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In this paper, the floating body effect (FBE) in indium-gallium-zinc-oxide (IGZO) thin-film transistor (TFT) and the mechanism of device failure caused by that are reported for the first time. If the toggle AC pulses are applied to the gate and drain simultaneously for the switching operation, the drain current of IGZO TFT increases dramatically and cannot show the on/off switching characteristics. This phenomenon was not reported before, and our study reveals that the main cause is the formation of a conductive path between the source and drain: short failure. It is attributed in part to the donor creation at the drain region during the high voltage (Vhigh) condition and in part to the donor creation at the source region during the falling edge and low voltage (Vlow) conditions. Donor creation is attributed to the peroxide formation in the IGZO layer induced by the electrons under the high lateral field. Because the donor creation features positive charges, it lowers the threshold voltage of IGZO TFT. In detail, during the Vhigh condition, the donor creation is generated by accumulated electrons with a high lateral field at the drain region. On the other hand, the floating electrons remaining at the short falling edge (i.e., FBE of the IGZO TFT) are affected by the high lateral field at the source region during the Vlow condition. As a result, the donor creation is generated at the source region. Therefore, the short failure occurs because the donor creations are generated and expanded to channel from the drain and source region as the AC stress accumulates. In summary, the FBE in IGZO TFT is reported, and its effect on the electrical characteristics of IGZO TFT (i.e., the short failure) is rigorously analyzed for the first time. [ABSTRACT FROM AUTHOR]

In this paper, a novel wideband antenna with a simple structure and low profile based on spoof surface plasmon polaritons (SSPPs) is proposed. The structure consists of periodically modulated corrugated metal strips as transmission lines, a CPW feed, and a ground metal plate as an antenna reflector. The SSPP transmission line is used to convert quasi-TEM to SSPP mode and achieve optimal impedance matching. The prototype of the end-fire antenna has been designed and fabricated. The simulation results show that this antenna can achieve a gain of 10.19 dB, a bandwidth of 146%, and an efficiency of 90% in a wide operating band from 7 to 45 GHz. The proposed design illustrates great potential that includes high efficiency, good directivity, high gain, wide bandwidth, and easy manufacturing. [ABSTRACT FROM AUTHOR]

A two-dimensional unsteady seepage model for coal using a finite element program is developed, and the temporal variations of key factors such as water pressure and hydraulic gradient are analyzed in this paper. Additionally, the triaxial rock mechanical experiment and utilized pneumatic fracturing equipment on raw coal samples to investigate both hydraulic and pneumatic fracturing processes are conducted. Through these experiments, the relationship between pressure and crack formation and expansion are examined. The analysis reveals that the pore pressure gradient at the coal inlet reaches its peak during rapid surges in water pressure but diminishes over time. Conversely, the pore pressure gradient at the outlet side exhibits a gradual increase. Hydraulic fracturing is most likely to occur at the water inlet during sudden increases in water pressure. Besides, as the permeability of coal decreases, the duration for seepage stabilization prolongs due to the intensified pore pressure gradient resulting from sudden increases in water pressure. Moreover, an extended period of high hydraulic gradient further increases the risk of hydraulic fracturing. The experimental findings indicate that coal samples initially experience tensile failure influenced by water and air pressure. Subsequently, mode I cracks form under pressure, propagating along the fracture surface and becoming visible. The main types of failure observed in hydraulic and pneumatic fracturing are diametrical tensile failure, and the development of fractures can be categorized into three distinct stages, which contains the initial stage characterized by slight volume changes while water pressure increases, the expansion stage when pressure reaches the failure strength, and the crack closure stage marked by little or even decreasing volume changes during pressure unloading. The acoustic emission signal accurately corresponds to these three stages. [ABSTRACT FROM AUTHOR]

Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to better understand land utilization on these lands vulnerable to rising sea levels. Current field boundaries delineation and classification methods, such as manual digitalization and traditional segmentation techniques, are labour-intensive and often require manual and time-consuming parameter selection. In recent years, deep learning (DL) techniques, including convolutional neural networks and Mask R-CNN, have shown promising results in object recognition, image classification, and segmentation tasks. However, there is a gap in applying these techniques to detecting surface drainage patterns on agricultural fields. This paper develops and tests a Mask R-CNN model for detecting land-formed fields on agricultural dykelands using LiDAR-derived elevation data. Specifically, our approach focuses on identifying groups of pixels as cohesive objects within the imagery, a method that represents a significant advancement over pixel-by-pixel classification techniques. The DL model developed in this study demonstrated a strong overall performance, with a mean Average Precision (mAP) of 0.89 across Intersection over Union (IoU) thresholds from 0.5 to 0.95, indicating its effectiveness in detecting land-formed fields. Results also revealed that 53% of Nova Scotia's dykelands are being used for agricultural purposes and approximately 75% (6924 hectares) of these fields were land-formed. By applying deep learning techniques to LiDAR-derived elevation data, this study offers novel insights into surface drainage mapping, enhancing the capability for precise and efficient agricultural land management in regions vulnerable to environmental changes. [ABSTRACT FROM AUTHOR]

