Base on building information modeling(BIM) plus with Robotic process automation (RPA) technology is rapid development in smart building operations and maintenance (O&M). RPA is a workflow-based automated execution tool that is particularly well suited for replacing manual execution of simple and repetitive tasks using computer systems. The use of RPA technology can saves man-hours and increases productivity, which is important for the automation of building operations and maintenance operations.. The BIM technology, as an advanced technology in the construction industry, it can provide new ideas and methods for building operation and maintenance based on its visualization characteristics. That’s integrating RPA with BIM technology which will enhance the digitalization and virtualization of smart building operation and management. In this article will first introduce the features of RPA and BIM technology and then discuss the technical roadmap for remote automated operation and maintenance of smart building equipment room to implemention a low-cost and high efficient building equipment room operation and maintenance solution.

The accurate of model attitude angel plays an important role on the aerodynamic test results in the wind tunnel test. The original method applies the spherical coordinate system transformation to obtain attitude angel calculation. The model attitude angel is obtained by coordinate transformation and spherical surface mapping applying the nominal attitude angel (the balance attitude angel in the wind tunnel coordinate system) indicated by the mechanism. First, the coordinate transformation of this method is not only complex but also difficult to establish the transformed relationship between the space coordinate systems especially after many steps of coordinate transformation, moreover it cannot realize the iterative calculation of the interference relationship between attitude angels; Second, during the calculate process to solve the problem the arc is approximately used to replace the straight line, the angel for the tangent value, and the inverse trigonometric function is applied. Therefore, in the calculation of attitude angel, the process is complex and inaccurate, which can be solved approximately when calculating small attack angel. However, with the advancing development of modern aerodynamic unsteady research, the aircraft tends to develop high or super large attack angel and unsteady research field. According to engineering practice and vector theory, the concept of vector angel coordinate system is proposed for the first time, and the vector angel coordinate system of attitude angel is established. With the iterative correction calculation and avoiding the problem of approximate and inverse trigonometric function solution, the model attitude calculation process is carried out in detail, which validates that the calculation accuracy and accuracy of model attitude angels are improved. Based on engineering and theoretical methods, a vector angel coordinate system is established for the first time, which gives the transformation and angel definition relations between different flight attitude coordinate systems, that can accurately calculate the attitude angel of the corresponding coordinate system and determine its direction, especially in the channel coupling calculation, the calculation of the attitude angel between the coordinate systems is only related to the angel, and has nothing to do with the change order s of the coordinate system, which simplifies the calculation process.

Both drag and lift forces impact an inclined plane when it is dragged through a granular bed. In this paper, the following results have been obtained: the drag and lift forces grow with the velocity of motion; when the immersion depth is constant, the inclination angle has no effect on drag force, however, the lift force increases linearly with this inclination angle; the ratio of drag and lift forces is exactly equal to the tangent value of the inclined angle. In order to describe this physical process macroscopically, a continuum wedge model based on the Coulomb model is established to predict drag and lift forces. Particularly，the dynamic friction angle in the assumed shear band is predicted as a function of both inclined angle and moving velocity.

To address the large hysteresis and susceptibility to instability in high-speed deposition fusion moulding (FDM) 3D printing’s temperature control, this paper proposes an improved ant-lion optimization algorithm (OCALO) to optimize the PID parameters.The improvement aims to solve the problem that the traditional ant-lion optimization algorithm (ALO) reduces the population diversity and tends to fall into the local optimal solution when it is close to the global optimal solution.The generation of the initial solution is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping. This integration into Elite Opposition-Based Learning aids in optimizing population diversity. Concurrently, the incorporation of cosine factors of a specified parameter into the elitist formula of the traditional ALO algorithm is aimed at reducing the tendency of ants to randomly gravitate towards ant-lions with lower fitness values. This adjustment helps in balancing the exploration-exploitation trade-off in the algorithmic process. Compared with two existing classical algorithms and three improved ALO algorithms, the algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The improved algorithm was combined with PID control to design an OCALO-PID temperature controller and simulated on a high-speed 3D printing temperature model identified by modelling. The results show that the method improves the transient and steady-state performance of reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a physical experimental platform to verify the feasibility of the algorithm.

Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy and explain its mechanism. A variety of weak classifiers classify eggs based on the spectra after preprocessing and feature wavelength extraction to obtain three classifiers with the highest accuracy. The three classifiers are used as metamodels of stacking ensemble learning to improve the highest accuracy from 96.25% to 100%. Moreover, the highest accuracy of scattering, reflection, transmission and mixed hyperspectral of eggs are 100.00%, 88.75%, 95.00% and 96.25%, respectively, indicating that the scattering hyperspectral for egg freshness detection is better than that of the others. In addition, the accuracy is inversely proportional to the angle of incidence due that the smaller the incident angle, the camera collects a larger proportion of scattering light, which contains more biochemical parameters of an egg than that of reflection and transmission. These results are very important for improving the accuracy of non-destructive testing and selecting the incident angle of the light source, and have potential applications in online non-destructive testing.

Background. This study aimed to establish a prognostic nomogram for predicting overall survival (OS) and cancer-specific survival (CSS) of patients with early-onset endometrial cancer (EOEC). Methods. Patients diagnosed with EOEC during 2004-2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were performed to ascertain the independent factors impacting the prognosis of EOEC patients, while a prognostic nomogram was established. Its performance was verified by the receiver operating characteristic (ROC) curves, and calibration plots. The prediction accuracy and clinical net benefit between the model, TNM stage and SEER stage were further compared by the concordance index (C-index) and decision curve analysis (DCA). Results. A total of 4415 eligible patients with EOEC were identified and enrolled in this study. Independent prognostic factors for OS and CSS were confirmed by multivariate Cox regression, which were further incorporated to constructed a nomogram. ROC curves and calibration curves indicated an optimal accuracy of the nomogram. Meanwhile, the C-index and DCA showed the predictive capability and the clinical applicability of the nomogram was superior over the TNM stage and SEER stage. Conclusions. Nomogram based on risk factors was developed as an individualized tool to predict OS and CSS for EOEC patients. The novel tool will assist in patient counseling and selection of appropriate therapeutic regimens.

Abstract: Plant types and soil bacterial communities had a close relationship, understanding the profound association between them contributes to better learn bacterial ecological function for plant growth. In this study, rhizosphere soil of six different chemotype Cinnamomum camphora trees were collected, including C. bodinieri var. citralifera, [C. camphora (Linn.) Presl], camphora-type, cineole-type, linalool-type and isoborneol-type. Soil properties content and bacterial communities were analyzed. Two chemotype C. camphora, including [C. camphora (Linn.) Presl] and linalool-type, shaped similar bacterial community structure, decreased Firmcutes relative abundance. richness estimators (Chao1 index and Ace index) of [C. camphora (Linn.) Presl] were decreased compared with the others. Furthermore, soil bacterial community structure was also similar among bodinieri var. citralifera, camphora-type, cineole-type and isoborneol-type. Hence, different chemotype C. camphora altered soil nutrient and shaped rhizosphere bacterial communities.

Pluripotent stem cells (PSCs) can differentiate into three germ layers and diverse autolo-gous cell lines. Being among the most commonly used large domesticated hoofed animals and serving as a vital source of food and bioreactors, cattle are among the many species in which PSCs have been developed. Bovine PSCs (bPSCs) are the "seed cells" of regenerative science and have immense potential to advance species propagation, genetic engineering, and disease treatment. Moreover, they can aid in screening new drugs for efficacy and toxicity, model human diseases, and establish a sustainable agricultural system that can cater to the needs of the growing global population. Establishing stable bPSCs in vitro has been a critical scientific challenge, and researchers have made numerous attempts to address this issue. This review primarily delves into the current research progress of bPSCs, the challenges faced, and their potential applications.

Hyperactivation of Janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) signaling is an attractive therapeutic target for tumor therapy. Herein, forty-eight novel meridianin derivatives were designed and synthesized, and their anti-tumor activity were evaluated in vitro both for activity optimization and structure-activity relationship (SAR) study. The results indicated that most derivatives exhibited significantly improved anti-tumor activity, especially for compound 6e. The compound 6e contains an isothiouronium linked by an alkyl chain consisting of 6 carbon atoms with IC50 ranging from 1.11 to 2.80 μM on various cancer cell lines. Consistently, 6e dose dependently induced the apoptosis of A549 and DU145 cells, in which STAT3 are constitutively active. Western blotting assays indicated that the phosphorylation levels of JAK1, JAK2 and STAT3 were inhibited by 6e at 5 μM without significant change in total STAT3 level. Moreover, 6e also suppressed the expressions of STAT3 downstream genes, including c-Myc, Cyclin D1 and Bcl-XL at 10 μM. An additional in vivo study revealed that 6e at the dose of 10 mg/kg could potently inhibit the DU145 xenograft tumor without obvious body-weight loss. These results clearly indicate that 6e could be a potential anti-tumor agent by targeting JAK/STAT3 signaling pathway.

The rapid and accurate detection of parasites is crucial for timely curative intervention in parasitosis and for epidemiological surveillance. To meet the needs of clinical diagnosis, it is imperative to develop a diagnostic tool based on nucleic acid that combines the sensitivity and specificity of established nucleic acid amplifica-tion tests with the speed, cost-effectiveness, and convenience of isothermal amplification methods. A new nu-cleic acid detection method, utilizing the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) nuclease, holds promise for point-of-care testing. Specifically, the CRISPR-Cas12a system has demonstrated numerous advantages for detecting parasites, with hopeful outcomes for identify-ing malaria, toxoplasmosis, and other parasitic ailments. This review provides an overview of how CRISPR-Cas12a can be utilized for parasite detection, evaluates its advantages and disadvantages, and sug-gests ways to improve the efficiency and sensitivity of CRISPR-Cas12a-based assays.

