Soil quality is related to food security and human survival and development. In recent years, due to the acceleration of urbanization and the increase of abandoned land, land degradation occurs, poor topsoil quality. In this study, the minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics and influencing reasons of topsoil quality in Tieling County. The re-sults showed that MDS included total potassium (TK), Clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu). The MDS indicators can well replace all indicators to evaluate the topsoil quality in the study area. The overall soil quality of Tieling County showed a trend of low in the east and high in the west, and gradually increased from the hilly area to the plain area. The evaluation results are consistent with field research, which can provide reference for other topsoil quality evaluation, and it also provide a basis for the formulation of soil quality improvement measures.

When the image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information of the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. To enable the model to focus on both global and local features, a multi-scale feature fusion module is proposed. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.

Seniors safety is a compelling need, which necessitates 24/7 real-time monitoring and timely dangerous action recognition. Being able to mirror characteristics of physical objects (PO) to corresponding logical objects (LO) and seamlessly monitor their footprints thus detect anomaly parameters, Digital Twins (DT) has been considered a practical way to provide virtual health services for seniors safety. Meanwhile, widely adopted Internet of Medical Things (IoMT) consisting of wearable sensors and non-contact optical cameras for self and remote health data monitoring also raises concerns on information security and privacy violation. Therefore, security of POs, LOs and reliable data sharing among healthcare professionals are challenging as constructing trust and privacy-preserving virtual health services. Thanks to characteristics of decentralization, traceability and unalterability, Blockchain is promising to enhance security and privacy properties in many areas like data analysis, finance and healthcare. This paper envisions a lightweight authentication framework (LAF) to enable secure and privacy-preserving virtual healthcare services. Leveraging Non-Fungible Token (NFT) technology to tokenize LOs and data streams on blockchain, anyone can certify the authenticity of a digital LO along with its synchronized data between PO without relying on a third-party agency. In addition, the NFT-based tokenization not only allows owners fully control their IoMT devices and data, but it also enables verifiable ownership and traceable transferability during data sharing process. Moreover, NFT only contains references to encrypted raw data that are saved on off-chain storage like local files or distributed databases, such a hybrid storage strategy ensures privacy-preservation for sensitive information. A proof-of-concept prototype is implemented and tests are conducted on a case study of seniors safety. The experimental evaluation shows the feasibility and effectiveness of the proposed LAF solution.

Paleoclimate information is key to understanding the climate system and predicting future climate. Currently, stable isotope analysis of whole molecules in tree rings is among the most advanced paleoclimate tools. Still, it only enables reconstructions of one climate parameter at a time (univariate climate reconstruction), a fundamental limitation. Here, we investigated whether this limitation can be overcome by analysing isotope variability at the level of intramolecular carbon positions in tree-ring glucose. We found that drought governs isotope variability at glucose C-1 to C-3, whereas radiation governs isotope variability at glucose C-5 to C-6. These isotope-climate relationships are statistically and mechanistically robust. According to cross-validation analysis, they can be reconstructed with high accuracy and precision. Thus, intramolecular isotope analysis enables multivariate climate reconstructions (proof of concept).

Purpose: Pitch plays an important role in auditory perception of music and language. This study provides a systematic review with meta-analysis to investigate whether individuals with autism spectrum disorder (ASD) have enhanced pitch processing ability and identify the potential factors associated with processing differences between ASD and neurotypicals. Method: We conducted a systematic search through six major electronic databases focusing on the studies that used nonspeech stimuli to provide a qualitative and quantitative assessment across existing studies on pitch perception in autism. We identified potential participant- and methodology-related moderators and conducted meta-regression analyses using mixed-effects models. Results: On the basis of 22 studies with a total of 464 participants with ASD, we obtained a small-to-medium positive effect size (0.26) in support of enhanced pitch perception in ASD.Moreover, the mean age and non-verbal IQ of participants were found to significantly moderate the between-studies heterogeneity. Conclusion: Our study provides the first meta-analysis on auditory pitch perception in ASD and demonstrates the existence of different developmental trajectories between individuals with ASD and neurotypicals. Non-verbal ability can be a significant contributor to the lower-level/ local processing bias in ASD. We highlight the need for further investigation of pitch perception in ASD under challenging listening conditions. Future neurophysiological and brain imaging research studies with a longitudinal design are also needed to better understand the nature of the atypical processing in ASD to obtain new insights into the underlying neural mechanisms and to help guide auditory-based interventions for improving language and social functioning.

Solar assisted hybrid cooling systems are promising for the energy saving of refrigeration systems. In most cases, the solar thermal gain is only able to power the heat-driven process of facilities in part of the working period. Therefore, the reduction of compressor power strongly depends upon the duration of heat-driven processes, which has not been addressed properly. Motivated by such knowledge gap, the thermodynamic understanding of solar assisted hybrid cooling systems is deepened through considering the duration in heat-driven processes. Three absorption-compression integrated cooling cycles were taken as examples. It is found that optimal parameters, e.g., inter-stage pressure and temperature, corresponding to various performance indicators trend to be identical, as the duration of heat-driven processes is taken into account. Furthermore, the optimal parameter for different working conditions was obtained. It is displayed that the dimensionless optimal intermediate temperature of layout with the cascade condensation process varies slightly, e.g., 4%, for different conditions. Moreover, the fall of compressor power in entire working periods is nearly independent upon the intermediate temperature. The paper is favorable for the efficient design and operation of solar assisted hybrid cooling systems.

To explore the impact of parenting styles on the perception of campus non-physical bullying, 492 students in upper elementary school grades were surveyed by using the Delaware Bullying Victimization Scale, the Negative Coping Style Scale, the Negative Affect Scale, and the Egna Minnen Beträffende Uppfostran Questionnaire. The questionnaire survey was conducted in the fifth and sixth grades of eight primary schools in Zhejiang province. The results showed that cyberbullying was not significantly related to an anxious parenting style, but negative affect experiences, negative coping styles, negative family parenting styles, and the perception of campus non-physical bullying were all positively correlated with each other (p < 0.05). The refusal parenting style was shown to be an important factor that affected students’ perception of campus non-physical bullying; it was observed to directly affect students’ perception of campus non-physical bullying and indirectly affect students’ perception of campus non-physical bullying by influencing negative affect experiences and negative coping styles. In conclusion, negative affect experiences and negative coping styles had a chain-like mediating effect between the refusal parenting style and students’ perception of campus verbal bullying. Moreover, negative affect experiences had a partial mediating effect between the refusal parenting style and students’ perceptions of campus cyberbullying, relationship bullying, and non-physical bullying total scores. Implications and suggestions based on these results are also discussed.

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are deployed around the world. Proliferation of artificial intelligence (AI) and machine learning (ML) technologies has gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference scale as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capture a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.

Species diversity in microbiome is a cutting-edge concept in metagenomic research. In this study, we propose a multifractal analysis for metagenomic research. From the chaos game representation (CGR) visualization of simulated and real metagenomes, we find that there exists self-similarity in the visualization of metagenomes. Then we compute the multifractal dimensions for simulated and real metagenomes. For simulated metagenomes, we also compute their diversity indices, such as species richness indices, Shannon’s diversity indices and Simpson’s diversity indices respectively for varying value of . Fom the Pearson correlation coefficients between their multifractal dimensions and traditional species diversity indices, we find that the correlation coefficients between the multifractal dimensions and species richness indices and Shannon diversity indices reach their maximums at respectively. The correlation coefficients between the multifractal dimensions and Simpson’s diversity indices reach their maximums at nearly. So the traditional diversity indices can be unified by the frame of multifractal analysis. These results coincided with the similar results in macrobial ecology. Finally, we apply our methods to real metagenomes of 100 infants’ gut microbiomes when they are newborn, 4 months and 12 months. Our results show that multifractal dimensions of infants’ gut microbiomes can discriminate the age difference.

Null-flux Electro-dynamic suspension (EDS) system promises to be one of the feasible high-speed maglev systems above 600 km/h. On account of its greater current-carrying capacity, superconducting magnet can provide super-magnetomotive force that is required for null-flux EDS system and cannot be provided by electromagnets and permanent magnets. There is already a relatively mature high-speed maglev technology with low temperature superconducting (LTS) magnets as the core, which works in the liquid helium temperature region (T≤4.2 K). 2-Generation high temperature superconducting (HTS) magnet winded by REBa2Cu3O7−δ (REBCO, RE=rare earth) tapes works above 20 K region and do not need to count on liquid helium which is rare on earth. This paper designed HTS no-insulation closed-loop coils applied for EDS system and energized with persistent current switch. The coils can work at persistent current model and has premier thermal quench self-protection. Besides, a full size double-pancake module was designed and manufactured in this paper, and it was tested in liquid nitrogen. The double-pancake module’s critical current is about 54 A and it is capable of working at persistent current model, whose average decay rate measured in 12 hours is 0.58%/day.

Nested structure is a structural feature that is conducive to system stability formed by the co-evolution of species in mutualistic ecosystems, and reflects the ability of ecosystem stability to be restored to a stable state again after being destroyed. The co-opetition relationship and value flow between industrial sectors in the global value chain are similar to the mutualistic ecosystem, and the pattern of the global economic system is always changing in dynamic equilibrium. Nestedness theory is used in this article to define the generalist and specialist sectors in the global value chain to analyze the changes in the global supply pattern. Then we study the mechanism of the global economic system to reach a stable equilibrium and the role of different sectors in the steady of the economic system, so as to provide countermeasures for enhancing the stability of the global economic system. At the end of the article, the domestic trade network, export trade network and import trade network of each country are extracted, and an econometric model is designed to analyze how the microstructure of the production system affects a country’s macroeconomic performance, thereby deriving the conclusion that the stability of the international trade network is crucial to a country's economic development.

Despite existing strong evidence on oxidative markers overproduction following ischemia/reperfusion (I/R), the mechanism by which oxidative enzyme Cytochrome P450-2E1 (CYP2E1) contributes to I/R outcomes is not clear. In this study, we sought to evaluate the functional significance of CYP2E1 in I/R. CYP2E1 KO mice and controls were subjected to middle cerebral artery occlusion (MCAo-90min) followed by 24hr of reperfusion to induce focal I/R injury models. Then, histological and chemical analyses were conducted to investigate the role of CYP2E1 in lesion volume, oxidative stress, and inflammation exacerbation. Also, the role of CYP2E1 on the BBB integrity was investigated by measuring 20-Hydroxyecosatetraenoic acid (20-HETE) activity, as well as, in vivo BBB transfer rate. Following I/R, the CYP2E1 KO mice exhibited a significantly lower lesion volume, and neurological deficits compared to controls (p<0.005). Also, ROS production, apoptosis, and the neurodegeneration were significantly lower in the CYP2E1(-/-) I/R group (p<0.001). The BBB damage was significantly lower in CYP2E1(-/-) mice compared to WT (p<0.001), while 20-HETE production was increased by 41%. Besides, inflammatory cytokines expression and the number of activated microglia were significantly lower in CYP2E1(-/-) mice following I/R. CYP2E1 suppression ameliorates I/R injury and protects BBB integrity by reducing both oxidative stress and inflammation.