This paper investigates trabecular bone ontogenetic changes in two different Polish populations, one prehistoric and the other historical. The studied populations are from the Brześć Kujawski region in Kujawy (north-central Poland), one from the Neolithic Period (4500–4000 BC) and one from the Middle Ages (twelfth-sixteenth centuries AD), in total 62 vertebral specimens (32 males, 30 females). Eight morphometric parameters acquired from microCT scan images were analysed. Two-way ANOVA after Box-Cox transformation and multifactorial regression model were calculated. A significant decrease in percentage bone volume fraction (BV/TV; [%]) with age at death was observed in the studied sample; Tb.N (trabecular number) was also significantly decreased with age; trabecular separation (Tb.Sp) increased with advancing age; connectivity density (Conn.D) was negatively correlated with biological age and higher in the Neolithic population. These data are found to be compatible with data from the current biomedical literature, while no loss of horizontal trabeculae was recorded as would be expected based on modern osteoporosis. [ABSTRACT FROM AUTHOR]

The reduction of interfacial interaction and the deterioration of processing properties of aluminum hydrides (AlH3) is the main challenges preventing its practical application. Here, a simple and effective core–shell structure aluminum hydrides@polydopamine (AlH3@PDA) complex was constructed through in-situ polymerization. The evolution of element states on the surface of AlH3 conducted by X-ray photoelectron spectroscopy indicated the successful introduction of PDA to form the core@shell structure, the thickness of the PDA coated layer increased with the increasing PDA dosage from 0.1 to 1.6% in mass fraction, and the maximum of thickness is 50 nm in TEM testing. Py GC/MS results proved that the increase of dopamine concentration leads to higher proportions of self-assemble units, whereas lower dopamine concentrations favor higher levels of chemical bonded components. Regarding whether PDA is a covalent polymer or a noncovalent aggregate of some species, the formation of intermediates, such as dopaminechrome and 5,6-dihydroxyindole played an important role to coordination interaction with AlH3 in FTIR, Raman, and UV–Vis spectra testing. Compared with pure AlH3, the formation of organic PDA coating improved AlH3 heat resistance. The adhesion work with GAP adhesive was also improved from 107.02 J/m2 of pure AlH3 to 111.13 mJ/m2 of AlH3@PDA-5 complex. This paper provides well support for further practical application of AlH3 in solid propellants. [ABSTRACT FROM AUTHOR]

To efficiently allocate medical resources at disaster sites, medical workers perform triage to prioritize medical treatments based on the severity of the wounded or sick. In such instances, evaluators often assess the severity status of the wounded or sick quickly, but their measurements are qualitative and rely on experience. Therefore, we developed a wearable device called Medic Hand in this study to extend the functionality of a medical worker's hand so as to measure multiple biometric indicators simultaneously without increasing the number of medical devices to be carried. Medic Hand was developed to quantitatively and efficiently evaluate "perfusion" during triage. Speed is essential during triage at disaster sites, where time and effort are often spared to attach medical devices to patients, so the use of Medic Hand as a biometric measurement device is more efficient for collecting biometric information. For Medic Hand to be handy during disasters, it is essential to understand and improve upon factors that facilitate its public acceptance. To this end, this paper reports on the usability evaluation of Medic Hand through a questionnaire survey of nonmedical workers. [ABSTRACT FROM AUTHOR]