Seedling emergence is crucial for the establishment and growth of crops and is influenced by topsoil temperature and moisture. Mulching has been widely used to promote seedling emergence in areas with a cold spring. This study was conducted to determine and simulate the effects of transparent plastic film mulch on surface soil moisture, temperature, and seedling emergence of spring maize in Northwest China. Four treatments were used: no mulching (CK), full plastic film mulch (PMF), and partial plastic film mulch, 60 cm in width (PM60, 40 cm uncovered between rows of mulch) or 30 cm in width (PM30, 20 cm uncovered between rows of mulch). The effects of soil water conservation and temperature enhancement gradually decreased as the plastic film width decreased, but there was little difference between PMF and PM60 treatments. Soil-water-modified growing degree days (WGDD) described the beneficial effects of soil moisture and temperature with respect to seedling emergence under mulching conditions. Plastic film mulching markedly accelerated seedling emergence and increased final seedling emergence rate because mulching can shorten the autotrophy duration and maintain surface soil moisture, which leads to increased germination. The effects of seedling emergence improvement decreased as the plastic film width decreased. The relationship between WGDD and cumulative relative seedling emergence (CRE) was adequately described by a Gompertz function.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

The major histocompatibility complex (MHC) genes are the most polymorphic genes in vertebrates, and their proteins play a critical role in adaptive immunity for defense against a variety of pathogens. MHC diversity was lost in many species after experiencing a decline in size. To understand the variation and evolution of MHC genes in the Siberian ibex, Capra sibirica, which has undergone a population decline, we analyzed the variation of the second exon of MHC class II DRB genes in samples collected from five geographic localities in Xinjiang, China, that belong to three diverged mitochondrial clades. Consequently, we identified a total of 26 putative functional alleles (PFAs) with 260 bp in length from 43 individuals, and found one (for 27 individuals) to three (for 5 individuals) PFAs per individual, indicating the presence of one or two DRB loci per haploid genome. The Casi-DRB1*16 was the most frequently occurring PFA, Casi-DRB1*22 came after found in only seven individuals, 14 PFAs occurred once (7 PFAs twice), implying high frequency of rare PFAs. Interestingly, more than half of the (15) PFAs were specific to clade I, only one and three PFAs were specific to clades II and III, respectively. So, we assume that the polygamy and sextual segregation nature of this species likely contributed to the allelic diversity of DRB genes. Genetic diversity indices showed that PFAs of clade II were lower in nucleotide, amino acid, and supertype diversity compared to those of the other two clades. The way of allele sharing and FST values between three clades were to some extent in agreement with pattern observed in mitochondrial DNA divergence. In addition, recombination analyses revealed no evidence for significant signatures of recombination events. Alleles shared by clades III and the other two clades diverged 6 million years ago, and systematic neighbor grids showed cross-species polymorphisms. Together with the PAML and MEME analyses, the results indicated that the DRB gene in C. sibirica evolved under balancing and positive selection. However, by comparison, it can be clearly seen that different populations were under different selective pressures. Our results are valuable in understanding the diversity and evolution of the DRB gene in a mountain living C. sibirica and in making decisions on future long-term protection strategies.

Entanglement is one of the most striking features in quantum systems. Today, quantum entanglement plays a vital role in quantum technology, the non-classical correlation of quantum entanglement is an important resource for quantum communication and quantum computing. Entanglement can be divided into two categories: between multi-particle systems and between various degrees of freedom in a single-particle system (intraparticle entangled states). This work first analogized the distinctions and connections between two kinds of entangled states. Then it designed three types of entanglement-swapping methods based on Bell state joint measurement, cross-Kerr medium, and linear optical elements for intraparticle entangled states by analogizing the entanglement swapping between multiple particles. Intraparticle entanglement swapping can be applied in super quantum dense encoding, quantum information transmission, and quantum teleportation. This work is beneficial for promoting the development of quantum information technologies.

The current management of colorectal cancer liver metastasis (CRCLM) patients involves a multidisciplinary approach with surgical resection remaining the primary curative option. The advances in liver surgery have improved outcomes, enabling more patients to undergo surgery successfully. In addition, the development of imaging software has improved the preoperative planning and patient selection for surgery and other interventions. Systemic therapies, such as targeted therapies and immunotherapies have enhanced the chances of complete resection. Targeted agents in combination with chemotherapy, have shown efficacy in downstaging tumors and increasing resectability.

We constrain two dynamical dark energy models that are parametrized by the logarithm form of $w(z)=w_{0}+w_{1}\left(\frac{\ln (2+z)}{1+z}-\ln 2\right)$ and the oscillating form of $w(z)=w_{0}+w_{1}\left(\frac{\sin(1+z)}{1+z}-\sin(1)\right)$. Comparing with the Chevallier-Polarski-Linder (CPL) model, the two parametrizations for dark energy can explore the whole evolution history of the universe properly. Using the current mainstream observational data including the cosmic microwave background data and the baryon acoustic oscillation data as well as the type Ia supernovae data, we perform the $\chi^2$ statistic analysis to global fit these models, finding that the logarithm parametrization and the oscillating parametrization are almost as well as the CPL scenario in fitting these data. We make a comparison for the impacts of the dynamical dark energy on the cosmological constraints on the total mass of active neutrinos. We find that the dark energy properties could significantly change the fitting results of neutrino mass. Looser constraints on $\sum m_{\nu}$ are obtained in the logarithm and oscillating models than those derived in the CPL model. Consideration of the possible mass ordering of neutrinos reveals that the most stringent constraint on $\sum m_{\nu}$ appears in the degenerate hierarchy case.

Additive manufacturing (AM) or 3D printing is a promising industrial technology that enables rapid prototyping of complex configurations. Powder Bed Fusion (PBF) is one of the most popular AM techniques for metallic materials. Until today, only a few metals and alloys are available for AM, e.g. titanium alloys, the most common of which is Ti-6Al-4V. After optimization of the PBF parameters, with or without post processing such as heat treatment or hot isostatic pressing, the printed titanium alloy can easily reach tensile strengths of over 1100 MPa due to the quickly cooling of AM process. However, attributed to the unique features of metallurgical defects and microstructure introduced by this AM process, their fatigue strength has been low, often only not less than 30% of the tensile strength, especially in very-high-cycle regime, i.e. failure life beyond 107 cycles. Here, we have reviewed the current state of research on the very-high-cycle fatigue (VHCF) of additively manufactured (AMed) titanium alloys, refined the basic quantities of AMed microstructure and metallurgical defects, summarized the main factors limiting their VHCF strengths, and suggested possible ways to improve VHCF performance.

This paper presents a review on numerical and experimental methods to predict fracture of ad-vanced high strength steels (AHSS). AHSS are widely processed through sheet metal forming processes. Although being an excellent choice when lightweight, high strength and ductility are critical factors, their multi-phase microstructure accentuates forming challenges. To accurately model forming behavior, necking based failure criteria as well as direct fracture models require improvements. As a necking based failure model, the conventional forming limit diagram/curve (FLD/FLC) presents limitations such as in estimating direct fracture (surface cracks, edge cracks, shear cracks), as well as deformation histories under nonlinear strain paths. Thus, significant re-search efforts are being made towards the needs of advanced fracture constitutive models capa-ble of predicting fracture scenarios without necking, more frequently observed in the realm of AHSS. Scientific community research is divided into several directions aiming at improving the forming and fracture behavior accuracy of parts subjected to sheet metal forming operations. In this review paper, a comprehensive overview on ductile fracture modeling is presented. Firstly, the FLD/FLC limitations modeling fracture behavior in sheet metal forming operations are stud-ied. Followed by the recent trends in constitutive material modeling. Afterwards, material char-acterization methods advancements to cover a broad range of stress states are comprised. Final-ly, damage and fracture models predicting failure in AHSS are investigated. This review paper supplies relevant information on the current issues the sheet metal forming community is chal-lenged with due to the trend towards AHSS employment in the automotive industry.

Alzheimer's disease (AD), which is characterized by the development of β-amyloid plaques and neurofibrillary tangles in the brain parenchyma, can result in synaptic and neuronal loss. Clinical signs, including deteriorating memory deficiency, may lead to catastrophic effects for the elderly. Research on brain imaging and its application in the study of AD has advanced extraordinarily over the past 30 years. However, widely used imaging methods are only capable of identifying structural or metabolic alterations at the anatomical level in the brain and can only be utilized for drawing inferential distinctions. As a disease with a complex pathophysiological mechanism, AD has many different molecular pathological features and molecular biomarkers. Therefore, molecular imaging for AD pathology indicators provides a practical method for the early diagnosis and impartial assessment of AD. The present review discusses the pathogenesis of AD, as well as the detection methods and principles of molecular imaging, such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) techniques. Meanwhile, various tracers and probes applied in molecular imaging that detect Aβ deposits, tau protein accumulation, and neurotransmitters are also reviewed. Molecular pathological tracers provide the possibility to accurately quantify brain changes through molecular imaging.

Human activities mostly impact the trend and direction of surface water, groundwater, and other river basin resources in the watershed in Africa. Human activities influence river flows and the water quality at both highlands and lowlands. A watershed is indeed a conserved area of land that collects rain and snow and empties or penetrates into ground water sources. The act of managing the activities around the watershed is the Integrated Watershed Management while considering the social, economic, and environmental issues, as well as community interests to manage water resources sustainably. These watersheds, river basins, and groundwater resources provide important services for communities and biodiversity. This paper reveals that the best way to protect groundwater resources is on a watershed basis using IWM. This technique enables us to handle a variety of concerns and objectives while also allowing us to plan in a complicated and uncertain environment. IWM involves cooperation and participation from a wide range of community interests and water users, including municipalities, companies, people, agencies, and landowners, for stakeholders' input to be successful. All of the strategies and plans are produced concerning one another, as well as the overall conditions of the watershed, local land uses, and specific issues.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

In recent years, video anomaly detection technology , which can intelligently analyze massive video and quickly find abnormal phenomena, has attracted extensive attention with the wide application of video surveillance technology . To address the complex and diverse problem of abnormal human behavior detection in surveillance videos, a surveillance video abnormal behavior detection and localization supervised method based on the deep network model and the traditional method is proposed. Specifically, we combined AGMM and YOLACT methods to obtain more accurate foreground information by fusing the foreground maps extracted by each technique. To further improve the accuracy , we use the PWC-Net technique to extract features of the foreground images and input them into an anomaly classification model for classification. The proposed method effectively detects and locates the abnormal behavior in the monitoring scene. In addition to the aforementioned methods, this paper also employs YOLOV5 and DeepSORT networks for object detection and tracking in the video, which allows us to track the detected objects for better understanding of the scene in the video. Experiments on the UCSD benchmark dataset and the comparison with state-of-the-art schemes prove the advantages of our method.