Hardened air void analysis provides essential information of concrete freeze-thaw durability based on the size and spacing of air voids in the material. As the physical freeze-thaw experiment is time-consuming and costly, the characteristics of concrete air voids are often deemed as a proxy of the freeze-thaw performance. This analysis is typically done by measuring the 2D air void intersections on polished samples, but the current interpretation of the 2D void characters does not accurately represent the actual void structure in 3D. To solve this problem, a 2D-to-3D unfolding technique has been proposed in the field of stereology. However, the unfolding analysis is known to be sensitive to several factors, such as void population and size along with a binning scheme, where improper unfolding can considerably bias the prediction of the actual concrete void system. This study investigates the optimal strategy of conducting the unfolding analysis for concrete. The investigation is carried out on both idealized void systems to interrogate the influence of the critical factors individually, and real concrete samples with varying levels of air entrainment to assess the concrete-specific impacts. The concrete void system is studied based on a stereological model emulating the intersected 3D air voids on the surface of polished concrete. The results highlight that, for unfolding concrete voids, logarithmic binning scheme is far more accurate to linear binning. The low unfolding error of the concrete samples indicates that the proposed methodology enables an accurate restoration of 3D void size distribution.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

Jumping side volley has created breathtaking moments and cherished memories for us. Regrettably, a scientific study on the skill has not been found in literature. Relying on talent of athletes to improvise on the fly can hardly be considered a viable learning strategy. This study targets to fill the gap by quantifying factors contributing to develop its coaching method. Using 3D motion capture (12-cameras, 200Hz) and full-body biomechanical modeling, our study aimed to identify elements that govern entrainment of the skill by examining jumping, kicking and falling phases of its execution. Given the rarity of players who have acquired this skill, we found five subjects for the study. Twenty-three trials were captured and quantified. The results unveil the following key elements: 1) the control of trunk rotation during the jumping, 2) the angle between thighs upon take-off, 3) the whip-like control of the kicking leg during airborne, 4) timing between ball motion and limbs’ coordination, and 5) damping mechanism during falling. An accurate kick can only be achieved through repetitive training. This underlines the need for athletes to master a safe landing technique that minimizes risk of injury during practice. Therefore, training should begin with learning a safe falling technique.

This study presents an exploration into identifying the interactions between ocean waves and the continental margin in the origination of double-frequency (DF, 0.1-0.5 Hz) microseisms recorded at 33 stations across East Coast of USA (ECUSA) during a ten-day period of ordinary ocean wave climate. Daily primary vibration directions are calculated in three frequency bands and projected as great circles passing through each station. In each band, the great circles from all stations exhibit largest spatial density primarily near the continental slope in the western North Atlantic Ocean. Generation mechanisms of three DF microseism events are explored by comparing temporal and spatial variations of the DF microseisms with the migration patterns of ocean wave fronts in Wavewatch III hindcasts. Correlation analyses are conducted by comparing the frequency compositions of and calculating the correlation coefficients between the DF microseisms and the ocean waves recorded at selected buoys. The observations and analyses lead to a hypothesis that the continental slope causes wave reflection, generating low frequency DF energy and that the continental shelf is where high frequency DF energy is mainly generated in ECUSA. The hypothesis is supported by the primary vibration directions being mainly perpendicular to the strike of the continental slope.

This paper investigates an inverse problem of option pricing in the extended Black--Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in financial markets using a Bayesian inference approach. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by Markov Chain Monte Carlo (MCMC), which explores the posterior state space. The efficient sampling strategy of MCMC enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown drift and volatility coefficients from the measured data.

Intended for the high voltage gain and wide-range operation of DC/DC converters for photovoltaic energy storage systems, a topology for four-phase interleaved DC/DC converters for photovoltaic power generation is proposed. This topology increases output voltage for output in series, and reduces the input current ripple by paralleling the input. Compared with traditional boost converter topology, the proposed topology reduces the output current and output voltage ripple, reduces the stress of the switching device, and reduces the withstanding voltage of the output capacitor under the premise of ensuring the boost ratio. Experimental results show that the maximum efficiency of the converter reaches 95.37%. Compared with traditional boost converters, the proposed converter offers obvious advantages in efficiency under the conditions that the output voltage and load are variable.

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

Based on previous research results, we propose a new preprocessing HSS iteration method (PHSS) for the generalized Lyapunov equation. At the same time, the corresponding inexact PHSS algorithm (IPHSS) is given from the angle of application. All the new methods presented in this paper have given the corresponding convergence proof. The numerical experiments are carried out to compare the new method with the existing methods, and the improvement effect is obvious. The feasibility and effectiveness of the proposed method are proved from two aspects of theory and calculation.

Ferrite body is the origin of crack and corrosion initiation of steels. Distribution and density of ferrite in seven steel ingots were examined by light optical microscopy and computational modeling in the study to explore the correlation of ferrite formation to chemical composition and mushy zone temperature in ingot forming. The central segregation phenomenon in ferrite distribution was observed in all the examined steel specimens except 0Cr17Ni4Cu4Nb stainless steel. No significant difference was found in distribution and density of ferrite amongst zones of the surface, ½ radius and core in neither risers nor tails of 0Cr17Ni4Cu4Nb ingots. Additionally, fewer ferrite was found in 0Cr17Ni4Cu4Nb compared to other examined steels. The difference of ferrite formation in 0Cr17Ni4Cu4Nb elicited a debate on the traditional models explicating ferrite formation. Considering the compelling advantages in mechanical strength, plasticity and corrosion resistance, further investigation on the unusual ferrite formation in 0Cr17Ni4Cu4Nb would help understand the mechanism to improve steel quality. In summary, we observed that ferrite formation in steel was correlated with mushy zone temperature. The advantages of 0Crl7Ni4Cu4Nb in corrosion resistance and mechanical stability could be resulted from that fewer ferrites formed and distributed in a scattered manner in microstructure of the steel.

In recent years, compound glass microsphere resonator devices have attracted increasing interest and have been widely used in sensing, microsphere lasers, and nonlinear optics. Compared with traditional silica resonators, compound glass microsphere resonators have many significant and attractive properties, such as high-Q factor, an ability to achieve high rare earth ion, wide infrared transmittance and low phonon energy. This review provides a summary and a critical assessment of the fabrication and the optical characterization of compound glasses and the related fabrication and applications of compound glass microsphere resonators.

NaoMaiTong (NMT: Radix et Rhi-zoma Rhei, Radix Ginseng, Radix Puerariae, and Rhizoma Ligustici Chuanxiong as 9: 9: 6: 6) is a traditional Chinese medicine prescription for treating ischemia cerebral apoplexy. In this work, four cell injury models of ischemic stroke were establish, namely hypoxic injury, glutamate damage, injury of potassium chloride and hydrogen peroxide damage model. The protective effects of NMT and its single herbs-containing sera of different time points on PC12 cell damage were evaluated respectively, and the corresponding efficacy-time curves were drawn. Cell viability was measured by MTT (3-(4,5)-Dimethylthiahiazo(-z-y1)-3,5-di-phenytetrazoliumromide) assay. Furthermore, an statistical methods of support vector machine (SVM) were used to establish the correlation between concentration-time-efficacy for the first time. These results revealed that NMT-containing serum has obvious protective effect on the four injury models and can significantly improve cell viability. The PK-PD correlation between fifteen ingredients in the NMT compound with four model efficacy indexes indicated that rhein, puerarin, and 3'-methoxy puerarin might be the most important constituents controlling the pharmacological effects of NMT. The study suggested that these fifteen components are likely to be the material basis of NMT and recommended to increase the amounts of Pueraria in the NMT compound. That provided the scientific basis and demonstration for the research of efficacy material base of Traditional Chinese Medicine (TCM).

A novel starch stabilized Mg/Al LDHs material (S-LDH) was prepared in a facile approach and its fluoride ion removal performance was developed. Characterization of S-LDH was employed by using XRD, FTIR and particle size distribution. The adsorption property was studied through the assessment of adsorption isotherms, kinetic models, thermal dynamics and pH influence. The result shows that a low loading of starch of 10 mg onto LDH could obviously improve fluoride removal rate. The S-LDH had 3 times higher adsorption capacity to fluoride than that of Mg/Al LDH to fluoride. The particle size was smaller and the particle size distribution was narrower for S-LDH than that for Mg/Al LDH. Langmuir adsorption isotherm model and pseudo-second-order kinetic model fitted well with the experimental data. In thermodynamic parameters, the enthalpy (△H0) value was 35.63 kJ mol-1 and the entropy (△S0) value was 0.0806 kJ mol-1K-1. The values of △G0 were negative, implying the adsorption process is spontaneous. S-LDH reveals stable adsorption property in a wide pH range from 3 to 9. The mechanism for fluoride adsorption on S-LDH included surface adsorption and interaction ion exchange.

While the Internet of Things (IoT) technology has been widely recognized as the essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today's AC is the centralized authorization server, which can be the performance bottleneck or the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable an effective protection for devices, services and information in large scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registering, propagating and revocating of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI node) and more powerful computing devices (i.e., laptops), and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

Typhoon Durian forming over the Western North Pacific Ocean and entering into the South China Sea (SCS), caused extreme and widespread damages in 2006. In this research, sensitivity analyses on different physical parameterization schemes of the Weather Research and Forecasting Atmospheric Model (WRF-ATM) have been utilized to study typhoon Durian. Model accuracy and performance testing were investigated with different initial conditions during the tropical cyclone simulation in the SCS. The initial and boundary conditions (IBCs) for all experiments were derived from the European Centre for Medium Range Weather Forecasts (ECMWF), Re-Analysis Interim (ERAI), and the National Centers for Environmental Prediction (NCEP) with Final (FNL) analysis data compiled through the WRF-ATM model. The sensitivity analysis results indicated a major improvement for the cumulus scheme by using the Grell-Devenyi scheme along with the PBL scheme of Yonsei University, mixed-phase microphysics scheme of the WRF Single Moment 5-class and IBCs for ECMWF-ERAI of TC simulation under the context of Wind-Pressure Relationships. This predicted better track and intensity comparing with these of the Joint Typhoon Warning Center. The results revealed that the TC track and intensity were well simulated by the WSM5-GD combination for the WRF-ATM model with an intensity error of 1.69 hPa for minimum surface level pressure, maximum wind speed of 1.83 knots and average track error of 25 km in 72 hours. The simulations showed that the potential track and intensity error decreased with the delayed IBCs, suggesting that the model simulation is more dependable when the coast is approached by the TC.

This paper introduces PatchFusion, an innovative approach for nonrigid tracking and reconstruction of deformable objects using a single RGB-D sensor. Existing methods face challenges in accurately capturing the rapid deformations of soft and flexible objects, thereby limiting their utility in diverse scenarios. Our approach overcomes this challenge by employing a dynamic patch-based framework that adapts to rapid inter-frame motions. Firstly, patch-wise rigid transformation fields for non-overlapping patches are solved via Iterative Closest Point (ICP) by incorporating geometric features as additional similarity constraints, thereby enhancing robustness and accuracy. Secondly, deformation optimization based on a nonrigid solver is applied to refine the coarse transformation fields. In order to enable simultaneous tracking and reconstruction of deformable objects, the patch-based rigid solver is designed to run in parallel with the nonrigid solver, serving as a plug-and-play module requiring minimal modifications for integration while enabling real-time performance. Following a comprehensive evaluation, PatchFusion showcases superior performance in effectively dealing with rapid inter-frame deformations when compared to existing techniques, rendering it a promising solution with broad applicability across domains such as robotics, computer vision, and human-computer interaction.

