To realize structural vibration control,a two parameters H2 optimization design was proposed to optimize the tuning ratio and damping ratio for electromagnetic tuned mass damper (EMTMD). The control effect of this two parameters optimization design is better than that of classical tuned mass damper (TMD).For this two parameters optimization,the most important thing is that the inductance of the coil can be set very small and the external load resistance can be positive ,which can avoid the use of complex negative impedance circuit. If Ref.[6] were designed according to the H2 optimization of two parameters, the EMTMD can be used for multi-modal vibration control of structures without connecting negative inductance and negative resistance spontaneously.

Accurate measurement of systemic financial risk is critical to maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was con-structed by integrating six dimensions and employing Gray Relation Analysis (GRA) for dimensionality reduction of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA) optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trend of financial pressure indicators, the relationship between financial pressure and economic activity can be effectively identified. The research findings indicate that: (1) The CFSI is capable of accurately reflecting the current financial stress situation in China; (2) The IGSA-RBFNN demonstrates good robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology benefits policymakers and regulatory authorities to formulate preventive measures and risk management by identifying potential risks and vulnerabilities in advance.

Temperature is a crucial environmental factor that affects embryonic development, particularly for marine organisms with long embryonic development periods. However, the sensitive period of embryonic development and the role of autophagy/apoptosis in temperature regulation in cephalopods, enigmatic creatures, remain unclear. In this study, we cultured embryos of Sepiella japonica, a typical species in the local area of the East China Sea, at different incubation temperatures (18 ℃, 23 ℃, and 28 ℃) to investigate various developmental aspects, including morphological and histological characteristics, mortality rates, the duration of embryonic development, and expression patterns of autophagy-related genes (LC3, BECN1, Inx4) and apoptosis marker genes (Cas3, p53) at 25 developmental stages. Our findings indicate that embryos in the high-temperature (28 ℃) group had significantly higher mortality and embryonic malformation rates than those in the low-temperature (18 ℃) group. Furthermore, high temperature (28 ℃) shortened the duration of embryonic development by 7 days compared to the optimal temperature (23 ℃), while low temperature (18 ℃) caused a delay of 9 days. Therefore, embryos of S. japonica were more intolerant to high temperatures (28 ℃), emphasizing the critical importance of maintaining an appropriate incubation temperature (approximately 23 ℃). Additionally, our study observed, for the first time, that the Early blastula, Blastopore closure, and Optic vesicle to Caudal end stages were the most sensitive stages. During these periods, abnormalities in the expression of autophagy-related and apoptosis-related genes were associated with higher rates of mortality and malformations, highlighting the strong correlation and potential interaction between autophagy and apoptosis in embryonic development under varying temperature conditions.

The teaching and learning environment have changed significantly over the last decade with the integration of an increasing amount of information and communication technologies, especially in distance education due to global pandemics, total lockdowns, isolation, and quarantine periods. The number of numerous virtual and non-virtual tools involved in educational processes is growing day by day. In terms of technological innovations used in learning environments, many researchers and educators are trying their best to bring more creative ideas into the technology- based learning environments. Although higher educational institutions have greatly benefited from the growing number of hardware and software resources in the educational process, they have also faced some setbacks that have harmed the learning processes. These technologies have in turn complicated some of the processes in the learning atmosphere. Therefore, we take Spanish teaching course as an example to present the challenging smarter teaching and learning systems that higher educational institutions face today.

In real financial markets, the certainty equivalent rate is an important factor for investors to consider. From the value of a specific company’s certainty equivalent rate, investors can judge if this company is worth investing in. In this paper, we will explore how to obtain the value of the certainty equivalent rate so that we can evaluate a given company. First, we will introduce the theoretical support of this paper, which is put-call parity. From put-call parity, we will derive the mathematical expression for the certainty equivalent rate. Second, we will use the mathematical expression for the certainty equivalent rate to perform an empirical study. We will consider some extreme examples, including some of the best- and worst-performing stocks up to this point in 2023. Based on these stocks’ data, we will visualize the performance of these stocks and explain the meaning of the data. Finally, we will apply the results to a very popular industry—the electric vehicle industry—to explore the current electric vehicle market situation in the U.S.A.

Chaotic switching is a newly evolve randomization method which can suppress conducted electromagnetic interference generated within the DC-DC converter. It can suppress the spectral peaks present in the frequency band effectively by spread spectrum technique and can spread it over the wide range of frequency band implying EMI suppression. In this paper, a chaotic PWM technique based on RCFMFD scheme is generated through Field programmable gate array (FPGA) for suppressing the conducted electromagnetic interference (EMI) generated within the Luo converter. A hardware prototype of Luo converter was developed in order to analyze EMI reduction through FFT analysis by comparing both traditional periodic PWM switching and chaotic PWM switching. The results obtained from the hardware setup shows significant reduction of EMI with Chaotic switching as compared to traditional PWM switching for both boost and buck operation of Luo converter.

Environmental estrogen pollution and estrogen effects on the female reproduction system are well-recognized scientifically. Among the estrogens, 17 β-estradiol is a priority in environmental estrogen pollution, and it is also a major contributor to estrogen which regulates the female reproduction system. 17 β-estradiol is carcinogen and has a tumor promotion effect relating to breast cancer, lung cancer and others. It also affects the psychological well-being such as depression, fatigue and others. Thus, a simple method of detection of 17 β-estradiol will be important for both environmental estrogen pollution and women’s health care. This study demonstrates a simple-use, cost effective 17 β-estradiol biosensor system which can be used for both environment and women’s health care applications. The bio-recognition mechanism is based on the influence of the redox couple, K₃Fe (CN) ₆/K₄Fe (CN) ₆ by the interaction between 17 β-estradiol antigen and its α-receptor (ER- α; α-estrogen antibody). The transduction mechanism is an electrochemical analytical technique, differential pulse voltammetry (DPV). The levels of 17 β-estradiol antigen studied was between 2.25 pg/mL to 2,250 pg/mL, Phosphate buffered saline (PBS), tap water from the Cleveland regional water district, and simulate urine were used as the test media covering the potential application areas for 17 β-estradiol detection. An interference study by testosterone which has a similar chemical structure and molecular weight as those of 17 β-estradiol was carried out, and this 17 β-estradiol biosensor showed excellent specificity without any interference by similar chemicals.

By repeated unloading and loading of the excavation dynamic and static loads, repeated train dynamic and static loads of multi-factors, the underpass tunnel, cross tunnel engineering influence area rock layer, the existing tunnel structure of the three constitutes a multifaceted structure, multi-factor interaction system. Existing tunnels into the prediction theory model of the new tunnel excavation process of dynamic and static loads, dynamic and static loads of existing tunnels are mostly analyzed individually, the results of synergistic analysis needs to be improved. Based on the theory of the current layer method, the paper comprehensively considers the stiffness of the existing tunnel, the stiffness of the new tunnel, the excavation method load and the existing tunnel load, coupled with Peck's empirical formula, the theoretical model of deformation prediction for excavation of a two-lane highway tunnel under the tunnel through the existing railroad is proposed, and verified and optimized by numerical calculations and monitoring data, which proves the applicability and reliability of the theoretical model in the similar strata and the project promotion.

The research on the excavation mechanical properties of underpass tunnels has already had certain results, but fewer of them consider the effects of dynamic and static loads on the excavation mechanical properties of underground tunnels at the same time, especially the lack of research cases of double-line highway tunnels with angled underpasses of existing railway tunnels. In this paper, based on the tunnel project of the new double-line Shiqian Highway Tunnel passing under the Hurong Railway at an oblique angle, the mechanical parameters of the rock body in the cross-tunneling area under the influence of static and dynamic load coupling are obtained by using the disturbance quantification coefficients and the Hoek-Brown criterion based on the method of over-advanced geological prediction and the investigation of the surrounding rock on the palisade face, and the engineering-scale finite element model is constructed and the mechanical characteristics of the excavation in the tunnel of the double-lane highway tunnel passing under the existing railroad under the influence of the static and dynamic loads are analyzed. The results show that: the new tunnel rock movement law for the top of the arch sinking, bottom plate bulging, side wall to move outward, the height and width of the arch and bottom plate arch with the tunnel excavation and increase, the side wall rock displacement effect is smaller; left and right lines of the tunnel disturbed area of the rule of change is similar; the existing tunnel bottom plate displacement is more than the top plate and the left and right side walls by the excavation of the time step influence is larger. Typical profile displacement is mainly determined by the distance from the excavation surface; the new tunnel in the four types of calculation conditions of the rock disturbance angle is the same; the existing tunnel roof and side wall Von Mises stress extreme occurs in the tunnel structure, the bottom plate there are pressure relief and pressure-bearing areas; based on the displacement control criteria, the recommended upper and lower step blasting method.

The accumulation of high levels of lead or lead ions in a human body is harmful, particularly to children. Its neurotoxic effect is profound, damaging the central and peripheral nervous systems, resulting in stunted growth, behavioral problems and learning disabilities. The major source of lead or lead ions comes from the drinking water and tap water. The assessment of the water quality, including lead or lead ion content, is usually completed by a regional water department professional. This assessment is time-consuming and requires expensive instruments and skilled operators. Therefore, there is a need to produce a simple-use and relatively inexpensive method to detect lead or lead ions in water samples. This research has developed a simple-use, cost effective sensor system for the detection of lead ions in tap water. An under-potential deposited bismuth sub-layer on a thin gold film based electrochemical sensor was designed, manufactured and evaluated. Differential pulse voltammetry (DPV) measurement technique was employed in this detection. Tap water from the Cleveland, Ohio, USA regional water district was the test medium. Concentrations of lead ion in the range of 8 X 10^-8 M to 8 x 10^-4 M were evaluated. This DPV detection system required 3 -6 minutes to complete the detection measurement. A longer measurement time of 6 minutes was used for the lower lead ion concentration. The selectivity of this lead ion sensor was very good, and Fe III, Cu II, Ni II and Mg II at a concentration level of 5×10^-4 M did not interfere with the lead ion measurement.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with the assembled truss beam composite floor (ATBCF), the pushdown analysis, and the nonlinear dynamic analysis as well, are conducted for such a structure by using the alternate load path (ALP) method. The bearing capacities of the remaining structures at three different work conditions, which are the side middle column failure, the edge middle column failure, and the corner column failure, are individually studied, and the collapse mechanism for the remaining structures is analyzed from the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient are discussed. The results show that the residual bearing capacity of the remaining structure with the bottom corner column failure is higher than that of the one with the side or edge middle column failure, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times of the first-order vertical vibration period of the remaining structure, and it is suitable to set the dynamic amplification coefficient as of 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

The rapid development of China's economy has led to a rapid increase in energy production and use. Among them, the excessive consumption of coal in fossil energy consumption is the leading cause of air pollution in China. This paper incorporates renewable energy innovation, fossil energy consumption and air pollution into a unified analysis framework, and uses spatial measurement models to investigate the spatial effects of renewable energy green innovation and fossil energy consumption on air pollution in China, and decomposes the total impact into direct and indirect effects. influences. The empirical results show that China's air pollution, renewable energy green innovation and fossil energy consumption are extremely uneven in geographical space, generally showing the characteristics of high in the east and low in the west, and showing a strong spatial aggregation phenomenon. Fossil energy consumption will lead to increased air pollution, and the replacement of fossil fuels with clean and renewable energy is an important means of controlling pollution emissions. The direct and indirect effects of renewable energy green innovation on air pollution are significantly negative, indicating that renewable energy green innovation not only suppresses local air pollution, but also suppresses air pollution in neighboring areas. The consumption of fossil energy will significantly increase the local air pollution, and the impact on the SO2 and Dust&Smoke pollution in the adjacent area is not very obvious. It is recommended to strengthen investment in renewable energy green innovation, reduce the proportion of traditional fossil energy consumption, and pay attention to the spatial connection and spillover of renewable energy green innovation.

