Detecting, especially locating local degradations at an incipient stage, is very important for mission-critical high voltage rotating machines. One particular challenge of the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially made defects. These defects include turn-to-turn short, surface creepage, loose coils, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that the FDR can identify and locate both structure and insulation degradations of both shielded and unshielded objects with good sensitivity. This makes the FDR a comprehensive tool for fault diagnosis and aging assessment.

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. The IoT devices in this computing model gather data from various sources and send it to edge servers for real-time processing, which presents a challenge to the existing message queue systems in Artificial Intelligence of Things (AIoT) Edge computing. These systems lack the adaptability to respond to the current state of the system, such as changes in the number of devices, message size, and frequency, and optimize the message transmission mechanism accordingly. Hence, it is critical to devise an approach that can effectively decouple message processing and mitigate workload fluctuations in the AIoT computing environment. To this end, this study introduces a distributed message queue system that is specifically tailored to the AIoT edge environment, utilizing a reinforcement learning approach to optimize message queue performance. Empirical findings reveal that this pioneering method significantly improves system throughput while handling varying message scenarios.

Artificial Intelligence (AI) is a breakthrough technology that has been widely applied in many fields and its use in pharmacy is also gaining increasing attentions. Recently, ChatGPT, a newly developed virtual assistant and large language model, is showing strong ability in writing, which makes it a potential tool for review writing. Here in our study, we employ the public available ChatGPT and try to instruct it to generate a mini review on the topic of “lipid-based drug delivery systems” by conversation. Our results showed that ChatGPT can give reasonable outline for this topic and give seemingly sentences with concluding wording, which can give useful information to the readers. However, the accuracy and consistence of the review are not fully guaranteed and there is lack of reliable citations. Therefore, it is concluded that ChatGPT can only give general knowledge on the selected topic, but not capable of giving in-depth discussion. The generated review article can be a useful but occasionally unprecise electronic encyclopedia instead of rigorous scientific literature, which can give benefit for pharmaceutical education and interchange to some extent.

In this paper, fiber sensor based on Vernier effect for simultaneous measurement of relative humidity (RH) and temperature is proposed. The sensor is fabricated by coating two kinds of ultraviolet (UV) glue with different refractive indexes (RI) and thicknesses on the end face of a fiber patch cord. Vernier effect is formed by the inner lower-RI polymer cavity and the cavity composed of both polymer films. By calibrating the RH and temperature response of two peaks on the envelope of the reflection spectrum, simultaneous measurement of RH and temperature is realized by solving a set of quadratic equations. Experimental results show that the highest RH and temperature sensitivities of the sensor are 387.3 pm/%RH (in 20%RH to 90%RH) and -533.0 pm/°C (in 15°C to 40°C), respectively. The sensor has merits of low cost, simple fabrication, and high sensitivity, which makes it very attractive for applications that need to simultaneously monitor these two parameters.

The prediction of drug--target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug--target interactions are either mediocre or rely heavily on data stacking. In this work, we merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end--to--end auto--encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state--of--art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.848. We found that the mutual information between the substructure and graph--level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node--level and graph--level representations contributes most in a relatively dense network.

In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To maximize the utilization, various machine and deep learning models have been developed, e.g. CNN [1] and RNN [2], to study data and extract valuable information from different perspectives. While data-driven applications improve countless products, training models for hyperparameter tuning are still time-consuming and resource-intensive. Cloud computing provides infrastructure support for the training of deep learning applications. The cloud service providers, such as Amazon Web Services [3], create an isolated virtual environment (virtual machines and containers) for clients, who share physical resources, e.g., CPU and memory. On the cloud, resource management schemes are implemented to enable better sharing among users and boost the system-wide performance. However, general scheduling approaches, such as spread priority and balanced resource schedulers, do not work well with deep learning workloads. In this project, we propose SpeCon, a novel container scheduler that is optimized for shortlived deep learning applications. Based on virtualized containers, such as Kubernetes [4] and Docker [5], SpeCon analyzes the common characteristics of training processes. We design a suite of algorithms to monitor the progress of the training and speculatively migrate the slow-growing models to release resources for fast-growing ones. Specifically, the extensive experiments demonstrate that SpeCon improves the completion time of an individual job by up to 41.5%, 14.8% system-wide and 24.7% in terms of makespan.

This paper presents an optimized feature-centered reflection symmetry axis detection and localization framework for object perception. The proposed framework is formed to obtain an improved reflection symmetry axis based on the salient symmetry feature. It starts with a refined Multi-scale Saliency Symmetry Model (MSSM), which is realized by applying isotropic symmetry operator on salient points in scale-space rather than all pixels. In each scale, salient points are initially extracted as local extremal from an image, and they are further refined by a multi-scale implementation for generating salient symmetry feature maps. A Symmetric Transformation Matrix is then computed using the optimal feature matching pairs, which can be explicitly used as an abstract representation of the constraint regions of symmetry objects in an image to optimize the performance of the potential symmetry axis detection. The framework has been investigated experimentally both on the classical dataset from a symmetry detection challenge and the latest dataset. It has shown that the framework can get a better or comparative result and also can be further adapted into terminated human--computer equipment for reflection symmetry object perception and tracking.

The COVID-19 pandemic caused by SARS-COV-2 has infected over 500,000 people causing over 25,000 deaths in the last 10 weeks. A key host cellular protein required for the virus entry is angiotensin-converting enzyme 2 (ACE2). Recent studies have reported that patients with hypertension and diabetes treated with ACE inhibitors or angiotensin receptor blockers might be at a higher risk of COVID-19 infection as these drugs have been reported to increase ACE2 expression. This has raised the need to systematically investigate the effect of different drugs including antihypertensives on modulating ACE2 expression. Here, we analyzed a publicly available CMAP dataset of pre/post transcriptomic profiles for drug treatment in cell lines for over 20,000 small molecules. We show that only one subclass of antihypertensives drugs - ACE inhibitors, are significantly enriched for drugs up-regulating ACE2 expression. Studying the effects of the 672 clinically approved drugs in CMAP, we chart the drug categories that affect ACE2 expression. Specifically, we find that panobinostat (an HDAC inhibitor) confers the highest up-regulation of ACE2 expression while isotretinoin (a vitamin A derivative) is its strongest down-regulator. Our results provide initial candidates guiding further in vitro and in vivo studies aimed at assessing drug effects on ACE2 expression.

During the extraction of hard coal in Polish conditions, methane is emitted, which is referred to as mine gas. As a result of the desorption of methane, a greenhouse gas is released from coal seams. In order to reduce atmospheric emissions, methane from coal seams is captured by a methane drainage system. On the other hand, methane, which has been separated into underground mining excavations, is discharged into the atmosphere with a stream of ventilation air. For many years, Polish hard coal mines have been capturing methane to ensure the safety of the crew and the continuity of mining operations. As a greenhouse gas, methane has a significant potential, as it is more effective at absorbing and re-emitting radiation than carbon dioxide. The increase in the amount of methane in the atmosphere is a significant factor influencing global warming, however, it is not as strong as the increase in carbon dioxide. Therefore, in Polish mines, the methane-air mixture captured in the methane drainage system is not emitted to the atmosphere, but burned as fuel in systems, including cogeneration systems, to generate electricity, heat and cold. However, in order for such use to be possible, the methane-air mixture must meet appropriate quality and quantity requirements. The article presents an analysis of changes in selected parameters of the captured methane-air mixture from one of the hard coal mines in the Upper Silesian Coal Basin in Poland. The paper analyses the changes in concentration and size of the captured methane stream through the methane capturing system. The gas captured by the methane drainage system, as an energy source, can be used in cogeneration, when the methane concentration is greater than 40%. Considering the variability of CH4 concentration in the captured mixture, it was also indicated which pure methane stream must be added to the gas mixture in order for this gas to be used as a fuel for gas engines. The balance of power of produced electric energy in gas engines is presented. Possible solutions ensuring constant concentration of the captured methane-air mixture are also presented.

It has recently been shown that, contrary to the wide belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-symmetric shift matrix. This paper extends this prior work, focusing on undirected graphs where the shift matrix is generally symmetric. We show that while, in this case, the shift matrix can be converted to satisfy the original shift-enabled condition, the converted matrix is not associated with the original graph, that is, it does not capture anymore the structure of the graph signal. We show via a counterexample, that a non-shift-enabled matrix cannot be converted to a shift-enabled one and still maintain the topological structure of the underlying graph, which is necessary to facilitate localized signal processing.

In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV can charge or change the UAV’s battery on the designated landing points to enable the UAV to continue its mission. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software, is constructed to describe this problem. In order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are improved. The computational experiments are set up to compare our model with the 2E-VRP. The results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.

Milk fat production is important in the New Zealand (NZ) dairy industry. Elsewhere, an amino acid substitution (K232A) in the enzyme diacylglycerol acyl-CoA acyltransferase (DGAT1) has been reported to explain variation in some milk traits, including variation in milk fatty acid (FA) profile. In this study, associations between K232A, and milk traits including milk FA composition, were studied in wholly pasture-fed NZ Holstein-Friesian × Jersey (HF×J) cross-bred dairy cows. With a high frequency of K variant (61.9%), the KK cows produced more milk fat than the AA cows (5.41 ± 0.04 % vs 4.42 ± 0.05 %). The milk volume, fat concentration and protein concentration of AK cows were between the genotypes, AA and KK. More C16:0, CLA and C18:3 cis-9, 12, 15 FA were found in the milk of Kiwi-cross cows feed in outdoor pasture grazing system., and the influence of DGAT1 K232A, on these FAs from mid- and late lactation stages were significant. The AA cows produced (P < 0.001) more CLA and C18:3 cis-9, 12, 15, but less C16:0 (1.137 ± 0.047, 0.855 ± 0.015 and 35.170 ± 0.355) than the KK cows (0.934 ± 0.025, 0.778 ± 0.009 and 38.010 ± 0.250).

NiFe alloy nanoparticles/graphene oxide hybrid (NiFe/GO) was prepared for electrochemical glucose sensing. The as-prepared NiFe/GO hybrid was characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The results indicated that NiFe alloy nanoparticles can be successfully deposited on GO. The electrochemical glucose sensing performance of the as-prepared NiFe/GO was studied by cyclic voltammetry and amperometric measurement. Results showed that NiFe/GO modified glassy carbon electrode had sensitivity of 173 μA mM⁻¹cm⁻² for glucose sensing with a linear range up to 5 mM, which was superior to commonly used Ni nanoparticles. Furthermore, high selectivity for glucose detection can be achieved by NiFe/GO. All the results demonstrated that NiFe/GO hybrid was promising for using in electrochemical glucose sensing.

According to the operation conditions of time unlimited passive containment heat removal system (TUPAC), a separate effect experiment facility was established to investigate the heat transfer performance of steam condensation in presence of non-condensable gas. The effect of wall subcooling temperature, total pressure and mass fraction of the air on heat transfer process was analyzed. The heat transfer model was also developed. The results showed that the heat transfer coefficient decreased with the rising of subcooling temperature, the decreasing of the total pressure and air mass fraction. It was revealed that Dehbi’s correlation predicted the heat transfer coefficient conservatively, especially in the low pressure and low temperature region. The novel correlation was fitted by the data obtained in the following range: 0.20~0.45 MPa in pressure, 20% ~ 80% in mass fraction, 15°C ~ 45°C in temperature. The discrepancy of the correlation and experiment data was with ±20%.