Our understanding of the environmental and occupational health implications of pollutants emitted in steel production is still lacking, despite the considerable amount of research devoted to this topic. Given the significance of steel recycling and the need to reduce greenhouse gas emissions, many steel factories are adopting electric arc furnace (EAF) technology. The use of a technological system designed for the capture of pollutants emitted through EAF steel production is highly ecological because of its utilization of iron scrap and low investment cost. Despite this, the main issue with the EAF is the environmental impact it poses, specifically the release of pollutants into the air, such as dust and organic substances, chlorinated dioxins and furans, dioxin-like polychlorinated biphenyls and brominated dioxins and furans. As a result, workers in this field have a considerable rate of morbidity. The main challenge for EAFs is to optimize the capture of powders produced during the techno-logical process, both from the EAF and the workplace. A state-of-the art solution for managing pollutants in modern steel manufacturing is highlighted in this paper, featuring a method used in Romania that employs the Best Available Techniques (BAT) reference document for iron and steel production to directly collect pollutants from the EAF. The system included a cylindrical fitting, a heat exchanger to cool the gases and a hood to collect contaminants. In comparison to other ventilation options, this equipment boasts lower investment and lower operational costs because of its effective and minimal air flow. Through the use of cutting-edge technology and progressive strategies, we can move closer toward our objective of a workplace free from injuries in the steel industry. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship among interlayer exchange coupling (IEC), Dzyaloshinskii–Moriya interaction (DMI), and multilevel magnetization switching within a Co/Pt/Co heterostructure, where varying Pt thicknesses enable control over the coupling strength. Employing Brillouin Light Scattering to quantify the effective DMI, we explore its potential role in magnetization dynamics and multilevel magnetization switching. Experimental findings show four distinct resistance states under an external magnetic field and spin Hall effect related spin current. We explain this phenomenon based on the asymmetry between Pt/Co and Co/Pt interfaces and the interlayer coupling, which, in turn, influences the DMI and subsequently impacts the magnetization dynamics. Numerical simulations, including macrospin, 1D domain wall, and simple spin wave models, further support the experimental observations of multilevel switching and help uncover the underlying mechanisms. Our proposed explanation, supported by magnetic domain observation using polar-magnetooptical Kerr microscopy, offers insights into both the spatial distribution of magnetization and its dynamics for different IECs, thereby shedding light on its interplay with DMI, which may lead to potential applications in storage devices. [ABSTRACT FROM AUTHOR]

The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm. [ABSTRACT FROM AUTHOR]

Refractive index stands as an inherent characteristic of a material, allowing non-invasive exploration of the three-dimensional (3D) interior of the material. Certain materials with different refractive indices produce a birefringence phenomenon in which incident light is split into two polarization components when it passes through the materials. Representative birefringent materials appear in calcite crystals, liquid crystals (LCs), biological tissues, silk fibers, polymer films, etc. If the internal 3D shape of these materials can be visually expressed through a non-invasive method, it can greatly contribute to the semiconductor, display industry, optical components and devices, and biomedical diagnosis. This paper introduces a novel approach employing deep learning to generate 3D birefringence images using multi-viewed holographic interference images. First, we acquired a set of multi-viewed holographic interference pattern images and a 3D volume image of birefringence directly from a polarizing DTT (dielectric tensor tomography)-based microscope system about each LC droplet sample. The proposed model was trained to generate the 3D volume images of birefringence using the two-dimensional (2D) interference pattern image set. Performance evaluations were conducted against the ground truth images obtained directly from the DTT microscopy. Visualization techniques were applied to describe the refractive index distribution in the generated 3D images of birefringence. The results show the proposed method's efficiency in generating the 3D refractive index distribution from multi-viewed holographic interference images, presenting a novel data-driven alternative to traditional methods from the DTT devices. [ABSTRACT FROM AUTHOR]

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out. [ABSTRACT FROM AUTHOR]

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future. [ABSTRACT FROM AUTHOR]

A fundamental understanding of the theory and measurement procedures for characterizing a solenoid is crucial for making a project cost-effective and achieving the desired performance for its use in a LINAC. This paper attempts to comprehend the beam dynamics of charged particles by considering the conservation of canonical angular momentum, which can take on positive, negative, and zero values. The analytical expression to generate a parallel beam using a solenoid is derived. The effect of the final beam size on the values of a solenoid's spherical aberration coefficient and the position of the minimum beam size with respect to the focal length is studied using electron trajectories. A practical procedure to tune the above two parameters of a solenoid is demonstrated by simply modifying the demountable disks connected at each end. A mathematical model is proposed to accurately determine the axis of a solenoid and the magnetic centre using the measured magnetic field data, which is validated experimentally. Several solenoids, used in the 9.5 MeV 10 kW electron LINAC suitable for sterilizing medical products and food irradiation, have been characterised using this procedure. [ABSTRACT FROM AUTHOR]