With the burgeoning growth of online retail, the issue of counterfeit products has become a significant threat to both consumer trust and brand integrity, posing challenges to sustainable market practices. Online retail platforms are promoted to redesign their channel structures and enhance brand traceability to tackle counterfeits using blockchain technology. To investigate the interactions between blockchain technology and the channel structures of online retail platforms, we design a game theory model incorporating a manufacturer with(out) blockchain participation selling products via an online retail platform that operates an agency channel and/or a reselling channel. We show that the critical trade-off of blockchain participation comprises the brand assurance effect of the blockchain, the channel competition intensification effect, and the blockchain adoption cost. The platform is capable of strategically designing the channel structure to encourage (while discouraging) blockchain participation by the manufacturer. Our analysis highlights the complementarity between blockchain participation and the channel structure. Furthermore, the effect of the consumer’s belief in the reselling channel, the commission fee in the agency channel, and the substitutability between channels are investigated in depth. The research provides valuable insights for online retail platforms and policymakers seeking to foster a sustainable and trustworthy e-commerce ecosystem.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

A macro network loading Model for multi-flow lines, time-varying and pedestrian congestion is proposed. The station hub is abstracted as a network of different types of nodes, and the flow of passengers at each node is calculated in real time for the purpose of simulating the hub's collection and distribution process. For correct transmission of passenger flow on heterogeneous networks, three types of indexes are proposed to distinguish the nodes, and then match the corresponding fundamental diagrams. This paper divides the update process of the dynamic network loading model into multiple processes by flow lines, and improves computational speed of DNL model. The proposed model is applied to the simulation of passenger flow collection and distribution in an actual hub station with multi-flow lines. The analysis results illustrate that the model can accurately reflect the realistic congestion facilities and explain the formation process of high-density areas. A rolling passenger flow control model based on optimal control theory is proposed. The effectiveness of the control model is verified based on simulation data.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

As noted in the introductory quotation, an ideal map was long ago seen as the map of the map, the map of the map, of the map, and so on endlessly. This recursive perspective on maps, however, has received little attention in cartography. Cartography, as a scientific discipline, is essentially founded on Euclidean geometry and Gaussian statistics, which deal with respectively regular shapes, and more or less similar things. It is commonly accepted that geographic features – such as rivers, cities, streets and building – are not regular and that the Earth’s surface is full of fractal or scaling or living phenomena with far more small things than large ones at different levels of scale. This paper argues for a new paradigm in mapping, based on fractal or living geometry and Paretian statistics, and – more critically – on the new conception of space, conceived and developed by Christopher Alexander, that space is neither lifeless nor neutral, but a living structure capable of being more living or less living. The fractal geometry is not limited to Benoit Mandelbrot’s framework, but is extended towards Christopher Alexander’s living geometry and based upon the third definition of fractal: A set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times. Paretian statistics deals with far more small things than large ones, so it differs fundamentally from Gaussian statistics, which deals with more or less similar things. Under the new paradigm, I make several claims about maps and mapping: (1) Topology of geometrically coherent things – in addition to that of geometric primitives – enables us to see a scaling or fractal or living structure; (2) Under the third definition, all geographic features are fractal or living, given the right perspective and scope; (3) Exactitude is not truth – to paraphrase Henri Matisse – but the living structure is; and (4) Töpfer’s law is not universal, but scaling law is. All these assertions are supported by evidence, drawn from a series of previous studies. This paper demands a monumental shift in perspective and thinking from what we are used to on the legacy of cartography and GIS.

Nuclear Magnetic Resonance (NMR) and its various forms are extensively utilized in both research and clinical settings to analyse molecules. NMR data pre-processing, while essential to NMR data analysis, can be uniquely complex. Despite the availability of software tools, understanding these processes can be challenging, complicating the selection of appropriate pre-processing steps. In this review, we elucidate pre-processing steps in the time domain from a mathematical and statistical perspective, explaining direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, and domain transformation using plain language and fundamental mathematical formulas. Our objective is to clarify these processes in simple terms and provide general guidance.

Magnetic Resonance Imaging (MRI) is widely used in clinics and research due to its accurate disease identification and non-invasive nature. MRI is based on Nuclear Magnetic Resonance (NMR), which is also extensively employed in various fields. NMR and its modern versions involve intricate pre-processing steps before data analysis. These steps are initially processed in the time domain and subsequently in the frequency domain. While our previous review focused on time domain pre-processing (https://www.preprints.org/manuscript/202310.2032/v1), this review delves into the mathematical and statistical aspects of frequency domain pre-processing. We discuss essential pre-processing steps like phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing and peak picking, peak fitting/deconvolution and compound identification, integration and quantification, normalization and transformation. Furthermore, we offer practical recommendations for each step.

As Christopher Alexander conceived and defined through his life’s work – The Nature of Order – wholeness is a recursive structure that recurs in space and matter and is reflected in human minds and cognition. Based on the definition of wholeness, a mathematical model of wholeness, together with its topological representation, has been developed, and it is able to address not only why a structure is beautiful, but also how much beauty the structure has. Given the circumstance, this paper is attempted to argue for the wholeness as the scientific foundation of sustainable urban design and planning, with the help of the mathematical model and topological representation. We start by introducing the wholeness as a mathematical structure of physical space that pervasively exists in our surroundings, along with two fundamental laws – scaling law and Tobler’s law – that underlie the 15 properties for characterizing and making living structures. We argue that urban design and planning can be considered to be wholeness-extending processes, guided by two design principles of differentiation and adaptation, to transform a space – in a piecemeal fashion – into a living or more living structure. We further discuss several other urban design theories and how they can be justified by and placed within the theory of wholeness. With the wholeness as the scientific foundation, urban design can turn into a rigorous science with creation of living structures as the primary aim.

To solve the problem of smooth switching between car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver's behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle's intention to change lane was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there is no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers' natural driving information was collected and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to driver's behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM.

Purpose: The treatment of renal fibrosis caused by long-term obstructive nephropathy is limited. The purpose of this study was to establish a mouse model of renal fibrosis with unilateral ureteral obstruction (UUO) and to treat it with exosomes derived from SIRT6 transfected urine-derived stem cells (USCs), which will determine whether exosomes have the effect of anti-fibrosis Methods: The renal fibrosis model of UUO mice was established by ligating unilateral ureter. USCs were extracted from human urine and transfected with SIRT6 lentivirus plasmid. The SIRT6-USCs-exosomes(SIRT6-USCs-exos) were collected and identified by transmission electron microscope, nanoparticle tracking analysis and western blot. SIRT6-USCs-exos were injected into the tail vein of mice and the renal tissue of mice was stained with HE. The relative gene expressions of α-SMA, E-cadherin and TGF-β1 were analyzed by RT-qPCR. Results: In this study, we successfully constructed the renal fibrosis model of UUO mice and hUSCs with overexpression of SIRT6. The SIRT6-USCs-exos significantly improved the renal fibrosis in UUO mice. The relative mRNA expressions of fibrosis-related genes α-SMA, E-cadherin and TGF-β1 in renal tissue were significantly down-regulated after SIRT6-USCs-exos treatment. Conclusion: Our results indicate that SIRT6-USCs-exos can effectively alleviate renal fibrosis caused by long-term obstructive nephropathy. The findings may be promising for dealing with renal fibrosis.

Soil amendments with composted organic materials are recommended to increase soil organic matter (SOM), promote soil fertility, and the Circular Economy. Growing areas of hedged olive groves in southern Iberia peninsula generate huge amounts of olive leaves whose potential as soil organic amendment is not fully studied. An experimental field trial in a hedged olive grove (cv. “Cobrançosa”) was set up near Portalegre, Portugal, to test a compost of olive leaves plus sheep manure (2:1) applied in the row, at soil surface. Three nominal rates (0 or control, 2.5 and 5.0 kg m-2) were used, in a complete randomized block setup (3 treatments, 3 replicas, 9 plots) and soil chemical properties of layers 0-5, 5-15, and 15-30 cm deep were annually monitored. Results (mainly in layer 0-5 cm) showed increases in total N, carbon of the particulate organic matter (POM-C), permanganate oxidizable carbon (POX-C), extractable phosphorus, and zinc, after one year, and increases in soil organic carbon (SOC), C-stock, pHKCl, extractable phosphorus, and zinc, after two years. POX-C was the most sensitive SOM-related indicator, showing increases up to 30 cm deep after one year. This compost improved soil fertility, but should be monitored over longer periods of time, namely for SOC and extractable zinc content.

Conceived and developed by Christopher Alexander through his life’s work: The Nature of Order, wholeness is defined as a mathematical structure of physical space in our surroundings. Yet, there was no mathematics, as Alexander admitted then, that was powerful enough to capture his notion of wholeness. Recently, a mathematical model of wholeness, together with its topological representation, has been developed that is capable of addressing not only why a space is good, but also how much goodness the space has. This paper develops a structural perspective on goodness of space – both large- and small-scale – in order to bridge two basic concepts of space and place through the very concept of wholeness. The wholeness provides a de facto recursive definition of goodness of space from a holistic and organic point of view. A space is good, genuinely and objectively, if its adjacent spaces are good, the larger space to which it belongs is good, and what is contained in the space is also good. Eventually, goodness of space – sustainability of space – is considered a matter of fact rather than of opinion under the new view of space: space is neither lifeless nor neutral, but a living structure capable of being more living or less living, or more sustainable or less sustainable. Under the new view of space, geography or architecture will become part of complexity science, not only for understanding complexity, but also for making and remaking complex or living structures.

Under the new pattern of “double carbon” development, good ESG performance is the best way to promote the sustainable development of enterprises, and the ESG investment strategies are directly affected by the strategic vision of managers. Based on the upper echelons theory and stakeholder theory, this paper takes Chinese A-share listed companies from 2011 to 2022 as samples to empirically analyze the impact of managerial myopia on corporate ESG performance. The results show that managerial myopia significantly inhibits corporate ESG performance, mainly by inhibiting corporate green investment and green innovation sustainability. Furthermore, for state-owned enterprises (SOE), heavy polluting enterprises (HPE) and non-high-tech enterprises, the inhibitory effect of manager myopia on ESG performance is stronger. When the enterprise is in the growth stage, the above inhibition effect is more severe. For external governance, the greater the analyst attention and public environmental attention are, the more conducive they are to alleviating the restraining effect of managerial myopia on enterprise ESG performance. Therefore, effectively improving the time cognition level of managers and strengthening external supervision have become important measures for comprehensively optimizing the ESG performance.

The existence of palindrome numbers of arbitrary length greater than 2 that are divisible by the prime number 37 is proven in this paper. The method to construct such a palindrome is also described. It turns out that a palindrome number divisible by 37 is also divisible by 3 and, consequently, divisible by 111.

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

Zearalenone is one of the most prevalent estrogenic mycotoxins produced mainly by Fusarium family fungi, and harmed the heath of animals. Zearalenone hydrolase is an important enzyme capable of degrading zearalenone into a non-toxic compound. Although previous research has investigated the catalytic mechanism of zearalenone hydrolase, information on its dynamic interaction with zearalenone remains unknown. This study aimed to develop a pipeline for identifying the allosteric pathway of zearalenone hydrolase. Using an identity analysis, we identified hub genes whose sequences can generalize a set of sequences in a protein family. We then utilized a neural relational inference (NRI) model to identify the allosteric pathway of the protein throughout the entire molecular dynamics simulation. The production run lasted 1 microsecond, and we analyzed residues 139-222 for the allosteric pathway using the NRI model. We found that the cap domain of the protein opened up during catalysis, resembling a hemostatic tape. We used umbrella sampling to simulate the dynamic docking phase of the ligand-protein complex and found that the protein took on a square sandwich shape. Our energy analysis using both MMPBSA and PMF analysis showed discrepancies, with scores of -8.45 kcal/mol and -1.95 kcal/mol, respectively. MMPBSA, however, obtained a similar score to that of a previous report.