Full-scale/model test comparison studies to validate the traditional ABL wind tunnel simulation technique are reviewed. According to the literature review, notable discrepancies between full-scale measurement results and model test results were observed by most performed comparison studies, but the causes of the observed discrepancies were not revealed in a scientific way by those studies. In this regard, a new research scheme for future full-scale/model test comparison studies is proposed in this article, which utilizes the multiple-fan actively controlled wind tunnel simulation technique. With the new research scheme, future full-scale/model test comparison studies are expected to reasonably disclose the main problems with the traditional ABL wind tunnel simulation technique, and the technique can be improved correspondingly.

Partial differential equations (PDEs) are used to describe a wide range of phenomena, such as heat transfer, fluid dynamics, and quantum mechanics. By solving PDEs, we can ob- tain insights into the behavior of the system and make predictions about its future evolution. Conventional numerical methods for obtaining the approximate solutions of PDEs may re- quire extensive computational resources and time, especially for complex PDEs and large- scale problems. The recently developed physics-informed neural network (PINN) has shown promise in a variety of scientific and engineering fields by incorporating physical laws into the loss functions of the neural network (NN). In addition, the training of PINN does not require ground truth data, but it has poor generalization ability to unseen domains. On the other hand, a convolutional neural network (CNN) has fast inference and better generalization ability, but it requires a large amount of training data. Taking the advantages of PINN and CNN by using Legendre multiwavelets (LMWs) as basis functions, we introduce a new method to approach the PDEs in this paper, namely Physics-Informed Legendre Multiwavelets CNN (PiLMWs-CNN), in order to continuously approximate a grid-based state representation that can be handled by a CNN. PiLMWs-CNN enable us to train our models using only physics-informed loss functions without any pre- computed training data, simultaneously providing fast and continuous solutions that gener- alize to previously unknown domains. In particular, the LMWs can simultaneously possess compact support, orthogonality, symmetry, high smoothness, and high approximation order. Compared to orthonormal polynomial (OP) bases, the approximation accuracy can be greatly increased and computation costs can be significantly reduced by using LMWs. We applied PiLMWs-CNN to approximate the damped wave equation, incompressible Navier-Stokes (N- S) equation, and two-dimensional heat conduction equation. The experimental results show that this method provides more accurate, efficient, and fast convergence with better stability when approximating the solution of PDEs.

Remote patient monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine are usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enables the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature. The ubiquitous problem of wearable devices is power demand for signal transmission, such devices require frequent battery charge which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report for collecting kinetic energy from human daily activities for monitoring Human vital signs, the harvested energy is used to sustain the battery autonomy of wearable devices, which enables longer monitoring time of vital data. A thorough review of available commercial ECG devices is first provided, and different methods evaluated in other literature to improve the monitoring time of wearable IOT devices. Besides, a novel type of Stress or exercise ECG monitoring device based on Microcontroller PIC18F4550 and Wi-Fi device ESP8266 is proposed in this study, which is cost effective and enables real time monitoring of heart rate on cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has smallest structure and low friction. Neodymium magnets are chosen for their highest magnetic force. Due to nonlinear magnetic force interaction of the magnets, the nonlinear part of the dynamic equation has inverse quadratic form. Electromechanical damping is considered in this study and the quadratic non linearity is approximated using MacLaurin expansion, which enables to find the law of motion for general case of studies using classical methods for dynamic equations and find the suitable parameters for the harvester. The oscillations are enabled by applying an initial force and there is loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab software and ODE45 solver is used to verify the accuracy of the method.

The perturbations of DNA methyltransferase 3 alpha (DNMT3A) may cause uncontrolled gene expression, resulting in cancers and tumors. The DNMT inhibitors Azacytidine (AZA) and Zebu-larine (ZEB) inhibit the DNMT family with no specificities and consequently would bring side effects during the treatment. Therefore, it is vital to understand their inhibitory mechanisms in DNMT3A to inform the new inhibitor design for DNMTs. Herein, we carried out molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the inhibitory mechanisms of AZA and ZEB. The results were compared to the methyl transfer of cytosine. We showed AZA might stop the methyl transfer process, whereas the ZEB might be stuck in a me-thyl-transferred intermediate (IM3). Such IM3 state then fails the elimination due to the unique protein dynamics that result in missing the catalytic water chain. Our results brought atomic-level insights into the mechanisms of the two drugs in DNMT3A, which could benefit the new generation of drug design for the DNMTs.

For the traditional indoor visible light communication heterogeneous network switching algorithm is only concerned with the research of vertical switching algorithm, however, the research of horizontal vertical collaborative switching combined with multi-carrier modulation technology is seldom mentioned, based on the above, this paper proposes a horizontal-vertical collaborative switching strategy based on the residency time of the communication blind area. Firstly, we use asymmetrically clipped optical OFDM (ACO-OFDM) for signal processing to reduce the bit error rate (BER) of the system by reducing the multipath interference generated during signal transmission.Secondly, the BER is used to determine the communication blind area and to calculate its dwell time. Finally, the threshold time is set as the channel interruption time in the communication blind zone, and the horizontal-vertical cooperative switching is performed by comparing with the dwell time. The simulation results show that the average network throughput is 195.28Mbps, and the average number of handovers is 1.35. Compared with instant vertical handover (IVHO) scheme and dwell vertical handover (DVHO) scheme, the average network throughput is improved by 37.21% and 28.45%, respectively, and the average switching times are reduced by 28.05% and 5.27%, respectively.

To study the anti-cracking performance of cast-in-place concrete inner shaft lining structure under the combined effects of temperature and constraint, this paper first developed a novel temperature stress test machine suitable for the shape of the inner shaft lining structure and with adjustable endpoint constraint, providing specialized experimental equipment for subsequent cast-in-place concrete inner shaft lining temperature stress simulation experiments. Then, this paper preliminarily studied the temperature, strain, and stress changes of the cast-in-place inner shaft lining under the condition of 100% endpoint constraint by simulating the internal hydration heating and cooling process of the cast-in-place inner shaft lining and using the temperature stress test machine to accurately control the endpoint constraint conditions of the inner shaft lining scaled model.

With the targets of petroleum exploration transferred to the deep and ancient strata, abundant oil and gas resources have been found in the Lower Paleozoic and older strata in central and western China. Due to complex evolutionary processes including multiple episodes of hydrocarbon accumulation and ubiquitously accompanied by secondary alterations, significant uncertainties were remained for the generation time and accumulation processes of these revealed petroleum. In this paper, relative pure Re and Os elements existing in the asphaltene fractions of Lower Cambrian solid bitumen collected from the Guangyuan area, western Sichuan Basin, SW China and another Middle–Lower Ordovician heavy oils in the Aiding area of the Tahe oilfield in the Tarim Basin, NW China were successfully obtained by the sample pre-treatments, and Re–Os isotopic analysis was subsequently carried out for the dating of these two petroleum. The Re–Os isotopic composition indicates a generation time of Guangyuan bitumen to locate between 572 Ma and 559 Ma, corresponding to the late Sinian period of Neoproterozoic era. By the means of Re–Os isochron ages, initial 187Os/188Os ratios, and carbon isotopic compositions, the Lower Cambrian bitumen is supposed to be originated from source rocks of the Doushantuo Formation in the Sinian strata and subsequently migrated into the reservoirs of Dengying Formation. These previously reserved petroleum had been transformed to present bitumen by the destruction of reservoirs caused by tectonic uplift. The Re–Os dating results of Middle–Lower Ordovician heavy oil of Tarim Basin supposed that it was formed between 450 Ma to 436 Ma, corresponding to the Late Ordovician–Early Silurian system, and the generated petroleum is likely to migrate into the Middle–Lower Ordovician karst reservoirs to form early oil reservoirs. With tectonic uplift, these oil reservoirs were degraded and reformed to be present heavy oil reservoirs.

The operation status of manufacturing equipment is directly related to the reliability of the operation of manufacturing equipment and the continuity of operation of the production system. Based on the analysis of the operation status of manufacturing equipment and its characteristics, it is proposed that the concept of assessing the operation status of manufacturing equipment can be realized by applying real-time acquisition of accurate inspection data of important parts of weak motion units and comparing them with their motion status evaluation criteria. Through the study of the application characteristics of fractional-order calculus theory, a differential data fusion model based on the fractional-order differential operator is established. The advantages of Internet of Things (IoT）technology and fractional order differential fusion algorithm are integrated to obtain real-time high-precision data of the operating parameters of manufacturing equipment, and the research objective of operating condition assessment of manufacturing equipment is realized. The feasibility and effectiveness of the method are verified by the application of the method in machining center operation status assessment.

This study aims to develop an in-situ field repair approach, special for aircraft composite structures, to enhance the interlaminar toughness of plain woven composites (PWCs) by adding multi-walled carbon nanotubes (MWCNTs). MWCNTs are dispersed at each interface between prepreg layers by means of solvent spraying with the density is 1.58 g/m2. And then, the layers are stacked with the predefined sequence and cured at 120℃ and 1 bar pressure using the heat repairing instrument. Moreover, double cantilever beam (DCB) standard test is used to investigate the interlaminar toughening effect due to the MWCNTs. For comparison, original samples are also prepared, the results indicate that the introduction of MWCNTs can favorably enhance the interlaminar toughness of PWCs at field repair approach and the Mode I fracture energy release rate GIC increases by 102.92%. Based on finite element method (FEM) of continuum damage mechanics, the original and MWCNTs toughening specimen under DCB Mode I fracture are modeled and analyzed. The simulation and experiment are in good agreement. Finally, the toughening mechanism of MWCNTs is explored by scanning electron microscope (SEM), it is founded that a large amount of Fiber-matrix (F-M) interface debonding and matrix cracking of mountain shape are the major modes of fracture accompanied with few fiber breakage and matrix peeling off for the MWCNTs toughening specimens.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.

Rotary tillage is a main management tillage practices and widely applied in the North China Plain. However, the long term rotary tillage (depth of 20 cm) results in soil compaction and plow pan formation, which reduces water use efficiency and nutrient uptake, and then impedes the yield increase. In this study, a 3-year field experiment was conducted to investigate the influence of different depths of tillage on soil bulk density, field capacity, water use, photosynthetic rate, nutrients and maize yields in the North China Plain. Three depths of tillage (D20, depth of 20 cm; D25, depth of 25 cm; D30, depth of 30 cm) were assessed. D25 and D30 significantly reduced soil bulk density and improved field capacity in 10-20 and 20-30 cm soil layer, compared to D20. Soil water consumption for D25 was significantly higher 10.12% and 6.61% than that for D20 and D30, respectively. Photosynthetic rate for D25 significantly improved than that for D20 and D30. Total nitrogen in 0-20cm soil layer decreased with the depths of tillage. The maize yields for D25 significantly increased by 20.92% and 21.56% compared to that for D20 and D30, respectively. Structural equation models showed that the total effects of tillage, total nitrogen, photosynthesis and soil water consumption on yields were 0.019, -0.628, 0.121, and 0.895 (path coefficients λ), respectively. The results demonstrated that D25 could improve maize yields, water use efficiency, photosynthetic rate by improving soil water consumption. Depth of 25 cm is optimal tillage practice for the maize production in the North China Plain.