This study derives space travel pricing by Walrasian Equilibrium, which is logical reasoning from the general relativity theory (GRT), the accounting equation, and economic supply and demand functions. The Cobb-Douglas functions embed the endogenous space factor as new capital to form the space travel firm's production function, which is also transformed into the consumer's utility function. Thus, the market equilibrium occurs at the equivalence of supply and demand functions, like the GRT presents the equivalence between the spatial geometric tensor and the energy-momentum tensor, which explains the principles of gravity and the motion of space matter in the spacetime framework. The mathematical axiomatic set theory of the accounting equation explains the equity premium effect that causes a short-term accounting equation inequality, then reaches the equivalence by suppliers' incremental equity through the closing accounts process of the accounting cycle. On the demand side, the consumption of space travel can be assumed as a value at risk (VaR) investment to attain the specific spacetime curvature in an expected orbit. Spacetime market equilibrium is then achieved to construct the space travel pricing model. The methodology of econophysics and analogy method was applied to infer the space travel pricing by the model of profit maximization, single-minded, and envy-free pricing in unit-demand markets. A case study with simulation was conducted for empirical verification of the math models and algorithm. Results show that space travel pricing remains the principle of market equilibrium but needs to be extended to the spacetime tensor of GRT.

We simulated the glass-blowing process to model a shell resonator, which is a key component in MRIG (Micro Rate Integrating Gyroscope), using the finite elements method to solve a non-isothermal problem. We also calculated the mechanical characteristics of the modeled resonator, including effective mass, eigenfrequencies, angular gain, and energy loss, to predict the resonator's performance as a gyroscope. One key aspect we addressed is the bending of the resonator's rim toward the stem during glass-blowing process. This phenomenon occurs due to increased heat loss at the mold's edge and reduced heat flux at the side of the Gaussian heat source. To mitigate rim bending and the subsequent reduction in capacitance between the rim and 3D electrodes, which can degrade performance in micro glass-blown shell resonators for gyroscopes, we propose two potential mold solutions. The first is using a short-stem mold, which requires extensive polishing to eliminate the bent rim. The second solution is using a mold with a suitable chamfer at the edge, resulting in a straight rim. To quantitatively compare resonators created by these two types of molds, we calculated the noise factor for predicting its gyroscope performance. From these calculations, we find out that using the chamfered mold enables us to increase in the resonator's effective mass, eigenfrequency, angular gain, and quality factor, consequently reducing the noise factor compared to using the short-stem mold. This simulation process will assist us in getting insight into understanding a shell resonator as a gyroscope, designing a mold and determining experimental conditions.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

Cellular organisms possess intricate DNA damage repair and tolerance pathways to manage various DNA lesions arising from endogenous or exogenous sources. Dysregulation of these pathways is associated with cancer development and progression. Synthetic lethality (SL), a promising cancer therapy concept, involves exploiting the simultaneous functional loss of two genes for selective cell death. PARP inhibitors (PARPis) have demonstrated success in BRCA-deficient tumors. Cisplatin (CPT), a widely used chemotherapy agent, forms DNA adducts and crosslinks, rendering it effective against various cancers but less so for prostate cancer (PCa) due to resistance and toxicity. Here, we explore the therapeutic potential of TLK1, a kinase upregulated in androgen-insensitive PCa cells, as a target for enhancing CPT-based therapy. TLK1 phosphorylates key homologous recombination repair (HRR) proteins RAD54L and RAD54B, critical for HRR alongside RAD51. The combination of CPT with TLK1 inhibitor J54 exhibits SL in androgen-insensitive PCa cells. The formation of double-strand break intermediates during inter-strand crosslink processing necessitates HRR for effective repair. Therefore, targeting TLK1 with J54 enhances the SL of CPT by impeding HRR, leading to increased sensitivity in PCa cells. These findings suggest a promising approach for improving CPT-based therapies in PCa, particularly in androgen-insensitive cases. By elucidating the role of TLK1 in CPT resistance, this study provides valuable insights into potential therapeutic targets to overcome PCa resistance to CPT chemotherapy. Further investigations into TLK1 inhibition in combination with other DNA-damaging agents may pave the way for more effective and targeted treatments for PCa and other cancers that exhibit resistance to traditional chemotherapy agents.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based system. In this paper, we present a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consume substantial computational resources and increase the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we propose a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduce a lightweight superpixel-based Graph Neural Network (GNN) and break new ground by introducing superpixel metric learning to segment nucles and cytoplasm. Remarkably, our proposed segmentation model (SMGNN) achieves state-of-the-art segmentation performance while utilizing at most 10000$\times$ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes show satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories.

Natural killer cells are associated with the pathogenesis of ulcerative colitis (UC), but their precise contributions remain unclear. The present study sought to investigate the diagnostic value of activated NK-associated genes (ANAGs) in UC. Bulk RNA-seq and scRNA-seq datasets were obtained from the Gene Expression Omnibus (GEO) and Single Cell Portal (SCP) databases. In the bulk RNA-seq, 92 differentially expressed genes (DEGs) were screened out by the “Batch correction” and “Robust rank aggregation” (RRA) methods. The immune infiltration landscape was estimated by single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT, which revealed a higher abundance of activated NK cells in noninflamed UC tissues. 54 DEGs correlated with activated NK cells were identified as ANAGs. Protein-protein interaction (PPI) analysis and least absolute shrinkage and selection operator (LASSO) regression were utilized to screen out 4 key ANAGs (SELP, TIMP1, MMP7, and ABCG2) and establish an activated NK-associated gene score (ANAG score). The ANAG score demonstrated excellent diagnostic value and was validated in three external datasets. The expression of the 4 key ANAGs was validated in UC patients and healthy controls (HC) samples. Through scRNA-seq data analysis, higher expression levels of SELP, TIMP1, MMP7, and ABCG2 were observed in post-capillary venules, inflammatory fibroblasts, enterocytes, and immature enterocytes. The cell scores based on the ANAGs showed enrichment in endothelial cells and fibroblasts. In conclusion, we established and validated an ANAG score with the ability to precisely diagnose UC. The 4 key ANAGs have the potential to serve as therapeutic targets in UC.

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem, aiming to find a smallest set of vertices such that every edge has at least one endpoint in the set, which has extensive applications in cyber security, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain a “good” vertex cover. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: A 3-improvements (TI) framework with tiny perturbation, and an edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with two state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.

This work aims to describe the dielectric behaviours of four main tissues in the human forearm using mathematical modelling, including fat, muscle, blood, and bone. Determining the electrical response of multiple tissues can help develop physiological monitoring of an organ or a body’s section by filtering out the impedance contributions from the surrounding tissues to blood flow-induced impedance variations. Multi-frequency bioimpedance analysis (MF-BIA) was initially performed using the finite element method (FEM) with a 3D forearm model followed by a pilot study to characterise the response of actual forearm tissues from 1 kHz to 349 kHz. Both the simulation and experimental results were fitted to a single-dispersion Cole model (SDCM) and a multi-dispersion Cole model (MDCM) to determine the Cole parameters for each tissue. The correlation analysis and Bland-Altman plot indicated a good fit between raw and fitted impedance values using both SDCM and MDCM. Overall, MDCM exhibited better performance in fitting and estimation of the Cole parameters with correlation coefficient (R2) of 0.99 and 0.97, root mean squared error (RMSE) of 0.09 Ω and 0.14 Ω, and mean difference (mean±standard deviation) of 0.00±0.09 Ω and -0.03±0.14 Ω for the real part and imaginary part of impedance, respectively.

Epsilon-near-zero nanoantennas can be used to tune the far-field radiation pattern due to their exceptionally large intensity-dependent refractive index. Here, we propose hybrid optical antenna based on indium tin oxide (ITO) to optically tune the deflection of radiation. In particular, a hybrid structure antenna of ITO and dielectric material, which makes the deflection angle changes 17 ∘ as incident intensities increases. Finally, the array of ITO or hybrid nanodisks can enhance the unidirectionality to be needle-like, with the angular beam width α&lt; 8∘ of main lobe. The deflection angle of radiation pattern response with needle-like lobe pave the way for further studies and applications in beam steering and optical modulation where dynamic control of the nanoantennas is highly desirable.

The connexin gene family is the most prevalent gene that contributes to hearing loss. Connexins 26 and 30, encoded by GJB2 and GJB6 respectively, are the most abundant expressed connexins in the inner ear. Connexin 43 which is encoded by GJA1 appears to be widely expressed in various organs, including the heart, skin, brain, and inner ear. The mutations that arise in GJB2, GJB6 and GJA1 can all result in comprehensive or non-comprehensive genetic deafness in newborns. As it is predicted that connexins include at least 20 isoforms in humans, the biosynthesis, structural composition, and degradation of connexins must be precisely regulated so that the gap junctions can operate properly. Certain mutations result in connexins possessing a faulty subcellular localization, failing to transport to the cell membrane and preventing gap junction formation, ultimately leading to connexin dysfunction and hearing loss. In this review, we provide a discussion of the transport models for connexin 43, connexins 30 and 26, mutations affecting trafficking pathways of connexin 26, the existing controversies in trafficking pathways of connexins, and the molecules involved in connexin trafficking and their functions. This review can contribute to a new way of understanding the etiological principles of connexin mutations and finding therapeutic strategies for hereditary deafness.

From the perspective of network attackers, finding attack sequences that can make significant damage on network controllability is an important task, which also helps defenders improve the robustness during network constructions. Therefore, developing effective attack strategies is a key aspect of research on network controllability and its robustness. In this paper, we propose a Leaf Node Neighbor-based Attack (LNNA) strategy that can effectively disrupt the controllability of undirected networks. The LNNA strategy targets the neighbors of leaf nodes, and when there are no leaf nodes in the network, the strategy attacks the neighbors of nodes with a higher degree to produce the leaf nodes. Results from simulations on synthetic and real-world networks demonstrate the effectiveness of the proposed method. In particular, our findings suggest that removing neighbors of low-degree nodes (i.e., nodes with degree 1 or 2) can significantly reduce the controllability robustness of networks. Thus, protecting such low-degree nodes and their neighbors during network construction can lead to networks with improved controllability robustness.

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security nature, Blockchain has a great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind Software Defined Network (SDN) technology, this paper envisions SAUSA, a blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud data sets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-enabled point cloud service architecture which allows for efficient data processing and delivery to satisfy diverse Quality-of-Service (QoS) requirements. A blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.