In real-life applications, nonlinear differential equations play an essential role in representing many phenomena. One well-known nonlinear differential equation that helps describe and explain many chemicals, physical, and biological processes is the Caudrey Dodd Gibbon equation (CDGE). In this paper, we propose the Laplace residual power series method to solve fractional CDGE. The use of terms that involve fractional derivatives leads to a higher degree of freedom, making them more realistic than those equations that involve the derivation of an integer order. The proposed method gives an easy and faster solution in the form of fast convergence. Using the limit theorem of evaluation, the experimental part presents the results and graphs obtained at several values of the fractional derivative order. [ABSTRACT FROM AUTHOR]

This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets. [ABSTRACT FROM AUTHOR]

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency. [ABSTRACT FROM AUTHOR]

In robot-assisted surgery, in which haptics should be absent, surgeons experience haptics-like sensations as “pseudo-haptic feedback”. As surgeons who routinely perform robot-assisted laparoscopic surgery, we wondered if we could make these “pseudo-haptics” explicit to surgeons. Therefore, we created a simulation model that estimates manipulation forces using only visual images in surgery. This study aimed to achieve vision-based estimations of the magnitude of forces during forceps manipulation of organs. We also attempted to detect over-force, exceeding the threshold of safe manipulation. We created a sensor forceps that can detect precise pressure at the tips with three vectors. Using an endoscopic system that is used in actual surgery, images of the manipulation of excised pig kidneys were recorded with synchronized force data. A force estimation model was then created using deep learning. Effective detection of over-force was achieved if the region of the visual images was restricted by the region of interest around the tips of the forceps. In this paper, we emphasize the importance of limiting the region of interest in vision-based force estimation tasks. [ABSTRACT FROM AUTHOR]

High-precision and high-efficiency Semantic segmentation of high-resolution remote sensing images is a challenge. Existing models typically require a significant amount of training data to achieve good classification results and have numerous training parameters. A novel model called MST-DeepLabv3+ was suggested in this paper for remote sensing image classification. It’s based on the DeepLabv3+ and can produce better results with fewer train parameters. MST-DeepLabv3+ made three improvements: (1) Reducing the number of model parameters by substituting MobileNetV2 for the Xception in the DeepLabv3+’s backbone network. (2) Adding the attention mechanism module SENet to increase the precision of semantic segmentation. (3) Increasing Transfer Learning to enhance the model's capacity to recognize features, and raise the segmentation accuracy. MST-DeepLabv3+ was tested on international society for photogrammetry and remote sensing (ISPRS) dataset, Gaofen image dataset (GID), and practically applied to the Taikang cultivated land dataset. On the ISPRS dataset, the mean intersection over union (MIoU), overall accuracy (OA), Precision, Recall, and F1-score are 82.47%, 92.13%, 90.34%, 90.12%, and 90.23%, respectively. On the GID dataset, these values are 73.44%, 85.58%, 84.10%, 84.86%, and 84.48%, respectively. The results were as high as 90.77%, 95.47%, 95.28%, 95.02%, and 95.15% on the Taikang cultivated land dataset. The experimental results indicate that MST-DeepLabv3+ effectively improves the accuracy of semantic segmentation of remote sensing images, recognizes the edge information with more completeness, and significantly reduces the parameter size. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel Secure Clustering Routing Method based on Blockchain and Swarm Intelligence (BS-SCRM) for Wireless Sensor Networks (WSNs), which serves as a cornerstone in the Internet of Things (IoT) infrastructure. Recognizing the limitations of existing clustering routing methods in addressing security threats, our approach integrates blockchain technology to fortify WSNs against vulnerabilities such as man-in-the-middle attacks. The proposed BS-SCRM method is structured in two phases: (1) an enhanced cluster head (CH) election utilizing an elite strategy-enhanced Whale Optimization Algorithm (WOA) that considers node energy and proximity to the base station, and (2) a secure data on-chain phase where blockchain comes into play, encrypting and validating cluster data to safeguard integrity and prevent tampering. We further tackle the challenge of implementing blockchain in resource-constrained WSNs by assigning distinct roles to devices, i.e., ordinary nodes with data viewing permissions and accounting nodes entrusted with both data viewing and consensus algorithm execution. Extensive simulations confirm that BS-SCRM not only improves clustering quality but also provides a more secure and energy-efficient routing solution compared to contemporary methods. More specifically, simulation results in different scenarios demonstrate that BS-SCRM enhances network lifetime by 24–73% compared to other clustering methods when facing attacks. [ABSTRACT FROM AUTHOR]