The Mouse Grimace Scale (MGS) was developed 10 years ago to assess pain through characterisation of changes in five facial features or action units. The strength of the technique is that it is proposed to be a measure of spontaneous or non-evoked pain. A comprehensive scoping review of the academic literature was performed. The MGS has been employed mainly in evaluation of acute pain, particularly in the pain and neuroscience research fields. There has however been use of the technique in a wide range of fields, and based on limited study it does appear to have utility for pain assessment across a spectrum of animal models. Use of the method does allow detection of pain of a longer duration, up to a month post-initial insult. There has been less use of the technique using real-time methods and this is an area in need of further research.

A simple in vitro biosensor for the detection of β-amyloid 42 in phosphate-buffer saline (PBS) and undiluted human serum was fabricated and tested based on our platform sensor technology. The bio-recognition mechanism of this biosensor was based on the effect of the interaction between antibody and antigen of β-amyloid 42 to the redox couple probe of K₄Fe (CN) ₆ and K₃Fe (CN) ₆. Differential pulse voltammetry (DPV) served as the transduction mechanism measuring the current output derived from the redox coupling reaction. The biosensor was a three-electrode electrochemical system, and the working and counter electrodes were 50 nm thin gold film deposited by sputtering technique. The reference electrode was a thick-film printed Ag/AgCl electrode. Laser ablation technique was used to define the size and structure of the biosensor. Cost-effective roll-to-roll manufacturing process was employed in the fabrication of the biosensor making it simple and relatively inexpensive. Self-assembled monolayers (SAM) of 3-Mercaptopropionic acid (MPA) was employed to covalently immobilize the thiol group on the gold working electrode. A carbodiimide conjugation approach using N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride (EDC) and N–hydroxysuccinimide (NHS) was undertaken for cross-linking antibody of β-amyloid 42 to the carboxylic groups on one end of the MPA. The antibody concentration of β-amyloid 42 used was 18.75µg/mL. The concentration range of β-amyloid 42 in this study was from 0.0675µg/mL to 0.5µg/mL for both PBS and undiluted human serum. DPV measurements showed excellent response in this antigen concentration range. Interference study of this biosensor was carried out in the presence of Tau protein antigen. Excellent specificity of this β-amyloid 42 biosensor was demonstrated without interference by other species such as T-tau protein.

Friction and wear take place on two solid surfaces in sliding contact as a result of the mechanical, thermal and chemical interactions with the participation of environmental species. Electron microscopy and the associated spectroscopic analyses are powerful in probing these mechanisms in spatial resolutions from micro to atomic scale. This article provides a review of the author’s work in the wear and friction mechanisms of PVD hard coatings in which various SEM- and TEM-based microscopic and spectroscopic techniques were employed. Understanding on the failure mechanisms and the origin of self-adaptive friction has been improved to nano-scale. Other related issues are also discussed, such as sample preparation techniques for cross-sectional electron microscopy, energy dispersive X-ray spectroscopy and electron energy loss spectroscopy.

With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.

Focused microwave breast hyperthermia (FMBH) employs a phased antenna array to perform beamforming that can focus microwave energy at targeted breast tumors. Selective heating of the tumor endows the hyperthermia treatment with high accuracy and low side effects. The effect of FMBH is highly dependent on the applied phased antenna array. This work investigates the effect of polarizations of antenna elements on the microwave focusing results by simulations. We explore two kinds of antenna arrays with the same number of elements using different digital realistic human breast phantoms. The first array has all the elements’ polarization in the vertical plane of the breast while the second array has half elements’ polarization in the vertical plane and the other half in the transverse plane, i.e., cross polarization. In total 96 sets of different simulations are performed, and the results show that the second array leads to a better focusing effect in dense breasts than the first array. This work is very meaningful for the potential improvement of the antenna array for FMBH, which is of great significance for the future clinical applications of FMBH. The antenna array with cross polarization can also be applied in micro-wave imaging and sensing for biomedical applications.

Leasing and recycling are important issues to achieve low-carbon sustainable development of construction machinery industry, however there are some dilemmas bothering which is caused by excessive restriction of product service life. Focusing on the problem of pricing and coordination, a two-stage model of leasing closed-loop supply chain based on the Industrial Internet platform practical application was proposed. Under the centralized and decentralized decision-making scenarios, the price of leasing and selling, maintenance effort and recovery rate are calculated respectively. By using the Shapley value method to optimize the selling price, and the two-part pricing contract to calculate the compensation fee, the global profit is reasonably distributed, while the supply chain achieving Pareto optimality. The performance of different contracts on the improvement of supply chain profit was investigated. And the correlation among parameters was also explored through sensitivity analysis and numerical simulation. The findings showed that maintenance and recycling of construction machinery can be improved with the advantage of the Industrial Internet platform, while achieving the supply chain coordination by contracts. The enhancement of maintenance effort can decrease the remanufacturing cost which is positively correlated to the selling price and leasing price of product, furthermore promoting the recovery rate of used products. In the future, reasonable control of platform cost can help improve the recycling of construction machinery.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

By using the Black-Derman-Toy (BDT) model, we predict the future trend of the riskless rate, and then we build an equation that relates the market price of zero-coupon bonds and the theoretical price of zero-coupon bonds calculated using a binomial option pricing model. Based on this, we can find the implied daily return μ, implied natural upturn probability p, and implied daily volatility σ with respect to different time-to-maturity values of zero-coupon bonds. With these results, we can give some suggestions to bond traders.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

Plant Cysteine Oxidase (PCO) is a plant O2-sensing enzyme of catalyzing oxidation of cysteine to Cys-sulfinic acid at the N-termini of target proteins. To better understand the Brassica napus PCO gene family, PCO genes in B. napus and related species were analyzed. In this study, 20, 7 and 8 PCO genes were identified in Brassica napus, Brassica rapa and Brassica oleracea, respectively. According to phylogenetic analysis, the PCOs were divided into five groups: PCO1, PCO2, PCO3, PCO4 and PCO5. Gene organization and motif distribution analysis suggested that PCO gene family was relatively conserved during evolution. Expression analysis showed that PCO genes were expressed in different tissues at different developmental stages. Interestingly, most of the Bna/Br/BoPCO5 members were expressed in leaves, roots, flowers, buds and siliques, suggesting an important role in both vegetative and reproductive development. Expression of BnaPCO was induced by various abiotic stress, especially waterlogging stress, which was consistent with the result of cis-element analysis. In this study, the PCO gene family of Brassicaceae was analyzed for the first time, which contributes to a comprehensive understanding of the origin and evolution of PCO genes in Brassicaceae and the function of BnaPCO in regulating plant responses to abiotic stresses.

A cuprous oxide (Cu₂O) thin layer served as the base for a non-enzymatic glucose sensor in an alkaline medium, 0.1 NaOH solution, with a linear range of 50-200 mg/dL using differential pulse voltammetry (DPV) measurement. An X-ray photoelectron spectroscopy (XPS) study confirmed the formation of the cuprous oxide layer on the thin gold film sensor prototype. Quantitative detection of glucose in both phosphate-buffered saline (PBS) and undiluted human serum were carried out. Neither ascorbic acid nor uric acid even at a relatively high concentration level of 100mg/dL in serum interfered with the glucose detection, demonstrating the excellent selectivity of this non-enzymatic cuprous oxide thin layer based glucose sensor. Chronoamperometry (CA) and single potential amperometric voltammetry were used to verify the measurements obtained by differential pulse voltammetry (DPV), and the positive results validated that the detection of glucose in a 0.1 M NaOH alkaline medium by DPV measurement was effective. Nickel, platinum and copper are commonly used metals for non-enzymatic glucose detection. The performance of these metal-based sensors for glucose detection using DPV were also evaluated. Cuprous oxide (Cu₂O) thin layer based sensor showed the best sensitivity for glucose detection among the sensors evaluated.

We generalize the idea of quantum clock time to quantum spacetime reference frame via physical realization of a reference system by quantum rulers and clocks. Omitting the internal degrees of freedom (such as spins) of the physical rulers and clocks, only considering their metric properties, the spacetime reference frame is described by a bosonic non-linear sigma model (NLSM). We study the quantum behavior of the system under approximations, and obtain (1) a cosmological constant valued (2/π)ρ_c0 (ρ_c0 the critical density at near current epoch) which is very close to the observations; (2) an effective Einstein-Hilbert term; (3) the ratio of variance to mean-squared of spacetime interval tends to a universal constant 2/π in the infrared region. This effect is testable by observing a linear dependence between the inherent quantum variance and mean-squared of the redshifts from cosmic distant spectral lines. The proportionality is expected to be the observed percentage of the dark energy. The equivalence principle is also generalized to the quantum level.

Local polynomial smoother (LPS) is a weighted local least-squares nonparametric method. It provides a local Taylor series fit of the data at any location and can be directly used in a differential equation to provide a numerical scheme. In this article, we introduce this new nonparametric idea based on local polynomial smoother, for acquiring the numerical solution of the Bagley-Torvik fractional-order differential equations. Furthermore, this paper will present a numerical comparison with some methods, such as legendre operational matrix and pseudo-spectral method. The efficiency and accuracy of the LPS method are demonstrated by two numerical examples.

To assure the opacity, an optimal problem is to retain the occurring events sequences as many as possible. Contrary to such problem, the other optimal problem is to preserve the minimal occurring events sequences. In the paper, based on the choosing cost, an optimal opacity-enforcing model is presented to minimize the sum of discount choosing cost, which is not only to preserve the opacity, but also to retain the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. And then, the solutions produced by the algorithms are proved correctly by theoretical proof. Finally, some illustrations for the algorithms are given.

Health care in Canada is managed by provinces/territories. We investigated regional differences in survival among Canadians diagnosed with central nervous system (CNS) tumours. We identified 50,670 patients diagnosed with a first-ever primary CNS tumour between 2008 and 2017 with follow-up until December 31, 2017. We selected the four highest incidence histologies and used proportional hazards regression to estimate hazard ratios (HRs) for five regions (British Columbia, Prairie provinces, Ontario, Atlantic provinces and the Territories), adjusting for sex, tumour behaviour and patient age. Ontario had the best survival profile for all histologies investigated. The Atlantic provinces had the highest HR for glioblastoma (HR=1.26, 95% CI:1.18-1.35) and malignant glioma not otherwise specified (NOS) (Overall: HR=1.87, 95% CI:1.43-2.43; Pediatric population: HR=2.86, 95% CI:1.28-6.39). For meningioma, the Territories had the highest HR (HR=2.44, 95% CI:1.09-5.45) followed by the Prairie provinces (HR=1.52, 95% CI:1.38-1.67). For malignant unclassified tumours, the highest HRs were in British Columbia (HR=1.45, 95% CI:1.22-1.71) and the Atlantic provinces (HR=1.40, 95% CI:1.13-1.74). There are regional differences in the survival of CNS patients at the population level for all four specific histological types of CNS tumours investigated. Factors contributing to these observed regional survival differences are unknown and warrant further investigation.