The rapid advancement of large language models (LLMs), such as ChatGPT, has revolutionized natural language processing, enabling these models to generate coherent and contextually relevant text. These capabilities hold significant potential for various applications, particularly in the medical field. LLMs can assist healthcare professionals by providing accurate information, supporting diagnostic processes, and enhancing medical education. However, the implementation of LLMs in clinical settings faces several barriers, including technical limitations, ethical concerns, regulatory constraints, and practical challenges. Ensuring accuracy, reliability, and interpretability is crucial, along with addressing biases, ensuring data privacy, and establishing ethical guidelines. Future research should focus on improving training techniques, developing explainable AI methods, and creating efficient, resource-saving models. Expanding applications through domain-specific models and multi-modal integration can further unlock the potential of LLMs. By addressing these challenges and prioritizing responsible development, LLM technology can be safely and effectively integrated into various domains, driving innovation and improving outcomes. This paper explores the current state of LLM technology, its medical applications, barriers to implementation, and directions for future research and development

Unpaid family caregivers might suffer losses in income as a result of care provision. Here we used data from the baseline survey of the China Health and Retirement Longitudinal Study to assess the relationship between hours of weekly caregiving provided to grandchildren/parents/parents-in-law and individual’s monthly employment income. Our study sample comprised 3,718 middle-aged Chinese adults who were of working age (45-60 years). For women and men separately, we used a likelihood-based method to determine a caregiving threshold in a two-stage Heckman selection procedure. Instrumental variables were used to rule out the endogeneity of caregiving hours. Our analysis revealed a negative association between caregiving and income for women that depended on a caregiving threshold of 63-hours per week. There was an absence of caregiving-income relationship among men. These results offer new insights into the opportunity costs of unpaid caregiving and support tailored policies to protect the financial well-being of female caregivers.

Saikosaponin (SS) a is a compound with various pharmacological properties and easily degraded into SSb1 and SSg in acid condition. In present work another two new degradation products of SSa formed under acid hydrolytic condition were detected and isolated by analytical and semi-preparative liquid chromatography technology, furthermore their structures were characterized as hydroxyl-saikosaponin a and SSb2 by spectral analysis, which is not only essential in stability-indicating method development and validation but also could be used as a worst case to assess the analytical method performance of SSa. Moreover their structural transformation pathways were also proposed.

A novel electrospun nanofiber was developed using polyamide-6 (PA) and ε-poly-L-lysine (PL). PA is a popular textile polymer having desirable mechanical and thermal properties, chemical stability, and biocompatibility. However, PA nanofibers are prone to bacterial growth and user discomfort. PL, a natural homopolyamide, is non-toxic, antimicrobial, and hydrophilic but lacks spinnability due to its low molecular weight. Given its similar backbone structure as PA, with an additional amino side chain, PL was integrated with PA to develop multifunctional nanofibers. This study explores a simple, scalable method to obtain PL nanofibers by utilizing the structurally similar PA as the base. The goal was to enhance the functionality of PA by addressing its drawbacks. The study demonstrates spinnability of varying concentrations of PL with base PA while exploring compositions with higher PL concentrations than previously reported. Electrospinning parameters were studied to optimize the nanofiber properties. The effects of PL addition on morphology, hydrophilicity, thermal stability, mechanical performance, and long-term antimicrobial activity of nanofibers were evaluated. The inclusion of PL in PA-based nanofibers resulted in super hydrophilicity, increased tensile strength, and efficient antimicrobial properties with long-term stability. These enhanced characteristics hold promise for the composite nanofiber’s application in medical and protective textiles.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

In pattern recognition domain, deep architectures are widely used nowadays and they have achieved fine grades. However, these deep architectures need special demands, especially big datasets and GPU. Aiming to gain better grades without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size, therefore it can gain more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis( PCA) and we apply softmax to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames to compare with their neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. This makes the salient areas found from different subjects have the same size. Besides, gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

7046 aluminum alloy has good fatigue property, corrosion resistance and weld property, but the moderate strength and plasticity limit its wider application and development. In the present study, severe plastic deformation (SPD) was applied prior to T6 treatment to significantly strengthen and toughen the 7046 aluminum alloy. Results indicate that the alloy processed by 4 passes of equal channel angular pressing (ECAP) at 300 ℃ prior to T6 treatment exhibits an excellent mechanical performance, with ultimate tensile strength (UTS) and elongation (EL) of 485 MPa and 19%, exhibiting an improvement of 18.6% and 375% compared to that of the T6 alloy, respectively. The mechanical properties of the alloy are further improved by the additional room temperature (RT) rolling process, with UTS of 508 MPa and EL of 23.4%. There are more precipitates of η' and Al6Mn phases in the 300℃4P-R80%-T6 and 300°C4P-T6 alloys, which contribute to the strengthening and toughening enhancement by SPD processed T6 alloy. The results of this work may shed some light on enhancing 7046 aluminum alloy.

Unpaid family caregivers must consider the economic trade-off between caregiving and paid employment. Prior literature has suggested labour force participation (LFP) to decline with caregiving intensity, but no study has evaluated this relationship by accounting for the presence of both kinks and discontinuities. Here we used respondents of the China Health and Retirement Longitudinal Study baseline survey who were non-farming, of working-age (aged 45-60) and had a young grandchild and/or a parent/parent-in-law. For women and men separately, a caregiving threshold-adjusted probit model was used to assess the association between LFP and weekly unpaid caregiving hours. Instrumental variables were used to rule out the endogeneity of caregiving hours. Of the 3,718 respondents in the analysis, for men, LFP was significantly and inversely associated with caregiving that involved neither discontinuities nor kinks. For women, a kink was identified at the caregiving threshold of 8 hours per week such that before 8 hours, each caregiving hour was associated with an increase of 0.0257 in the marginal probability of LFP, but each hour thereafter was associated with a reduction of 0.0014 in the marginal probability of LFP. These results have implications for interventions that simultaneously advance policies of health, social care and labour force.

The digital economy (DE) has become a major force driving the optimization and upgrading of industrial structures (ISOU), and research on the driving mechanism has yet to be verified. To this end, the driving influence of DE on the ISOU is targeted, and the role played by economic resilience (ER) and innovation is explored. Based on the panel data of 31 provinces in China from 2011 to 2020, the driving influence of the DE on the ISOU is analyzed using a two-way fixed-effects model. The empirical study finds that the DE presents a positive driving effect on both industrial structure advancement and rationalization, and the driving effect is still significant after the addition of control variables and the robust-type test with one period of lag; the DE presents regional differences in ISOU, with the driving effect of the DE on industrial structure advancement in the eastern region having a more significant performance than that of the central and western regions, and the driving influence on the rationalization of industrial structure in the eastern and western regions exceeds that in the central region; the impact of DE on the ISOU has a mediating role through innovation, ER has a moderating role in the process, and innovation as a partial mediator also has a threshold effect, and finally countermeasure suggestions are put forward.

Background: primary sclerosing cholangitis (PSC) is a chronic cholestatic disease characterized by inflammation and fibrosis of the bile ducts. Cholestasis may lead to hepatic inflammation and fibrosis, and amelioration of cholestasis may allow recovery from inflammatory and fibrotic pathological damage. Prevotella copri (P. copri) intervention have been reported could significantly improve cholestasis and liver fibrosis in DDC-induced PSC mice model. Despite alone P. copri treatment could not recover DDC-induced inflammation, it increased the abundance of Lactobacillus murinus (L. murinus) compared to DDC treatment, which has been reported to have anti-inflammatory effects. The abundance of L. murinus still not recover to normal level may underlie hepatic inflammation in P. copri+DDC mice. Method: alone or in combination interventions of P. copri and L. murinus were used to investigate the molecular mechanism of improvement of PSC inflammation and fibrosis. Results: P. copri and L. murinus significantly reduced the hepatic inflammatory cell aggregation and inflammatory factor expression, as well as the hepatic collagen content and fibrin factor expression in PSC mice. Further analysis of phosphorylation and dephosphorylation levels revealed that P. copri and L. murinus combined intervention in PSC mice inhibited the activity of DDC-activated TGF-β1/Smad pathway, thereby reducing liver inflammation and fibrosis. Conclusion: combination of P. copri and L. murinus inhibits the TGF-β1/Smad pathway and reduces inflammation and fibrosis in PSC.

Inside hippocampal circuits, neuroplasticity events that individual cells may undergo during synaptic transmissions occur in the form of Long Term Potentiation (LTP) and Long Term Depression (LTD). The high density of NMDA receptors expressed on the surface of the dendritic CA1 spines confers to hippocampal CA3-CA1 synapses, the ability to easily undergo NMDA-mediated LTP and LTD, that is essential for some forms of explicit learning in mammals. Providing a comprehensive kinetic model that can be used for running computer simulations of the synaptic transmission process is currently a major challenge. Here, we propose a compartmentalized kinetic model for CA3-CA1 synaptic transmission. Our major goal was to tune our model in order to predict the functional impact caused by disease associated variants of NMDA receptors related to severe cognitive impairment. Indeed, for variants Glu413Gly and Cys461Phe, our model predicts negative shifts in the glutamate affinity and changes in the kinetic behavior, consistent with experimental data. These results pinpoint to the predictive power of this multiscale viewpoint, which aims to integrate the quantitative kinetic description of large interaction networks typical of system biology approaches with a focus on the quality of few, key, molecular interactions typical of structural biology ones.

In this study, we discuss the thermospheric density response to the solar activity and QBO. By using the wavelet power spectrum method, the thermospheric density from 1967 to 2013 shows ~11-year period, semiannual and annual variations, while the seasonal variation is usually more significant under high solar activity conditions. Importantly, we investigate the possible link between the density and the QBO, with the aid of GAMDM model and different density residual method. As a result, the difference between the measured density and empirical model seemingly has QBO signal, but the ratio of them revels that the QBO signal does not exist in the thermospheric density. Comprehensively, we thought the stratospheric QBO cannot impact on the thermosphere, and more data and numerical modeling are needed for further validation.