This paper is concerned with a kind of Bobwhite quail population model x n + 1 = A + B x n + x n x n - 1 x n - 2 , n = 0 , 1 , ⋯ , where the parameters and initial values are positive parabolic fuzzy numbers. According to g-division of fuzzy sets and based on the symmetrical parabolic fuzzy numbers, the conditional stability of this model is proved. Besides the existence, boundedness and persistence of its unique positive fuzzy solution. When some fuzzy stability conditions are satisfied, the model evolution exhibits oscillations with return to a fixed fuzzy equilibrium no matter what the initial value is. This phenomena provided a vivid counterexample to Allee effect in density-dependent populations of organisms. As a supplement, two numerical examples with data-table are interspersed to illustrate the effectiveness. Our findings have been verified precise with collected northern bobwhite data in Texas, and will help to form some efficient density estimates for wildlife populations of universal applications. [ABSTRACT FROM AUTHOR]

In the field of population health research, understanding the similarities between geographical areas and quantifying their shared effects on health outcomes is crucial. In this paper, we synthesise a number of existing methods to create a new approach that specifically addresses this goal. The approach is called a Bayesian spatial Dirichlet process clustered heterogeneous regression model. This non-parametric framework allows for inference on the number of clusters and the clustering configurations, while simultaneously estimating the parameters for each cluster. We demonstrate the efficacy of the proposed algorithm using simulated data and further apply it to analyse influential factors affecting children’s health development domains in Queensland. The study provides valuable insights into the contributions of regional similarities in education and demographics to health outcomes, aiding targeted interventions and policy design. [ABSTRACT FROM AUTHOR]

This study explores the potential of repurposing mosque minarets as solar chimneys in hot arid regions to facilitate natural ventilation and diminish the reliance on energy-intensive cooling systems. Originating as a means to call the faithful to prayer, minarets have become iconic landmarks within Islamic cities. This research focuses on Cairo, Egypt, as a representative hot arid environment. The paper traces the evolution of the minaret, underscoring the variations in form that influence the experimental design. The investigation proceeded in two stages: the construction of physical mosque models with variably positioned minarets for laboratory testing, ensuring standardized measurements, followed by computational fluid dynamics (CFD) simulations for comparison. Findings indicate that mosque minarets can be effectively adapted for passive ventilation, with their performance significantly influenced by orientation and placement. This study concludes that traditional mosque minarets offer a viable, sustainable option for passive cooling in hot climates. [ABSTRACT FROM AUTHOR]

Based on the system dynamics theory, this paper establishes an environmental mass event evolution model and explores the evolution law of mass events caused by environmental problems. From a methodological point of view, the mixed-strategy evolutionary game principle and dynamic punishment measures are combined, and simulation analysis is carried out by Anylogic software, and the results show that there is no stable evolutionary equilibrium solution for the two sides of the game in the traditional asymmetric mixed-strategy game model, and after adjusting the game payoff matrix and incorporating the dynamic punishment strategy, stable evolutionary equilibrium solutions appear in the evolutionary game model, and the system begins to tend to be stabilized. The process and conclusions of the simulation experiment provide methodological reference and theoretical support for the analysis of the evolution of environmental mass events. [ABSTRACT FROM AUTHOR]

The precision of workpiece machining is critically influenced by the geometric errors in the kinematics of grind robots, which directly affect their absolute positioning accuracy. To tackle this challenge, this paper introduces a logistic-tent chaotic mapping Levenberg Marquardt algorithm designed to accurately identify and compensate for this geometric error. the approach begins with the construction of a forward kinematic model and an error model specific to the robot. Then the algorithm is adopted to identify and compensate for the geometric error. The method establishes a mapping interval around the initial candidate solutions derived from iterative applications of the Levenberg Marquardt algorithm. Within this interval, the logistic-tent chaotic mapping method generates a diverse set of candidate solutions. These candidates are evaluated based on their fitness values, with the optimal solution selected for subsequent iterations. Empirical compensation experiments have validated the proposed method's precision and effectiveness, demonstrating a 6% increase in compensation accuracy and a 47.68% improvement in efficiency compared to existing state-of-the-art approaches. This process not only minimizes the truncation error inherent in the Levenberg Marquardt algorithm but also significantly enhances solution efficiency. Moreover, simulation experiments on grind processes further validate the method's ability to significantly improve the quality of workpiece machining. [ABSTRACT FROM AUTHOR]