With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

Parental warmth plays an important role in the development of adolescents’ physical and mental health. There are numerous empirical studies indicating a relationship between parental warmth and prosocial behavior among adolescents, although the underlying mechanisms of this association remain unclear. Adopting a longitudinal design across two time points, the present study proposes a moderated mediation model to explore the mediating role of gratitude and the moderating role of the school climate between parental warmth and prosocial behavior. The sample consisted of 943 participants (483 boys and 451 girls) who participated in the second assessment and completed questionnaires assessing gratitude, school climate, and prosocial behavior in April 2019. Their parents participated in the first assessment and completed a questionnaire pertaining to parental warmth in October 2018. After controlling for the gender and age of the adolescents, the results showed that the positive association between parental warmth and prosocial behavior is mediated by gratitude, and school climate does play a moderating role in the second half of the mediating path. Specifically, the school climate can play a protective role in adolescents with low levels of gratitude. For adolescents with less gratitude, a strong school climate can promote more prosocial behaviors and can effectively alleviate the negative prediction of low levels of gratitude. This study provides a theoretical explanation for the generation of adolescents’ prosocial behavior, and provides theoretical guidance for the interventions of schools and parents.

Compared with liquid crystal clad waveguide, MEMS mirror has some merits, such as high-er-transmissivity, lager-angle of scanning, faster scanning speed and so on. Furthermore, MEMS mirror arrays perform more superior than MEMS mirror when they steer laser beam, which make MEMS arrays much more suitable to be used in devising compact Lidar. Before assembling a Lidar with MEMS arrays, the optical intensity and distributions of the laser diffracted by MEMS arrays should be analyzed, but few published papers about this issue are available so far, this paper will focus on this issue about MEMS arrays. Firstly, the complex amplitudes of laser which is diffracted by 1-D and 2-D arrays are presented, respectively. Then the optical intensity and distributions on the observation plane are presented. Finally, the simulation diagrams of these distributions are shown, and the correctness of the results is indirectly verified by Young’s dou-ble-slit experiment. The results gotten in this letter are essential to design a compacted Lidar based on MEMS arrays.

Biosensors have shown great potential in realizing rapid, low cost and portable on-site detection for diseases. This work reports the development of a new bioelectronic sensor called AC electrokinetics-based capacitive (ABC) biosensor, for the detection of genomic DNA (gDNA) of methicillin-resistant Staphylococcus aureus (MRSA). The ABC sensor is based on interdigitated microelectrodes biofunctionalized with oligonucleotide probes. It uses a special AC signal for direct capacitive monitoring of topological change on nanostructured sensor surface, which simultaneously induce dieletrophoretic enrichment of target gDNAs. As a result, rapid and specific detection of gDNA/probe hybridization can be realized with high sensitivity. It requires no signal amplification such as labelling, hybridization chain reaction, or nucleic acid sequence-based amplification. This method involves only simple sample preparation. After optimization of nano-structured sensor surface and signal processing, the ABC sensor demonstrated fast turnaround of results (~10 s detection), excellent sensitivity (a detection limit of 4.7 DNA copies /µL MRSA gDNA) and high specificity, suitable for point of care diagnosis. As a bioelectronic sensor, the developed ABC sensors can be easily adapted for detection of other infectious agents.

Effectively avoiding gas accident is vital to the security of mineral manufacture, and the coal mine gas accident is often caused by gas concentration overrun. The prediction accuracy of gas emission quantity in coal mine is the key to solve this problem. To maintain concentration of gas in a secure range，grey theory and neural network model increasingly diffusely used in forecasting gas emission quantity in coal mine critically. Nevertheless, the limitation of the grey neural network model is that researchers merely bonded the conventional neural network and grey theory. To enhance accuracy of prediction, a modified grey GM(1,1) and RBF neural network model is proposed combined amended grey GM(1,1) model and RBF neural network model. Then the proposed model was put into simulation experiment which is built based on Matlab software. Ultimately, conclusion of the simulation experiment verified that the modified grey GM(1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This showed that the modified grey GM(1,1) and RBF neural network model achieves effectiveness in precision of prediction much better than grey GM(1,1) model and RBF neural network model.

Antimicrobial and anti-viral materials have attracted significant interest in recent years due to increasing occurrences of nosocomial infections and pathogenic microbial contamination. One method to address this is the combination of photoactive compounds that can produce reactive oxygen species (ROS), such as hydrogen peroxide and hydroxyl radicals to disinfect microbes, with carrier materials that meet the application requirements. Using anthraquinone (AQ) and cellulose nanocrystals (CNCs) as the photoactive and carrier components respectively, this work demonstrates the first covalent incorporation of AQ onto CNCs. The morphology and the photoactive properties were investigated, revealing the structural integrity of the CNCs, and high degree of photoactivity of AQ-CNC materials upon UVA exposure. The AQ-CNCs also exhibited an unexpected persistent generation of ROS under darkness, which adds advantages for antimicrobial applications.

For traffic management under sudden disaster in high-density areas, the first and foremost step is to prevent traffic congestion in the disaster-affected area by traffic flow control, as to provide enough and flexible traffic capacity for emergency evacuation and emergency rescue. Catastrophe border identification is the foundation and the key to traffic congestion prediction under sudden disaster. This paper uses a mathematical model to study the regional traffic flow in the high-density area under sudden fire disaster based on the Cusp Catastrophe Theory (CCT). The catastrophe border is identified by fitting the CCT-based regional traffic flow model to explore the stable traffic flow changing to the instable state, as to provide a theoretical basis for traffic flow manage and control in disaster-affected areas, and to prevent the traffic flow being caught into disorder and congestion. Based on VISSIM simulator data by building simulation scenarios with and without sudden fire disaster in a Sudoku traffic network, the catastrophe border is identified as 439pcu/lane/h, 529pcu/lane/h, 377pcu/lane/h at 5s, 10s, 15s data collection interval respectively. The corresponding relative precision, which compares to the method of Capacity Assessment Approach (CAA), is 89.1%, 92.7% and 76.5% respectively. It means that 10s data collection interval would be the suitable data collection interval in catastrophe border identification and regional traffic flow control in high-density area under sudden fire disaster.

The coexistence of human beings and environmental sustainability becomes individually and even globally concerned issue. In addition to environment issues, people also encounter negative issues of infectious diseases, gap between rich and poor, violence in society, uneven resource distribution, people’s health decline, and population structure aging, which would affect the sustainable development of cities. When taking sustainable development from the world to cities, it appears sustainable cities. The goal of a healthy city is to pursue the sustainability of a city. Aiming at residents in Shanghai, total 360 copies of questionnaire are distributed, and 277 valid copies are retrieved, with the retrieval rate 77％. The research results conclude that 1.“safety” is the most emphasized dimension, followed by “convenience & prosperity”, “sustainable ecology”, “vitality & health”, and “culture friendly” and 2.security, Internet city, pollution control, space use, and inheritance education are top five indicators, among 15 evaluation indicators. According to the results, suggestions are proposed to provide the government with correct, objective, and simply understandable healthy city indicators and sustainable development indicators for the appropriate planning and review of the administration objectives to enhance the public awareness of healthy city and the participation. It would stop the worsening of environment and promote residents’ health and the sustainable development of cities to have the city and the public moving towards healthy development.

HybridAC/DC microgrids(HMG) are emerging as an attracting method for integrating the AC/DC distributed energy resources(DERs) with the features of high-performance and low-cost. In the isolated hybrid AC/DC microgrid (IHMG), the key problem is how to balance the power variation and regulate the voltage and frequency. Various energy storage systems (ESS)and interlinking converter (IC) technologies are viable for this application. The present study proposes a novel unified power flow model to evaluate and compare the abilities of the ESS with different connection topologies and ICs with different control approaches to maintain the voltage and frequency stability of the IHMG. In order to investigate the performance of the proposed scheme, five operation modes of the IHMG are defined and explained. The classification is based on the connection topologies and control modes of the ESS/IC in the IHMG. Then, a set of generic PF equations are derived. Moreover, three binary matrices are applied in the construction of the unified power equations. These matrices are used for describing the running state of the IHMG. Finally, in order to verify the proposed scheme, it is applied to several case studies of the IHMG. The operation characteristics of multi-DC subgrids IHMG in different modes, particularly when an external disturbance occurs, are investigated.

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this paper, focusing on the problem of weak feature learning ability when dealing with continuous data. In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer functions, the contrastive divergence is designed in hidden layer training process to raise convergence speed, an improved dropout strategy is then implemented in unsupervised training to realize features learning by de-cooperating between the units, and then the network is fine-tuned using back propagation algorithm. Besides, hyper-parameters are analysed through stability analysis to assure the network can find the optimal. Finally, the experiments on Lorenz chaos series, CATS benchmark and other real world like CO2 and waste water parameters forecasting show that cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than other methods.

UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

Spirit of science is one of the important components of science education theory. It not only directly affects the level of science education, but also indirectly affects the selection, education and evaluation of scientific researchers, and the development of science and technology in a country or region. Although more than a century ago, the researchers began to discuss the topic of spirit of science. However, to date, the research of the definition, attribute, structural model and its characteristics of the spirit of science has not yet made a breakthrough. Based on the detailed literature review, the related theoretical analysis and the research of the structural model of the spirit of science, this paper puts forward the new definition, the attribute and the establishment of the structural model of the spirit of science, and analyzes the match the situation from new structural model of the spirit of science and the scientific nature published by the American Society for the Advancement of Science. The results of this study are of great significance for raising the level of scientific education and cultivating future scientific researchers, enhancing their motivation and skills in innovation in scientific research and promoting the development of future scientific undertakings.

Aiming at the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

This paper analyzes the measurement error, caused by the position of the current-carrying conductor, of circular array of magnetic sensors for current measurement. The circular array of magnetic sensors is an effective approach for AC or DC non-contact measurement, as its low cost, large linear range, wide bandwidth, light weight and low noise. Especially it has claimed that such structure has the excellent reduction ability for the errors caused by the position of the current-carrying conductor, crosstalk current interference, shape of the conduction cross section and the earth magnetic field. However, the positions of the current-carrying conductor, including un-center and un-perpendicularity, has not analyzed in detail until now. In this paper, the theoretical analysis has been proposed based on vector inner and exterior product. In the presented mathematical model of relative error, the un-center offset distance, the un-perpendicular angle, the radius of the circle and the number of the magnetic sensor are expressed in one equation. The comparison of the relative error caused by the position of the current-carrying conductor between four and eight sensors is conducted. The Tunnel Magnetoresistance (TMR) sensors are used in the experimental prototype to verify the mathematical model. The analysis results can be the reference to design the detail of circular array of magnetic sensors for current measurement in practical situation.

Cropland area per capita and pressure index on cropland is the important parameters measuring the social vulnerability and sustainability in the perspective of food security in a certain region in China during the historical periods. This study reconstructs the spatial distribution change of cropland area per labor/household and pressure index on cropland during the 17th-20th century by the methods of historical documents, regression analysis, pressure index model and GIS. Then it analyzed the impacting process of climate change and sustainability of cropland use during the different periods. It draws conclusions: (i) the spatial difference of labor/household density was obvious which had the same pattern as cropland distribution during the same periods, which is higher density in three agricultural areas. (ii) Cropland area per capita was relatively higher during the 17th-18th century, which were above 0.4 ha/person in majority counties and distributed homogenously. Till the 19th century and the beginning of 20th century, cropland area per capita in large amount of regions decreased below 0.2 ha/person embodying the increase of social vulnerability and unsustainability at that time. (iii) Pressure index on cropland also showed the similar spatial pattern as cropland area per capita which presented lower threshold than nowadays. During the 17th-18th century there was no pressure on cropland. While, in the 19th century and at the beginning of 20th century, two high value centers of pressure index on cropland appeared in Middle Shandong and the Jiaodong region, pressure on sustainable cropland use increased obviously and a food crisis is probably created. (iv) Higher sustainable extent of cropland use corresponds to cold period, and lower sustainable extent of cropland use corresponds to warm period in Shandong over the past 300 years. The turning point of 1680s from dry to wet attributes to the decrease of sustainable extent of cropland use in Shandong not very distinctively. More and more pressure on sustainability of cropland use finally since the beginning of 20th century would intensify the social conflict and increase the probability of social revolts.