Molecular communication (MC) is a promising bio-inspired paradigm for exchanging molecule information among nanomachines. This paper proposes a synchronisation-assist photolysis MC system that aims to transmit the bio-sensing signal of the tumour microenvironment, facilitated by mitigating redundant molecules for improved bit error rate (BER) performance. Benefits from bio-compatible MC, biosensors could transmit bio-sensing signals of the tumour in $vivo$ instead of converting them to electrical signals. Due to diffusion motion's slow and stochastic nature, inter-symbol interference (ISI), resulting from previous symbols' residual information molecules, inevitably occurs in diffusion-based MC. ISI is one of the challenges in diffusion-based MC, which significantly impacts signal detection. Inspired by on-off keying (OOK) modulation, the proposed modulation implements a switch of molecules and light alternatively. The light emitted is triggered by a synchronisation signal, and the photolysis reactions could reduce the redundant molecules. An expression for the relevant channel impulse response (CIR) is derived from a hybrid channel model of diffusion and photolysis-reaction. This paper implements the maximum posterior estimation scheme to find the optimal decision threshold and analysis the BER performance in terms of different time intervals of the system. Numerical simulations demonstrate that the proposed method can improve the channel capacity and BER performance. We believe that our work may pave the way for MC application in bio-sensing.

The energy efficient problem of parallel-connected pumps in heating, ventilation, and air-conditioning (HVAC) systems has received an increasing attention in recent years. While many pump optimization methods are proposed and show great performance, pumps are not always energy efficient and lack flexibility. In this paper, we propose a distributed control algorithm for parallel pumps in HVAC system in a peer-to-peer setting. Based on a spanning tree of the network of the intelligent nodes and a population of potential solutions randomly sampled, the algorithm makes optimal control decision for pumps to minimize energy consumption and meet the system demand. The theoretical analysis on convergence of the algorithm is established. Unlike traditional control structure, the whole system is fully distributed and each pump is controlled by an intelligent node that runs identical control code and coordinates with other nodes through direct data exchange. Simulation experiments on 6 parallel-connected pumps are provided for different working cases to demonstrate the effectiveness of the proposed algorithm and compare with other four methods. The results show that our method strictly satisfies the demand constraint and presents a good energy saving potential, the convergence guarantee, the flexibility. The maximum energy saving can be up to 29.92%. Besides, the hardware test clearly presents that our method can perform on low-cost Raspberry Pi3 and reduce system cost.

COVID-19 greatly challenges human beings in the health sector and leaves behind a large amount of medical waste that poses many potential threats to the environment. In this paper, we compiled relevant data released by official agencies and the media, and conducted data supplementation based on previous studies to calculate the net value of medical waste production in Hubei Province during COVID-19 with the help of a neural network model. Then, we reviewed the data related to the environmental impact of medical waste per unit and designed four scenarios to estimate the environmental impact of new medical waste generated during the epidemic. The results showed that at a medical waste generation rate of\ 0.\ 5\ kg/(\ bed\ \cdot\ d) COVID-19 resulted in a net increase in medical waste volume of about 3366.99 tons in Hubei Province. In the four scenario assumptions, if the medical waste brought by COVID-19 is completely incinerated, it will have a large impact on the air quality. If it is disposed by distillation sterilization, it will produce a large amount of wastewater and waste residue. Based on the results of the study, three policy recommendations are proposed in this paper: strict control of medical wastewater discharge, reduction and transformation of the emitted acidic gases, and attention to the emission of metallic nickel in exhaust gas and chloride in soil. These policy recommendations provide a scientific basis for controlling medical waste pollution.

In this study, we de novo assembled whole genomes of two small body-sized West Pacific sciaenids (Larimichthys polyactis and Collichthys lucidus) and compared them with published genome data of two closely-related, large body-sized species (Larimichthys crocea and Miichthys miiuy) and one distantly-related, large body-sized outgroup species (Dicentrarchus labrax). The phylogeny constructed using 7,403 single-copy orthologs shared among the five species indicated that L. crocea and L. polyactis diverged about 42 MYA. The two sibling taxa are more closely-related to C. lucidus than M. miiuy. We further identified four growth-related genes (CDHR2, PGC, PTN and PDGFA) that host five diagnostic amino acid variants on body size traits in the fishes, splitting small-body sized L. polyactis and C. lucidus from large-body sized L. crocea, M. miiuy and D. labrax. The results provide new genomic resources and guidelines to facilitate future endeavors in studying functional genomics and developing selective breeding programs for desirable growth traits in sciaenids.

Poverty alleviation is a hallmark of post-revolution Chinese policymaking. Since 1978, the Communist Party of China (CPC) has implemented successive waves of poverty alleviation policies whose effects have become the focus of an ever-increasing body of academic literature. This paper reviews this diverse but limited literature that evaluates the impact of the CPC’s poverty reduction programs through four major channels, namely fiscal investment programs, social safety nets, rural governance on the village-, county- and provincial level, and the relocation of rural populations from destitute regions. This paper aims to synthesize results and evaluate whether and how the abovementioned poverty alleviation programs have had distinct positive or negative impacts on regional development outcomes. Furthermore, I highlight contradictions in empirical findings to motivate the discussion about contextual importance when designing and implementing future poverty alleviation programs. Finally, I suggest that an exhaustive and critical appraisal of the empirical strategies used in this literature would further the development and application of more accurate and informative methodologies.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of modeling-based control design. System identification from observed input-output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression, are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KVLCC2 ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics and prediction accuracy.

Badminton tactics refer to the techniques and strategies employed by players to win a match. Analyzing these tactics can help players improve their performance and outsmart their opponents. To study the tactics of top players, we use a gaming tree to analyze matches between two of the most powerful badminton players in history: Lin and Lee. By employing the Nash Equilibrium, we can discover the most beneficial strategies for both players, which reflect their most powerful techniques. Additionally, with the help of this gaming tree, we can precisely predict how players will implement their tactics. Empirical experimental results demonstrate that our proposed method not only evaluates and identifies each player's weaknesses and strengths but also has powerful capabilities to predict their tactics.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations

Pancreatitis and alcoholic pancreatitis are serious health concerns, and there is an urgent need for effective treatment strategies. Alcohol is a known etiological factor for pancreatitis, including acute pancreatitis (AP) and chronic pancreatitis (CP). Excessive alcohol consumption induces many pathological stress responses; of particular note is endoplasmic reticulum (ER) stress and adaptive unfolded protein response (UPR). ER stress results from the accumulation of unfolded/misfolded protein in the ER and is implicated in the pathogenesis of alcoholic pancreatitis. Here we summarize the possible mechanisms by which ER stress contributes to alcoholic pancreatitis. We also discuss potential approaches targeting ER stress and UPR for developing novel therapeutic strategies for the disease.

The effects of different pulse frequencies on the microstructure grain size and solid solubility of Al-9Si alloy were systematically investigated using OM, SEM, and EDS. The effects on the mechanical properties of the alloy were analyzed using micro-Vickers hardness tester and multifunctional friction tester. During the solidification process, the Al-9Si alloy is exposed to high-frequency electric current pulses with a current density of 300A/cm2 and frequencies of 0Hz, 500Hz, 1 000Hz, and 2 000Hz. The experimental results show that as the high-frequency pulse frequency increases, the Lorentz force also increases. The intense electromagnetic stirring leads to grain refinement. However, as the pulse frequency continues to increase, the combined effect of Joule heating and Lorentz force results in an enlargement of the melt zone and an increase in grain size. At a pulse frequency of 1 000Hz, the eutectic structure size of the Al-9Si alloy is optimal, with the average size reduced to 13.87μm and a dense distribution, effectively eliminating primary Si. The EDS results revealed that the high-frequency pulse led to a more uniform distribution of Si elements within the matrix, and the solid solubility of Si in the α-Al matrix increased to a maximum value of 1.99%, representing a 39.2% increase. At a pulse frequency of 1 000Hz, the sample demonstrates the most favorable mechanical properties, with the friction coefficient reaching a minimum value of 0.302, representing a 37.7% decrease in the average friction coefficient. The results demonstrate that high-frequency pulsing is an effective method for enhancing the mechanical properties of Al-9Si alloy.

Flexible implementations of Monte Carlo Tree Search (MCTS), combined with domain specific knowledge and hybridization with other search algorithms, can be a very powerful for the solution of problems in complex planning. We introduce mctreesearch4j, a standard MCTS implementation written as a standard JVM library following key design principles of object oriented programming. We define key class abstractions allowing the MCTS library to flexibly adapt to any well defined Markov Decision Process or turn-based adversarial game. Furthermore, our library is designed to be modular and extensible, utilizing class inheritance and generic typing to standardize custom algorithm definitions. We demon- strate that the design of the MCTS implementation provides ease of adaptation for unique heuristics and customization across varying Markov Decision Process (MDP) domains. In addition, the implementation is reasonably performant and accurate for standard MDP’s. In addition, via the implementation of mctreesearch4j, the nuances of different types of MCTS algorithms are discussed.

The research on the impact of excavation of underpass tunnels has already had certain results, but there is a lack of research cases of double-line highway tunnels with oblique angles underpass the existing railway tunnels, especially the method of determining the area of the impact of the dynamic and static loads of the new tunnels and existing trains to be enriched by considering them at the same time. In this paper, based on the tunnel project of the new double-line Shiqian Highway Tunnel with oblique angles underpass the Hurong Railway in Wanshoushan. By constructing a three-dimensional finite element model and simulating the application of static tunnel excavation load, dynamic blasting load and dynamic train operation load of the existing tunnel, the overall displacement of the existing tunnel and the settlement value of the bottom plate of the track surface by the static tunnel excavation load were analyzed. Then, the stresses, vibration speeds and displacements of the tunnel due to the dynamic blasting loads and the dynamic train operation loads were obtained. The results show that: the area of influence of the static loads of the new tunnel excavation on the existing tunnel is divided into three types of perturbations presenting strong, weak and slight; the area affected by the blasting dynamic load of new tunnel excavation is a circular domain with the datum point in the tunnel section as the center; the area affected by the dynamic load of train operation in the existing tunnel is an ellipse with the center of the track surface of the existing tunnel as the datum; there is an anomalous shape in the area affected by the blasting dynamic load of new tunnel excavation and the area affected by the dynamic load of train operation in the existing tunnel.