The health of young people is crucial for the future and development of a nation. It is the collective responsibility and imperative mission of society to ensure the holistic well-being, both physically and mentally, of young individuals. Therefore, it is essential to thoroughly comprehend the factors that influence their health in order to expedite the exploration of effective solutions. The objective of this study is to comprehend the mechanisms that underlie the correlation between physical exercise behavior and psychological resilience among teenagers, while also examining the mediating role played by social sensitivity and need to belong. So put forward the hypothesis: (1) physical exercise behavior can positively predict the psychological resilience. (2) Social sensitivity and need to belong plays a mediating role between physical exercise behavior and psychological resilience. (3) Social sensitivity and need to belong plays a chain mediating role between physical exercise behavior and psychological resilience. Using the cluster sampling method, a total of 1106 students (with an average age of 15.7 and a standard deviation of 0.598) who met the requirements were surveyed from Shandong Province in China. Standard scales were utilized to assess Physical Exercise Behavior, Psychological Resilience, Social Sensitivity, and Need to Belong. For data analysis, Pearson's correlation analysis and bias-corrected percentile Bootstrap method were sequentially conducted. (1) The present study did not find any significant methodological bias, and the observed correlations between physical exercise behavior, psychological resilience, social sensitivity, and need to belong were all statistically significant; (2) Based on the self-determination theory, this study elucidates the relationship between physical exercise behavior and psychological resilience among teenagers. The findings indicate that physical exercise behavior positively predicts the need to belong and psychological resilience, while negatively predicting social sensitivity. Similarly, social sensitivity negatively predicts the need to belong and psychological resilience. Moreover, the need to belong directly and positively predicts psychological resilience. Importantly, all hypotheses proposed in this paper were empirically supported. (3) The indirect effect of the path mediated by social sensitivity is 0.009, while the indirect effect of the path mediated by need to belong is 0.033. Additionally, the combined indirect effect of both social sensitivity and need to belong as mediating variables is 0.014. (4) The cumulative sum of all these indirect effects amounts to 0.056. Based on the self-determination theory, we propose a chain mediation model, specially, physical exercise behavior can significantly positively predict psychological resilience, among which, social sensitivity and need to belong play a significant mediating role between Physical exercise behavior and psychological resilience. In addition, the adoption of good physical exercise behavior can enhance the psychological resilience of adolescents by diminishing social sensitivity and augmenting the need to belong. [ABSTRACT FROM AUTHOR]

Kagome superconductors AV3Sb5 (A = K, Rb, and Cs) have attracted enormous interest due to the coexistence of charge density wave (CDW) order, unconventional superconductivity (SC) and anomalous Hall effect (AHE). In this paper, we reported an intensive investigation on Cs(V1−xTax)3Sb5 single crystals with systematic Ta doping. Ta was confirmed to be doped into V-site in the Kagome layer from both single crystal X-ray diffraction structural refinement and scanning transmission electron microscopy observation. The highest Ta doping level was found to be about 16%, which is more than twice as much as 7% in Nb-doped CsV3Sb5. With the increase of Ta doping, CDW order was gradually suppressed and finally vanished when the doping level reached to more than 8%. Meanwhile, superconductivity was enhanced with a maximum critical temperature (Tc) of 5.3 K, which is the highest Tc in the bulk crystal of this Kagome system at ambient pressure so far. The μ0Hc2(T) behavior demonstrates that the system is still a two-band superconductor after Ta doping. Based on the electrical transport measurement, a phase diagram was set up to exhibit the evolution of CDW and SC in the Cs(V1−xTax)3Sb5 system. These findings pave a new way to search for new superconductors with higher Tc in the AV3Sb5 family and establish a new platform for tuning and controlling the multiple orders and superconducting states. [ABSTRACT FROM AUTHOR]

Phase-to-height mapping is one of the important processes in three dimensional phase measurement profilometry. But, in traditional phase-to-height mapping method, the measurement accuracy is affected by device attitude, so it needs saving a large amount of mapping equations to achieve high-quality phase-to-height mapping. In order to improve that, this paper proposes an improved phase-to-height mapping method combine with device attitude. Firstly, we get the unwrapped phase of the target. Then, using generalized regression neural network is used to reduce the offset of phase information at the same height due to the randomness of device attitude. Last, the phase-to-height mapping is completed by substituting the unwrapped phase (the difference between having detected object and no detected object) of eliminate the offset into improved phase-to-height mapping method. Experimental results show that the proposed method could achieve high-quality phase-to-height mapping with less mapping equation and less memory space. Compared with the nonlinear phase-to-height mapping method (probabilistic neural network to eliminate phase offset), its accuracy is improved by 44.30%. Compared with the nonlinear phase-to-height mapping method (radial basis function neural network to eliminate phase offset), the accuracy is improved by 39.58%. [ABSTRACT FROM AUTHOR]