In order to quickly develop a multi ship collaborative collision avoidance plan in multi ship encounter scenarios, a multi ship collision avoidance decision model is proposed in accordance with the requirements of the International Maritime Collision Avoidance Regulations, taking into account the collision avoidance operations in multi ship encounter scenarios. Firstly, the collision risk between ships is determined by the closest encounter distance (DCPA) and closest encounter time (TCPA). Secondly, a decision objective function is established based on rules, degree of ship deviation, and ship risk. Finally, the prediction mechanism is used to calculate the minimum value of the objective function and complete the collision avoidance decision. The simulation results show that in the scenario of multiple ship encounters, the collision avoidance model can achieve safe collision avoidance with a smaller steering frequency and a shorter col-lision avoidance time. The proposed multi ship collision avoidance model can provide reference for collision avoidance decision-making in multi ship encounter scenarios, which helps to im-prove the collision avoidance efficiency in multi ship collision avoidance

In recent years, the Chinese government pays more and more attention to agricultural development and ecological protection. While improving the cultivated land green use efficiency(CLGUE) is the key to promote the sustainable development of agriculture. This study aims to study the current situation and influencing factors of agricultural production from the perspective of green utilization efficiency of cultivated land. It takes 39 cities in the upper, middle and lower reaches of the Yangtze River basin in China as an example. The CLGUE values in those 39 cities from 2011 to 2020 were specifically measured, using the Super-SBM model, kernel density estimation and geographic detector method. Their temporal and spatial heterogeneity was described, and the influencing factors were detected at both single and interactive levels. The results showed that: （1）From 2011 to 2020, the green utilization efficiency value of cultivated land in the Yangtze River Basin showed an upward trend on the whole; （2）There is a clear spatial heterogeneity the CLGUE values in the Yangtze River Basin cities, as shown by: downstream region > midstream region > upstream region; (3) Cultivated land resource endowment, socioeconomic development, and agricultural production technology are important factors affecting the variability of CLGUE values. However, there are some differences in the degree and direction of influence of different influencing factors on different sample subgroups.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

A (2,6)-fullerene F is a 2-connected cubic planar graph whose faces are only 2-length and 6-length. Furthermore, it consists of exactly three 2-length faces by Euler's formula. The (2,6)-fullerene comes from Do\v{s}li\'{c}'s (k,6)-fullerene, a 2-connected 3-regular plane graph with only k-length faces and hexagons. Do\v{s}li\'{c} showed that the (k,6)-fullerenes only exist for k = 2, 3, 4 or 5, and some of the structural properties of (k,6)-fullerene for k = 3, 4 or 5 are studied. In this paper, we study the properties of (2,6)-fullerene. We obtain that the edge-connectivity of (2,6)-fullerenes is 2. Every (2,6)-fullerene is 1-extendable, but not 2-extendable. F is said to be k- resonant (k>0) if the deleting of any i (0=< i<= k) disjoint even faces of F results in a graph with at least one perfect matching. We have that every (2,6)-fullerene is 1-resonant. An edge set S of F is called an anti-Kekule set if F-S is connected and has no perfect matchings, where F-S denotes the subgraph obtained by deleting all edges in S from F. The anti-Kekule number of F, denoted by ak(F), is the cardinality of a smallest anti-Kekule set of F. We have that every (2,6)-fullerene F with |V(F)|>6 has anti-Kekule number 4. Further we mainly prove that there exists a (2,6)-fullerene F having f_{F} hexagonal faces, where f_{F} is related to the two parameters n, m.

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a computer data capture and visualization platform foundation for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design theory based on extensive data analysis. Additionally, the system presents information on occupants' behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human-machine communication and enabling timely adjustments to design strategies for new buildings and operation and maintenance strategies for existing buildings.

Nowadays, considering the growing tendency of companies towards e-commerce as well as green production, and dual-channel green supply chain management has become particularly important. This paper investigates pricing decisions within a dual-channel green supply chain framework while considering the capital constraints faced by risk-averse manufacturer. Simultaneously, the impact of the degree of risk aversion, consumer green preferences, and channel preferences on the optimal decision-making process of green supply chain members is analyzed. We address the issue of financial constraints faced by manufacturer through bank loans and trade credit financing and compare the two cases. Our findings illustrate that trade credit financing outperforms bank loan financing in the dual-channel green supply chain. Moreover, pricing decisions under different models are influenced by consumer preferences and risk aversion. Specifically, the greenness of the product, wholesale price, and sale price exhibit negative correlations with manufacturers' risk aversion but positive correlations with consumer preferences.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

Image encryption based on chaotic maps is an important way to ensure the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed by employing a compound chaotic map and random cyclic shift. Firstly, an improved Hybrid chaotic system is designed by coupling Logistic, ICMIC, Tent, and Chebyshev maps (HLITC). Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and Kolmogorov entropy illustrate the new hybrid chaotic map has better chaotic performance. Then the proposed HLITC chaotic system is used to develop a new chaotic image encryption scheme with the double cyclic shift. The improved HLITC chaotic system is performed to generate key sequences used in the image scrambling and diffusion stage. The spiral transformation controlled by chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments of statistical analysis, key sensitivity, and keyspace analysis are conducted. Experimental results show the proposed encryption scheme has good robustness against brute force attacks, statistical attacks, and differential attacks, and is more effective than many existing chaotic image encryption algorithms.

This article explores the specific impact of artificial intelligence (AI) -driven data visualisation technologies in the user experience of the Internet industry. Through the introduction of machine learning and data mining technologies, AI has significantly improved the intelligence and automation of data visualization tools, promoting innovation in visualization methods and improving the quality and efficiency of data cleaning, processing, and analysis. In addition, AI has also promoted the development of personalised data visualization, providing users with a richer and more intuitive way to present data. This paper analyses the applications of AI-driven data visualization in machine learning, natural language processing, and computer vision. It demonstrates the positive effects of AI data visualization on user understanding and optimisation of models through practical feedback from Yellowbrick tools.

Previous studies found that corilagin inhibits the growth of many cancer cells. Ovarian cancer is the deadliest gynecological malignancy, but research on the effects of corilagin on ovarian cancer is limited. This experiment aims to explore the impact of corilagin on ovarian cancer A2780 cell apoptosis and its mechanism of action to evaluate the prospects of corilagin in treating ovarian cancer. Corilagin significantly affects the A2780 cell cycle, reducing the proportion of G0/G1 and G2/M phase cells and blocking them in the S phase. Corilagin at low concentrations can cause A2780 cell apoptosis, mitochondrial membrane potential decline, and calcium influx. After the cells were stained with Acridine Orange, autophagosomes were observed under a fluorescence microscope and increased with the increase in concentration. The results of transcriptome sequencing showed that the effect of corilagin could cause differential expression of apoptosis-related genes in A2780 cells and concentrated on the PI3K-AKT pathway. The qPCR results showed that the expression of genes P53 and Bax in cells increased while the expression of BCL-2 decreased. The expressions of apoptotic factors caspase-9, caspase-3, p53-upregulated modulator of apoptosis (PUMA), and cytochrome C increased after the action of corilagin, indicating that corilagin can induce cell apoptosis. Corilagin can effectively inhibit the proliferation of A2780 cells, cause cell cycle arrest, and induce cell apoptosis. Moreover, it can trigger autophagy and has in vitro antitumor effects. It is a promising drug for treating ovarian cancer through the PI3K/P53 pathway.

There is an overall scientific consensus that public space and, mainly, space with a high ecological index – most notably experienced in green areas – positively affects individual and collective well-being and urban dwellers’ physical and mental health. However, the ‘reality check’ indicates difficulties in translating the assumed benefits of green space into implementable interventions in urban environments. To examine such an ‘implementation gap’ on the case of Zurich seems valid, given that the ‘green urban agenda’ debate has been embedded in numerous Swiss policies (from the federal to the municipal levels). In narrow terms, the research first focuses on Zurich’s district 9 consisting of two neighborhoods (Altstetten and Albisrieden) – the area under ongoing densification yet with a variety of green spaces, to then elucidate four green space clusters and their 400-meter catchment areas in the mentioned district. The mixed-method approach has been applied at three analytical levels: 1) documentary analysis of multi-scale national policy toolkits, to identify the critical tenets associated with the ‘green urban agenda’, 2) assessment of green spaces in Zurich’s district 9 based on online available quantitative data and on-site observation, to determine their provision, types, size and mutual connectivity, and 3) analysis of four green space clusters within the mentioned district through on-site observation, to identify green space attributes and main activities. Through critical mutual examination of the identified ‘green urban agenda’ principles in the national policies, on the one hand, and green space attributes, on the other, the research findings reveal the extent of the convergence and/or divergence between the ‘green urban agenda’ policy trends and their implementation in practice.

This research provides a computationally efficient numerical tool for predicting 3-D turbulent flows over hydrofoils. It addresses a gap in existing numerical methods by extending the capabilities of the laminar VIScous Vorticity Equation (VISVE) solver to handle 3-D turbulent flow scenarios. The implementation integrates the k − ω SST model into the 3-D VISVE solver using the Finite Volume Method (FVM), extending its capability to handle turbulent flows. The turbulence model is also parallelized to improve computational efficiency. The application of the newly developed model to 3-D hydrofoil cases expands its scope and serves as preparatory work for future rotational propeller applications. Rigorous testing includes a convergence analysis concerning grid numbers and time step sizes, and validation against a Reynolds-Averaged Navier-Stokes (RANS) solver ensures the reliability and accuracy of the enhanced VISVE solver for turbulent flow simulations. The turbulent VISVE solver offers advantages such as reduced computational domain and costs through a vorticity-based approach. Turbulence concentration within boundary layers and free shear flows does not compromise the advantages of the small computational domain. The simplified meshing process, automatically generated by denoting the number of panels on the hydrofoil, makes the solver accessible for researchers and engineers.

The ZX Spectrum was a popular 8-bit home computer by Sinclair Research in the 80s. Even though some of these computers may still work, the audio tapes, the TV with an analog tuner and the micro-switch joystick, used with the original ZX Spectrum, nowadays are outdated and hard to find in good working order or replicate. Since many other old closed systems are also very difficult to update to support modern peripherals, there is a necessity to provide a methodology to adapt such systems to support new peripherals while being compatible with existing software. The work proposed in this paper is focused on recreating a ZX Spectrum+/48k computer and interface it with modern peripherals on an FPGA. This is accomplished by adding a co-processor to assist with the control of the new peripherals which would either require to complex architectural changes to the original system and in the end it would perform poorly due to the low performance of the Z80 CPU. This work distances from previous ones on emulating a ZX Spectrum since it focuses on the use of different upgraded peripherals and the use of a NIOS II soft-processor as a co-processor to manage the SD Card accesses. A demonstration of the proposed modernized architecture was made by successfully running a diagnostics ROM and playing original ZX Spectrum games from an SD card for game with a PS/2 keyboard and a pair of joysticks.

This article explores the relationship between green energy and cryptocurrencies in the sustainable energy finance sector. The research findings contribute to our understanding of the application of green economy practice, enabling investors in financial markets, policymakers, and stakeholders to make informed decisions and develop specific strategies. Adopting the green economy paradigm makes it possible to promote collaboration and innovation by integrating ethical and responsible principles that can improve the overall quality of processes and boost sustainable growth. Cryptocurrencies have been widely used as financial instruments over the last decade. Given the development of the cryptocurrency market and the growing awareness of greener and more energy-efficient tokens, the green economy has become a popular topic for understanding economic and political issues. However, the literature still lacks clear evidence on how cryptocurrencies interact with green energies. Therefore, this study examines the long- and short-term relationships between dirty cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), clean cryptocurrencies such as Cardano (ADA), Ripple (XRP), Stellar (XLM), and green energies such as ISE Clean Edge Global Wind Energy, S&amp;P Global Clean Energy, S&amp;P TSX Renewable Energy and Clean Technology, Solactive China Clean Energy, in the period from January 2020 to September 2023. The results show that diversification is key, with clean cryptocurrencies such as ADA, XLM and XRP offering diversification opportunities alongside "dirty" cryptocurrencies such as BTC and ETH. Although sustainable energy indices show mixed evidence in the long and short term, they remain relevant for those who focus on clean energy investments. It is also becoming increasingly relevant for investors in sustainable portfolios to assess their environmental impact, especially for energy-intensive cryptocurrencies, and it is advisable to explore sustainable blockchain technologies.