Chemistry has been viewed as one of the most fruitful near-term applications to science of quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Much less effort has been placed on how one finishes these calculations by using the results from the quantum computer (on the active region of the molecule) and embeds them back into the remainder of the molecule in order to determine the properties of the entire molecule. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with classical algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.

The oncologic merits of laparoscopic technique for colorectal cancer surgery remain debatable. Eligible patients with non-metastatic colorectal cancer who were scheduled for an elective resection by only one surgeon in a medical institution were randomized to either laparoscopic or open treatment. During this period, total 188 patients received laparoscopic surgery and other 163 patients to open approach. The primary endpoint was cancer-free 5-year survival after operative treatment and secondary endpoint was the tumor recurrence incidence. We found there was no statistically significant difference between open and laparoscopic groups regarding average number of lymph nodes dissected, overall mortality rate, cancer recurrence rate or cancer-free 5-year survival. Nevertheless, laparoscopic approach was more effective for colorectal cancer treatment with shorter hospital stay and less blood loss despite operation time was significantly longer. Meanwhile fewer patients receiving laparoscopic approach developed postoperative urinary tract infection, wound infection, pneumonia or anastomosis leakage, which reached statistical significance. For non-metastatic colorectal cancer patients, laparoscopic surgery resulted in better short-term outcomes whether in total complications and intra-operative blood loss. Though there was no significant statistical difference in terms of cancer-free 5-year survival and tumor recurrence, we favor patients receiving laparoscopic surgery if not contraindicated.

Carbon fiber reinforced polymer (CFRP) bending structure is widely used in the aviation field. The occurrence and propagation of delamination damage will reduce the safety of in-service bending structures. As a detection tool with high damage sensitivity, Lamb wave can effectively identify delamination damage. In this study, the signal difference coefficient (SDC) was introduced to quantify delamination damage and evaluate the sensitivity of A0 and S0 mode lamb waves to delamination damage. The simulation results show that compared with the S0 mode Lamb wave, the A0 mode Lamb wave exhibits higher delamination damage sensitivity. The delamination damage can be quantified based on the strong correlation between the SDC and the delamination damage size. The control effect of the linear array PZT phase time-delay method on Lamb wave mode was investigated by simulation. The phase time-delay method realizes the generation of a single-mode Lamb wave, which can separately excite the A0 mode and S0 mode Lamb wave to identify delamination damage of different sizes. The A0 mode Lamb wave was excited by the developed one-dimensional linear comb transducer (LCT), which was used to conduct the detection experiment on the CFRP bending plate with delamination damage sizes of Φ6.0 mm, Φ10.0 mm, and Φ15.0 mm. The experimental results verify the correctness of the simulation. According to the Hermite interpolation results of the finite-element simulation data, the relationship between the delamination damage size and the SDC was fitted by the Gaussian function, which can accurately quantify the delamination damage. The absolute error of the delamination damage quantification with fitting expression is not larger than 0.8 mm, and the percentage error is not more than 8%.

Lung adenocarcinoma (LUAD) is the most common lung cancers, which accounts for about 35%-40% of all lung cancer patients. Despite therapeutic advancements in recent years, the overall survival time of the LUAD patients still remain poor, especially KRAS mutant LUAD. Therefore, it is necessary to further explore novel targets and drugs to improve the prognosis of LUAD. Ferroptosis, an iron-dependent regulated cell death (RCD）caused by lipid peroxidation, has attracted much attention recently as an alternative target for apoptosis in LUAD therapy. Ferroptosis has been found closely related with LUAD, at its every stage, including initiation, proliferation and progression. In this review, we will provide a comprehensive overview on ferroptosis mechanisms, it’s regulation in LUAD, and application of targeting ferroptosis for LUAD therapy.

Biliary tract cancer, encompassing intrahepatic and extrahepatic cholangiocarcinoma, as well as gallbladder carcinoma, stands as a prevalent malignancy characterized by escalating incidence rates and unfavorable prognoses. The onset of cholangiocarcinoma involves a multitude of risk factors and could potentially be influenced by microbial exposure. The human microbiome, encompassing the entirety of human microbial genetic information, assumes a pivotal role in regulating key aspects such as host digestion, absorption, immune responses, and metabolism. The widespread application of next-generation sequencing technology has notably propelled investigations into the intricate relationship between the microbiome and diseases. An accumulating body of evidence strongly suggests a profound interconnection between biliary tract cancer and the human microbiome. This article critically appraises the existing evidence pertaining to the microbiome milieu within patients afflicted by biliary tract cancer. Furthermore, it delves into potential mechanisms through which dysregulation of the human microbiome could contribute to the advancement of biliary tract cancer. Additionally, the article expounds on its role in the context of chemotherapy and immunotherapy for biliary tract cancer.

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

Copper toxicity has been a selective pressure on the sea-ice bacteria due to its widespread occur-rence in Antarctica. Here, with a combined biochemical and metabolomic approach, the Cu2+ ad-aptation mechanisms of Antarctic bacteria were analyzed. Heavy metal resistance pattern of Pb2+ > Cu2+ > Cd2+ > Hg2+ > Zn2+ was observed. Copper treatment did increase the activity of antioxidants and enzymes, maintaining cellular redox state balance and normal cell division and growth. Metabolomics analysis demonstrated that fatty acids, amino acids, and carbohydrates played dominant roles in copper stress adaptation. The results indicated that the adaptation mechanisms of strain O5 to copper stress included protein synthesis and repair, accumulation of organic per-meable substances, up-regulation of energy metabolism, and formation of fatty acids. This study increases the resistance mechanism understanding of Antarctic strains to heavy metals in extreme environments.

Understanding and evaluating urban investment environment is essential for effectively improving the efficiency of resource allocation between cities and promoting overall development of the regional economy. This paper takes 15 node cities on maritime Silk Road covered by the “Belt and Road” as the research object, establishes a dynamic evaluation index system for investment environment, and uses projection pursuit cluster to analyze and evaluate the investment environment of the cities. It is found that the investment environment potential of a city is directly related to the level of social development, economic development, and the degree of opening to the outside world. It is recommended that node cities should seize the important opportunity of the construction of the Maritime Silk Road, introduce world-wide human, financial and material resources to promote regional resources allocation and flow, and continuously improve and upgrade the investment environment quality.

A dual-platform colorimetric and ratiometric chemosensor, namely 6-(2-hydroxynaphthalen)-N-n-butyl-naphthalimide (HNA), based on Schiff base constructed from N-n-butyl-4-amino-1,8-naphthalimide and 2-hydroxy-1-naphthaldehyde has been designed and fabricated for detecting fluoride anion (F−) in DMSO solution. HNA is a colorimetic and ratiometric probe with superior selectivity and sensitivity. The color changes of HNA from yellow to purple was observed by the naked eyes in the presence of F−. The absorbance in the UV-Vis spectra of HNA was decreasing in 422 nm, while gradually increasing in 583 nm by adding F−. The limit of detection (LOD) of HNA for detecting F− is low to 0.61 μM and the binding model of HNA and F− is in 3:2 stoichiometry. Meanwhile, HNA can be used to detect F− in real samples. Finally, the mechanism of HNA for the detection of F− was investigated. The present work indicated that HNA would be a superior potential chemosensor in monitoring F− selectively and sensitively.

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

In the case that ship operators may not be aware of the potential risks of environmental factors in situation of high causation probability during the initial stage of the geometric probability analysis process, it is likely that higher-grade collision accident measures will not be taken. However, if any risks is told to the ship operators, more effective and intentional measures can be taken in time. Moreover, if the causation probability corresponding to environmental factors is no less than the risk early warning critical value calculated based on historical collision accident data, there would be a high-risk level that a collision may occur. A new method is put forward based on quantitative analysis of environmental factors and previous collision statistics to provide early warning of any accident risk, and a risk early-warning critical value (REWCV) can be obtained based on this simple but highly operational and practical method. A case study of Three Gorges Reservoir in China indicates that the range of environmental factors where the probability of collision accident grows rapidly is consistent with environmental limits defined by Chinese maritime standards. In addition, the relationship between the risk early-warning critical value and the number of previous collision accident is also clarified.

Background: Objective quantitative texture characteristics may be helpful in salivary glandular tumor differential diagnosis. This study uses machine learning (ML) to explore and validate the performance of ultrasound (US) texture features in diagnosing salivary glandular tumors. Material and methods: 122 patients with salivary glandular tumors, including 71 benign and 51 malignant tumors, are enrolled. A representative brightness mode US pictures are selected for further Gray Level Co-occurrence Matrix (GLCM) texture analysis. We use t-test to test the significance and use receiver operating characteristic curve method to fund optimal cut-point for these significant features. After splitting 80% data into training set and 20% data into testing set, we use five machine learning models k-nearest Neighbors (kNN), Naïve Bayes, Logistic regression, Artificial Neural Networks (ANN) and supportive vector machine (SVM) to explore and validate the performance of US GLCM texture features in diagnosing salivary glandular tumors. Results: This study includes 49 female and 73 male patients, with a mean age of 53 years old, ranging from 21 to 93. We find that six GLCM texture features (contrast, inverse difference movement, entropy, dissimilarity, inverse difference and difference entropy) are significantly different between benign from malignant tumors (p&lt;0.05). On ML, the overall accuracy rates are 74.3% (95%CI: 59.8-88.8%), 94.3% (86.6-100%), 72% (54-89%), 84% (69.5-97.3%) and 73.5% (58.7-88.4%) for kNN, Naïve Bayes, Logistic regression, one node ANN and SVM, respectively. Conclusion: US texture analysis with ML has potential as an objective and valuable tool for assessment of salivary gland tumors.

The integration of robotic devices into healthcare settings for rehabilitation purposes represents a rapidly advancing field with significant benefits for both therapists and patients. However, standalone robot systems face limitations such as inefficient processing of patient data and logistical challenges for patients, such as travel time to rehabilitation centers. To address these shortcomings, this paper proposes the integration of cyber-physical systems into rehabilitation robotics. Cyber-physical systems leverage network connectivity and real-time data processing to enhance the efficiency and convenience of robot-based rehabilitation. By seamlessly integrating sensors, actuators, and computational algorithms, cyber-physical rehabilitation systems empower therapists to more effectively treat patients by providing actionable insights derived from comprehensive data analysis. This enables therapists to tailor treatment plans precisely to each patient’s unique needs, ultimately leading to improved outcomes. Additionally, cyber-physical rehabilitation systems facilitate remote monitoring and intervention, allowing therapists to oversee patient progress and make timely adjustments to treatment protocols without the need for in-person visits. This not only saves patients valuable time and resources but also ensures continuity of care, particularly for individuals residing in remote or underserved areas. Furthermore, cyber-physical rehabilitation systems enable collaborative decision-making by facilitating communication and information sharing among healthcare professionals, further enhancing the quality and efficacy of rehabilitation services. In summary, integrating cyber-physical systems into rehabilitation robotics represents a significant advancement in leveraging technology to optimize patient care and outcomes, promising to revolutionize rehabilitation therapy.