This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels (m), the central angle of the corner (α) and the parameter of the dealiasing degree (n) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤ α < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number (m0) within the allowable range of error, based on the α-m0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m. The study's results can be applied to dealiasing and shape analysis of complex closed contours. [ABSTRACT FROM AUTHOR]

The h-BN/diamond mix-dimensional heterostructure has broad application prospects in the fields of optoelectronic devices and power electronic devices. In this paper, the electronic properties and band offsets of hexagonal boron nitride (h-BN)/(H, O, F, OH)-diamond (111) heterostructures were studied by first-principles calculations under biaxial strain. The results show that different terminals could significantly affect the interface binding energy and charge transfer of h-BN/diamond heterostructure. All heterostructures exhibited semiconductor properties. The h-BN/(H, F)-diamond systems were indirect bandgap, while h-BN/(O, OH)-diamond systems were direct bandgap. In addition, the four systems all formed type-II heterostructures, among which h-BN/H-diamond had the largest band offset, indicating that the system was more conducive to the separation of electrons and holes. Under biaxial strain the bandgap values of the h-BN/H-diamond system decreased, and the band type occurred direct–indirect transition. The bandgap of h-BN/(O, F, OH)-diamond system increased linearly in whole range, and the band type only transformed under large strain. On the other hand, biaxial strain could significantly change the band offset of h-BN/diamond heterostructure and promote the application of this heterostructure in different fields. Our work provides theoretical guidance for the regulation of the electrical properties of h-BN/diamond heterostructures by biaxial strain. [ABSTRACT FROM AUTHOR]

The absence of biodegradability exhibited by plastics is a matter of significant concern among environmentalists and scientists on a global scale. Therefore, it is essential to figure out potential pathways for the use of recycled plastics. The prospective applications of its utilisation in concrete are noteworthy. The use of recycled plastic into concrete, either as a partial or complete substitution for natural aggregates, addresses the issue of its proper disposal besides contributing to the preservation of natural aggregate resources. Furthermore, the use of agricultural wastes has been regarded as a very promising waste-based substance in the industry of concrete manufacturing, with the aim of fostering the creation of an environmentally sustainable construction material. This paper illustrates the impact of nano sunflower ash (NSFA) and nano walnut shells ash (NWSA) on durability (compressive strength and density after exposure to 800 °C and sulphate attack), mechanical properties (flexural, splitting tensile and compressive strength) and fresh characteristics (slump flow diameter, T50, V-funnel flow time, L-box height ratio, segregation resistance and density) of lightweight self-compacting concrete (LWSCC). The waste walnut shells and local Iraqi sunflower were calcinated at 700 ± 50 °C for 2 h and milled for 3 h using ball milling for producing NSFA and NWSA. The ball milling succeeded in reducing the particle size lower than 75 nm for NSFA and NWSA. The preparation of seven LWSCC concrete mixes was carried out to obtain a control mix, three mixtures were created using 10%, 20% and 30% NWSA, and the other three mixtures included 10%, 20% and 30% NSFA. The normal weight coarse aggregates were substituted by the plastic waste lightweight coarse aggregate with a ratio of 75%. The fresh LWSCC passing capacity, segregation resistance, and filling capability were evaluated. The hardened characteristics of LWSCC were evaluated by determining the flexural and splitting tensile strength at 7, 14 and 28 days and the compressive strength was measured at 7, 14, 28 and 60 days. Dry density and compressive strength were measured after exposing mixes to a temperature of 800 °C for 3 h and immersed in 10% magnesium sulphate attack. The results demonstrated that the LWSCC mechanical characteristics were reduced when the percentages of NWSA and NSFA increased, except for 10% NWSA substitution ratio which had an increase in splitting tensile strength test and similar flexural strength test to the control mixture. A minor change in mechanical characteristics was observed within the results of LWSCC dry density and compressive strength incorporating various NSFA and NWSA` contents after exposing to temperature 800 °C and immersed in 10% magnesium sulphate attack. Furthermore, according to the findings, it is possible to use a combination of materials consisting of 10–20% NSFA and 10–20% NWSA to produce LWSCC. [ABSTRACT FROM AUTHOR]