Background: Thyroid cancer incidence has increased globally in recent decades, especially in females, although its trends in Taiwan have not been studied extensively. This study aimed to investigate changes in female incidence and possible causes of thyroid cancer in Taiwan. Methods: Using the Taiwan Cancer Registry (TCR) Database, age-standardized incidence rates, age-specific incidence rates and birth cohorts were calculated. Correlation between female thyroid cancer incidence and cohort fertility rates were examined. Results: Thyroid cancer incidence increased in Taiwanese female, with age-adjusted rates per 100,000 people increasing from 7.37 during 1995-1999 to 20.53 during 2015-2019, APCs were 5.9% (95% CI, 5.3-6.5). Age-specific incidence rates increased with age, with peak rates occurring at younger ages. The APCs in the 50-54 age group were the highest (6.8%, 95% CI, 6.1-7.5). Incidence rates also increased with later birth cohorts. We observed a significant negative correlation between thyroid cancer incidence and fertility rates in the same birth cohort. Conclusion: We hypothesize that overdiagnosis may be a main reason for the rapidly increasing thyroid cancer incidence in Taiwanese female. Notably, we observed a strong negative correlation between fertility and thyroid cancer incidence. The associations with fertility will be an important subject for future thyroid cancer research.

BACKGROUND: The number of waitlisted patients requiring mechanical circulatory support (MCS) as a bridge to heart transplantation is increasing. The data concerning the results of double-bridge strategy are limited. We sought to investigate the post-transplant outcomes across the different bridge strategies. METHODS: We retrospectively reviewed a heart transplantation database from Jan 2009 to Jan 2019. Intra-aortic balloon pump (IABP), extracorporeal membrane oxygenation (ECMO), and ventricular assist devices (VAD) were the MCS that we investigated. The pre- and post-transplant characteristics and variables of patients bridged with the different types of MCS were collected. The post-transplant survival was compared using Kaplan-Meier survival analysis. RESULTS: A total of 251 heart transplants were reviewed; 115 without MCS and 136 with MCS. The patients were divided to five groups: Group 1 (no MCS): n=115; Group 2 (IABP): n=15; Group 3 (ECMO): n=33; Group 4 (ECMO-VAD): double-bridge (n=59); Group 5 (VAD): n=29. Survival analysis demonstrated that the 3-year post-transplant survival rates were significantly different among the groups (Log-rank p < 0.001). There was no difference in survival between group 4(ECMO-VAD) and group 1(no MCS)1 (p = 0.136), or between group 4(ECMO-VAD) and group 5(VAD) (p = 0.994). Group 3(ECMO) had significantly inferior 3-year survival than group 4(ECMO-VAD) and group 5(VAD). CONCLUSION: Double bridge may not lead to worse mid-term results in patients who could receive a transplantation. Initial stabilization with ECMO for critical patients before implantation of VAD might be considered as a strategy for obtaining an optimal post-transplant outcome.

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

The objective of this study was to develop an ultrasonic-assisted procedure for the extraction of total phenolics from Citrus aurantium L. blossoms (CAB) and evaluate the free radical scavenging activity, anti-HMG-CoA reductase activity of total phenolics. In this work, a Box-Behnken design based on the single-factor experiments was used to explore the optimum extraction process. Under the optimum conditions (extraction solvent 70.31% ethanol, extraction temperature 61.94 °C, extraction time 51.73 min and liquid-to-solid ratio 35.63 mL/g), the extraction yield of total phenolics was 95.84 mg gallic acid equivalents (GAE)/g dry matter (DM), which was highly consistent with the theoretical value (96.12 mg GAE/g DM). The total phenolic extract showed excellent free radical scavenging properties against DPPH·, ABTS+·, ·OH and ·O2-, with the IC50 values of 197.007, 83.878, 218.643 and 158.885 μg/mL, respectively, and the extracts also showed good inhibition of HMG-CoA reductase activity, with the IC50 value of 117.165 μg/mL. Total phenolics from CAB could be a potential source of natural free radical scavenger and HMG-CoA reductase inhibitor.

Biological neural network (BNN) is the core brain for creature to accomplish its intelligent behaviors through unique network structure and mechanisms. It also inspires controlling human-designed autonomous agents including robots to generate more advanced intelligent behaviors by mimicking the neural control mechanism of creatures at a deeper and more interactive level. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of cellular synapse. The neural network simulation integrates computational programming and visualization of neurons and synapse connections of C. elegans, containing the specific controllable circuits summarization with their dynamic characteristics within the whole network. To illustrate its particular intelligent control capability in terms of robotics, we introduce the first innovative methodology for applying the BNN model to a 12-legged robot’s movement control based on the established numerical simulation platforms. We accomplished and designed two methods and corresponding encoding processes, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of BNN. Both simulation and experiment results indicate that the robot exhibits stronger autonomy and more intelligent moving performance under the control of BNN. We then summarized the contributions for digitalizing the C. elegans’ whole-brain neural network in real-time and utilizing it to control robot in a closed loop to validate its advanced intelligence control ability in terms of a scientific robot.

Emission trading schemes (ETSs) have become vital tools in striving toward global emission reduction objectives and promoting a sustainable economy. This paper empirically investigates the impact of market conditions on interconnectedness within precious metals, clean energy, green bonds, and carbon markets, focusing on China and the EU ETSs as the study subjects. A TVP-VAR-based asymmetric connectedness approach is adopted to capture the evolving relationships between the variables of interest in the time–frequency domain. The empirical findings reveal that: First, time-varying asymmetric connectedness and spillover effects among clean energy, precious metals, green bonds, and carbon markets are identified in both time and frequency domains under diverse market conditions, with long-term connectedness asymmetry being particularly sensitive, although spillovers exhibit variations. Second, the role and intensity of net transmitters or receivers vary across market conditions and frequency domains, indicating the complex and nuanced nature of market interactions. The EU ETS demonstrates a higher level of connectivity with global markets for clean energy, precious metals, and green bonds when compared to the Chinese ETS. However, it also shows greater susceptibility to fluctuations in market conditions. These results have significant implications for both investors striving to build sustainable diversified portfolios and regulators endeavoring to craft effective policies for risk regulation and sustainable development.

To solve the problem of a uneven load for a dual-input counter-rotating transmission system, this research is based on Small Displacement Torsor Jacobian-Torsor theory.Considering the influence of the local parallel chain of a gearbox on the calculation accuracy of the backlash,the processing method of a meshing tooth pair parallel chain is proposed. The probability distribution of the tolerance band of gearbox manufacturing and assembly errors is modeled, the three-dimensional tolerance analysis model of a dual-input counter-rotating gearbox is established, and the Monte Carlo method is used to solve the backlash of the output end. The output backlash is solved with the Monte Carlo method, and the theoretical design backlash is combined with the output backlash to derive the gearbox side clearance range. The gearbox shaft system is analyzed statically, the range of the equal load coefficient is calculated, and the equal load performance optimization method based on the matching relationship between the gear backlash and the elastic shaft torsional stiffness is proposed. The correctness of the three-dimensional tolerance analysis combined with the elastic deflection angle used to optimize the equal load performance of the dual-input counter-rotating transmission system is verified by comparison with the relevant test data.

Rational delineation of urban–rural boundaries is a foundational prerequisite for holistic urban and rural development planning and rational resource allocation. However, the results of division of urban–rural boundaries extracted using a single data source are non-comprehensive. To address this problem, the present study proposes a method for using multiple sources such as population data, nighttime light data, land use, and points of interest (POI) data to extract urban–rural boundaries. Considering Guizhou Province for a case study, we here present a two-step method for identifying urban–rural boundaries. First, the random forest model was combined with the dasymetric mapping method to obtain the population spatialization data with a 30-m resolution in the studied province. Second, using the breaking point method, we extracted the urban–rural boundary for Guizhou Province in 2020 based on the spatialized population. This method fully integrated the benefits of various data and judiciously extracted the boundaries of the main urban areas and small- and medium-sized towns of each city in the study province at the same spatial scale. The stratified random sampling method revealed that the average overall accuracy was 88.05%. The method proposed has certain universality and application value and allows identifying the urban–rural boundaries more accurately and practically.

Neurodegeneration is becoming one of the leading causes of death worldwide as the population expands and grows older. There is a growing desire to understand the mechanisms behind prion proteins as well as the prion-like proteins that make up neurodegenerative diseases (NDs) including Alzheimer’s Disease (AD). Both amyloid β (Aβ) and hyperphosphorylated tau (p-tau) proteins behave in ways similar to that of the infectious form of the prion protein, PrPSc, such as aggregating, seeding, and replicating under not yet fully understood mechanisms, thus the designation of prion-like. This review aims to highlight the shared mechanisms between prion-like proteins and prion proteins in the structural variations associated with aggregation and disease development. These mechanisms are largely focusing on the dysregulation of protein homeostasis, self-replication, and protein aggregation, and how this knowledge could contribute to diagnoses and treatments for the given NDs.

Mallard (Anas platyrhynchos) are currently one of the most popular species in rare bird breeding in several southern provinces of China, but there are very few studies on the gut microbial communities of domestic and wild mallards. In this study, 16S rRNA gene high-throughput sequencing technology was used to compare the composition and diversity of gut microbial communities in domestic and wild mallards. Alpha diversity analysis revealed significant differences in the gut microbial communities of domestic and wild mallards. Beta diversity analysis showed that the two groups of stool samples were mostly separated on the principal coordinate analysis (PCOA) plot. In domestic mallards, Firmicutes (67.97%) was the most abundant bacterial phylum, followed by Proteobacteria (24.48%), Bacteroidetes (3.13%), Fusobacteria (2.21%), and Actinobacteria (1.10%). The dominant bacterial phyla in wild mallards were Firmicutes (79.02%), Proteobacteria (12.85%), Fusobacteria (3.37%), and Bacteroidetes (2.79%). At the genus level, a total of 10 dominant genera (Streptococcus, Enterococcus, Clostridium, Lactobacillus, Soilbacillus, Bacillus, Acinetobacter, Comamonas, Shigella, and Cetobacterium) with an average relative abundance greater than 1% were detected in the fecal samples of both groups. The average relative abundance of five potential pathogens (Streptococcus, Enterococcus, Acinetobacter, Comamonas, and Shigella) was higher in domestic mallards than in wild mallards. The enrichment of pathogenic bacteria in the intestinal tract of domestic mallards should be of sufficient concern.