As a stimulus signal, coded electrical signals can control the motion behavior of animals, which has been widely used in the field of animal robots. In current research, most of the stimulus signals used by researchers are traditional waveform, such as square waves. To enrich the stimulus waveform, a wireless animal robot stimulation system based on neuronal electrical signal characteristics is presented in this paper. The stimulator uses the CC1101 wireless module to control animal behavior through brain stimulation. The LabVIEW-based graphical user interface(GUI) can manipulate brain stimulation remotely while the stimulator powered by battery. Additionally, the spikes of animals have been simulated by this system through Direct Digital Synthesizer(DDS) algorithm. The GUI enable users to customize the combination of these analog spike signals. The recombined signals are sent to the stimulator through CC1101 as stimulus signals. In vivo experiments conducted on five pigeons verified the efficacy of the stimulation mechanism. The analog spike signal with an amplitude of 3-5V successfully caused the pigeon’s turning behavior. The feasibility of the analog spike signals as stimulus signals was successfully verified. Increased the diversity of stimulus waveform in the field of animal robots.

Brain-stimulation reward, also known as intracranial self-stimulation (ICSS), is a commonly used procedure for studying brain reward function and drug reward. In electrical ICSS, an electrode is surgically implanted into the medial forebrain bundle in the lateral hypothalamus or the ventral tegmental area in the midbrain. Operant lever responding leads to the delivery of electrical pulse stimulation. The alteration in the stimulation frequency-lever response curve is used to evaluate the impact of pharmacological agents on brain reward function. If a test drug induces a leftward or upward shift in the ICSS response curve, it implies a reward-enhancing or abuse-like effect. Conversely, if a drug causes a rightward or downward shift in the functional response curve, it suggests a reward-attenuating or aversive effect. A significant drawback of electrical ICSS is the lack of cellular selectivity in understanding the neural substrates underlying drug reward versus aversion. Excitingly, recent advancements in optical ICSS (oICSS) have facilitated the development of at least three cell type-specific oICSS models – dopamine-, glutamate-, and GABA-dependent oICSS. In these new models, a comparable frequency-rate response curve has been established and employed to study the substrate-specific mechanisms underlying brain reward function and a drug's rewarding versus aversive effects. In this review article, we summarize recent progress in this exciting research area. The findings in these new behavioral models have not only increased our understanding of the neural mechanisms underlying drug reward and addiction but have also introduced a novel experimental approach in preclinical medication development for treating substance use disorders.

A novel nano-laminated GdB2C2 material was successfully synthesized using GdH2, B4C, and C via an in-situ solid state reaction approach for the first time. The formation mechanism of GdB2C2 was revealed based on the microstructure and phase evolution investigation. A purity of 96.4 wt. % GdB2C2 was obtained at a low temperature of 1500 ℃, while a near full pure GdB2C2 can be obtained at a temperature over 1700 ℃. The as-obtained GdB2C2 presented excellent thermal stability at high temperature of 2100 ℃ in Ar atmosphere due to the stable frame work formed by the high covalent four-members and eight-members B-C rings in GdB2C2. The minimum reflection loss value (RLmin) of -47.01 dB with an effective absorption bandwidth (EAB) of 1.76 GHz at a thickness of 3.44 mm was obtained for the GdB2C2 synthesized at 1500 ℃. The possible EMWA mechanism could be ascribed to the nano-laminated structure and appropriate electrical conductivity, which facilitated to the good impedance matching, remarkable conduction loss, interfacial polarization as well as multiple interface reflection and scattering. The as-obtained GdB2C2, with excellent EMWA performance as well as remarkable ultra-high temperature thermal stability, could be a promising candidate for the EMWA materials applications in extreme ultra-high temperatures.

The Gulong shale oil reservoir is formed in freshwater to slightly saline lacustrine basins, mainly consisting of pure shale geological structure, which is quite different from other shale reservoirs around the world. Currently, the development of Gulong shale oil mainly relies on hydraulic fracturing, while the subsequent shut-in period for imbibition has been proven to be an effective method for enhancing shale oil recovery. To clarify the characteristics of fluid occurrence space and the variation of fluid occurrence during saltwater imbibition in Gulong shale, this paper carried out porosity and permeability tests on Gulong shale cores, and analyzed the fluid occurrence space characteristics and imbibition oil recovery based on nuclear magnetic resonance (NMR). In the porosity and permeability tests, the porosity measured by saturation method was calibrated using NMR T2 spectra. Combined with the identification of fractures in shale cores using micro-CT and the analysis of porosity and permeability parameters, it was found that the permeability of shale cores was related to the development of fractures in shale cores. Through the testing and analysis of NMR T1-T2 two-dimensional spectra of the shale cores before and after saturation with oil, it was found that the shale mainly contains heavy oil, light oil, and clay-bound water, and they were distributed in different positions in the T1-T2 spectrum. Finally, the T1-T2 two-dimensional spectra of the shale core at different imbibition stages were analyzed, and it was found that the saltwater mainly entered the minuscule inorganic pores of clay minerals during the imbibition process, and squeezed the larger-sized inorganic pores containing light oil through the hydration expansion effect, thus expelling the light oil from the shale core and achieving the purpose of enhanced oil recovery.

Sustainable urban design or planning is not a LEGO-like assembly of prefabricated elements, but an embryo-like growth with persistent differentiation and adaptation towards a coherent whole. The coherent whole has a striking character – called living structure – that consists of far more small substructures than large ones. To detect the living structure, natural streets or axial lines have been previously adopted to be topologically represent an urban environment as a coherent whole. This paper develops a new approach to detecting the underlying living structure of urban environments. The approach takes an urban environment as a whole and recursively decomposes it into meaningful subwholes at different levels of hierarchy or scale ranging from the largest to the smallest. We compared the new approach to natural street and axial line approaches and demonstrated, through four case studies, that the new approach is better and more powerful. Based on the study, we further discuss how the new approach can be used not only for understanding, but also for effectively designing or planning the living structure of an urban environment to be more living or more livable.

The popularity and growth of additive manufacturing(AM) in recent years are due to its ability to provide innovative solutions to complex manufacturing challenges and its potential to transform the way products are designed, developed, and produced. In this work, the numerical simulation geometry of the model extruder from nozzle is established along with the material extrusion process. The high gravity material extrusion system (HG-MEX) properties and characteristics during filament fabrication are evaluated. This work investigates the effects of gravity on the material extrusion process from nozzle extrusion. This work is unique in the incorporation of fluid surface characteristics to represent the performance and contact with the material extrusion in additive manufacturing. The use of simulation in high gravity material extrusion in additive manufacturing is a meaningful and valuable approach to improving the quality and efficiency of the process.

ABSTRACT Background It is important to be able to predict, for each individual patient, the likelihood of later metastatic occurrence, because the prediction can guide treatment plans tailored to a specific patient to prevent metastasis and to help avoid under- or over-treatment. Deep Neural Network (DNN) learning, commonly referred to as deep learning, has become popular due to its success in image detection and prediction, but questions such as whether deep learning outperforms other machine learning methods when using non-image clinical data remain unanswered. Grid search has been introduced to deep learning hyperparameter tunning for the purpose of improving its prediction performance, but the effect of grid search on other machine learning methods are under-studied. In this research, we take the empirical approach to study the performance of deep learning and other machine learning methods when using non-image clinical data to predict the occurrence of breast cancer metastasis (BCM) 5, 10, or 15-years after the initial treatment. We developed DNN models as well as models using 9 other machine learning methods including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), LASSO, Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forrest (RF), AdaBoost (ADB), and XGBoost (XGB). We used grid search to tune hyperparameters for all methods. We then compared the deep learning models to the models trained using the 9 other machine learning methods. Results Based on the mean test AUC results, DNN ranks 6th, 4th, and 3rd when predicting 5-year, 10-year, and 15-year BCM respectively, out of 10 machine learning methods. The top performing methods in predicting 5-year BCM are XGB(1^st), RF(2^nd), and KNN(3^rd). For predicting 10-year BCM the top performers are XGB (1^st), RF(2^nd), and NB(3^rd) . Finally, for 15-year BCM the top performers are SVM (1^st), LR and LASSO (tied for 2^nd), and DNN (3^rd). The ensemble methods RF and XGB outperform other methods when data are less balanced, while SVM, LR, LASSO, and DNN outperform other methods when data are more balanced. Our statistical testing results show that at a significance level of 0.05 DNN overall performs no worse than other machine learning methods when predicting 5-year, 10-year, and 15-year BCM. Conclusions Our results show that deep learning with grid search overall performs at least as well as other machine learning methods when using non-image clinical data. It is interesting to note that some of the other machine learning methods such as XGB, RF, and SVM are very strong competitors of DNN when incorporating grid search. It is also worth noting that the computation time required to do grid search with DNN is way more than that required to do grid search with the other 9 machine learning methods.

Considering the significance of thermodynamic cycles in the global energy system, it is necessary to develop new general classes of thermodynamic cycles to relieve current energy and environmental problems. Inspired by the relationship between power cycles and refrigeration cycles, we realize that general classes of thermodynamic cycles should occur in pairs with opposite functions. Here we reverse class 1 heating cycles to obtain another new general class of thermodynamic cycles named class 2 heating cycles (HC-2s). HC-2s have two basic forms, and each contains six thermodynamic processes. HC-2s present the simplest and most general approach to utilizing the temperature difference between a medium-temperature heat source and a low-temperature heat sink to achieve efficient high-temperature heating. HC-2s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in the global sustainable energy system.

Thermodynamic cycles are not only the core concepts of thermal science, but also key approaches to energy conversion and utilization. So far, power cycles and refrigeration cycles have been the only two general classes of thermodynamic cycles. While diverse types of systems have been developed to perform thermodynamic cycles, no new general classes of thermodynamic cycles have been proposed. Based on the basic principles of thermodynamics, here we propose and analyze a new general class of thermodynamic cycles named class 1 heating cycles (HC-1s). Two basic forms of HC-1s are obtained by connecting six essential thermodynamic processes in the proper order and forming a thermodynamic cycle. HC-1s present the simplest and most general approach to utilizing the temperature difference between a high-temperature heat source and a medium-temperature heat sink to achieve efficient medium-temperature heating and/or low-temperature cooling. HC-1s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in energy conservation and emission reduction.