There have been relatively few researches that determine the role of bricolage in mediating the empirical relationship between strategic flexibility and product innovation. However, most of the research has studied strategic flexibility to engage the absorptive capacity and facilitate product innovation. The Bricolage approach emphasizes utilizing existing resources in product innovation and explores the mechanisms behind bricolage. A resource-based approach to product innovation involves combining resources in the development process in a concrete manner. This is a groundbreaking study since it is the first to examine empirically how absorptive capacity affects product innovation through bricolage and the serial mediation of strategic flexibility. As a consequence of presenting our results, we concluded that absorptive capacity positively and significantly influences product innovation through the serial mediation of strategic flexibility and bricolage. For strategic flexibility to play a role in supporting product innovation, bricolage is one of the mechanisms that can be utilized. This study contributes to the literature on strategic flexibility by examining the effect of strategic flexibility on bricolage and product innovation from the standpoint that absorptive capacity enhances the strategic flexibility of high-tech SMEs in China. Furthermore, this research offers new insights into the relationship between absorptive capacity and product innovation. In addition, it also provides insight into the economic opportunities that may result from product innovation in transition economies such as China.

The Ising model, a cornerstone in the understanding of critical points, phase transitions, and magnetic systems, has been pivotal in advancing our knowledge of statistical physics. Although analytical solutions exist for the 1D and 2D Ising models, complexities rise significantly with the inclusion of external magnetic fields and in higher dimensions. Here, we demonstrate a novel quantum computational approach to simulate the 1D Ising model with basic quantum gates in the QISKIT(Python) that is both accessible to beginners and executable on personal computers, bypassing the need for advanced computational infrastructure. By initializing the system in a comprehensive superposition state, we effectively utilize quantum gates to replicate the intricate interactions between spins and external fields. This method offers a more intuitive and direct observational means of the system's evolution, distinguishing it from previous methodologies. Our results not only repeat all the physics behavior in the 1D Ising model but also showcase the expanding capabilities of quantum computing to tackle complex physical systems, promising advancements in both theoretical and applied physics.

Astrocytes are essential for normal brain development and functioning. They respond to brain injury and disease through a process referred to as reactive astrogliosis, where the reactivity is highly heterogenous and context dependent. Reactive astrocytes are active contributors to brain pathology and can exert beneficial, detrimental, or mixed effects following brain insults. Transforming growth factor-β (TGF-β) has been identified as one of the key factors regulating astrocyte reactivity. Genetic and pharmacological manipulation of TGF-β signaling pathway in animal models of CNS injury and disease alters pathological and functional outcomes. This review aims to provide recent understanding regarding astrocyte reactivity and TGF-β signaling in brain injury, aging, and neurodegeneration. Further, it explores how TGF-β signaling modulates astrocyte reactivity and function.

Engineering application of additively manufactured (AM) metallic materials is quite limited by their fatigue behaviors which are very inconsistent with that of conventionally wrought or cast ones. Here, based on advanced material characterization techniques, such as focused ion beam (FIB), scanning electron microscopy (SEM) and transmission electron microscopy (TEM), the mi-crostructures underneath fracture surfaces were thoroughly investigated in an AM aluminium (AlSi10Mg) alloy with horizontal and vertical building orientation endured very-high-cycle fa-tigue (VHCF) loading under stress ratios R = –1, 0 and 0.5. Two VHCF failure specimens A and B were representatively selected to further SEM examine and TEM samples preparation via FIB milling. Specimen A was horizontally printed and failed at R = –1; specimen B was vertically printed and failed at R = 0. TEM samples A1 and B1 were lifted from locations near the crack ini-tiation sites on the fracture surfaces of specimens A and B; The locations of TEM samples A2 and B2 kept away from the crack origin sites but still within the “fish-eye” region of crack steady growth. TEM observations show that there was no characteristic microstructure induced by VHCF in different oriented specimens and under various R values.

A co-curing resin system consisting of 9368 epoxy resin for prepreg and 6808 epoxy resin for resin transfer molding (RTM) was developed. A corresponding preparation method of novel polymer composite bolted T-joint with internal skeleton and external skin was proposed based on prepreg-RTM co-curing process, and novel T-joints were fabricated. A series of conventional configuration T-joints based on RTM process and T-joints made of 2A12 aluminum alloy were prepared simultaneously. Bending performances were studied on these T-joints experimentally. The results indicate that 9368 epoxy resin and 6808 epoxy resin exhibit good compatibility in rheological and thermophysical properties. The novel T-joints prepared with prepreg-RTM co-curing process show no obvious fiber local winding or resin-rich regions inside, and the interface quality between internal skeleton and external skin is excellent. The main failure modes of the novel T-joint under bending load include separation of skin and skeleton and fracture along the thickness on the base panel; the skeleton carries the main bending load, but there is still load transfer between external skin and internal skeleton through their interface. The internal damages of the novel T-joint are highly consistent with surface damages observed visually, facilitating the detection and timely discovery of damages. The initial stiffness, damage initiation load, and ultimate load of the novel T-joint are 1.65 times, 5.89 times, and 3.45 times that of conventional T-joint, respectively. When considering the influence of density, the relative initial stiffness and relative ultimate load of the novel T-joint are 1.44 times and 2.07 times that of aluminum alloy T-joint, respectively.

A set of polymer composite bolted T-joints with novel configuration consisting of internal skeleton and external skin was fabricated using a prepreg-RTM co-curing molding process. Experiments were conducted to study their mechanical properties under bending load. A finite element model with a polymer resin area between the skin and skeleton was established and verified by the experimental results. Then, the damage propagation process and failure mechanism of the joint and the influence of three factors related to the layer characteristics of the skin and skeleton were investigated by the validated models. The results show that the bending stiffness and the yield limit load of the novel composite T-joint are 0.81 times and 1.65 times that of the 2A12 aluminum T-joint respectively, while at only 55.4% of its weight. The damage of the joint initiates within the resin area and leads to a degradation of the joint bending performances. The preferred stacking sequence of the skeleton is [0/+45/90/-45]ns when primarily subjected to bending loads. The decrease in bending performances is within 5% with the inclining angle of the skeleton less than 12 degrees. The more layers of 90° in the skin, the better bending performances of the joints, while the more layer of 0°, the poorer bending performances.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN’s use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyper-parameters—learning rate and batch size of the online learning phase—highlights their roles in optimizing the learning process and enhancing the framework’s overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

Few studies have examined the role of mediating mechanisms played by bricolage in the relationship between strategic flexibility and new product development, even though most of the studies view strategic flexibility as a capability to cope with environmental turbulence and promote product innovation. The bricolage approach embraces the resource-based understanding of the product development process as a concrete process of recombining resources in the process and exploring the mechanisms behind the bricolage approach. This study is the first of the kind that has examined the empirical effect of environmental turbulence on new product development through serial mediation of strategic flexibility and bricolage. Based on our results, environmental turbulence positively and significantly affects new product development through the serial mediation of strategic flexibility and bricolage. Bricolage is one of the mechanisms by which strategic flexibility may play a part in supporting product innovation. Our contribution to strategic flexibility research comes from examining the effect of strategic flexibility on bricolage and new product development from the point that environmental turbulence compels the high-tech industries to remain strategically flexible for their sustainability and growth. Besides providing new evidence on the relationship between environmental turbulence and product innovation, this research also provides new insight into the economic opportunity of new product development in transition economies such as China.

Nuclear protein of testis (NUT), a protein product of the NUTM1 gene (located on the long arm of chromosome 15) with highly restricted physiologic expression in post-meiotic spermatids, is the oncogenic driver of a group of emerging neoplasms when fused with genes involved in transcription regulation. Although initially identified in a group of lethal midline carcinomas in which NUT forms fusion proteins with bromodomain proteins, NUTM1-rearrangement has since been identified in tumors at non-midline locations, with non-bromodomain partners and with varied morphology. The histologic features of these tumors have also expanded to include sarcoma, skin adnexal tumors, and hematologic malignancies that harbor various fusion partners and are associated with markedly different clinical courses varying from benign to malignant. Most of these tumors have nondescript primitive morphology and therefore should be routinely considered in any undifferentiated neoplasm. The diagnosis is facilitated by the immunohistochemical use of the monoclonal C52 antibody, fluorescence in situ hybridization (FISH), and, recently, RNA-Sequencing. The pathogenesis is believed to be altered expression of oncogenes or tumor suppressor genes by NUT-mediated genome-wide histone modification. NUTM1-rearranged neoplasms respond poorly to classical chemotherapy and radiation therapy. Targeted therapies such as bromodomain and extraterminal domain inhibitor (BETi) therapy are being developed. This current review provides an update of NUTM1-rearranged neoplasms, focusing on the correlation between basic sciences and clinical aspects.

This chapter aims to construct a relatively complete, universal, and highly reliable road surface state monitoring and perception system through the research of the A1 contact road reconstruction project of an international airport (hereinafter referred to as the “airport”). The system design is based on on-the-spot investigation, theoretical analysis, and engineering practice, using vibrating string type and resistive strain sensors as sensing means, real-time high-frequency signal acquisition technology as the guide, 4G router for wireless communication technology as the media, and storage technology for monitoring system based on server storage terminals. In combination with engineering background, aircraft motion state, main landing gear configuration, sensor selection and evaluation, and other major factors, the actual monitoring information needs of the plane are fully taken into account, as well as the difference in sensor information types, and the system design is carried out by stratification and classification.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

The 1060 aluminium alloy is widely applied to electronic industry, construction area, aerospace field, traffic engineering, decoration and consumption goods market for its good chemical, physical and mechanical properties. In general, excellent processing property is necessary and important for manufacturing of complicated panels. In this paper, a special 2D ultrasonic vibration incremental forming method is designed to improve its plasticity and mechanical properties. Three kind of processing methods including traditional single point incremental forming, longitudinal ultrasonic vibration incremental forming and 2D ultrasonic vibration incremental forming are conducted for flexible manufacture of conical parts of 1060 aluminium alloy sheet. Then micro-hardness tests, residual stress tests and scanning electron microscopy tests are carried out to probe its revolution of micro-structure and mechanical properties and to analysis the effects of different types of ultrasonic vibration on plasticity and fracture characteristic of 1060 aluminium alloy. It is proved that 2D ultrasonic vibration is facilitated to improvement of plasticity and surface qualities of 1060 aluminium alloy than the other two processing methods. So, the novel 2D ultrasonic vibration incremental forming possesses substantial application values on flexible and rapid manufacturing of complicated thin-walled component of aluminium alloy.

Nanoparticle (NP)–surfactants formed by the self-assembly of NPs and end-functionalized polymers at the hydrophilic/hydrophobic interface have a wide range of applications in many fields. In this study, the influence of density of amino groups, NPs dimension and pH on the interaction between end-functionalized polymers and NPs were extensively investigated. Single amino-terminated polystyrene (PS-NH2, Mw ≈ 0.6k, 2.5k, 3.5k, 3.9k) and diamino-terminated polystyrene (H2N-PS-NH2, Mw ≈ 1.1k, 2.8k) were prepared using reversible addition–fragmentation chain transfer polymerization and atom transfer radical polymerization. NPs with different dimensions (zero-dimensional carbon dots with sulfonate groups, one-dimensional cellulose nanocrystals with sulfate groups and two-dimensional graphene with sulfonate groups) in the aqueous phase were added into the toluene phase containing the aminated PS. The influence of pH and the molecular weight of amino-terminated PS on the interfacial tension between two phases were investigated. The results indicate that aminated PS exhibited the strongest interfacial activity after compounding with sulfonated NPs at a pH of 3. Terminating PS with amino groups on both ends leads to better performance in in reducing the water/toluene interfacial tension than modifying the molecular structure of PS on a single end. The dimension of sulfonated NPs also contributed significantly to the reduction of the water/toluene interfacial tension. The minimal interfacial tension was 4.49 mN/m after compounding PS-NH2 with sulfonated zero-dimensional carbon dots. Molecular dynamics simulation on the evolution of the water/toluene interface in the presence of sulfonated carbon dots and H2N-PS-NH2 revealed that these opposite charged substances moved towards the interface in an extreme short time and orderly assembled in a thermodynamic equilibrium.