Malaysian healthcare institutions still use ineffective paper-based vaccination systems to manage childhood immunization schedules. This may lead to missed appointments, incomplete vaccinations, and outbreaks of preventable diseases among infants. To address this issue, we proposed a text messaging vaccination reminder and recall system named Virtual Health Connect (VHC) to simplify and accelerate the immunization administration for nurses that may result in improving the completion and timeliness of immunizations among infants. Considering the narrow research about the acceptance of these systems in the healthcare sector, we examined factors influencing nurses’ attitude and intention to use VHC using the extended technology acceptance model (TAM). The novelty of the conceptual model is proposing new predictors of attitude, namely, perceived compatibility and perceived privacy and security issues. We conducted a survey among 121 nurses in Malaysian government hospitals and clinics to test the model. We analyzed the collected data using partial least squares-structural equation modeling (PLS-SEM) to examine the significant factors influencing nurses’ attitude and intention to use VHC. Moreover, we applied artificial neural network (ANN) to determine the most significant factors of acceptance with higher accuracy. Therefore, we could offer more accurate insights to decision-makers in the healthcare sector for the advancement of health services. Our results highlighted that the compatibility of VHC with the current work setting of nurses developed their positive perspectives toward the system. Moreover, the nurses felt optimistic about the system when considering it to be useful and easy to use in the workplace. Finally, their attitude toward using VHC played a pivotal role in raising their intention. Based on the ANN models, we also found that perceived compatibility was the most significant factor influencing nurses' attitude towards using VHC, followed by perceived ease of use and perceived usefulness.

In the polar region, the gravity vector and Earth's rotation vector tend to be in the same direction, leading to slower convergence speed and longer alignment time of the moving base alignment. When the alignment time is short, the alignment cannot converge, resulting in low azimuth accuracy. To address this issue, we propose a polar moving base alignment method based on backtracking scheme. Notably, this work first derives a polar coarse alignment method with the inertial frame based on the transverse Earth model. On this basis, we design a polar coarse alignment method based on backtracking scheme and optimize the data storage scheme. Then, an inverse navigation algorithm based on the transverse inertial navigation mechanical arrangement scheme was derived, and a polar fine alignment method based on backtracking scheme was designed. Semi-physical simulation experiments showed that the alignment algorithm based on backtracking scheme can converge in a short time with high alignment accuracy.

We construct a novel family of summation-integral type hybrid operators in terms of shape parameter $\alpha\in \lbrack 0,1]$ in this paper. Basic estimates, rate of convergence, and order of approximation are also studied using the Korovkin theorem and the modulus of smoothness. We investigate the local approximation findings for these sequences of positive linear operators utilising Peetre's K-functional, Lipschitz class, and second-order modulus of smoothness.

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa, previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. This is the first report in alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

The time’s arrow of macroscopic physical phenomenon is reflected by irreversible physical process, which essentially occurs from small probability state to high probability state. In this paper, simplified models are proposed to understand the macroscopic physical process. In order to describe the information of a physical system, we defined the full self-information as "information height" to describe the complexity or difficulty of a macrostate of physical system. In this way, the direction of macroscopic physical process is from high information height to low information height. We can judge the direction of physical process by the information height. If we want the macroscopic physical process to evolve from the low information height state to the high information height state, the system need to add extra information and corresponding energy to increase the information height.

Background: In the present study, cisplatin, artemisinin and oleanolic acid were evaluated alone and in combination, on human ovarian A2780, A2780ZD0473R and A2780cisR cancer cell lines with aim of overcoming cisplatin resistance and side effects. Methods: Cytotoxicity was assessed by MTT reduction assay. CI values were used as a measure of combined drug effect. MALDI TOF/TOF MS/MS and 2-DE gel electrophoresis were used to identify protein biomarkers in ovarian cancer and to evaluate combination effects. Results: Synergism from combinations was dependent on concentration and sequence of administration. Generally, bolus was most synergistic. 49 proteins differently expressed by 2 ≥ fold were: CYPA, EIF5A1, Op18, p18, LDHB, P4HB, HSP7C, GRP94, ERp57, mortalin, IMMT, CLIC1, NM23, PSA3,1433Z, and HSP90B were down-regulated, whereas hnRNPA1, hnRNPA2/B1, EF2, GOT1, EF1A1, VIME, BIP, ATP5H, APG2, VINC, KPYM, RAN, PSA7, TPI, PGK1, ACTG and VDAC1 were up-regulated, while TCPA, TCPH, TCPB, PRDX6, EF1G, ATPA, ENOA, PRDX1, MCM7, GBLP, PSAT, Hop, EFTU, PGAM1, SERA and CAH2 were not-expressed in A2780cisR cells. The proteins were found to play critical roles in cell cycle regulation, metabolism and biosynthetic processes and drug resistance and detoxification. Conclusion: Results indicate that appropriately sequenced combinations of cisplatin with ART and OA may provide a means to reduce side effects and circumvent platinum resistance.

The outcomes of the present scientometric analysis of research in catalysis provide chemistry and catalysis scholars with a closer bibliometric knowledge of an old and central field of chemical research which is being reshaped by fundamental and technological advances spanning from single-atom heterogeneous catalysis to flow chemistry. Improving and widening research and education in catalysis is a strategic need for national economies. Four research policy guidelines aimed at fostering progress in catalysis research and education conclude the study.

Objectives: This study proposes a model that integrates three determinants of social cognitive theory (SCT) to explain the impact of FoMO on SNS usage. Design: A cross-sectional study is conducted using data for 259 participants recruited from a website. Main Outcome Measures: The analysis focuses on FoMO, social influence, positive outcome expectancy, refusal self-efficacy, and SNS-related behavior cloud-based sites. Data are examined using descriptive analysis and structural equation modeling. Results: The proposed model reported proper goodness of fit. FoMO does not directly or indirectly impact SNS usage through the determinants of SCT. However, social influence and refusal self-efficacy have a direct effect. Conclusions: The roles of the three determinants of SCT vary by stage of SNS usage. FoMO and refusal self-efficacy are more strongly related with SNS addiction. Further research, particularly longitudinal and intervention studies, is needed to examine the effects of specific factors on SNS addiction.

The lean supply chain of construction engineering projects is to achieve the maximum satisfaction of the owners' needs in order to effectively achieve the goal of supply chain management. This paper explores an effective method of lean supply chain cost management for construction engineering projects with target cost management, so that each participating unit on the supply chain node can fully utilizes its core competencies to minimize internal consumption and waste, and achieve the optimal overall efficiency of the supply chain. According to the requirements of the goal planning theory of the construction project company, establish a lean supply chain cost planning system for the construction project, realize the basic model of the lean supply chain cost management of the construction project, and set the target cost from the lean project of the construction project. The technical decomposition is established by the process of cost decomposition and cost pressure transmission and sub-target cost planning.

In 2015, the new installed capacity of global renewable energy power generation exceeded the newly installed capacity of conventional energy power generation, marking a structural change in the construction of the global power system. With the continuous improvement of wind energy utilization technology, the global wind power industry has developed rapidly in recent years. The world's available wind energy is 20 billion kilowatts and has become one of the most economical green power. In China, wind power has become the third largest source of electricity, with the installed capacity increasing from 3.1% in 2010 to 9.2% in 2017. In 2017, China's new installed capacity was 19,660 MW, accounting for 37.45% of the world's new installed capacity. This paper evaluates and compares the efficiency of wind power industry in the four regions of eastern, central, western and northeastern China through EBM models based on radial and non-radial factors. This paper discusses the contribution of China's wind power industry to CO2 emission reduction from the relationship between installed capacity efficiency and CO2 emission reduction efficiency. The conclusions show that the overall efficiency score and ranking of wind power in 2013-2017 is the best in the eastern region, followed by the northeast region and the western and central regions.

Both sand and fly ash were found to be promising for phosphorus removal in bioretention systems. However, nutrient removal in bioretention systems with sand, soil and fly ash was still uncertain due to a lack of data about the influence of layer structure and submerged zone. In this study, a mixture with sand, soil and fly ash (1:1:1) was selected as the base in bioretention systems with different packed layer structures and heights of submerged zone. The comparison of multi-layered structure with mixed structure implied that the used bioretention system with multi-layered structure was superior to that with mixed structure for nitrogen and phosphorus removal. The investigation of the influence of submerged zones on nutrient removal indicated that the submerged zone could significantly improve nitrate removal efficiency with 67.52%-86.32%, while sharply reduce the removal of ammonia nitrogen (from 95.15% to 51.81%) and TP (from 88.66% to 44.50%). Overall evaluation of the effect of packed layer structures and submerged zones suggested that the bioretention system with multi-layered structure at the height of submerged zone at 20-40cm was the most satisfactory, due to its microbial environment.

Novel high energy density materials N14 (1,6-dihydro-1,2,3,3a,4,5,5a,6,7,8,8a,9,10,10a-tetradecazapyrene) and N18 (1,2,2a,3,4,4a,5,6,6a,7,8,8a,9,10,10a,11,12a-octadecazacoronene) were designed, and their structures, detonation performance and stabilities were calculated employing density functional theory (DFT). Calculations reveals that they have a good balance between high energy and stability. Their energy gaps between LUMO and HOMO are all lower than that of TATB, while their impact sensitivity h_50% is estimated close to that of RDX. Concerning energy, detonation performance of the N14 (P = 43.6 GPa, D = 10040 m/s, Q = 2214 cal/g) and the N18 (P = 37.4 GPa, D = 9400 m/s, Q = 2114 cal/g) are comparable to CL-20.

A fast algorithm is established to transform points of the unit sphere into fundamental region symmetrically. With the resulting algorithm, a flexible form of invariant mappings is achieved to generate aesthetic patterns with symmetries of the regular polyhedra. This method avoids the order restriction of symmetry groups, which can be similarly extended to treat regular polytopes in n-dimensional space for n>=4.

Swarm intelligence (SI) methods are nature-inspired metaheuristics for global optimization that exploit a coordinated stochastic search strategy by a group of agents. Particle Swarm Optimization (PSO) is an established SI method that has been applied successfully to the optimization of rugged high-dimensional likelihood functions, a problem that presents the main bottleneck across a variety of gravitational wave (GW) data analysis challenges. We present results from a first application of PSO to one of the most difficult of these challenges, namely, the search for Extreme Mass Ratio Inspiral (EMRI) in data from future spaceborne GW detectors such as LISA, Taiji, or Tianqin. We use the standard Generalized Likelihood Ratio Test formalism, with minimal use of restrictive approximations, to search 6 months of simulated LISA data and quantify the search depth, in signal-to-noise ratio (SNR), and breadth, in the ranges of the EMRI parameters, that PSO can handle. Our results demonstrate that a PSO-based EMRI search is successful over a search region that is in the ballpark of the one that current hierarchical schemes can identify. Directions for future improvements, including computational bottlenecks to be overcome, are identified.