The growing shortage of human resources to manage landscape in the water environment has led to problems concerning preservation of the landscape in Japan. To cultivate people with awareness about preserving the landscape, it is important to understand the process of how a region’s residents develop an attachment toward the water environment in their area. This paper identified factors influencing the attachment of residents toward the Fukushima-gata lagoon, Niigata prefecture, Japan, as a case study while focusing on changes in the lifestyle of the regional residents. The findings are as follows: (1) The target households frequently used Fukushima-gata lagoon for “fishing and for picking edible wild plants” and “gathering reeds to make thatched roofs” before “the reclamation project was carried out by the government in Fukushima-gata lagoon.” They also frequented Fukushima-gata lagoon for “walking and cherry blossom viewing” after “the reclamation project” was implemented. (2) Even the fact that distance from the lagoon to their place of residence increased after the project did not lose their attachment to the lagoon. (3) It was suggested that the project contributed to increase the attachment of regional residents towards Fukushima-gata lagoon because of multiple factors such as “playing in the water,” “livelihood opportunity,” “recreational use,” and so on. (4) Changes in the attachment of regional residents toward Fukushima-gata lagoon before and after the reclamation project was carried out by the government corresponded to “changes in the lifestyle of the local residents” before and after the project.

A new mesogenic non-symmetric dimeric monomer with a terminal olefin function, forming a twist bend nematic (Ntb) as well as a nematic (N) phase was synthesized, using an enhanced synthetic methodology, which avoids isomerization of the terminal double bond in the preparation of the dimer. This monomer was attached to a pentamethyldisiloxane group, resulting in SmA LC phase behaviour of the ensuing material. Linking the monomer to a siloxane main chain resulted in nematic phase behaviour. Detailed studies with the Ntb phase forming dimer DTC5C7 show full miscibility of the dimer and the new LC polymer in the LC state, suggesting that the side-chain LC polymer forms a Ntb phase as the low temperature nematic phase. Copolymerizing the monomer with a cyanobiphenyl based monomer allows to tune the glass transition and phase behavior further.

We present an investigation on the fracture of a mold, comparing it with a normal part using specific techniques, such as EDX, SEM, and AES. The EDX analysis revealed that the composition of the normal part was consistent with that of low carbon steel, mainly comprising Fe and C. In contrast, the fractured part exhibited cracks due to nonconforming nonmetallic inclusions and reticular carbides, with fractures resulting from microporosity agglomeration and cleavage fracture. The SEM and AES analyses further presented the causes of mold fracture, highlighting the mechanism by which dimples on the specimen edge contributed to the fracture. The EDX analysis confirmed that the mold experienced thermal brittleness during use. To enhance mold durability and extend its lifespan, a double-pulse electrodeposition method was employed to create a NiCo alloy coating as a replacement for the Cr layer on the metal surface. The coating exhibited a smooth and scratch-free surface. The prepared NiCo special coating significantly increased the mold yield strength by approximately 313.8%, facilitated a 13% increase in plastic deformation, and reduced fracture strain by 25%, effectively preventing mold fracture and 3improving its service life.

Obtaining classification confidence at the pixel level is a challenging task for accuracy assessment in remote sensing image classification. Among the various methods for estimating classification confidence at the pixel level, interpolation-based methods have drawn special attention in the literature. Even though they have been widely recognized in the literature, their usefulness has not been rigorously evaluated. This paper conducts a comprehensive evaluation of three interpolation-based methods: local error matrix method, bootstrap method, and geostatistical method. We applied each of the three methods to three representative datasets with different spatial resolutions, spectral bands, and the number of classes. We then derive the estimated classification confidence and true classification confidence and compared the results with each other using both exploratory data analysis (bi-histogram) and statistical analysis (Willmott's d and Binned classification quality). The results indicate that the three interpolation methods provide some interesting insights on various aspects of estimating per-pixel classification confidence. Unfortunately, the interpolation assumes that classification confidence is smooth across the space, which is usually not true in practice. In other words, interpolation-based methods have limited practical use.

Authorities define cities – or human settlements in general – through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city based on the notion of natural cities that is defined based on head/tail breaks, a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations – all building footprints of America (mainland) or their centroids more precisely – to derive 2.1 million natural cities in the country (http://lifegis.hig.se/uscities/). These natural cities – in contrast to government defined city boundaries – constitute a valuable data source for city-related research.

Bacterial biofilms can cause widespread infection. In addition to causing urinary tract infections and pulmonary infections in patients with cystic fibrosis, biofilms can help microorganisms adhere to the surfaces of various medical devices, causing biofilm-associated infections on the surfaces of biomaterials such as venous ducts, joint prostheses, mechanical heart valves, and catheters. Biofilms provide a protective barrier for bacteria and provide resistance to antimicrobial agents, which increases the morbidity and mortality of patients. This review introduces the formation process and drug resistance mechanism of biofilms in detail, and further summarizes the main characteristics of clinical persistent infection caused by biofilms and the many methods of treating biomaterial-related biofilm. This provides ideas and directions for the development of new biofilm infection strategies related to therapeutic materials.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

As a widely used plastic, the aging and degradation of polyethylene (PE) are inevitable problems, whether the goal is to prolong the life of PE products or address the issue of white pollution. Molecular simulation is a vital scientific tool in elucidating the mechanisms and processes of chemical reactions. To obtain the distribution and evolution process of PE's thermal oxidation products, this work employs the self-consistent charge density functional tight binding (SCC-DFTB) method to perform molecular simulations of the thermal oxidation of PE with different crystallinity and branched structures. We discovered that crystallinity does not affect the thermal oxidation mechanism of PE, but higher crystallinity makes PE more susceptible to cross-linking and carbon chain growth, reducing the degree of PE carbon chain breakage. The branched structure of PE results in differences in pore size between the carbon chains, with larger pores leading to a concentrated distribution of O2 and free radicals subsequently formed. The breakdown of PE is slowed down when free radicals are localized in low-density regions of the carbon chain. The specifics and mechanism of PE's thermal oxidation are clearly revealed in this paper, which is essential for understanding the process in-depth and for the development of anti-aging PE products.

In order to study the effect of explosion load on the flexural performance of RC beams, two RC beams of the same size were carried out explosion test and bearing capacity test successively. Piezoelectric intelligent aggregate was embedded in the beams in advance to monitor the damage state of the beams, which realized the whole process of damage monitoring of the concrete inside the structure from explosion damage to compression damage.Based on wavelet packet analysis and residual bearing capacity damage evaluation criteria, the beam damage was quantitatively evaluated, and the bending bearing capacity of RC beam after explosion damage was compared and analyzed.The results show that the change of concrete strain gauge signal, the phenomenon of crack development and the increase of internal crack development index indicate that the RC beam is damaged by explosion load.After explosion damage, the damage index of the beam increases and the residual bending capacity decreases, which proves that the piezoelectric intelligent aggregate sensor can effectively monitor the bending capacity change of the component after explosion damage.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

In the ground-based infrared optical remote sensing system, the target signal is very weak due to the dynamic strong light background and the movement of small and small targets. In order to improve the limit detection capability, the background suppression and target enhancement methods are required to be more suitable for this scene. To solve this problem, we first analyze the image features in the current scene, and propose a more perfect point target and noise model. Then, we propose a new WY distribution function based on the Fermi Dirac distribution function, and propose an anisotropic filtering method based on this function, and further suppress the back-ground through the difference results of two steps. Then, based on the distribution function, we further designed an energy concentration accumulation strategy in 9 scale directions, through which the SNR of the target is effectively improved and the suppression ability of the background is enhanced. Through quantitative and qualitative experimental analysis, compared with the same type of algorithms, the proposed method has better robustness against extremely weak targets and dynamic backgrounds.

Climate and land-use changes have been contributing to increase the occurrence of extreme wild-fires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the effects of land-use change on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini index) between 1984 and 2019, which was combined with 2018 carbon stock data (above- and below-ground), and a landscape typology re-sulting from cluster analysis over land-use composition, diversity and configuration, to explore relationships between landscape types and the two major ecosystem services at stake: wildfire reduction and carbon stock. Cross tabulations, logistic and linear regressions were performed on these data and results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt forest plantations and high-hazardous fire regime but hold the highest carbon stock. Shrubland-mixed landscapes were associated with lower carbon stocks but less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agro-forestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes however, this good wildfire performance is achieved at the cost of the poorest carbon stock, de-noting land degradation, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

A plant’s early response to pathogen stress is a vital indicator of its disease resistance. In order to study the response mechanism of resistant and susceptible flax cultivars to Fusarium oxysporum f. sp. lini (Foln), we applied RNA-sequencing to analyze transcriptomes of flax with Foln 0.5, 2 and 8 hours post inoculation (hpi). We found a significant difference in the number of differential expression genes (DEGs) between resistant and susceptible flax clutivars. The number of DEGs in the Fusarium-resistant cultivar increased dramatically at 2 hpi, and a large number of DEGs participated in the Fusarium-susceptible cultivar response to Foln infection 0.5 hpi. GO enrichment analysis determined that the up-regulated DEGs of both flax cultivars were enriched such as oxidoreductase activity and oxidation-reduction process. At the same time, the genes involved in diterpenoid synthesis were up-regulated in resistant cultivar, while those involved in extracellular region, cell wall and organophosphate ester transport were down-regulated in susceptible cultivar. KEGG enrichment analysis showed the genes encoded WRKY 22 and WRKY33 which involved in MAPK signaling pathway were up-regulated expressed in S-29 and down-regulated expressed in R-7, negatively regulated the disease resistance of flax; The genes encoded Hsp 90 family which in involved in plant pathogen interaction pathway were up-regulated in R-7 and down-regulated in S-29, which positively regulated the disease resistance of flax; The genes encoded MYC2 transcription factor and TIFY proteins which involved in plant hormone signaling pathway were up-regulated in R-7, and regulated the jasmonic acid metabolism of flax and the signal transduction of plant hormones. Meanwhile seven regulatory genes with the most correlation were screened out, Among Lus10025000.g and Lus10026447.g regulated other genes expressed both in plant hormone signal transduction pathway and MAPK signal pathway. In conclusion, these findings will facilitate further studies on the function of these candidate genes in flax of response to Fusarium stress, and the breeding of disease-resistant flax cultivar.

Commercial classification is essential to spatially describe commerce. Although most of the proposed classifications have been classic functional analyses, rooted on qualitative studies, some recent classifications efforts are found to rely heavily on more quantitative methods, due eventually to increasing data availability and technological advancements. In this paper, a classification is proposed, using k-means clustering and a minimal set of variables (density, diversity and clustering) to derive the commercial structure of Lisbon. The classification is implemented for 1995, 2002 and 2010, using a 150m-sided square grid. The cross-sectional analysis of the results shows the rise of shopping-malls against city centre decline and gentrification, along with changes in cluster composition considering 9 different commercial categories (6 categories of retail, and restaurants, cafes and bars). These findings are in line with literature, thus supporting the obtained classification. Since the classification can be used to accurately describe the commercial structure of the city in different time periods, it is implied that it may also be generalized to different cities. Furthermore, the potential use of cluster membership in retail location models, which is an advantage of the proposed classification, could help strengthen the relationship between location modelling and commercial classification, thus reinforcing the role of commercial studies in urban planning and policymaking.