The increasing amount of municipal sludge in China requires safe and effective management to protect human health and ensure environmental sustainability. Pyrolysis is a thermochemical process that that decompose organic matter at elevated temperature and under anaerobic conditions, and it has attracted an increasing attention in sludge treatment in the recent years. However, comprehensive environmental and economic assessment of sludge pyrolysis in China's context is rare, due to the small quantities of full-scale sludge pyrolysis plant. In this paper, we applied our design and operation parameters of full-scale sludge pyrolysis plants to generate the material and energy consumptions of the pyrolysis system under various of conditions, including sludge organic content and moisture content, system size, system energy distribution, and whether or not heat substitution is applied. Life cycle assessment and techno-economic assessment were then applied to investigate the environmental and economic performance of the system Our results demonstrate the significant environmental and economic impacts associated with sludge properties and system size. Generally, sludge with higher organic content and lower moisture content requires less natural gas consumption, which leads to a simultaneous improvement of the system environmental and economic performance. The system economic performance is more sensitive to the system size, and centralized sludge handling using a larger pyrolysis is more economic favorable. In the most ideal case, the average global warming potential and minimum sludge handling price of sludge pyrolysis could be as low as -32.5 kg CO₂-Eq/t DS and 188.8 $/t DS, respectively. Based on these results, we discussed the pathways that could be taken to further optimize the environmental and economic performances of the pyrolysis system.

We research the interaction between six representative carbon-based nanoparticles (CBNs) and 20 standard amino acids through all-atom molecular dynamics simulations. The six carbon-based nanoparticles are fullerene(C60), CNT55L3, CNT1010L3, CNT1515L3, CNT2020L3, and two-dimensional-graphene(Graphene33). Their curvatures decrease sequentially, and all of CNT are single-walled carbon nanotubes. We have observed that as the curvature of CBNs decreases, the adsorption effect of 20 amino acids with them has an increasing trend. In addition, we also used multi-dimensional clustering to analyze the adsorption effects of 20 amino acids on six carbon-based nanoparticles. We observed that the π-π interaction still plays an extremely important role in the adsorption of amino acids on carbon-based nanoparticles. Individual long-chain amino acids and “Benzene-like” Pro also have a strong adsorption effect with carbon-based nanoparticles.

Aiming for the problems emerging in the PWM drive system with long cables, accurate modeling of power cables is the premise to predict and analyze the relevant phenomenon, and a proper filter design is the key solution to these problems. This paper proposes high-frequency cable models to represent the frequency-dependent characteristics, especially for the high-frequency resistance of the cable that is an easily overlooked factor but determines the damping of overvoltage. The proposed models can be used for accurately representing the cable parameters in a wide frequency range, and correctly simulating the differential mode (DM) overvoltage and common mode (CM) current, including the peak value, oscillation frequency and damping of the transient waveform. In addition, improved filter networks are proposed to suppress the DM voltage and CM current, with the merit of low losses, small volume and an excellent ability of suppressing overvoltage. The proposed cable models and the filter design have been validated in a 750W PWM drive system with 200m power cables.

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

The diamondback moth, Plutella xylostella (Linnaeus), is one of the notorious pests causing substantial loses to many cruciferous vegetables across the nations. We have got the result that 200Gy dosage irradiated 6-day male pupae of P. xylostella inducing F1 generation sterile. First, in our research, we detected Oxidase system and stress response mechanism of irradiated pupae, the results displayed that 200Gy irradiation significantly alters the antioxidant enzyme regulation in 6-day male pupae of P. xylostella. The level of SOD, CAT were increased significantly in contrast the level of POD and GST were decreased in 12-24h post-treatment. The heat shock proteins (Hsps) gene expression level was significant increasing, maximum > 2 folds up-regulation of genes were observed in peak. But they also had a trend of gradual recovery with development. Second, in order to explore the irradiated sterility further, we detected the testis LDH and ACP activity found that in male adults testis they increased significantly than control during its development. Thus the present research investigation highlights that the ⁶⁰Co-γ radiation treatments alters the physiological development of diamondback moth. The results showed that 200Gy dosage resulted stress damage to the body and reproductive system of the diamondback moth.

In this study, we report a new epoxy acrylate based shape memory polymer(SMP) fabricated by Liquid crystal display (LCD) Stereolithographic 3D printing. The printed 3D object has a high resolution and high transparency in visible light region. The uniaxial tensile tests showed enhanced tensile toughness and tunable mechanical properties. The fix-recovery and cycle tests indicated high shape recovery properties including high shape recovery rate and excellent cycling stability. In addition, a smart electrical valve actuator was fabricated that can be used in fast heat or electricity responsive electrical circuits. LCD 3D printing provides a low-cost and high efficient way to fabricate fast responsive SMP, which can be used in wide applications in various fields on aerospace engineering, biomedical devices, soft robots and electronic devices.

Bacillus subtilis NCD-2 showed a promising biocontrol effect against plant soil-borne diseases, and was developed as a commercial microbial fungicide against cotton verticillium wilt in China. Spores were main ingredient of the fungicide and played the crucial role in biological control of plant diseases. Therefore, the key to reducing the cost of fungicide was to find ways to increase the number of spores of strain NCD-2 during fermentation. In this study, 755 substances were evaluated by Phenotype Microarray technology, and 5 kinds of carbon sources and 1 kind of nitrogen source were found to promote the metabolism of strain NCD-2. Among carbon sources L-arabinose showed strongest ability to promote the bacteria growth and sporulation of strain NCD-2. When L-arabinose was used as a single carbon source, it could increase the bacteria concentration and the sporulation efficiency of strain NCD-2 by 2.04 times and 1.99 times, respectively, compared with D-glucose. Moreover, L-arabinose could significantly decrease the autolysis of strain NCD-2 revealed by microscopy observation. The mechanism for promoting the sporulation by L-arabinose was analysed by RNA-seq sequencing. Results showed that total of 790, 923 and 1270 genes were up-regulated and 639, 850 and 1001 genes were down-regulated under L-arabinose supplies when cultured at 8 h, 12 h and 16 h, respectively. Fourteen differentially expressed genes associated with arabinose transportation and sporulation were selected for qRT-PCR analysis, and the result showed basically consistent expression trend with transcriptome. Notably, genes associated with arabinose metabolism, sporulation, spore resistance to heat and spore coat formation were significantly up-regulated, and genes associated with sporulation-delaying protein were significantly down-regulated under L-arabinose supplies. msmX gene which was involved in arabinose transport in Bacillus genus was deleted, and the mutant decreased the growth and sporulation by 53.71% and 86.46%, respectively, when compared to strain NCD-2 wild type. Complementary of the mutant by importing intact msmX gene could restore the growth and sporulation of the mutant strain. In conclusion, arabinose played an important role in regulating the growth and sporulation of strain NCD-2.

Vibro-fluidized dry coal beneficiation involves the unavoidable mixing of -1 mm coal dust into the fluidized bed, which compromises the stability and homogeneity of the bed density and reduces the effectiveness of fine-grained coal separation. In order to understand the features of coal powder separation and mixing (-1 mm) in a vibrating fluidized bed, as well as to achieve a uniform and stable bed density, a wide-grained dense medium consisting of -1 mm coal dust and 0.3-0.15 mm magnetite powder was homogeneously mixed in this work. The degree of density separation in the entire bed was examined, as well as the local mixing properties of the Wide-grained Dense Medium. It was discovered that when low vibrational energy was introduced at the same gas velocity, particle separation was enhanced in comparison to the usual fluidized bed. Little bubbles were found to ensnare fine coal dust and migrate upward, increasing density separation. The introduction of high vibrational energy leads to particle disorder within the bed and an increase in the level of particle mixing. The beneficial effects of vibration on the bed are diminished when the gas velocity is gradually increased under specified vibration circumstances. This causes big bubbles to develop more frequently, which increases particle mixing. Among them, the 1-0.5 mm fine-grained coal exhibits the best mixing state; it is less impacted by variations in air velocity and vibration levels, whereas the -0.5 mm coal dust is more affected by each of these variables. Furthermore, a wide range of air velocity regulation without a significant segregation state change was discovered to be achievable by the particle system under vibration circumstances of f=25Hz A=1mm and f=25Hz A=2mm.

Biological parameters extracted from electrical signals from various body parts have been used for many years to analyze the human body and its behavior. In addition, electrical signals from cancer cell lines, normal cells, and viruses, among others, have been widely used for the detection of various diseases. Single-cell parameters such as cell and cytoplasmic conductivity, relaxation frequency, and membrane capacitance are important. There are many techniques available to characterize biomaterials, such as scanning tunneling microscopy (STM), nanotechnology, microstrip cavity resonance measurement, dielectric impedance measurement (DEPIM), etc. This article reviews the single cell isolation and sorting techniques, summarizes the common single cell operations such as impedance flow cytometry, cell electrical impedance, electrical impedance sensing, and summarizes the current single cell analysis techniques and their applications.

Facing the charging difficulties of free-floating shared electric vehicle and the high cost of mobile charging for single demand, this article proposes a collaborative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the minimization model of system charging total cost, seeking the optimal charging scheme and the number of mobile charging vehicles. The lower model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% compared with the pure fixed charging scheme and 14.9% compared with the pure mobile charging scheme

A series of water diversion projects to address the uneven distribution of water resources in China involve the construction of a large number of hydraulic tunnels. As the lining structure that maintains the stability and durability of the tunnels, it is prone to durability damage during the operation process, which in turn affects the water transmission safety and water supply capacity. Therefore, it is important to evaluate the durability of hydraulic tunnel lining structure. Considering the randomness and fuzziness of the factors affecting the durability of hydraulic tunnel lining structure, this paper proposes a comprehensive evaluation model based on the coupling of set-pair analysis and extension. The G1 method and the simple correlation function method are used to determine the subjective and objective weights of the evaluation indexes respectively, and the combination weight is assigned based on the principle of minimum entropy; the set-pair analysis principle is used to establish the linkage affiliation function, calculate the comprehensive linkage affiliation of the object to be evaluated, and the maximum affiliation principle is used to judge the durability level of the hydraulic tunnel lining structure. Finally, taking a section of hydraulic tunnel as an example, the model proposed in this paper is used to calculate its durability grade as Class III, with the set-pair potential SHI(H) = 7.5856, which is consistent with the actual engineering practice, and a comparative study is done in combination with the AHP-Extenics method. It is verified that the evaluation model can scientifically and reasonably evaluate the durability of hydraulic tunnel lining structure, providing a basis for subsequent maintenance and reinforcement.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

The lack of tactile experience is a significant flaw in online product evaluation and purchase, but visual information can be utilized to compensate for tactile deficits. In this study, we constructed a mechanistic model of the role of tactile perceived diagnosticity on consumer purchase intention based on visual compensation based on mental imagery theory, innovativeness theory, and personal goals framework. We collected data from consumers (n=406) in the Chinese market and tested the research hypotheses using structural equation modeling. The findings suggest that visually compensated tactile perceptual diagnosticity promotes mental imagery and sensory similarity, which in turn affects purchase intention. Theoretically, this study enriches current explanations of online haptics by explaining the mechanisms by which haptic demonstration videos influence consumers' haptic evaluations and behavioral responses, as well as the moderating role of personal goals therein; practically offers advice for retailers seeking to build or expand their tactile marketing strategies.