In 2023, the technological landscape witnessed an unprecedented surge of transformative innovations, including Web 3.0 and machine learning, poised to revolutionize various aspects of our daily lives. At the forefront of this revolution stands artificial intelligence (AI), which has evolved from a concept once confined to science fiction into an indispensable force reshaping industries on a global scale. Notably, recent years have seen remarkable strides in AI functionalities, particularly in machine learning, computer vision, and natural language processing. These advancements have empowered AI to generate diverse interactions and media autonomously. This study ultimately provides invaluable insights into the dynamic intersection of AI and Advertising, con-tributing to a deeper comprehension of this evolving field. This study conducts a comprehensive review of Artificial Intelligence within the advertising domain, employing network analysis. By meticulously examining the evolution of AI through co-occurrences of terms, keywords, and co-authorship analysis, we aim to unveil the relationship of AI & Advertising through the lens of Generative models to identify pivotal trends, core concepts, and seminal studies that have profoundly influenced the industry. The analysis charts the trajectory leading to the emergence of Generative Artificial Intelligence (GenAI), a transformative development poised to revolutionize advertising practices and consumer engagement. In tandem with these objectives, the results highlight vital trends, emphasizing the growing prominence machine learning tools and techniques such as deep learning techniques and advanced natural language processing methods like word2vec, GANs, and more in shaping the future of advertising practices.

The previous studies on the diversion pipe of pumped storage power stations (PSPS) primarily focused on numerical simulations of pulsating pressure propagation within the pipe and its inducing factors. However, there was a lack of field monitoring and analysis of measured data to support surface ambient vibration, resulting in insufficient credibility and practicality of the numerical results. Based on ambient monitoring and data analysis, this paper examines the factors contributing to the ambient vibration of the water diversion pipeline in Changlongshan (CLS) PSPS. It is concluded that: (1) The primary source of vibration originates from units operation-induced ambient vibrations; (2) Vibration during pumping conditions exceeds that during power generation conditions; (3) The ground measuring point's vibration acceleration primarily depends on the distance from the vibration source; (4) Different types of units have varying effects on water diversion pipeline vibrations.

The influences of doped hole transporting layer (HTL) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effect by solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared by addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL layer. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity by doping HTL was studied by X-ray diffraction. The conductivity of HTL was measured using four probe methods and was higher for doped devices. Enhanced conductivity of PEDOT:PSS film was also proved using fluorescence emission quenching. Long-term stability of the device was enhanced by doping HTL. The device with solvent additive exhibited much enhanced stability, retained over 80% of the initial PCE for 400 hours, while PCE of the device without solvent additive decreased to less than 80% after 200 hours. A very weak power microwave treatment was used for post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different size of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased, the extent of lifetime enhancement by doping was increased.

Algal-bacterial granular sludge, a new biological technology, has been widely recognized due to its highly effective pollutant treatment and energy efficiency. This study investigated the effects of environmental concentrations of Cr(VI) (0.5-2.5 mg/L) on the performance of algal-bacterial gran-ular sludge and self-defensive responses after 90 days of cultivation. The results showed that Cr(VI) affected chemical oxygen demand (COD), ammonia-N and phosphate removal with different trends being apparent. A linear decline in COD removal was observed, whereas an initial de-creasing and then increasing ammonia-N and phosphate removal took place. Algal-bacterial granular sludge effectively removed Cr(VI) from wastewater through biological adsorption and reduction, showing the potential to treat Cr(VI)-contaminated wastewater. Cr(VI) affected the community abundance of the algal-bacterial granular sludge, in which Chlorophyceae and cya-nobacteria were vulnerable under Cr(VI)-induced stress. To reduce the toxicity of Cr(VI), over-produced EPS-PN and antioxidant enzymes (MDA, SOD and CAT) acted as self-defensive responses to resist oxidative damage. This study showed that algal-bacterial granular sludge can remove 00.5 mg/L of Cr(VI) without performance loss. It is hoped that this study can provide useful information for improved engineering feasibility of algal-bacterial granular sludge.

A new malachite beetle, Baltamauroania mirabilicornis gen. et sp. nov., belonging to the tribe Am-auroniodini (Coleoptera: Dasytidae) embedded in Eocene Baltic amber is described and illustrated. The new genus differs from the congeners of the tribe Amauroniodini in possessing a black, elongate and subparallel body and a pronotum with scalloped lateral sides; long and 11-segmented antennae, with three apical antennomeres enlarged and forming a ‘club’, with cylindrical an-tennomere 1; tarsi shortened and compressed, 5-segmented, with tarsomeres 1 and 2 equal in length, and tarsomere 5 the longest in all legs; pubescence of the dorsal surface consisting of short strong brown semi-erect and fine adpressed hairs; punctation of dorsal surface irregular, elytra lacking grooves; pronotum almost equilateral, slightly elongate, with acute anterior and obtuse posterior angles, with wide margination of basal side and with scalloped and finely margined lateral sides; lateral edges of pronotum weakly rounded, with obtuse protuberance before middle. This is the second extinct genus and third record of the subfamily Danaceinae found in Eocene amber. The fossil records of the family Dasytidae are discussed. Three recent records of different Amauroniodini taxa from Eocene East European amber (Baltic and Rovno) shows there to have been a high Danaceinae biodiversity in both the southern coast of the Subparatethys (documented by Rovno amber) and in its northern coast (documented by Baltic amber). The phenomenon of Danaceinae diversity in the Eocene Epoch is discussed.

Applying the improved social network analysis method and the idea of rolling window regression, this paper explores the impact of HSR network on the quality of urbanization and its dynamics. Based on a sample of 273 cities in China over 2009–2019, we find that the high-speed railway network has an increasing positive effect on the quality of urbanization, which proves the existence of network effect. The empirical results further show that this effect is closely related to the cov-erage rate of high-speed rail network. In addition, heterogeneity analysis reveals that urban ag-glomeration cities are the main beneficiaries. Academically, our study provides a plausible expla-nation and evidence from network size differences for the two conflicting views of the HSR effect. Practically, we also propose some important policy implications for countries in different high-speed rail network construction stages.

Mallard (Anas platyrhynchos) are currently one of the most popular species in rare bird breeding in several southern provinces of China, but there are very few studies on the gut microbial communities of domestic and wild mallards. In this study, 16S rRNA gene high-throughput sequencing technology was used to compare the composition and diversity of gut microbial communities in domestic and wild mallards. Alpha diversity analysis revealed significant differences in the gut microbial communities of domestic and wild mallards. Beta diversity analysis showed that the two groups of stool samples were mostly separated on the principal coordinate analysis (PCOA) plot. In domestic mallards, Firmicutes (67.97%) was the most abundant bacterial phylum, followed by Proteobacteria (24.48%), Bacteroidetes (3.13%), Fusobacteria (2.21%), and Actinobacteria (1.10%). The dominant bacterial phyla in wild mallards were Firmicutes (79.02%), Proteobacteria (12.85%), Fusobacteria (3.37%), and Bacteroidetes (2.79%). At the genus level, a total of 10 dominant genera (Streptococcus, Enterococcus, Clostridium, Lactobacillus, Soilbacillus, Bacillus, Acinetobacter, Comamonas, Shigella, and Cetobacterium) with an average relative abundance greater than 1% were detected in the fecal samples of both groups. The average relative abundance of five potential pathogens (Streptococcus, Enterococcus, Acinetobacter, Comamonas, and Shigella) was higher in domestic mallards than in wild mallards. The enrichment of pathogenic bacteria in the intestinal tract of domestic mallards should be of sufficient concern.

This study aimed to evaluate the diagnostic roles of various immunohistochemical markers in urothelial carcinoma in situ (uCIS) through a meta-analysis and review of diagnostic test accuracy. Immunohistochemical markers CK20, CD44, AMACR, and p53 were evaluated in the present study. We analyzed the expression rates of immunohistochemical markers and compared their diagnostic accuracies. The estimated expression rates were 0.803 (95% confidence interval [CI]: 0.726–0.862), 0.142 (95% CI: 0.033–0.449), 0.824 (95% CI: 0.720–0.895), and 0.600 (95% CI: 0.510–0.683) for CK20, CD44, AMACR, and p53, respectively. In the comparison between uCIS and reactive/normal urothelium, the expression of CK20, AMACR, and p53 in uCIS was significantly higher than in reactive/normal urothelium. CD44 showed significantly lower expression in uCIS than in the reactive/normal urothelium. Among the markers, AMACR had the highest sensitivity, specificity, and diagnostic odds ratio. The AUC on SROC was the highest for CK20. In conclusion, immunohistochemical markers, such as CK20, CD44, AMACR, and p53, can be useful in differentiating uCIS from reactive/normal urothelium.

In the study of rough sets, there are many covering approximation spaces, how to classify covering approximation spaces has become a hot issue. In this paper, we propose concepts covering approximation $T_{1}$-space, $F-$symmtry, covering rough continuous mapping, covering rough homeomorphism mapping to solve this question. We also propose a new method for constructing topology in Theorem 5.1, and get the following properties: (1) For each $x\in U$, $\{X_{i}:i\in I\}\subseteq \mathcal{P}(U)$ is all the subsets of $U$ which contains $x$ and $*$ is a reflexive relation on $U$. If $V\in \tau$ is a subset of $U$ and $x\in V$, then $\underline{*}(\bigcap \limits_{i\in I}X_{i})$ is the smallest subset of $U$ and $x \in \underline{*}(\bigcap \limits_{i\in I}X_{i})\subseteq V$. Denoted by $C(x)=\bigcap \{\underline{*}(X_{i}):x\in \underline{*}(X_{i}),i\in I\}$. (2)If $V\in \tau$ is a subset of $U$, then $V =$ $\bigcup \limits_{x\in V} C(x)$. (3) Let $\{\underline{*}(X_{i}):x\notin \underline{*}(X_{i}), i\in I\}$, then $\overline{\{x\}}$ $=$ $U \setminus \bigcup \limits_{x\notin \underline{*}(X_{i}), i\in I}\underline{*}(X_{i})$; (4) Let $*$ be a reflexive relation on $U$. For every $X\subseteq U$, we have $int(\underline{*}(X))=\underline{*}(X)$. Where $int(\underline{*}(X))$ represents the interior of $\underline{*}(X)$. (5) Let $\{\underline{*}(X_{i}):x\in \underline{*}(X_{i}),i\in I\}$ be a family subsets of $U$, then $\{\underline{*}(X_{i}):x\in \underline{*}(X_{i}),i\in I\}$ is a base for $(U,\tau)$ at the point $x$. \\

This study aimed to evaluate the diagnostic and prognostic roles of GATA3 immunohistochemistry in urothelial carcinoma (UC) through a meta-analysis. We investigated GATA3 immunohistochemical expression rates and performed a subgroup analysis based on tumor site, study location, and histological subtypes. The overall survival rates of patients with GATA3-positive and negative UC were compared. The estimated GATA3 expression rate was 0.748 (95% confidence interval [CI]: 0.704–0.787). GATA3 expression rates in the urinary bladder and urinary tract were 0.775 (95% CI: 0.727–0.818) and 0.614 (95% CI: 0.426–0.774), respectively. The GATA expression rates of noninvasive and invasive UCs were 0.965 (95% CI: 0.938–0.980) and 0.644 (95% CI: 0.581–0.702), respectively. In invasive UCs, there was a significant difference in GATA3 expression between non-muscular invasion and muscular invasion subgroups (0.937, 95% CI: 0.883–0.967 vs. 0.753, 95% CI: 0.645–0.836). GATA3 expression was the highest in the microcytic subtype among the histologic subtypes (0.952, 95% CI: 0.724–0.993). There was a significant correlation between GATA3 expression and better prognosis (hazard ratio: 0.402, 95% CI: 0.311–0.521). Taken together, GATA3 expression significantly correlated with low-stage and better prognosis in UC. GATA3 expression is highly variable across histological subtypes and it is careful interpreting GATA3 expression.