This paper proposes a solution for the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Secondly, the concept of voxel is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph building and loop closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Aluminum (Al) toxicity is the most common factor limiting the growth of alfalfa in acidic soil conditions. Melatonin (MT), a significant pleiotropic molecule present in both plants and animals, has shown promise in mitigating Al toxicity in various plant species. This study aims to elucidate the underlying mechanism by which melatonin alleviates Al toxicity in alfalfa through a combined physiological and transcriptomic analysis. The results reveal that 5 μM MT significantly alleviates Al-induced growth inhibition and preserves the morphology of root tip cells. Moreover, MT application reduces Al accumulation in alfalfa roots and shoots by 28.6% and 27.6%, respectively. Additionally, MT plays a crucial role in scavenging Al-induced excess H2O2 by enhancing the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), consequently reducing malondialdehyde (MDA) levels. The RNA-seq results reveal that MT application significantly upregulates the expression of xyloglucan endotransglucosylase/hydrolase (XTH) and carbon metabolism-related genes, including those involved in the glycolysis process, as well as sucrose and starch metabolism, suggesting that MT application may mitigate Al toxicity by facilitating the binding of Al to the cell walls, thereby reducing intracellular Al accumulation, and by improving respiration and the content of sucrose and trehalose. Taken together, our study demonstrates that MT alleviates Al toxicity in alfalfa by restoring redox homeostasis, enhancing Al binding to the cell wall, and modulating carbon metabolism. This research advances our understanding of the mechanisms underlying MT's effectiveness in mitigating Al toxicity, offering potential strategies for reducing Al accumulation in alfalfa.

The human interferon-induced transmembrane (IFITM) proteins inhibit the fusion of a broad spectrum of enveloped viruses, both when expressed in target cells and when present in infected cells. Upon expression in infected cells, IFITMs incorporate into progeny virions and reduce their infectivity by a poorly understood mechanism. Since only a few Env glycoproteins are present on HIV-1 particles, and Env clustering has been proposed to be essential for optimal infectivity, we asked if IFITM protein incorporation modulates HIV-1 Env clustering. The incorporation of two members of the IFITM family, IFITM1 and IFITM3, into HIV-1 pseudoviruses verified by Western blotting correlated with a marked reduction of infectivity. Super-resolution imaging of Env distribution on single HIV-1 pseudoviruses did not reveal significant effects of IFITMs on Env clustering. However, IFITM3 markedly reduced the Env processing and incorporation into virions relative to control and IFITM1-containing viruses. These results suggest that IFITM1 and IFITM3 restrict progeny HIV-1 infectivity via distinct mechanisms. We propose that IFITM1 interferes with viral fusion by altering the properties of the viral membrane or Env functionality by means other than disrupting Env clusters, while IFITM3 also targets Env processing and incorporation into progeny virions.

This paper has designed a four-channel wide dynamic microwave rectifier circuit for the shortcomings of uneven output voltage of each branch of the multi-channel rectification technology. A voltage-doubling rectifier circuit with identical structure was used in each branch. The four rectifier branches shared the same load at the output terminal, allowing the rectifier circuit to have an RF-DC conversion efficiency greater than 50% over the input power range from 12mW to 274mW, widened by 274% compared to a single channel. Not only the advantages of multi-channel technology with wide dynamics were retained, but also the output voltage was highly consistent and could be directly output in parallel without complex power electronics conversion.

With the urgent need of precise positioning faced by internet of things (IoT) applications, the universality and cost of indoor positioning devices become key factors. Since the 5G network has been widely deployed, new opportunity is brought by tightly fusing the traditional low-cost sensors, i.e., the magnetometer. In this study, using 5G channel state information (CSI) and geomagnetic data, a multi-input convolutional neural network (CNN) localization system was proposed. First, to generate a data tensor that is easy for CNN processing, the raw data was reconstructed individually. Then, to comprehensively incorporate the features of 5G CSI and geomagnetic strength data, the ReLU function was chosen as the activation function of the convolutional layer. After that, a multi-input CNN was trained using the incorporated geomagnetic strength and CSI amplitude in the off-line side, and the trained CNN was recorded as a location fingerprint, which can be used for the user position prediction. Finally, in the online side and using a multi-input CNN, the 2-D coordinates are estimated and tested indoors in a typical conference room scenario. The results showed that longer sampling time of fingerprint data result in better uniqueness of the reference point, while the data collection time of locating points does not need to be long. Taking the positioning efficiency and accuracy into consideration, a sampling time of 3s at the reference point and 0.2s at the locating point are recommended. The positioning accuracy using the proposed method was 1.41 m, with an improvement of 22.9% compared with the 5G positioning, and an improvement of 18.0% compared with the 5G and geomagnetic fusing positioning using single CNN.

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved super-resolution model which involves the self-attention augmented WGAN is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the HR results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we therefore design a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features, this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.

Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

Dry mouth, hyposalivation, or xerostomia is a significant problem in diabetic patients; however, there was no way to relieve these symptoms. This study was aimed to evaluate the effects of Ixeris dentata (IXD) in combination with lactobacillus extract on the salivation rate in diabetes-induced dry mouth, and its mechanism was also investigated. In the streptozotocin-induced diabetes model, dry mouth condition was established as a model. Both control and diabetic rats were treated with a sublingual spray of either water or IXD and subsequently treated with or without a spray of lactobacillus extract. In diabetes condition, the salivary flow rate, amylase activity, and aquaporin-5 and Na+/H+ exchanger (NHE-1) expressions were markedly decreased, whereas they were more significantly recovered in the sequential treatment of IXD-lactobacillus extract than each single treatment. Furthermore, oxidative stress and its related ER stress response were especially regulated in the IXD/lactobacillus extract condition, where the following anti-oxidative enzymes; GSH:GSSG ratio, superoxide dismutase (SOD), and glutathione peroxidase (GPx) were involved. This study suggests that the combination of IXD and lactobacillus would be a potential alternative medicine against diabetes-induced hyposalivation and xerostomia.

This paper focuses on quantifying the uncertainty in the specific absorption rate values of brain induced by the uncertain positions of the electroencephalography electrodes placed on patient's scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

Importance: Because of the high prevalence of myopia in Taiwan, understanding the risk factors for its development and progression is important to public health. Background: This study investigated the risk factors for myopia and their influence on the progression of myopia in schoolchildren in Taiwan. Design: Patients’ clinical records were obtained retrospectively from ophthalmologists. Questionnaires were given to collect demographic information, family background, hours spent on daily activities, myopia progression, and treatment methods. Participants: A total of 522 schoolchildren with myopia from a regional medical hospital in northern Taiwan participated the study. Written informed consent was obtained from the participants of legal age or the parents or legal guardians. Methods: Multivariable regression analyses were performed. Myopia measured in dioptres was analysed, controlling for patients’ family and demographic information as well as their daily behaviours. Main Outcome Results: Children with high myopic parents were more myopic. Earlier onset age of myopia was associated with a higher level of myopia and greater annual myopic progression. Children reporting more near work activities had higher levels of myopia and greater progression of myopia. Lower levels of myopia were associated with more exercise, longer periods of sleep, and better vision care knowledge in children and parents. Intake of food supplements had no effect on myopia. Conclusions and Relevance: In addition to genetics, education, environment, and near work activity can influence the development of myopia. Health policies for schoolchildren should promote protective activities and vision care knowledge in order to protect the eyesight of schoolchildren.

In the steeply inclined and extremely thick coal seams (SIETCS) mined using the horizontal sublevel top-coal caving (HSTCC) method, the uncertainty of gas emission is a safety threat to the mining operations. In order to reduce the occurrence of accidents, the determination of gas emission is crucial. In this paper, we first proposed a prediction model for workers at the floor coalbed to calculate gas emission on site. We then put forward a finite element numerical simulation for researchers to predict gas emission from the floor coalbed. At last, we measured gas emitted from the floor coalbed of SIETCS in Wudong Coal Mine in a specific mining period and used the data to verify the applicability and accuracy of these two gas-emission prediction methods. The results showed that the gas emission from Wudong Coal Mine was 1.08 m³/min calculated based on the prediction model and 1.07 m³/min obtained using the user-defined integration method. Both methods have their own advantages, disadvantages and applicable objects, and are important in predicting gas emission from SIETCS mined using HSTCC method.

A high step-up DC-to-DC converter that integrates an isolated transformer and a switched-clamp capacitor is presented in this study. The voltage stress of the main power switch should be clamped to 1/4 V by using the turn ratio and switched-clamp capacitor of an isolated transformer to achieve a high voltage gain. In addition, a passive clamp circuit is employed reduce voltage stress on the main power switch. The energy of the leakage inductor can be recycled by the clamp capacitor because of the passive clamp circuit, thereby improving the power converter efficiency. The converter consists of one isolated transformer, one main switch, three capacitors, and four diodes. Operating principle and steady-state analyses are also discussed. Finally, a 24-V-input voltage to 200-V-output voltage and a 150 W output power prototype converter are fabricated in the laboratory. The maximum efficiency of the converter is 95.1 at 60 W.

The main focus of this paper is to propose a comprehensive framework for the cognitive meas-urement and modelling of the built environment. This will involve exploring and measuring neural mechanisms. The aim is to create a foundation for further studies in this field that are consistent and rigorous. Additionally, this framework will facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling. By doing so, the broader intention is to design urban spaces that prioritize human needs and well-being, mak-ing them more liveable.

Since the development of CBCT had been utilized in dentistry, the images of the CBCT can assist the surgeon to evaluate the anatomy carefully. Despite the value of the radiology evaluation, the implant procedures may require additional consideration rather than only evaluating the anatomical factors. The purpose of this study is to evaluate the predictability of using CBCT alone to plan for implant placement on the edentulous patient digitally CBCT images were analyzed by clinicians, measuring the ridge heights and widths of 4 selected implant sites in the maxillary and 2 selected implant sites in the mandibular arches for 91 patients planning for the implant-supported overdenture. (A total of 47 patients out of the 91 had completed the implant placement on the edentulous ridge, contributing to 55 upper and/or lower arches (136 dental implants). . Both predictabilities are low, implying the CBCT planning for implant placement on the edentulous ridge is not a good index and is insufficient to predict the surgical procedures as a solo method. The finding of this study indicates that digital planning by CBCT is insufficient to serve as an individual tool to predict implant procedures. Further information and evaluation must be considered for implant placement on the edentulous ridge.

To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method is proposed. First, a scenario generation scheme considering waterlogged faults of power facilities is constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios will be merged to several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines’ thermal limit violation. Semi-invariant and Gram-Charlier level expansion methods are adopted to analytically depict the probability density function and cumulative probability function of each line’s power flow. The optimal solution is performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance are considered to quantitatively access the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method is verified in case studies based on the IEEE 24-bus system.

The layout of deep chamber for coal-gangue separation (DCCS) based on the horizon of the weak stratum can affect the stability of its surrounding rocks. First, the work summarized the main structural characteristics of DCCS. Similar simulation tests were performed to analyze the deformation and failure law of surrounding rocks of DCCS under different horizons of the weak stratum. Then, 60 numerical simulation schemes were designed by considering the lateral pressure coefficient and the thickness and horizon of the weak stratum. A comprehensive evaluation method was proposed for the stability of surrounding rocks. The optimal layout methods of the DCCS were determined based on the thickness and horizon of the weak stratum in different ground stress fields. If the thickness of the weak stratum was large and lateral pressure coefficient λ &lt; 0.6 or λ &gt; 1, the stability of surrounding rocks was optimal to arrange the side of DCCS along the weak stratum. When 0.6 ≤ λ ≤ 1, the optimal approach for controlling surrounding rocks was to position the floor of DCCS along the weak stratum. If the thickness of the weak stratum was small and λ&lt;0.6 or λ&gt;1, the stability of surrounding rocks was optimal to arrange the side of DCCS near roof along the weak stratum. When 0.6 ≤ λ ≤ 1, the optimal approach for controlling surrounding rocks was to arrange the side of DCCS near the floor along the weak stratum. The results can provide an important theoretical reference for the spatial layout and support design of DCCS.