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation, that is, the target distribution function, the PMF potential energy is extracted, and then the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have gone through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

ATP-binding cassette (ABC) transporters, acting as cellular "pumps," facilitate solutes translocation through membranes via ATP hydrolysis. Their overexpression is closely tied to multidrug resistance (MDR), a major obstacle in chemotherapy and neurological disorder treatment, hampering drug accumulation and delivery. Extensive research has delved into the intricate interplay between ABC transporter structure, function, and potential inhibition for MDR reversal. Cryo-electron microscopy has been instrumental in unveiling structural details of various MDR-causing ABC transporters, encompassing ABCB1, ABCC1, ABCG2, as well as recently revealed ABCC3 and ABCC4 structures. The newly obtained structural insight has deepened our understanding of substrate and drug binding, translocation mechanisms, and inhibitor interactions. Given the growing body of structural information available for human MDR transporters and their associated mechanisms, we believe it is timely to compile a comprehensive review of these transporters and compare their functional mechanisms in the context of multidrug resistance. Therefore, this review primarily focuses on the structural aspects of clinically significant human ABC transporters linked to MDR, with the aim of providing valuable insights to enhance the effectiveness of MDR reversal strategies in clinical therapies.

Based on the multiple coordination framework of “promising government-effective market-ordered society”, this paper takes 68 cases of adding elevators to old buildings as samples, and selects six conditional variables: approval optimization, financial subsidies, financing innovation, technological innovation, capable people leading and neighborhood committee guidance. Using Crisp-Set Qualitative Comparative Analysis (csQCA), this paper discusses the combination of conditions that affect the success or failure. It is found that: approval optimization and neighborhood committee guidance are sufficient conditions for the success; successful cases can be divided into three types: “political and social dual-drive type”, “market and social dual-drive type” and “ternary co-drive type”; lack of approval optimization, lack of financing innovation, and the existence of capable people leading are necessary conditions for the failure. The failure cases can be divided into “government and market double failure type” and “market failure type”.

. The impedance change of an induction coil surrounding a metal tube adapter is investigated by the truncated region eigenfunction expansion (TREE) method. Conventional TREE method is inapplicable to this problem as a consequence of the numerical overflow of eigenfunctions of the air-metal multi-subdomain regions. The difficulty is surmounted by a normalization procedure for the numerical eigenfunctions obtained from the finite element method (FEM). Efficient algorithm is devised by the Clenshaw-Curtis quadrature rule for integrals involving the numerical eigenfunctions. Numerical results of the TREE and FEM simulation coincide very well in all cases, and the efficiency of the proposed method is also confirmed.

After sperm enter the female reproductive tract, the physicochemical and biochemical microenvironment undergoes significant changes. Thus, the response of sperm to environmental factors is vital for successful fertilization. The cation channel of sperm (CatSper) is a sperm-specific, pH-sensitive, and Ca2+-permeable ion channel. It is responsible for the predominant Ca2+ entry in mammalian sperm and is involved in nearly every event of sperm to acquire fertilizing capability. CatSper also serves as a pivotal polymodal chemosensor in mammalian sperm by responding to multiple chemical cues. Physiological ligands (such as progesterone, prostaglandins, β-defensins, and odorants) provoke Ca2+ entry into sperm by activating CatSper and thus triggering sperm functions including hyperactivation, capacitation, the acrosome reaction, and chemotaxis. Additionally, synthetic and natural chemicals (such as medicines, endocrine disrupting chemicals, drugs of abuse, and antioxidants) affect sperm functions by regulating CatSper-dependent Ca2+ signaling. Therefore, understanding the interactions between CatSper and extracellular ligands sheds light on the mechanisms underlying male infertility and offers innovative diagnostic and treatment approaches. This underscores the importance of CatSper as a crucial regulatory target in male reproduction, linking sperm function with the extracellular environment. This review comprehensively summarizes the relevant studies describing the environmental factors that affect CatSper in humans and rodents.

A mathematical model is proposed to analyze the spreading dynamics of COVID-19. By using the parameters of the model, namely the basic reproduction number (R0) and the attenuation constant (k), the daily number of infections (DNI) and the cumulative number of infections (CNI) are deduced and shown to be in good agreement with experimental data. This model effectively addresses three key issues: explaining the waveform pattern of DNI, predicting the occurrence of the second wave of infection, and understanding the competitive spread of two viruses in a region. The findings demonstrate that these significant challenges can be comprehensively tackled using a simple mathematical framework. The theoretical insights derived from this model hold potential in guiding the estimation of the severity of an infection wave and formulating effective strategies for the control and mitigation of epidemic outbreaks.

In this paper we study a conjecture proposed by P.Duxbury , C.laror , L.Leduino de Salles Neto in 2021\cite{conjecture} on the Golumb Ruler Problem which is a classical optimization model in discrete case . In \cite{conjecture} the authors constucted a continuous model for the Golumb Ruler Problem associated to the discrete case and conjectured that the optimal value of both cases are equal . We deal with this conjecture via algebraic geometrical methods .

In order to establish a new discipline specializing in accident science from the perspective of safety science. Under the guidance of the current research theories and methods of safety science, combined with the research paradigm of humanities and social medicine, this paper puts forward new viewpoints, new theories and new models about accident research. First of all, through literature retrieval, this paper analyzes the relevant research results of accidents at home and abroad, and expounds the existing problems and the basic trend of accident science research. Secondly, it puts forward eight kinds of attribute relations of the accident, and makes clear the characteristics and connotation of the accident. In the study of accident types, a hierarchical classification model based on accident cognition is created for the first time. It also points out the logical relevance of five levels of accident science research and the realistic relevance of three levels. At the same time, according to the thought of science of science, this paper puts forward a new definition of safety under the thinking of accident science and other basic concepts related to safety science, and explains the connotation. In addition, it creates and constructs the basic concept of accident science, establishes the conceptual model of accident science, and points out the “3-4-5” model of accident science research and its connotation. Thirdly, draw lessons from the interdisciplinary paradigm to study the relevant theoretical basis and discipline classification relationship of accident science, and construct the tree of accident science. Finally, the research contents of three main aspects of accident science are summarized. The results show that the research results in this paper not only play a fundamental role in the basic construction of accident science, but also further enrich and perfect the discipline system of safety science, which has a certain theoretical significance.

Previous studies generally reckon that the full converter-base wind power generation (FCWG) is a ’decoupled’ power source from the grid, which hardly participates in electromechanical oscillations. However, it is found recently that strong interaction could be induced which might incur severe resonance incidents in electromechanical dynamic timescale. In this paper, the participation of FCWG in electromechanical dynamics is extensively investigated, and particularly, an unusual transition of electromechanical oscillation mode (EOM) is uncovered for the first time. The detailed mathematical models of open-loop and closed-loop power systems are firstly established, and modal analysis is employed to quantify the FCWG participation in electromechanical dynamics, with two new mode identification criteria, i.e., FCWG dynamics correlation ratio (FDCR) and quasi-electromechanical loop correlation ratio (QELCR). On this basis, the impact of different wind penetration levels and controller parameter settings on the participation of FCWG is investigated. It is revealed that if an FOM has a similar oscillation frequency to the system EOMs, there is a high possibility to induce strong interactions between FCWG dynamics and system electromechanical dynamics of the external power systems. In this circumstance, an interesting phenomenon may occur that an EOM may be dominated by FCWG dynamics, and hence is transformed into a quasi-EOM, which actively involves the participation of FCWG quasi-electromechanical state variables.

The coordination planning between land use and transportation system is an important premise of solving urban transportation problems and realizing land use integration. This study investigates the interactive and feedback relationship between land use and transportation system from the perspective of access management. By integrating the land use and traffic data from Las Vegas Metropolitan area with the system dynamics model, the causal relationship and causal loop diagrams (CLDs) are introduced to analyze the cause-and-effect relationship and quantitative relationship between the factors of the combined system of land use and transportation, and then sub-models partition and system simulation are performed. The systems dynamics model is established by analyzing the relationship between a series of access management techniques, traffic characteristics, and land use features. The results show that system dynamics model can be used as an effective alternative to model the symbiosis relationship of land use and transportation system for urban planning and construction.

Translational rockslides caused by toe excavation are one of the commonly seen geohazards in mountainous regions due to line traffic construction. Quantifying their failure extension length (FEL) and travel distance are of significant interest as well as huge challenges in landslide hazard assessment. In this paper, a simple criterion is proposed for predicting these two factors based on the principles of rigid body limit equilibrium and kinetic energy theorem. Further, the proposed criterion is validated against the field observations and numerical results with a practical case of the Xinjianan landslide, a medium-sized translational rockslide that occurred in 2013 in Nanchuan District, Chongqing. The findings indicate that a tiny discrepancy can be found in FEL between the field observations and the proposed criteria, while this discrepancy could be considerable in travel distance between different methods. However, the relative discrepancies all fall within 20%, deemed acceptable.

With the development of the volume of information exchange and perception increases, the demands for intelligent, miniaturized and integrated optical devices for information acquisition are also increasing. As the core component of optical networks for transmitting information, how to further optimize its structural characteristics to generate richer optical characteristics and apply them to information exchange and optical field control has become a key research hotspot. The introduction of chiral twist characteristics has led to new phenomena and applications in optical field transmission and transformation for traditional optical fibers or microstructured optical fibers (MOF). Therefore, this review mainly starts from the principle of chiral optical fibers, introduces their preparation and latest application scenarios, and finally looks forward to their potential future development prospects.

The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

Modified ammonium polyphosphate (MAPP) as a novel mono-component intumescent flame retardant (IFR) was prepared via the ionic exchange between ammonium polyphosphate (APP) and piperazine sulfonate, which is synthesized by self-assembly using 1-(2-Amioethyl) piperazine (AEP) and p-amino benzene sulfonic acid (ASC) as raw materials. This all-in-one IFR integrating three functional elements (carbon, acid, and gas source) showed more efficient flame retardancy and excellent smoke suppression as well as better mechanical properties than the conventional APP. The incorporation of 22.5 wt.% MAPP into polypropylene (PP) eliminated the melt dripping phenomenon and passed the UL-94 V-0 rating. The results of the cone calorimetry test (CCT) revealed that the release of heat, smoke, and CO is significantly decreased, demonstrating that this novel IFR endows PP with excellent fire safety more effectively. For PP/MAPP composites, a possible IFR mechanism was proposed based on the analysis of the pyrolysis gas and char residues.

Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.

Aims: To assess the occurrence of Chemotherapy-induced nausea and vomiting (CINV) after standard antiemetic therapy in the acute (24 h post-chemotherapy) and delayed (2–5 days post-chemotherapy) phases, as well as to identify risk factors for CINV in the acute and delayed phases. Methods: This prospective longitudinal and observational study analyzed the data of 400 breast cancer patients scheduled for chemotherapy over two cycles in two hospitals. The self-report survey was developed to assess the occurrence of CINV and their associated factors. CINV was evaluated with a Multinational Association of Supportive Care in Cancer Antiemetic Tool (MAT) on days 2 and 6 of chemotherapy. The incidence of acute and delayed CINV were presented by frequency and percentage. Generalized equation estimates (GEE) was used to identify risk factors of acute and delayed CINV. Results: There were 400 evaluable patients with complete Round 1 data, 334 for Round 2 data. Among 400 patients, 29.8% and 23.5% experienced acute and delayed CINV, respectively. Risk factors associated with for acute CINV were pain/insomnia, history of CINV, history of motion sickness (MS), and highly emetogenic chemotherapy regimen, while history of MS, CINV history, number of completed chemotherapy cycle number < 3, and the incidence of acute CINV were risk factors of delayed CINV (all p < 0.05). Conclusions: The findings may help nurses working for Chinese population in identifying patients at risk for CINV and in planning effective program to reduce the occurrence of CINV.

Tumor invasion, metastasis, and recrudesce remain a considerable challenge in the treatment of gastric cancer (GC). Herein, we first identified that RBFOX3 (RNA binding protein fox-1 homolog 3) was significantly up-regulated in GC tissues and negatively linked to the survival rate of GC patients. RBFOX3 promoted cell division and cell cycle progression in vitro as well as in vivo. Furthermore, RBFOX3 increased cell invasion and migration ability. Interestingly, both the suppression of GC cell multiplication and invasion moderated by the silencing of RBFOX3 was rescued by HTERT up-regulation. Additionally, RBFOX3 augmented the resistance of GC cells to 5-fluorouracil (5-Fu) by repressing RBFOX3. Mechanistically, exogenous up-regulation of RBFOX3 triggered promoter activity and HTERT expression thereby enhancing the division and development of GC cells. Importantly, our findings revealed that RBFOX3 interacted with AP-2β to modulate the HTERT expression as demonstrated by co-immunoprecipitation analysis. In conclusion, our study indicates that high expression of RBFOX3 promotes GC progression and development but predicts worse prognosis by stimulating HTERT signaling. Moreover, the results suggest that the RBFOX3/AP-2β/HTERT pathway is a novel target for the development of therapeutic agents for the prevention and treatment of GC reappearance and metastasis.

Light Detection and Ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation. To surmount these obstacles, this paper proposes a low-power, low-cost, SPAD-based system-on-chip (SoC) which packages the microlens arrays (MLA) and incorporates with a light-weight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8x8 Single-Photon Avalanche Detectors (SPADs) macro pixel array with time-to-digital converters (TDC) and charge pump, fabricated using a 180nm bipolar-CMOS-DMOS (BCD) process. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8x8 to quarter video graphics array level (QVGA; 256x256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. This integrated state-of-the-art hardware-software solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications.

Background: As the most invasive breast cancer (BrCa), triple-negative BrCa (TNBC) has the worst survival. The use of dexmedetomidine potentially affected BrCa surgery and dexmedetomidine was reported to have direct effects on TNBC cells. The objective of this study is to explore the mechanisms underlying the effect of dexmedetomidine on TNBC. Methods: Dexmedetomidine targets were predicted using The Cancer Genome Atlas data SwissTargetPrediction. Cell lines MDA-MB-231, MCF7, and MCF10A were used to validate the targets in TNBC with both clinical samples and cell lines. Cancer cell lines and normal breast cell lines were grouped in cancer and normal groups respectively. Both groups were exposed to dexmedetomidine treatment. Cell Counting Kit-8 was used to determine the effect of dexmedetomidine on cells with target silencing. The binding model of the candidate targets was docked and critical amino acids were mutated to validate the binding model. Results: Dexmedetomidine selectively inhibits cancer cells. Catalytic subunit of the DNA-dependent protein kinase (PRKDC), indoleamine 2,3-dioxygenase 1 (IDO1), opioid receptor kappa 1 (OPRK1), glutaminyl-peptide cyclotransferase (QPCT), macrophage migration inhibitory factor (MIF), potassium voltage-gated channel, subfamily H (Eag-related), member 2 (KCNH2), cholinergic receptor, muscarinic 3 (CHRM3), and potassium intermediate/small conductance calcium-activated channel, subfamily N, member 4 (KCNN4) were identified as dexmedetomidine targets in TNBC. The expression levels of PRKDC, IDO1, MIF, KCNH2, CHRM3, and KCNN4 were found to be upregulated in TNBC tissues compared to non-TNBC tissues(p<0.05). Silencing of these genes was found to reduce the sensitivity of TNBC cells to dexmedetomidine(p<0.05). This effect was counteracted when the silenced genes were overexpressed, resulting in an increase in the sensitivity of cells to dexmedetomidine (p<0.05). Furthermore, a direct interaction between dexmedetomidine and IDO1 and CHRM3 was observed, which regulated the sensitivity of cells to dexmedetomidine(p<0.05).Conclusion: IDO1 and CHRM3 are direct targets of dexmedetomidine in TNBC.

Background: Bladder cancer (BC) development is highly related to immune cell infiltration and inflammation. This study aimed to construct a new classification of bladder cancer (BC) molecular subtypes based on immune cells-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then methylation sites corresponding to immune cells-associated genes were acquired. Differentially methylation sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylation sites was performed to divide into several subtypes. Then the potential mechanism of different subtypes was exploded. Result: Bladder cancer patients were divided into three groups. Cluster 3 (methylation-L) subtype had the best prognosis. Cluster 1 (methylation-M) had the worst prognosis. The distribution of immune cells, level expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC_co_inhibition, APC_co_stimulation, HLA, MHC_class_I, Type_I_IFN_Reponse, and Type_II_IFN_Reponse were significant difference among three subgroups. The distribution of genomic alterations was different among them. Conclusion: The classification was accurate and stable. BC patients could be divided into three subtypes based on the immune cells-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were various among three subgroups. High level immune infiltration was a correlation with high level methylation. The lower RNAss score was associated with higher immune infiltration and higher level expression of CD274.

We analyzed the plant-litter-soil continuum to investigate the carbon and nitrogen distribution and ecological stoichiometry of an evergreen broad-leaved forest at Dagangshan Mountain, Jiangxi. The results showed that the average C and N contents and C:N ratios in the leaves and fine roots among 6 different tree species were 401.87g/kg, 21.41g/kg, 19.27 and 348.64g/kg, 15.73g/kg, 23.97, respectively; the average C and N contents and C:N ratios were 323.06 g/kg, 12.76 g/kg, 25.58 respectively in leaf litter, and 16.40 g/kg, 1.09 g/kg, 16.27 respectively for soil. In contrast with the C content, the total N content of the fine roots and litter had a high coefficient of variation and a high spatial heterogeneity. We ranked the six different representative tree species according to total C and N content in leaves and fine roots. The results for each species were generally consistent with each other, showing a positive correlation relationship between total C and N content in the leaves and roots. Among them, S. discolor (Champ. ex Benth.) Muell. plants displayed high carbon and nitrogen storage capacities, and on the other hand, C. fargesii Franch., C. myrsinifolia (Blume) Oersted, A. fortunei (Hemsl.) Makino, and V. fordii (Hemsl.) Airy Shaw showed a high nitrogen transfer rate. Total soil N and C decreased with depth. Soil organic carbon (SOC), soil resistant organic carbon (ROC), total N, alkali nitrogen, NH4+-N and NO3--N contents were all also negative correlated with soil depth, but the contents of the NH4+-N and NO3--N did not change significantly; The spatial distribution of soil NO3--N was significantly heterogeneous. At 0-10 cm soil depth, SOC was positively correlated with alkaline nitrogen, and at 10-20 cm soil depth, SOC was significantly positively correlated with total N. In general, when soil carbon was abundant, nitrogen supply capacity was also high.

Plasmonic gold nanorods play important roles in nowadays state-of-the-art plasmonic sensing techniques. Most of the previous studies and applications focused on gold nanorods with relatively small aspect ratios, where the plasmon wavelengths are smaller than 900 nm. Gold nanorods with large aspect ratios are predicted to exhibit high refractive-index sensitivity (Langmir 2008, 24, 5233–5237.), which therefore should be promising for developing of high-performance plasmonic chemical- and bio-sensors. In this study, we developed gold nanorods with aspect ratios over 7.9, which exhibit plasmon resonances around 1064 nm. The refractive index (RI) sensitivity of these nanorods have been evaluated by varying their dielectric environment, whereby a sensitivity as high as 473 nm/RIU can be obtained. Furthermore, we have demonstrated the large-aspect-ratio nanorods as efficient substrate for surface enhanced Raman spectroscopy (SERS), where an enhancement factor (EF) as high as 9.47×10⁸ was measured using 4-methylbenzenethiol (4-MBT) as probe molecule. Finally, a type of flexible SERS substrate is developed by conjugating the gold nanorods with the polystyrene (PS) polymer. The results obtained in our study can benefit the development of plasmonic sensing techniques utilized in the near-infrared spectral region.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

In agriculture, soil-borne fungal pathogens, especially Fusarium oxysporum strains, are posing a serious threat to efforts to achieve global food security. In the search for safer agrochemicals, silica nanoparticles (SiO2NPs) have recently been proposed as a new tool to alleviate pathogen damage including Fusarium wilt. Hollow mesoporous silica nanoparticles (HMSNs), a unique class of SiO2NPs, have been widely accepted as a desired pesticide carrier. However, their role in enhancing the disease resistance and the specific mechanism remain unknown. In this study, three sizes of HMSNs (19, 96 and 406 nm as HMSNs–19, HMSNs–96 and HMSNs–406, respectively) were synthesized and characterized to determine their effects on seed germination, seedling growth, and Fusarium oxysporum f. sp. phaseolus (FOP) suppression in cowpea roots by foliar spray using phenotypes, fresh biomass and disease progression as indicators. The results revealed that three HMSNs exhibited no adverse impacts on seed germination and tended to improve plant growth. Also, they exert their FOP suppression with a size- and concentration-dependent manner. HMSNs–406 possessed the best control effect at a concentration of 1000 mg/L showing an upto 40.00% decline in the disease severity. The Si(OH)4 control was also effective on FOP suppression at a lower concentration of 100 mg/L, whereas its higher concentrations exhibited obviously adverse impacts on FOP control, seed germination and plant growth. Moreover, we conformed that HMSNs posed their Fusarium wilt suppression in cowpea plant by activating SA (salicylic acid)-dependent SAR (systemic acquired resistance) responses rather than directly suppressing FOP. A higher level of SA content and elevated expression of its maker genes of PR-1 and PR-5 in HMSNs–406 treated cowpea roots provided substantial evidences of this mode of action. Other resistance-related genes, as well as defense-responsive enzymes, were also involved in the HMSNs-activated SAR pathway. Overall, for the first time, our results extended a new role of HMSNs as a potent elicitor to serve as a versatile alternative for plant disease protection of low cost, highly efficiency and sustainability.