The coastal zone, situated at the sensitive interface between land and sea, serves as a pivotal area of extensive human economic activities. As one of China's economic special zones, Xiamen exemplifies the comprehensive trajectory of coastal governance in China. This research adopts an interdisciplinary approach grounded in historical geography and political ecology, employing data collection methods encompassing historical literature, field surveys, and interviews. The research findings indicate that:(1) The year 1994 witnessed the pivotal role played by the United Nations Convention on the Law of the Sea (UNCLOS) in connecting Jimei Peninsula from a local context to the international arena, thus signifying a momentous turning point in the process of scale reconstruction. However, the subsequent implementation of the Coastal Exclusion Policy in 2002 had detrimental consequences, leading to the disintegration of the local scale, the erosion of maritime culture, and the disregard for the livelihood security of displaced coastal populations.(2)The Coastal Exclusion Policy serves as an instrumental tool for implementing scale politics, while its functional realignment has had profound implications for the coastal zone, encompassing economic transformation, ecological degradation, and the marginalization of the socio-cultural fabric that intertwines humans and the coastal environment.

Apple chlorotic leaf spot virus (ACLSV) is a widespread, deleterious and the most damaging pathogen of fruit tree plants including domesticated apple (Malus domestica) ―a great threat to apple production worldwide. The positive-sense single-stranded RNA genome of ACLSV (7.5 Kbp) encodes three proteins: RNA polymerase (Rep), movement protein (MP) and coat protein (CP). RNA interference (RNAi)-mediated antiviral innate immunity is a key sequence-specific biological phenomenon in eukaryotes to control plant viruses. The aim of this study was to analyze apple (M.domestica) locus-derived microRNAs (mdm-miRNAs) with predicted potential for targeting the ACLSV +ssRNA-encoded mRNAs, using ‘four algorithms’ approach. The ultimate goal in this research is to mobilize the in silico endogenous predicted mdm-miRNAs to trigger RNAi catalytic pathway experimentally and generate apple tree varieties for evaluating potential antiviral resistance monitoring capability and capacity for ACLSV. Experimentally validated mature apple (M.domestica, 2n = 2X = 34) mdm-miRNAs (n = 322) were acquired from miRBase database and tested for alignment with the ACLSV genome. Of the 322 targeting mature locus-derived mdm-miRNAs investigated, nine apple mdm-miRNA homologs (mdm-miR395k, mdm-miR5225c and mdm-miR7121 (a, b, c, d, e, f, g, h) were predicted by all ‘four algorithms’. Only fifty eight mdm-miRNAs were predicted consensus binding sites by union of consensus between two algorithms. The miRanda, RNA22, TAPIR algorithms predicted binding of mdm-miR395k at unique nucleotide position 4691, as the most effectively interacting mdm-miRNAs in targeting the ORF1 sequence. In order to validate target prediction accuracy, whether the apple mdm-miRNAs might bind predicted ACLSV mRNA target(s), we created an integrated Circos plot. Genome-wide in-silico-predicted miRNA-mediated target gene regulatory network validated interactions that warrant in vivo analysis. The current work provides valuable evidence and biological material for generating ACLSV-resistant apple varieties.

Powder mixture with given molar ratio Ca/P = 1.67 consisting of brushite (calcium hydrophosphate dihydrate) CaHPO4·2H2O, calcium oxalate monohydrate CaC2O4·H2O in form of whewellite and weddellite and some quantity of quasi-amorphous phase was obtained as a result of the interaction of hydroxyapatite powder Ca10(PO4)6(OH)2 with an aqueous solution of oxalic acid H2C2O4 at a molar ratio of Ca10(PO4)6(OH)2/H2C2O4 = 1:4 under mechanical activation conditions. This powder mixture was used to produce microporous monophase ceramics based on hydroxyapatite Ca10(PO4)6(OH)2 with aperient density of 1.25 g/cm3 after firing at 1200 oC. Microporosity of sintered ceramics was formed due to presence of particles with plate-like morphology, restraining shrinkage during sintering. Microporous ceramics based on hydroxyapatite Ca10(PO4)6(OH)2 with roughness of the surface as a consequence of the created microporosity can be recommended as a biocompatible material for the bone defects treatment and as a substrate for bone cell cultivation.

Calcium (Ca) is an essential element constituting sedimentary carbonate in subducting sediments. Ca isotopic characteristics of subduction-related rocks could provide insight into the behavior and budget of carbonate and carbon cycles in subduction zones, due to the distinctive δ^44/40Ca ranges of sedimentary carbonate with respect to the mantle. Here, we studied the Ca isotopic compositions of arc magmas from the Northern Luzon arc (NLA), which are evolved from a depleted mantle metasomatized by slab-derived fluids and sediment melts. The δ^44/40Ca values range from 0.76 ± 0.04‰ to 1.01 ± 0.03‰ and cover the typical ranges for bulk silica earth (BSE, ~ 0.94‰) and fresh mid-ocean ridge basalt (MORB, ~ 0.83‰). The Ca isotopes of NLA volcanics are not dominantly determined by the effects of mantle partial melting or fractional crystallization, nor significantly modified by secondary alteration. Instead, the δ^44/40Ca values of NLA volcanics are controlled by the subduction-related metasomatism. The metasomatism by slab-derived fluids (mainly expelled from altered oceanic crust, AOC) dramatically elevated the contents of fluid-mobile elements (e.g., Ba and Pb) with respect to fluid-immobile elements (e.g., Ce). This process, however, rarely modified the Ca isotopes, possibly ascribed to the δ^44/40Ca similarity between AOC and the depleted mantle. The δ^44/40Ca values significantly correlated with subduction indicators (e.g., Sr-Nd isotopes, Ba/Nb, Ce/Pb, and Nb/La), demonstrating the Ca isotopes of NLA volcanics are mainly controlled by the metasomatism of sediment melts subducting from the South China Sea (SCS). Based on the thermal structures and chemical compositions of sediments subducting into global trenches, we propose that carbonate Ca isotopic signals can only be observed in the arcs with high sedimentary Ca fluxes and temperature-pressure conditions well beyond the solidus of H₂O-saturated sediment melting, e.g., NLA, Nicaragua, Guatemala, Colombia, Peru, South Chile, North Vanuatu, New Zealand, and Kermadec. The absence of such signals in other arcs suggests either limited sedimentary fluxes or much of the subducting sedimentary carbonate has been survived during plate subduction to enter the deep mantle.

Rainfall retrieval using geostationary satellites provides critical means to the monitoring of extreme rainfall events. Using the relatively new Himawari 8 meteorological satellite with three times more channels than its predecessors, the deep learning framework of “convolutional autoencoder” (CAE) was applied to the extraction of cloud and precipitation features. The CAE method was incorporated into the Convolution Neural Network version of the PERSIANN precipitation retrieval that uses GOES satellites. By applying the CAE technique with the addition of Residual Blocks and other modifications of deep learning architecture, the presented derivation of PERSIANN operated at the Central Weather Bureau of Taiwan (referred to as PERSIANN-CWB) expands four extra convolution layers to fully use Himawari 8’s infrared and water vapor channels, while preventing degradation of accuracy caused by the deeper network. The development of PERSIANN-CWB was trained over Taiwan for its diverse weather systems and localized rainfall features, and the evaluation reveals an overall improvement from its CNN counterpart and superior performance over all other rainfall retrievals analyzed. Limitation of this model was found in the derivation of typhoon rainfall, an area requiring further research.

With the revolution of computing and biology technology, data sets containing information could be huge and complex that sometimes are difficult to handle. Dynamic computing is an efficient approach to solve some of the problems. Since neighborhood multigranulation rough sets(NMGRS) were proposed, few papers focused on how to calculate approximations in NMGRS and how to update them dynamically. Here we propose approaches for computing approximations in NMGRS and updating them dynamically. First, static approaches for computing approximations in NMGRS are proposed. Second, search region in data set for updating approximations in NMGRS is shrunk. Third, matrix-based approaches for updating approximations in NMGRS while decreasing or increasing neighborhood classes are proposed. Fourth, incremental algorithms for updating approximations in NMGRS while decreasing or increasing neighborhood classes are designed. Finally, the efficiency and validity of the designed algorithms are verified by experiments.

An improved method for Fluctuation Enhanced Sensing (FES) is introduced. We enhanced the old binary fingerprinting method, where the fingerprint bit values were ± 1, by introducing ternary fingerprints utilizing a reference odor. In the ternary method, the fingerprint bit values are -1, 0, and +1 where the 0 value stands for the situation where the slope of the spectrum is identical to that of the reference odor. The application of the reference odor spectrum makes the fingerprint relative to the reference. This feature increases the information entropy of the fingerprints. The method is briefly illustrated by sensing bacterial odor in cow manure isolates.

The pile foundation in the permafrost region is in a negative temperature environment, so the concrete is affected by the negative temperature of the surrounding soil.It not only affects the formation of concrete strength, but also leads to engineering quality accidents in serious cases.Based on the actual measurement of temperature at different strata depths and the comprehensive consideration of surface temperature, terrestrial heat flux and other parameters, the law curve of temperature change along depth in Greater Khingan is established.The calculated results of the curve are consistent with the measured results of ground temperature.The results show that the variation trend of ground temperature along the strata depth at different monitoring sites is basically the same. From June to November, the ground temperature at different depths tends to be constant.From December to May, the ground temperature at any depth within the depth range of 0 to 5.5m follows the law of the cosine function.Below 5.5m, the earth temperature no longer varies with depth.The research results can be used as reference for pile foundation construction under negative temperature environment.

Plasma microRNAs are considered to be potential diagnostic biomarkers for endometriosis. Increasing evidence has shown that a huge amount of miRNAs are abnormally expressed in endometriosis plasma and play irreplaceable roles in diagnosis. The aim of the our study was to identify the differential expression of circular miRNA by reviewing the PubMed, ScienceDirect, and Cochrane databases between normal women and women with endometriosis and analyzing the miRNA data downloaded from the GEO database. Because of the differential miRNA expression in this review, we evaluated the diagnostic values of the differentially expressed miRNAs, particularly during the menstrual phases. According to the cut-off criteria with |log 2 FC|>1.0 and P < 0.05, 36 differentially expressed miRNAs were identified, including 13 upregulated miRNAs and 23 downregulated miRNAs. We developed miR-155, miR-574, miR-23a, and miR-520d via a Venn diagram. Functional enrichment analysis considered that the target miRNAs might be involved in various pathways related to endometriosis, including neurotrophin, Hippo, oocyte meiosis, ubiquitin mediated proteolysis, HTLV-Infection, FoxO, and Rap1 signaling pathways. CTNNB1, MYC, and ES R1 of transcription factors were related to the differentially expressed miRNAs. In summary, our study suggested that a four-miRNA could be included as a prognostic marker in endometriosis.