Transition delaying is of great importance for the drag and heat flux reduction of hypersonic flight vehicles. The first mode within low frequency and the second mode within high frequency exist simultaneously during the transition of hypersonic boundary layer. This paper proposes a novel bi-frequency synthetic jet to suppress low- and high-frequency disturbances at the same time. Orthogonal table and variance analysis are used to compare the control effects of jet with different frequencies, amplitudes and positions. Linear stability analysis results show that, low frequency synthetic jet can suppress the first mode when it is arranged upstream of synchronization point, while the second mode control effect is relatively weak. The higher the high frequency is, the stronger the suppression effect is on the first mode. For the second mode, the suppression effect is only at f2=89.09kHz. The larger the amplitude, the weaker the promoting effect for the first mode and the second mode, and the more obvious the suppressing effect. For the cases with synthetic jet downstream of synchronization point, all levels of the three parameters promote the unstable mode. In terms of the growth rate with the spanwise wave number, the control effect of the same factor and level under different spanwise wave number is different. In order to obtain the optimal control effect on transition, the three factors and the arrangement position of the synthetic jet should be selected as follows: the position is arranged in the upstream, with f1 = 17.82kHz, f2 = 89.9kHz, a =0.007, so that the maximum growth rate of the first mode is reduced by 9.06% and that of the second mode is reduced by 1.28% compared with the uncontrolled state.

The bilateral trade relationship between PRC and ASEAN is close. As early as 2012, China has been the largest trading partner of ASEAN, and ASEAN has also taken the place of the United States as PRC’s second largest trading partner in 2019.This paper uses the theory of ecological population evolution, selects the total import and export volume of PRC and the ten ASEAN countries from 2000 to 2019 as the data sample, studies the bilateral trade relations between PRC and the ten ASEAN countries, analyzes the competition and symbiotic relationship of bilateral trade between PRC and the ten ASEAN countries and the possibility of trade friction according to the results, and proposes corresponding improvement strategies. According to the results of the population evolution competition model, China has a competitive relationship with Indonesia, Malaysia, the Philippines, Singapore and Thailand, which is most likely to cause trade friction. China has a biased relationship with Brunei, Cambodia, Laos, Myanmar and Vietnam, which is more likely to cause trade friction. On this basis, in order to further strengthen the trade relations between PRC and the ten ASEAN countries, this paper puts forward policy suggestions on improving infrastructure, strengthening independent innovation, seeking cooperation fields and actively investing abroad.

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviours and urban mobility. Here we propose a framework, ActivityNET, using machine learning (ML) algorithms to predict passengers’ trip purpose from smart card data and Points-of-Interest (POIs) data. The feasibility of the framework is demonstrated in two phases. Phase I focuses on extracting activities from individuals’ daily travel patterns from smart card data and combining them with POIs using the proposed ‘activity-POIs consolidation algorithm’. Phase II feeds the extracted features into an artificial neural network (ANN) with multiple scenarios and predicts trip purpose under primary activities (home and work) and secondary activities (entertainment, eating, shopping, child drop-offs/pick-ups and part-time work) with high accuracy. As a case study, the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose. The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.

Cloud robot is becoming popular and security of cloud robot is important. However, the researches of cloud robot safety are a few. This work develops a security policy to defense DDoS attack of cloud robot. In this policy, complex, but accurate calculation models are deployed on the cloud, simple but efficient calculation models are deployed on the robot. In the cloud, there are master server and standby server. The master server transfers parameters of complex but accurate models to the standby server periodically and the master executes the start-stop backup policy. Specifically, this work proposes and proves an algorithm to dynamically adjust the interval of parameter transfer. According to a PDRA feature of Netflow, when DDoS attacks, the master server sends warning signals to the robot and standby server. The robot runs local models to avoid stopping work until it is connected to the standby server. Then, standby server provides service to the robot until the master server recover. Finally, this work implemented a gesture recognition cloud robot based on convolutional neural network, hidden Markov model and PDRA feature of Netflow to verify the policy. Experiment shows that the security policy to defense DDoS attack for cloud robot is effective.

In the process of expanding the application of electronic licenses in the Chinese agricultural sector, challenges such as inconsistent information standards, poor interoperability, significant data barriers, and lack of regulatory basis are encountered. By examining online information and practical data of agricultural electronic licenses in the 31 provincial-level administrative units in mainland China, variations in the reasons for promoting the expanded application of electronic licenses in different regions were identified. It is believed that strategies should be proposed around key factors such as government capacity, economic development, social needs, and intergovernmental relations. Suggestions include enhancing the intensity of information aggregation, diversifying the management entities, advancing the intelligence of underlying technology, and improving the completeness of legal rules. New conceptual proposals, such as constructing a database for agricultural information collection, establishing a multi-entity management authorization mechanism, implementing intelligent applications of electronic licenses at the grassroots level, and developing specialized legal rules, require further validation.

Pain is unpleasant sensory and emotional experience accompanied by tissue injury. Often an indi-vidual experience, it can be influenced by different physiological, psychological, and social factors. Fibromyalgia, one of the most difficult-to-treat types of pain, is characterized by general muscle pain accompanied by obesity, fatigue, sleep, and memory and psychological concerns. Fibromyal-gia has increased nociceptive sensations via central sensitization in the brain and spinal cord level. We used intermittent cold stress to create a mouse fibromyalgia pain model (Day 0: 3.69  0.14 g; Day 5: 2.13  0.12 g). Mechanical pain could be reversed by eicosapentaenoic acid (EPA) admin-istration (Day 0: 3.72  0.14 g; Day 5: 3.69  0.13 g). A similar trend could be also observed for thermal hyperalgesia. The levels of elements in the transient receptor potential V1 (TRPV1) sig-naling pathway were increased in the ascending pain pathway, including the thalamus, medial prefrontal cortex, somatosensory cortex, anterior cingulate cortex, and cerebellum. EPA intake significantly attenuated this overexpression. Novel chemogenetics method was used to indeed in-hibit SSC to ACC activity presented an analgesic effect through TRPV1 downstream pathway. The present results provide insights as to the role of TRPV1 signaling pathway as in fibromyalgia and its potential as a clinical target.

Novel Ca3(BTC)2 metal-organic frameworks with different sizes and shapes constructed by Ca2+ and trimesic acid as metal center and ligands respectively were synthesized by a surfactant-assisted method. The relationship between the quantity of surfactant and the particle morphology of the product have been further studied. The samples were characterized by scanning electron microscope, powder X-ray diffraction and Inductively coupled plasma mass spectrometry. The results indicated that the size and shape of Ca3(BTC)2 could be regulated by changing the quantity of surfactant. Rod- and sheet-like Ca3(BTC)2 MOFs have been readily availabled. The removal kinetics of heavy metals of Pb(II) were also studied by rod-like Ca3(BTC)2. The results showed that as-synthesized Ca3(BTC)2 could effectively remove heavy metals of Pb(II) from the sewage.

Neuropathic pain is initiated by a malfunction of the somatosensory cortex system. Acupuncture had been shown to have therapeutic effects for neuropathic pain, although with uncertain mechanisms. We used electroacupuncture (EA) to treat mice spared nerve injury (SNI) model and explore the underlying molecular mechanisms through novel chemogenetic techniques. Both me-chanical and thermal pain were found in SNI mice till four weeks (mechanical: 3.23 ± 0.29 g; thermal: 4.9 ± 0.14 s). Hyperalgesia was attenuated by 2 Hz EA (mechanical: 4.05 ± 0.19 g; ther-mal: 6.22 ± 0.26 s) but not with sham EA (mechanical: 3.13 ± 0.23 g; thermal: 4.58 ± 0.37 s), sug-gesting EA’s specificity. In addition, animals with transient receptor potential V1 (Trpv1) deletion showed no significant induction of neuropathic pain (mechanical: 4.43 ± 0.26 g; thermal: 6.24 ± 0.09 s). Moreover, we found increased levels of inflammatory factors such as interleukin-1 beta (IL1-β), IL-3, IL-6, IL-12, IL-17, tumor necrosis factor alpha, and interferon gamma after SNI modeling, which decreased in EA and Trpv1-/- group rather than sham group. Western blot and immunofluorescence analysis showed similar tendency in the dorsal root ganglion, spinal cord dorsal horn, somatosensory cortex (SSC), and anterior cingulate cortex (ACC). In addition, a novel chemogenetics method was used to precisely inhibit SSC to ACC activity showed an analgesic effect through TRPV1 pathway. In summary, our findings indicate a novel mechanism underlying neuropathic pain as beneficial targets for neuropathic pain.

Microbiota-based strategies are a novel auxiliary therapeutic and preventative way of moderating chronic kidney disease. Lactobacillus mixture (Lm) was previously demonstrated to exert a renal-protective function in the CKD mice model. The efficacy of probiotics in pet foods is a relatively new area of study, and thus verifying the potential health benefits is necessary. This study evaluated the efficacy of Lm treats in feline CKD and elucidated the mechanisms underlying host-microbe interactions. CKD cats (2 and 3 stages) were administrated probiotic pet treats daily (10 g) for 8 weeks. The results demonstrated that during the eight weeks of Lm administration, creatinine was reduced or maintained in all cats with CKD. Similarly, gut-derived uremic toxins (GDUT), IS, and TMAO were significantly downregulated in the serum (confidence intervals = 90%). The life quality of the cats also improved. Feline gut microbiome data, KEGG pathway, and renal function indicator analyses revealed the possible mechanisms involved in modulating CKD feline microbial composition. Further regulation of the microbial functions in amino acid metabolism after Lm administration contributed to downregulating deleterious IS, PCS, and TMAO. The current study provides potential adjuvant therapeutic insights into probiotic pet foods or treats for pets with CKD.

Taiwan’s Chi Mei Medical Center has a vision of becoming a sustainable organization. They found they violated the sustainable development goal (SDG) 13 (“Climate Action”) since its workflows consume excessive tangible resources and waste much operation time. To fulfill the vision, the Chi Mei Medical Center first introduced electronic invoicing. Implementing this electronic invoicing included adopting cloud E-invoice data and creating a software robot for auditing them. This study presents this software robot and its contributions. The software robot replaces an internal auditor to log into Taiwan’s E-invoice platform, download cloud E-invoice data, and detect the inconsistency between these cloud and on-premise data. Internal auditors of the Chi Mei Medical Center only needed to confirm the detection results. They welcome this software robot since it significantly reduces their work burdens. They approved more software robots for assisting other workflows. Besides, the Chi Mei Medical Center earned profits after eliminating paper invoices. This elimination helped release less carbon. This study concludes that chasing a sustainable organization is unnecessarily equivalent to the requirement of more costs. A software robot represents the last puzzle for constructing a paperless workflow since it can resolve the resistance of existing workers to a new workflow.