Background: Gestational diabetes is a common complication during pregnancy that can lead to numerous adverse outcomes. Some studies suggest that probiotics may be used to treat gestational diabetes, however, the results remain controversial. We conducted a systematic review and meta-analysis to evaluate the effect of probiotics on blood glucose and pregnancy outcomes in women with gestational diabetes. Methods: A systematic search of PubMed, Embase, and Cochrane databases was performed (start date to August 22, 2023). Primary outcomes included fasting blood sugar (FBS), fasting serum insulin (FSI), homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI). Secondary outcomes included pregnancy and neonatal outcomes. Results: 15 articles (n = 1006 women) met the inclusion criteria and were included in the analysis. Compared to a placebo, probiotics can decrease FBS (MD -2.58, 95% CI -4.38 to -0.79, p &lt; 0.01), FSI (MD -2.29, 95% CI -3.40 to -1.18, p &lt; 0.01), HOMA-IR (MD -0.56, 95% CI -0.81 to -0.32, p &lt; 0.01), birthweight (MD -101.20, 95% CI -184.62 to -17.77, p = 0.02), neonatal intensive care unit (NICU) (RR 0.60, 95% CI 0.40 to 0.89, p = 0.01), and hyperbilirubinemia (RR 0.31, 95% CI 0.16 to 0.61, p &lt; 0.01), alongside higher QUICKI (MD 0.01, 95% CI 0.00 to 0.01, p &lt; 0.01). However, no other significant results were obtained. Conclusion: Probiotics may improve blood glucose indicators and reduce neonatal hyperbilirubinemia, NICU admissions, and birth weight in women with GDM.

In this paper, we investigated the formation isolation regulation issue regarding multiple autonomous underwater vehicles (AUVs) characterized by a "leader-follower" framework. Considering the cooperative-competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer was designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme was designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source and the structure converges was achieved, with the absence of zeno behavior also demonstrated.

The 3x+1 problem is a problem of continuous iteration for integers. According to the basic theorem of arithmetic and the way of iteration, we derive a general formula for continuous iteration for odd integers. Through this formula, we can construct a loop iteration equation and obtain the result of the equation: the equation has only one positive integer solution. In addition, this general formula can be converted into a linear indeterminate equation. The process of solving this equation shows that the relationship between the iteration result and the odd number being iterated is linear. Extending this result to all positive even numbers, we get the answer to the 3x + 1 question.

Aims: The sequencing data of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) are rapidly emerging. The mutation profile across SARS-CoV-2 populations is an important inference of the evolution of coronaviruses. Materials & Methods: With 4,521 lines of SARS-CoV-2, we obtained 3,169 unique point mutation sites in the SARS-CoV-2 genome. We counted the numbers and calculated the MAF (minor allele frequency) of each mutation type. Results: Nearly half of the point mutations are C-T mismatches and 20% are A-G mismatches. The MAF of C-T and A-G mismatches is significantly higher than MAF of other mutation types. Conclusions: The excessive C-T mismatches do not resemble the random mutation profile, and are likely to be explained by the cytosine-to-uridine deamination system in hosts. Not only the population analyses in previous studies are questionable, but also the 17% divergence between SARS-CoV-2 and RaTG13 could be erroneous due to the deamination.

This study examines the influences of live streaming servicescape on Chinese consumers' luxury purchase intention. Interestingly, for the sample without luxury purchase experience, the mediating effect of the perceived value of luxury goods is significant but effect of consumer trust is not. For the sample with luxury purchase experience, the result of mediating effect is just the opposite. The interaction between conspicuous consumption and live streaming servicescape plays a certain role in regulating the perceived value of luxury goods. These results show that luxury brands can adopt innovation in the live streaming servicescape for different types of consumers to promote live streaming marketing. This study enriches the servicescape theory of live streaming.

Eugenol is a widely used fishery anaesthetic. This study investigated the effects of various concentrations of eugenol on blood physiological and biochemical indexes, and muscle flavor, in crucian carp (Carassius auratus). To determine the appropriate concentration of eugenol anesthetic for use in crucian carp transportation and production operations, we evaluated seven anaesthesia groups of 20, 30, 40, 50, 60, 70 and 80 mg/L and one control group (without eugenol) to determine the effects on blood physiological and biochemical indexes, and muscle flavor. The red blood cells and platelets of crucian carp decreased significantly (P<0.05) with eugenol treatment. With increasing eugenol concentration, the white blood cells and hemoglobin did not change significantly, whereas lactate dehydrogenase, alkaline phosphatase, alanine aminotransferase and aspartate aminotransferase increased significantly (P<0.05). The content of phosphorus, magnesium and sodium increased after anaesthesia, whereas the content of potassium decreased with increasing eugenol concentration. After anaesthesia, the content of albumin and total protein in the serum decreased with increasing eugenol concentration (P<0.05); triglyceride first increased and subsequently decreased (P<0.05); blood glucose content first increased and then decreased (P<0.05); and no significant difference was observed in total cholesterol content (P>0.05). No significant difference was observed in muscle glycogen and liver glycogen content after eugenol anaesthesia (P>0.05). The eugenol-based anaesthesia test did not indicate major liver histomorphological effects, but the very small number of gill sheet edema cases observed requires further study. Analysis of electronic nose data indicated that eugenol treatment affected the flavor of the fish. The anaesthesia concentration of 20–80 mg/L had little effect on the physiology and biochemistry of crucian carp, thus providing a reference for the application of eugenol in crucian carp transportation and experimental research.

Cold chain logistics of Agricultural Products demand forecasting can provide the scientific basis for the country to formulate logistics strategy, which further promotes the development of social economy and the improvement of living standards in China. In this paper, a new mathematical combined model is proposed to Agricultural Products Demand. Shandong, one of a China’s province, serves as the main producer and distributor of agricultural products. Based on the index system created from multiple related factors influencing cold chain logistics demand of agricultural products in Shandong, this paper employs principal component analysis to reduce the dimension of various indexes and predicts principal components with time series. Thereafter, multiple linear regression model and neural network model were constructed to forecast the cold chain logistics demand of agricultural products in Shandong, and their combined forecast models were compared. What's more, the paper provides insight for reference and decision-making concerning the development of cold chain logistics industry of agricultural products in Shandong province.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

To promote low-carbon and green travel, this study proposes a tripartite evolutionary game model among new energy vehicle manufacturers, consumers, and government agencies. The game strategy combinations of each party and the stability conditions of the equilibrium point in the evolutionary game system are analyzed, and the validity of the conclusions is verified through simulation results. The study shows a strong correlation between the three parties in the evolutionary game system. From the perspective of car manufacturers, the probability of producing new energy vehicles is positively proportional to the subsidy received from new energy policy, the additional revenue gained from the management of the carbon trading scheme, and the fine received for producing conventional fuel vehicles. However, from the consumer perspective, the likelihood that consumers purchase new energy vehicles is positively related to the utility value to consumers and the coverage of public charging points in the city, and inversely related to the sales price of new energy vehicles. From the government perspective, a combination of direct subsidy policies and indirect support strategies should be considered to stimulate the rapid development of the new energy vehicle industry.

By using the particle swarm optimization algorithm for crystal structure prediction, we reveal a newly orthorhombic Cmcm structure of HfB4, which is more energetically superior to the previously proposed YB4-, ReP4-, FeB4-, CrB4-, and MnB4-type structures in the considered pressure range. The phonon dispersion and elastic constants calculations confirm that the new phase is dynamically and mechanically stable. The calculated large shear modulus (240 GPa) and high hardness (45.7 GPa) imply that the predicted Cmcm-HfB4 is a potential superhard material. Meanwhile, the directional dependences of the Young's modulus, bulk modulus, and shear modulus for HfB4 are systematically investigated. Further analyses of the density of states and electronic localization function indicate that the strong B-B and B-Hf covalent bonds greatly contribute to its high hardness and stability.

The world is struggling with the increasing rate at which water bodies are constantly polluted due to rapid modernization and industrialization. The different types of water contaminants include both anthropogenic and natural contaminants. Environmentalists are mainly concerned about heavy metals such as cadmium ions, as they are considered poisonous and carcinogenic even if they are present at a minimum concentration, and they are considered an immediate concern, according to the World Health Organization. Recently, many methods, such as solvent extraction, ion exchange, electrochemical precipitation, and reverse osmosis, have been developed to remove heavy metals from wastewater. These methods have been reported to be expensive and inefficient; hence, more cost-effective and eco-friendly alternative strategies are needed. Amidst these challenges, a ray of hope emerges in the form of biosorbents. These abundant, eco-friendly, and cost-efficient materials have shown promise as an alternative method for removing heavy metals from wastewater. They are equipped with functional groups that can effectively absorb metal ions onto their surfaces, offering a potential solution to our water contamination woes. This review is significant as it delves into the potential use of several biosorbents to decontaminate cadmium ions from wastewater. This review is significant as it delves into the potential use of several biosorbents to decontaminate cadmium ions from wastewater

To address the insufficient mechanical properties of calcined phosphogypsum, this research aims to investigate the utilization of basalt fiber and calcium carbonate whiskers as reinforcing agents. The study examines their impact on the flexural and compressive strength, toughness, water resistance, and tensile strength of calcined phosphogypsum. In the individual tests, basalt fibers with different lengths (3 mm, 6 mm, 9 mm, and 18 mm) were added at dosages of 0%, 0.5%, 1.0%, and 1.5% respectively. The findings demonstrate that basalt fiber effectively enhances the flexural and compressive strength, toughness, and tensile strength of calcined phosphogypsum, albeit potentially compromising water resistance. Among the various fiber lengths, the 6 mm fibers exhibit the most advantageous influence on the performance of calcined phosphogypsum. A subsequent test was conducted to investigate the impact of cross-scale fibers on the properties of calcined phosphogypsum by mixing 6 mm basalt fibers and calcium carbonate whiskers. The results illustrate that calcium carbonate whisker refines the pores, thereby improving the flexural strength and toughness of calcined phosphogypsum. Furthermore, it compensates for the water resistance limitations associated with the sole utilization of basalt fiber while further augmenting the tensile strength and strain capacity. However, it should be noted that increasing the dosage of both calcium carbonate whiskers and basalt fibers concurrently leads to increased porosity of phosphogypsum and reduced compressive strength.

Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. Experimental results show that the strategy we designed can effectively combine the advantages of the linear layer and self-attention mechanisms, compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.