The COVID-19 pandemic over the past years has shown a great need for rapid, low-cost and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild type SARS-CoV-2 spike protein (SP) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S protein of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

Neptunea cumingii is an economically important marine shellfish found in the Yellow Sea and Bohai Sea areas of China. However, due to environmental factors, such as global warming, Neptunea cumingii resources are in decline along the coast of China. In this study, samples of Neptunea cumingii were collected in Zhangzidao and Yantai during spring, summer， autumn, and winter to clarify the gene expression patterns and regulatory mechanisms in the gills in different seasons. Transcriptome analysis was conducted using Neptunea cumingii gill tissues and genes with significantly different expression levels were extracted for functional verification. The most genes with differences in expression（DEGs） were found in comparisons of the winter and summer samples. Gene enrichment analysis based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes terms showed that these DEGs were mainly involved in immune and metabolic pathways, and they had significant effects on oxidative stress, body metabolism, and protein synthesis in Neptunea cumingii. Further screening of DEGs identified 34 genes related to temperature regulation comprising 13 genes with roles in innate immunity in shellfish, 12 genes related to oxidative stress, and nine genes related to protein synthesis and energy metabolism. Eleven DEGs were randomly selected for qPCR verification and the results were consistent with the transcriptome analysis results. In summary, the transcriptome results differed significantly between seasons in the gill tissues of Neptunea cumingii. The expression levels of immune regulatory genes could be promoted in Neptunea cumingii during the high temperature season, whereas the expression of these genes may be inhibited in the low temperature season. The results obtained in this study provide insights into the molecular defense mechanisms that might allow Neptunea cumingii to adapt to climate change.

Purpose: Pancreatic cancer (PAAD) is one of the most lethal malignancies. Immunotherapy is largely ineffective in PAAD. T-cell exhaustion contributes to the resistance for immunotherapies. We investigated the prognostic potential of T cell exhaustion-related genes (TEXGs). Methods: Single-cell RNA (scRNA) sequencing dataset from TISCH, and bulk sequencing datasets from TCGA and GTEx were applied to screen differently expressed TEXGs. Kaplan-Meier survival, LASSO regression, and Univariate/Multivariate Cox regression analysis were performed to construct TEXGs risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. PAAD cohorts from ICGC and GSE71729 datasets were used to validate this risk model. Pan-cancer expression of SPOCK2 was performed in TISCH database. Results: A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Low-risk PAAD patients had longer survival time in both training dataset (TCGA-PAAD, n=178) and validation datasets (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n=412). Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PAAD. High-risk patients correlated with cancer-related KEGG pathway enrichment, higher mutation frequency of KRAS and TP53 genes, and immunosuppressive status. Immunohistochemistry staining confirmed the changes of TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 might be a novel marker of exhausted CD8+ T cells. Conclusion: In conclusion, we established and validated a T cell exhaustion-related prognostic signature for PAAD patients. Moreover, our study suggests that SPOCK2 may be a novel marker of exhausted CD8+ T cells.

It is important to reduce the computation complexity while maintaining the accuracy of convolution neural networks. We deem it is possible to further reduce the network complexity while ensuring the accuracy. In this paper, we propose a novel feature extraction network called DSRNet which is lightweight but effective. DSRNet follows the basic ideas of stacking modules and short connection, introduces Depthwise Separable convolution and utilizes the Dilated convolution. The proposed network has fewer parameters and achieves outstanding speed. We conducted comprehensive experiments on CIFAR10, CIFAR100 and STL10 datasets, and the results showed the DSRNet has great performance improvement in terms of accuracy and speed.

The metastasis of lung cancer can spread to the lymph nodes around the lungs. Metastasis, rather than the primary cancer, judges patients survival. Wherefore, a more detailed study on transcriptome of metastatic lung adenocarcinoma including primary carcinoma was carried out. LUAD RNA-seq data and the corresponding clinical information were available from The Cancer Genome Atlas (TCGA), which included 522 cases but only 515 cases have transcriptome data. Differential expression analyses between cases and controls, between primary cancer and metastasis subgroup, or between TNM stages, were respectively carried out using edgeR package. Then, the Kruskal-Wallis tests were used to verify the gradient changes of cancer metastasis or staging with the differential expression genes. The survival analyses were calculated using the Kaplan-Meier algorithm and log-rank test. The functional predictions for the differentially expressed genes were porformed with the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (GO/KEGG). Single gene set enrichment analysis (single GSEA) was run to explore the biological pathways associated with the expressions of RN7SL494P gene based on the Molecular Signatures Database (MSigDB). 406 and 439 differentially expressed genes were identified respectively in lymph node metastasis or TNM stages. 112/296 intersection genes were associated with nodal metastasis and/or staging, among them only 25 genes were associated with the nodal metastasis, 13 genes were associated with the staging with gradient changes. Only one gene (RN7SL494P) was found to be associated with prognosis. But RN7SL494P was not found joining any biological functions or processes or cellular components with GO/KEGG analyses. Finally, single GSEA enrichment and pathway analyses showed that RN7SL494P might be involving in cancer development process and poor outcome in lung adenocarcinoma. These findings highlight the potential applications of RN7SL494P as a promising molecular predictor not only in nodal metastasis but prognosis evalution in lung adenocarcinoma patients.

Leaf mechanical wounding triggers a rapid, within minutes, release of a blend of volatile organic compounds (VOCs). Wounding-induced VOC blend is mainly composed of oxygenated ubiquitous stress volatiles such as methanol and volatile products of lipoxygenase (LOX) pathway (mainly C5 and C6 alcohols and aldehydes and their derivatives), but also includes multiple minor VOCs that collectively act as infochemicals inducing defences in non-damaged plant leaves, neighbouring plants and attracting herbivore enemies. Till present, interspecific variability of the rate of induction and magnitude of wounding-induced emissions, and the extent to which plant structural traits and physiological activity alter these emissions are poorly known. Particularly scarce is the information of the induced emissions in tropical agricultural plant species despite their economic importance and large area of cultivation at regional to global scales. We chose five tropical crops with varying photosynthetic activity and leaf structural characteristics: Abelmoschus esculentus, Amaranthus cruentus, Amaranthus hybridus, Solanum aethiopicum and Telfairia occidentalis to characterize the kinetics and magnitude of wounding-induced emissions, hypothesizing that the induced emission response is greater and faster in physiologically more active species with greater photosynthetic activity than in less active species. Rapid highly repeatable leaf wounds (12-mm cuts) were generated by a within-leaf-chamber cutting knife. Wounding-induced VOC emissions were measured continuously with a proton-transfer reaction time-of-flight mass spectrometer and gas-chromatography mass spectrometry was used to separate isomers. Twenty-three ion VOCs and twelve terpenoid molecule structures were identified, whereas ubiquitous stress volatiles methanol (on average 40% of total emissions), hexenal (24%), and acetaldehyde (11%) were the main compounds across the species. Emissions of low-weight oxygenated compounds (LOC, 70% of total), and LOX products (29%) were positively correlated across species, but minor VOC components, monoterpenoids and benzenoids were negatively correlated with LOC and LOX, indicating a reverse relationship between signal specificity and strength. There was a large interspecific variability in the rate of induction and emission magnitude, but the hypothesis of a stronger emission response in physiologically more active species was only partly supported. In addition, the overall emission levels were somewhat lower with different emission blend compared to the data reported for wild species, as well as different shares for the VOCs in the blend. The study demonstrates that wounding-dependent emissions from tropical agricultural crops can significantly contribute to atmospheric volatiles, and these emissions cannot be predicted based on current evidence of wild plant model systems.

A large body of research has examined people’s attitudes toward mandatory vaccination to cope with the COVID-19 pandemic. However, studies have yet to explore how Chinese people view the COVID-19 vaccine mandates, let alone placing such views in the country’s highly politicized context. On the other hand, facing the high pressure of loosening its zero-COVID policies amidst the worldwide practices of co-existing with the COVID-19 virus, the country has to improve its vaccination coverage quickly. In this situation, it is more than necessary to systematically investigate the Chinese public’s attitude to mandatory vaccination against the virus and the underlying determinants. The current study utilizes data from a national survey adopting quota sampling to analyze the Chinese public’s medical and non-medical considerations when judging compulsory COVID-19 vaccination. The study reveals that thanks to China’s successful lockdown policies, personal risk, and benefit perceptions did not dominate their views regarding vaccination mandates. Instead, conspiracy beliefs regarding the pandemic outbreaks, conspiratorial thinking, and nationalism more strongly predicted their willingness to accept mandatory vaccination. Given the potentially massive impacts of the COVID-19 infection, if China opens its door and loosens its strict quarantine restrictions, the Chinese public needs to be educated with more medically valuable and relevant information to improve their vaccination protection against COVID-19.

Side-scan sonar (SSS) detection is a key method in applications such as underwater environmental security and subsea resource development. The use of acoustic images for seabed target detection has gradually become a mainstream underwater detection method. However, many existing detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, aiming at the underwater detection requirements of small scenes, we introduce a novel method for optimize and improve the detection sensitivity of different frequency ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels and space. Moreover, recognizing that SSS images can provide richer insights after frequency domain conversion, we introduce a Dual-Domain Feature Pyramid Network (D2FPN). By incorporating frequency domain information representation, D2FPN significantly augments the depth and breadth of feature information in underwater small datasets. Our methods are seamlessly designed for integration into existing networks, offering plug-and-play functionality with substantial performance enhancements. We have conducted extensive experiments to validate the efficacy of our proposed techniques, and the results showcase their state-of-the-art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. We will make our code and models publicly available at https://dagshub.com/estrellaww00/D2MFNet.

Objective: This study aims to investigate the relationship between sports psychological capital, coach leadership behavior, and satisfaction among middle school students in the 2023 sports college entrance examination in Chongqing. Methods: Using the structural questionnaire design, this paper carries out a questionnaire survey on the candidates who come to participate in the volleyball specialty college entrance examination in Chongqing, the corresponding database was established and the data were statistically processed by spss21.0 and amos18.0. Results: 1) there was a significant positive correlation between sports psychological capital, coach leadership behavior and examinees satisfaction; 2) sports psychological capital and coach leadership behavior had a certain influence on five dimensions of examinees' personal treatment, personal performance, team integration, team interaction and support from relatives and friends; 3) The sense of self-efficacy in sports psychological capital and coach's leading behavior are the most important to examinees' satisfaction; 4) Both coaches' leadership behavior and sports psychological capital can directly affect examinees' satisfaction, but sports psychological capital is the most important. It can not only directly affect examinees' satisfaction, but also act as an intermediary between coaches' leadership behavior and examinees' satisfaction. Conclusion: Sports College Entrance Examination Students can improve training satisfaction and sports performance by strengthening sports psychological capital training and perceiving coach's leadership behavior; In the leadership behavior of coaches, we should emphasize training and appropriately weaken authoritative teaching, while the training of sense of hope and toughness should be highlighted in sports psychological capital.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.