Clinical ketosis is a crucial metabolic disease in dairy cows that is often accompanied by severe lipolysis and inflammation in adipose tissue. Our previous study suggested that the fold change of calmodulin (CaM) was upregulated 2.401-fold in the adipose tissue of cows with clinical ketosis. Therefore, we hypothesize that CaM may regulate lipolysis and inflammatory responses in clinical ketosis cows. To verify the hypothesis, we conducted a thorough veterinary assessment of clinical symptoms and serum β-hydroxybutyrate (BHB) concentration before collecting subcutaneous adipose tissue samples from six healthy and six clinically ketotic Holstein cows at 17 ± 4 days postpartum. Commercial kits were used to test the abundance of BHB, Non-esterified fatty acid (NEFA), Liver function index (LFI), interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α). We found that Clinical ketosis cows had higher levels of BHB, NEFA, LFI, IL-6, IL-1β, TNF-α, and lower glucose levels compared to healthy cows. Furthermore, the abundance of CaM, toll-like receptor 4 (TLR4), inhibitor of nuclear factor κB kinase subunit β (IKK), phosphorylated nuclear factor κB/nuclear factor κB, (p-NF-κB/NF-κB), adipose triglyceride lipase (ATGL) and phosphorylated hormone-sensitive lipase/hormone-sensitive lipase (p-HSL/HSL) were increased, while that of perilipin-1 (PLIN1) was decreased in the adipose tissue of cows with clinical ketosis. To investigate the mechanistic underlying the responses, we isolated the primary bovine adipocytes from the adipose tissue of healthy cows and induced the inflammatory response mediated by TLR4/IKK/NF-κB with lipopolysaccharide (LPS), in addition to treating with CaM overexpression adenovirus and CaM small interfering RNA. In vitro, LPS upregulated the abundance of CaM, TLR4, IKK, NF-κB, ATGL, p-HSL/HSL, and CaM and downregulated PLIN1. CaM silencing downregulated the abundance of LPS-activated p-HSL/HSL, TLR4, IKK, and NF-κB and upregulated PLIN1 in bovine adipocytes, except for ATGL. By contrast, CaM overexpression upregulated the abundance of LPS-activated p-HSL/HSL, TLR4, IKK, and NF-κB and downregulated PLIN1 expression in bovine adipocytes. In summary, these data suggest that CaM promotes lipolysis in adipocytes through HSL and PINL1 while activating the TLR4/IKK/NF-κB inflammatory pathway to promote an inflammatory response. There is a positive feedback loop between CaM, lipolysis, and inflammation. Overall, inhibiting CaM may act as an adaptive mechanism to alleviate metabolic dysregulation in adipose tissue, contributing to relief from lipolysis and inflammatory response.

Our previous retrospective observational study demonstrated the safety of laparoscopically assisted subtotal colectomy with ileorectal anastomosis and preservation of the superior rectal artery (SRA), without instances of leakage, in patients with slow-transit constipation (STC). Thus, we extended the enrollment period and enlarged the sample size to detect the differences in the post-operative complications, and surgical and functional outcomes between patients who underwent laparoscopically assisted subtotal colectomy with and without SRA preservation. We conducted a retrospective single-center analysis of patients with STC who underwent laparoscopically assisted subtotal colectomy between 2016 and 2020. The diagnosis of STC was based on the colonic transit and anal functional tests, and barium enema to exclude secondary causes. Patients were divided into group A that underwent surgery with SRA preservation, and group B that underwent ligation of the SRA during surgery. Outcome assessments for both groups included the incidence of anastomotic breakdown, intraoperative complications, length of hospital stay, estimated blood loss, time to first flatus, and complications. Propensity-score matching allocated 34 patients to groups A and B each. Postoperative bowel function, including time to first flatus, stool, and oral intake, recovered better in group A than that in group B. Anastomotic leakage, a significant postoperative complication, was less frequent in patients with SRA preservation. Preservation of the SRA in patients undergoing laparoscopically assisted subtotal colectomy with ileorectal anastomosis for STC is associated with favorable postoperative bowel function recovery and lower anastomotic leakage rates.

The buildup dynamics of diode-pumped passively mode-locked solid-state laser is thoroughly explored by the real-time measurement with temporal sampling rate up to 40 GHz. A concise cavity is developed to ensure the transient dynamics purely arising from the gain medium and saturable absorber. Experimental results reveal that the laser output in the buildup process exhibits numerous passively Q-switched pulses followed with a damped relaxation oscillation prior to the stable mode locking. Furthermore, it is confirmed that the laser output has already displayed single clean mode-locked pulses inside the first several Q-switched envelopes before stepping into the stage of relaxation oscillation. The present real-time exploration is expected to provide important information for practical applications with temporal modulation of the pump intensity.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

In recent years, Malaysia has faced environmental challenges caused by municipal solid waste, especially household waste, during the Covid-19 pandemic. Among all the household waste, plastic and paper are the most prominent waste that causes environmental hazards. Several recycling associations in Malaysia have carried out their practices for better waste sustainability and management to curb the increasing amount of household waste. However, the effectiveness is still vague in achieving smart and effective household waste management. Therefore, this paper aims to investigate Malaysia’s household waste management, mainly in three significant municipalities in Malaysia for Kuala Lumpur, Penang and Melaka, in becoming a resilient and sustainable city by addressing two main research questions 1) What are the key factors for ensuring the more success move for future household waste management in cities? and 2) How will the 3 municipalities address their waste issues based on the key factors from RQ1, respectively? This paper reviewed 13 waste management articles and explores the potential of the four factors of waste management from the perspective of technology and data, economy, social and governance. The discussed factors and models contributed to an integrated future-proofing framework that focuses on smart waste tracking, an gamified awareness education, and strict policies to control waste management are the way forward for the future of smart cities household waste management.

This study aims to implement a SSDBI (single-stage differential boost inverter, SSDBI) applying in a single-stage BESS (battery energy storage system, BESS) topology, which can supply power from lower voltage battery module to AC load. Compared with the common two-stage topology, which has a two-stage converter and higher voltage battery module array, the single-stage topology can reduce the number of cells and components, and improve the power density. In addition, a modified SPWM control was proposed to reduce the control complexity of the SSDBI while improving the THD of the inverter. The modified SPWM control can reduce the duty ratio of the SSDBI and the stress on the components to improve the AC voltage output waveform and reduce the THD value about 2.654%.

Excitation-inhibition imbalance of GABAergic interneurons is predisposed to develop chronic temporal lobe epilepsy (TLE). We have previously shown that virtually every neuronal nitric oxide synthase (nNOS)-positive cell is a GABAergic inhibitory interneuron in the denate gyrus. The present study was designed to quantify the number of nNOS-containing hilar interneurons using stereology in pilocapine- and kainic acid (KA)-exposed transgenic adult mice that expressed GFP under the nNOS promoter. In addition, we studied the properties of miniature excitatory postsynaptic current (mEPSC) and paired-pulse response ratio (PPR) of evoked EPSC in nNOS interneurons using whole cell recording techniques. Results showed that there were fewer nNOS-immunoreactive interneurons of chronically epileptic animals. Importantly, patch-clamp recordings revealed reduction in mEPSC frequency, indicating diminished global excitatory input. In contrast, PPR of evoked EPSC following the granule cell layer stimulation was increased in epileptic animals suggesting reduced neurotransmitter release from granule cell input. In summary, we propose that impaired excitatory drive onto hippocampal nNOS interneurons may be implicated in the development of refractory epilepsy.

Detailed kinetics for a 3-wavelength photopolymerization confinement (PC) system is presented for both numerical solutions and analytic formulas. The dynamic profiles are simulated for oxygen, free radical, and conversion for various situations of: blue-light only, 2-light (red and UV), and 3-light (red, blue, UV). An effective PC requires two conditions: (i) a strong N-inhibition for uncured regime with a low conversion (triggered by the UV-light); and (ii) a weak S-inhibition (oxygen-induced) for high conversion under the blue-light or blue and red-light initiation. Good PC candidates are governed by collective factors of: (i) the double ratio of light-intensity and initiator-concentration, (ii) monomers rate-constant; and (iii) effective absorption constants at specific wavelength and initiators. A new reverse feature for the role of N-inhibition on the blue-conversion is found. Higher oxygen concentration leads to a lower conversion, which could be enhanced by reducing the S-inhibition via a red or blue-light pre-irradiation, having a pre-irradiation time TP=200 s for red-light only, and reduced to 150 s, when both red and blue-light. System under UV-only leads a conversion lower than that of blue-only. However, conversion could be improved by the dual-light (blue and UV), and further enhanced by the pre-irradiation of red-light. The two competing factors, N-inhibition and S-inhibition, could be independently and selectively tailored to achieve: (a) high conversion of blue-light (without UV-light), enhanced by red-light pre-irradiation for minimal S-inhibition; and (b) efficient PC initiated by UV-light produced N-inhibition for reduced confinement thickness and for high print speed.

In our previous study, a novel barrier processing on a porous low-dielectric constant (low-k) film was developed: an ultrathin Mn oxide on a nitrogen-stuffed porous carbon-doped organosilica film (p-SiOCH(N)) as a barrier of the Cu film was fabricated. To form a better barrier Mn₂O_3-xN film, an additional annealing at 450°C was implemented. In this study, the electrical characteristics and reliability of this integrated Cu/Mn₂O_3-xN/p-SiOCH(N)/Si structure is investigated. The proposed Cu/Mn₂O_3-xN/p-SiOCH(N)/Si capacitors exhibited poor dielectric breakdown characteristics in the as-fabricted stage, although, less degradation was found after thermal stress. Moreover, its time-dependence-dielectric-breakdown electric-field acceleration factor slightly increased after thermal stress, leading to a larger dielectric lifetime in a low electric-field as compared to other MIS capacitors. Furthermore, its Cu barrier ability under electrical or thermal stress was improved. As a consequent, the proposed Cu/Mn₂O_3-xN/p-SiCOH(N) scheme is a promising integrity for back-end-of-line interconnects.

The rapid development of multicopters has led to many security problems. In order to prevent multicopters from invading restricted areas or famous buildings, an Anti-UAV Defense System (AUDS) has been developed and become a research topic of interest. Topics under research in relation to this include electromagnetic interference guns for unmanned aerial vehicles (UAVs), high-energy laser guns, US military net warheads, and AUDSs with net guns. However, these AUDSs use either manual aiming or expensive radar to track UAVs. This paper proposes a dual-axis rotary platform with UAV automatic tracking. The tracking platform uses visual image processing technology to track and lock the dynamic displacement of a UAV. When a target UAV is locked, the system uses a nine-axis attitude meter and laser rangefinders to measure its flight altitude and calculates its longitude and latitude coordinates through sphere coordinates to provide UAV monitoring for further defense or attack missions. Tracking tests of UAV flights in the air were carried out using a DJI MAVIC UAV at a height of 30 meters to 100 meters. It was set up for UAV image capture and visual recognition for tracking under various weather conditions by a thermal imager and a full-color camera respectively. When there was no cloud during the daytime, the images captured by the thermal imager and full-color camera provide a high-quality image recognition result. However, under dark weather, black clouds will emit radiant energy and seriously affect the capture of images by a thermal imager. When there is no cloud at night, the thermal imager performs well in UAV image capture. When the UAV is tracked and locked, the system can effectively obtain the flight altitude and longitude and latitude coordinate values.

Background: Few studies have investigated the longitudinal association between cervical spondylosis (CS) and migraine by using a nationwide population-based database. Methods: We conducted a retrospective cohort study from 2000 to 2011 identifying 27,930 cases of cervical spondylosis and 111,720 control subjects (those without cervical spondylosis) from a single database. The subjects were frequency-matched on the basis of sex, age, and diagnosis date. The non- cervical spondylosis cohort was four times the size of the cervical spondylosis cohort. To quantify the effects of cervical spondylosis on the risk of migraine, univariate and multivariate Cox proportional hazard regression analyses were used to calculate the hazard ratio (HR) and 95% confidence interval (CI). Results: After a 10-year follow-up controlling for potential confounding factors, overall migraine incidence was higher in the cervical spondylosis cohort than in the non- cervical spondylosis cohort (5.16 and 2.09 per 1,000 people per year, respectively; crude hazard ratio = 2.48, 95% confidence interval = 2.28–2.69) with an adjusted hazard ratio of 2.03 (95% confidence interval = 1.86–2.22) after accounting for sex, age, comorbidities, and medication. Individuals with myelopathy in the cervical spondylosis cohort had a 2.19 times (95% confidence interval = 1.80–2.66) higher incidence of migraine compared than did those in the non- cervical spondylosis cohort. Conclusion: Individuals with cervical spondylosis exhibited a higher risk of migraine than those without cervical spondylosis. The migraine incidence rate was even higher among individuals with cervical spondylotic myelopathy.

Numerical simulations of turbulent flows in a stirred dead-end membrane bioreactor are performed by the RNG k-ε model based on finite volume method using Fluent codes. Comparisons of numerical and experimental results confirm the reliability and feasibility of the constructed model. Flow structures such as wake flows and circulation loops in stirred flows were well simulated. An increase of stirring speed is proposed to use to minimize the low velocity region. The single vane stirrer is found to be beneficial for biological separations. Results reveal that the increase of vane number can enhance the mixing effect in flow domains. However, a circular disk stirrer goes against the formation of vertical circulations. The six-vane stirrer is found to be able to provide a uniform distribution of high shear stress.

Background: We aimed to evaluate the effect of early pelvic binder use in emergency management of suspected pelvic trauma, compared with the conventional stepwise approach. Methods: We enrolled trauma patients with initial stabilization using a pelvic binder for suspecting pelvic injury. Inclusion criteria were traumatic injury requiring a trauma team and at least one of the following: loss of consciousness or Glasgow coma score (GCS) < 13; systolic blood pressure < 90 mmHg; falling from ≥6 m; injury to multiple vital organs; and suspected pelvic injury. Various parameters, including gender, age, mechanism of injury, GCS, mortality, hospital stay, initial vital sign, revised trauma score, injury severity score, and outcome, were assessed and compared with historical controls. Results: A total of 204 patients with high-energy multiple-trauma from single level I trauma center in North Taiwan were enrolled in the study from August 2013 to July 2014. The two group baseline patient characteristics were all collected and compared. The trauma patients with suspected pelvic fractures initially stabilized with a pelvic binder had shorter hospital and ICU stays. The study group achieved statistically significantly improved survival and lower mean blood transfusion volume and mortality rate although they were more severe in the trauma score. Conclusions: We recommend prompt pelvic binder use for suspected pelvic injury before definitive imaging is available, as a cervical spine collar is used to protect the cervical spine from further injury prior to definitive identification and characterization of an injury.

Sinulariolide, a natural product extracted from cultured-type soft coral Sinularia flexibilis, possesses bioactivity against the movement of several types of cancer cell. However, the molecular pathway of its effects on human bladder cancer remain poorly understood. Using a human bladder cancer cell line as an in vitro model, this study investigated the underlying mechanism of sinulariolide against cell migration/invasion in TSGH-8301 cells. We found that sinulariolide inhibited TSGH-8301 cell migration/invasion, and the effect was concentration-dependent. Furthermore, the protein expressions of matrix metalloproteinases (MMPs) MMP-2 and MMP-9, as well as urokinase, were significantly decreased after 24-h sinulariolide treatment. Meanwhile, the increased expressions of tissue inhibitors of metalloproteinases (TIMPs) TIMP-1 and TIMP-2 were in parallel with an increased concentration of sinulariolide. Finally, the expressions of several key phosphorylated proteins in the mTOR signaling pathway were also downregulated by sinulariolide treatment. Our results demonstrated that sinulariolide has significant effects against TSGH-8301 cell migration/invasion, and its effects were associated with decreased levels of MMP-2/-9 and urokinase expression, as well as increased TIMP-1/TIMP-2 expression. The inhibitory effects were mediated by reducing phosphorylation proteins of the PI3K, AKT and mTOR signaling pathway. The findings suggested that sinulariolide is a good candidate for advanced investigation with the aim of developing a new drug for the treatment of human bladder cancer.

The parameter estimation problem for polynomial phase signals (PPSs) arises in a number of fields, including radar, sonar, biology, etc. In this paper, a fast algorithm of parameter estimation for monocomponent PPS is considered. We propose the so-called LSU-EKF estimator, which combines the least squares unwrapping (LSU) estimator and the extended Kalman filter (EKF). First, the coarse estimates of the parameters of PPS are obtained by the LSU estimator using a small number of samples. Subsequently, these coarse estimates are used to initial the EKF. Monte-Carlo simulations show that the computation complexity of the LSU-EKF estimator is much less than that of the LSU estimator, with little performance loss. Similar to the LSU estimator, the proposed algorithm is able to work over the entire identifiable region. Moreover, in the EKF stage, the accurate estimated results can be output point-by-point, which is useful in real applications.

The modeling of flood-induced bend scour near embankment toes can provide important information for river engineering, embankment safety warnings, and emergency action management. During the rainy seasons, short-term general scour and bend scour are the most common causes for the failure of reinforced concrete embankments in Taiwan. To gain a deeper understanding of the scouring process near levee foundations, this study proposed a straightforward and practical method for bend scour simulation. The proposed simulation method is subdivided into three stages: two-dimensional flow simulation, general scour estimation, and bend scour estimation. A new bend-scour computation equation is proposed and incorporated into a two-dimensional hydraulic finite-volume model for simulating the evolution of bend scour depth around embankment toes. The proposed method is applied to simulate the temporal evolution of bend scouring near the Shuideliaw Embankment on the Cho-Shui River in Taiwan, where serious failure occurred during the June 2012 monsoon. Field data were gathered using the numbered-brick technique at the Shuideliaw Embankment to demonstrate the accuracy of the proposed method. The results of the bend scour simulations compared reasonably well with field measurements, indicating close agreement in terms of water levels and bend scour depths near the Shuideliaw Embankment. The proposed method was found to quickly estimate the maximum short-term general scour and bend scour depths for further enhancement of the safety of the embankment toe.

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies and impacts. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

The fall armyworm, Spodoptera frugiperda (Lepidoptera, Noctuidae), is a notorious migratory pest. After invading China in 2019, the species has established as a year-round breeding population in most of the southern provinces. The area of winter corn in this region has been increasing due to the huge demand of fresh corn consumption which is potentially at risk from this invasive, although S. frugiperda growth and development under changing climate in this region is unclear, particularly the rising temperature during night period. Here we used highest daily temperatures of 27℃, 24℃, 20℃ and night temperatures that were 2℃, 4℃, 6℃ lower giving three daily temperature treatments reflective of winter conditions; GroupⅠ: 27-21℃, 27-23℃, 27-25℃, Group Ⅱ: 24-18℃, 24-20℃, 24-22℃, and Group Ⅲ: 20-14℃, 20-16℃, 20-18℃; to evaluate the effect of increasing night temperature on growth and development of S. frugiperda. Results show that the survival rate of larvae and pupae significantly declined with the daily temperature declining and the night temperature range increasing. Significant developmental effects were observed in all day-night temperature treatment, except for adults, with significant interaction effects for all stages except egg and generation time. Development rate accelerated along with the increasing daily temperature and night temperature, except for the intermediate treatments (Group Ⅱ). The uniformity of pupation and emergence times are higher in high daily temperatures and night temperature. Predictions of FAW development and warnings to local farmers need to be adjusted to take into account the more rapid development. These results will support decision makers to develop long-term management strategies for FAW in southern China.

Genetic engineering and directed evolution are effective methods to address the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions, improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase, as well as high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

The exchange coupling between two ferromagnetic thin films, one with magnetically hard and the other with soft phases, separated by a thin non-magnetic layer is studied. Nd-Fe-B/ Ti / Fe thin film heterostructures were fabricated using DC magnetron sputtering on Si substrates which were heated in situ at 650°C using a house-built vacuum-compatible heater. The effect of the thickness of the Ti buffer layer and the annealing temperature on the formation of various phases of NdFeB was investigated. The effect of the thickness of the non-magnetic Ti spacer layer on the exchange coupling strength between the hard phase Nd-Fe-B ferromagnetic thin layer and the soft phase transition metal Fe layer was experimentally investigated. Hysteresis loops of multilayer thin films indicate an antiferromagnetic coupling was observed when the thickness of the spacer layer was 2 nm. This is within the range of antiferromagnetic coupling calculation based on RKKY theory predictions.

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions in-situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for the Lake Orajärvi in 2011-12. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarisation (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarised σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

In the future, the pursuit of high-quality economic development and a focus on ecological environmental protection in China will inevitably result in significant conflicts between land use and ecological land use. The challenge lies in achieving sustainable high-quality development while simultaneously protecting the ecological environment, optimizing the land use structure, and promoting a harmonious relationship between humans and the land. These challenges are faced by all regions. Land use conflicts primarily occur in peri-urban areas characterized by prominent economic development and urban agglomeration. Previous studies have mainly focused on analyzing the effects of land use on habitat quality during intense urbanization. However, it is important to recognize that land pressure encompasses economic, ecological, and social aspects. To gain a comprehensive understanding of the spatial conflict of land use and the impact on habitat quality in Ankang, a city that has been advocating ecological protection for the past two decades, this study aims to objectively analyze the spatial trends in land use changes in such cities. Additionally, it aims to provide insights for the harmonious development of land use in eco-region-oriented cities. Using the SSP-RCP scenarios provided by CMIP6, this paper employs a system analysis method, PLUS model, InVEST model, and land use conflict measurement model to dynamically simulate the future habitat quality and spatial conflict patterns of land use in Ankang City. The study explores the spatial coupling effect of both factors under different scenarios. The results indicate the following:(1) Under different future shared socio-economic path scenarios, land use intensity and land conflict levels follow the order of SSP585 (high forcing scenario), SSP370 (medium to high forcing scenario), SSP245 (medium forcing scenario), and SSP126 (low forcing scenario), with intensity and conflict decreasing accordingly. (2) The overall development trend in Ankang City reveals an intensification of land use conflicts and a decrease in habitat quality. The expansion rate of construction land is increasing and exhibiting aggregation, while agricultural land area is expanding and forest land area is continuously decreasing. (3) Land use intensity exhibits a significant positive correlation with land conflict levels, while land conflict levels demonstrate a significant negative correlation with habitat quality. These findings suggest that land use has had some impact on the ecological environment, with indications of habitat degradation. Even in Ankang, where ecological development is highly valued, the city will gradually face conflicts between ecological protection and economic development in future scenarios. The study highlights that Ankang's future development space will be constrained within the context of environmental protection, leading to greater land use conflicts in urban and surrounding areas. Consequently, the quality of habitats will inevitably decline. Therefore, it is crucial to allocate sufficient space for economic development while simultaneously prioritizing ecological protection. This approach will ensure a healthy economic development trajectory and foster a harmonious relationship between humans and the land.

This study aimed at exploring what role artificial intelligence techniques could play in the futural numerical analysis. In this paper, a convolutional neural network techniques based on modified loss function is proposed as a surrogate of finite element method(FEM). Several surrogate-based physics-informed neural networks(PINNs) are developed to solve representative boundary value problems based on elliptic partial differential equations (PDEs). Results from the proposed surrogate-based approach are in good agreement with ones from conventional FEM. It is found that modification of the loss function could improve the prediction accuracy of the neural network. It is indicated that to some extent the artificial intelligence technique could replace conventional numerical analysis as a great surrogate model.

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robot, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon or heuristic search algorithm based CCPP methods, when coping with the field with irregular boundaries, obstacles and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, which introduces the idea of chromosome pairs and multi-points mutation to improve its global optimization ability in complex environments, reduce the repeated work areas and the number of turns and U-turns, and thereby improve the operation efficiency. The simulation and experimental results on simple regular fields showed that the proposed improved genetic algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, although the proposed CCPP method increased the number of turns by 13.76%, the area of repeated operation and the number of U-turns were decreased by 38.54% and 35.00% respectively, and saves the battery voltage by 7.82% on average compared to the boustrophedon-based CCPP method. This proved that the proposed CCPP method has strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

As solar energy is a low-cost and clean energy source, there has been a great deal of interest in how to harvest it. To absorb solar energy efficiently, we have designed a broadband metamaterial absorber based on the principle of Fabry–Pérot (FP) cavity and surface plasmon resonances (SPRs). We propose a broadband perfect absorber consisting of a four-layer structure of silica-tungsten-silica-titanium (SiO2-W-SiO2-Ti) for the incident light wavelength range of 300–2500 nm. The structure achieves perfect absorption of incident light in the wavelength range of 351.8–2465.0 nm (absorption >90%), with an average absorption of 96.3%. The advantage of our proposed structure is that it combines the characteristics of both high and broadband absorption and has a high overall absorption efficiency for solar radiation. It is also independent of polarization and insensitive to incident angle. We investigated how absorption was affected by different structures, materials, geometrical parameters, and refractive indices for different dielectric materials, and we explored the reasons for high absorption. This structure is refractory and ultra-thin, and it offers a good trade-off between bandwidth and absorption. It therefore has premium application prospects and value.

Although several protocols for genetic transformation of citrus have been published, it is highly desirable to further improve its efficiency. Here we report treatments of Agrobacterium cells and citrus explants prior to and during co-cultivation process to enhance transformation efficiency using a commercially used rootstock 'Carrizo' Citrange as a model plant. We pre-cultured Agrobacterium cells in a 1/10 MS, 0.5 g/L 2-(N-morpholino) ethanesulfonic acid (MES) and 100 µM acetosyringone liquid medium for 6 hours at 25 ℃ before used to infect citrus explants. We incubated epicotyl segments in an MS liquid medium containing 13.2 µM 6-BA, 4.5 µM 2,4-D, 0.5 µM NAA for 3 hours at 25℃ prior to Agrobacterium infection. In the co-cultivation medium, we added 30 µM paclobutrazol and 10 µM lipoic acid. Each of these three treatments significantly increased the efficiencies of transformation. When the three treatments were combined, we observed that the transformation efficiency was enhanced from 11.5% to 52.3%. The improvement of genetic transformation efficiency mediated by these three simple treatments may facilitate more efficient applications of transgenic and gene editing technologies for functional characterization of citrus genes and for genetic improvement of cultivated citrus varieties.

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we propose an association rule mining method based on clustered events of spatial auto-correlation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high-high), LL (low-low), HL (high-low), and LH (low-high) groups by using local Moran’s I clustering map (LMIC). Then the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Indosinian granodiorite is enriched in sulfophile elements, e.g., Zn, Ag and Cd, and the Variscan granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining co-existence patterns of elements and provides an insight into their enrichment mechanisms.

Excretory-secretory products (ESPs) are the main research targets for investigating the hosts and helminths interaction. Parasitic worms can migrate to parasitic sites and avoid the host immune response by secreting this product. Angiostrongylus cantonensisis an important food-borne zoonotic parasite that causes severe neuropathological damage and symptoms, including eosinophilic meningitis or meningoencephalitis in humans. Benzaldehydes are organic compounds composed of a benzene ring and formyl substituents. This compound has anti-inflammatory and antioxidation properties. Previous studies showed that 3-hydroxybenzaldehyde (3-HBA) and 4-hydroxybenzaldehyde (4-HBA) can reduce apoptosis in A. cantonensis ESPs treated astrocytes. These results on the protective effect underlying benzaldehyde have primarily focused on cell survival. The study was designed to investigate the molecular mechanisms of endoplasmic reticulum stress (ER stress) and oxidative stress in astrocytes in A. cantonensis ESPs treated astrocytes and to evaluate the therapeutic consequent of 3-HBA and 4-HBA. First, we initially established the RNA-seq dataset in each group, including Normal, ESPs, ESPs+3-HBA, and ESPs+4-HBA. We also found that benzaldehyde (3-HBA and 4-HBA) can stimulate astrocytes to express ER stress-related molecules after ESP treatment. The level of oxidative stress could also be decreased in astrocytes by elevating antioxidant activity and reducing ROS generation. These results suggested that benzaldehyde may be a potential therapeutic compound for human angiostrongyliasis to support brain cell survival by inducing the expression levels of ER stress- and oxidative stress-related pathway.

Understanding how species that follow different life-history strategies respond to stressful temperature can be essential to efficient treatments against agricultural pests. Here we focus on how the development and reproduction of C. medinalis is influenced by exposure to different rearing temperatures in the immature stage. We found that low (18 or 22 °C) or high (30 and 34 °C) rearing temperatures negatively affected the immature development and flight performance compared to the normal temperature 26 °C, with higher larval and pupal mortality, lower pupation and eclosion rates and shorter flight duration and distance. Low rearing temperatures significantly slowed down the immature process, but accelerated adult reproduction. However, high rearing temperatures had the opposite impacts. Interestingly, the flight of adults with un-mature rearing low temperatures (18 and 22 °C) significantly decreased their lifetime fecundity and mating frequency, but increased pre-oviposition period of females compared to the control; On contrast, high rearing temperatures (30 and 34 °C) significantly accelerated onset of oviposition and enhanced synchrony of spawning. These results indicate high rearing temperatures in the immature stage are more likely to trigger the onset of migration, but low rearing temperatures induces adults to have a greater resident propensity with stronger reproductive ability.

Existing methods enhance mid-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.

The zebrafish Danio rerio is suitable to study gametes as a model organism. There were &gt; 70% of zebrafish spermatozoa activated, because they were contaminated with urine or excrement. The movement of spermatozoon in water was propagated along the flagellum at 16 s after sperm activation, then damped from the end of the flagellum for 35 s and fully disappear at 61 s after activation. For artificial fertilization, milt must be added to an extender, which stops the movement of sperm and keeps the sperm motionless until fertilization. E400 was shown to be the most suitable extender as it allows to store sperm for fertilization for 6 to 12 h at 0-2oC. Sperm motility decreased only to 36% at 12 h post stripping (HPS) for E400 extender and to 19% for Kurokura extender. To achieve an optimal level of fertilization and hatching, a test tube with a well-defined amount of 6,000,000 spermatozoa in E400 extender per 100 eggs and 100 µl of activation solution has proved to be more successful than using a Petri dish. The highest fertilization and hatching rates reached 80% and 40-60%, respectively, with milt stored for 1.5 h in E400 extender at 0-2oC.

Simply Summary: Translation of new cancer treatments between pets and human were noticed in comparative oncological investigation. The current study aims at evaluating a polyacetylenic glucoside purified from an edible herb, Bidens pilosa, to present its anti-angiogenic effects. We innovatively find this polyacetylenic glucoside, cytopiloyne, shows anti-angiogenic effect on different in vitro assays and various in vivo animal models under hypoxia. Based on results of this study, cytopiloyne will be a prospective herb angiogenesis inhibitor candidate to control animal or human cancer formation as adjuvant therapy. Abstract: Anti-angiogenesis is a pivotal combination treatment approach in cancer therapy but rare using on companion animals. This study aimed at evaluating the anti-angiogenic effect of a B. pilosa sourced polyacetylenic glucoside, cytopiloyne, on various in vitro assays and in vivo models. We provide evidences showing that CP has anti-angiogenic activities. Firstly, CP inhibited sponge/ Matrigel plug angiogenesis from tumor cells and decreased the survival of tumor cells on hypoxic conditions. Besides, CP declined PKCα protein expression which a protein leads to the growth and spread of tumors under hypoxia. Secondly, inhibitory effects of CP on endothelial angiogenesis were confirmed by chick chorioallantoic membrane assay, tube formation of SVEC4-10 cells and Matrigel plug assay. A dose-dependent CP treatment inhibited 4T1 cells proliferation under hypoxia and migration. It also suppresses VEGF transcription under hypoxia. Finally, we found that CP decreased PDIA4, a novel regulator of cancer growth, expression in endothelial cells. This effect was confirmed by PDIA4 knockout mice with reduced angiogenesis in Matrigel plug assay. Taken together, these results suggest that CP might act as a promising anti-angiogenic herbal agent candidate to be used in animal hypervascularized cancer of veterinary medicine or in combination to control human cancer as adjuvant therapy.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Although the Slicing Method (SM) is effective for calculating the volume of point cloud objects (PCOs), it is restricted in terms of applicability and practicability because of a certain contingency and directional defects. The Co-Opposite-Direction Slicing Method (CODSM) proposed in this paper is an improved method for calculating PCO volume by increasing parallel (co-opposite-direction) observation and considering the two-way mean as the result. This method takes full advantage of the mutual offsetting of random errors and the compensation of systematic directional errors, which can effectively overcome (or mitigate) the effect of random errors and reduce the effect of systematic errors in SM. In this paper, two typical objects, a cone model and a stone lion base, are the examples for calculating PCO volume using CODSM. The results show that CODSM has all the inherent advantages of SM and effectively weakens the volatility of random errors and the directionality of systematic errors from SM. Therefore, CODSM is a robust configuration upgrade of SM.

M87 is the best available source for studying the AGN jet-launching region. To enrich our knowledge of this region, with quasi-simultaneous observations using VLBA at 22, 43 and 86~GHz, we capture the images of the radio jet in M87 on a scale within several thousand R_s. Based on the images, we analyze the transverse jet structure and obtain the spectral-index distribution in the jet. We find that the absorption is enhanced at the collimation regions and the interruption of the jet. The external medium, which may not be uniform, may have contributed a lot to the absorption in the jet-launching region. Additionally, the temporal morphology of the jet in its launching region may be largely affected by the local, short-lived kink instability growing in itself.