Maintaining a power balance between generation and demand is generally acknowledged as being essential to maintaining a system frequency within reasonable bounds. This is especially important for linked renewable-based hybrid power systems (HPS), where disruptions are more likely to occur. This paper suggests a prominent modified “Fractional order-proportional-integral with double derivative (FOPIDD2) controller” as an innovative HPS controller in order to navigate these obstacles. The recommended control approach has been validated in power systems including wind, reheat thermal, solar, and hydro generating, as well as capacitive energy storage and electric vehicle. The improved controller’s performance is evaluated by comparing it to regular FOPID, PID, and PIDD2 controllers. Furthermore, the gains of the newly structured FOPIDD2 controller are optimized using a newly intended algorithm terms as squid game optimizer (SGO). The controller’s performance is compared to benchmarks such as the grey wolf optimizer (GWO) and jellyfish search optimization. By comparing performance characteristics such as maximum frequency undershoot/overshoot, and steadying time, the SGO-FOPIDD2 controller outperforms the other techniques. The suggested SGO optimized FOPIDD2 controller was analyzed and validated for its ability to withstand the influence of power system parameter uncertainties under various loading scenarios and situations. Without any complicated design, the results show that the new controller can work steadily and regulate frequency with an appropriate controller coefficient. [ABSTRACT FROM AUTHOR]

To enhance the vibration system characteristic distortion and pressure loss, we propose a novel rotary valve control vibration system. The paper presents the designed structural composition and generation mechanism of the rotary valve control vibration system. It also derives the mathematical model for the rotary valve distribution process and the overall system. The flow field inside the rotary valve is dynamically simulated using the multiple reference frame model, allowing for the determination of the change rule of the rotary valve’s output characteristics. An AMESim model was developed to analyze the vibration characteristics of the rotary valve control system. The effects of parameters such as inlet pressure, motor speed, and oil supply pump displacement were investigated. A rotary valve control vibration system experimental bench was constructed to experimentally verify the output characteristics of the rotary valve and the vibration characteristics of the system. The results indicate that the characteristic curve of the designed vibration system closely resembles a sinusoidal wave. Additionally, the rotary valve exhibits low pressure loss, making it more suitable for vibration stress relief applications. By appropriately increasing the inlet pressure and decreasing the motor speed, the vibration characteristics of the system can be improved. [ABSTRACT FROM AUTHOR]

Realizing precise and fast position control of the gear is a challenging issue because of its nonlinearity, parameter uncertainty and external disturbance. Therefore, this paper researches the clutch position control considering the influence because of the factor on the system performance. By virtue of the traditional adaptive control method, an improved strategy based on finite time theory is proposed to further improve the convergence rate as well as the position tracking precision. First, a model of electromechanical clutch actuator system is established by theoretical analysis. Then, an enhanced adaptive controller is designed using finite time idea by introducing power function in the virtual control. And parameter update rate is adopted in the control action. Next, the stability of the control system is proved theoretically. Finally, Matlab simulations and experimental bench test are carried out to exhibit the effectiveness of the presented method. The results show that the satisfactory performance has been achieved with accurate position tracking and fast convergence speed. [ABSTRACT FROM AUTHOR]

Tramways in urban areas for mass transit has been suggested to have a lower environmental footprint than roads. However, studies on the impact of tramways and the surrounding infrastructure on biodiversity is extremely rare despite the potential ecological effects associated with this anthropogenic feature. Surprisingly, we found fewer than 10 papers published on tramway-wildlife interactions, which is significantly lower (vs dozens of thousands) than that of other transportation methods. As tramways and stations may be managed sustainably by planting short vegetation on the track and roofs of tramway stations, they may be good examples of land-sharing policies in green urban planning, improving both biodiversity and people’s well-being. The potential environmental benefits of green practices for commercially available tramways should be strictly tested and applied, especially in the context of the growing popularity of tramway systems worldwide. [ABSTRACT FROM AUTHOR]

Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2–7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section—water level—rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings. [ABSTRACT FROM AUTHOR]

In this paper, a double-layer patterned graphene-based frequency-selective absorber (DGFSA) is proposed as a means of reducing an antenna's radar cross-section (RCS) while simultaneously increasing its gain. The antenna consists of a patch antenna with Multi-Graphene Frequency Selective Absorber (MGFSA) mounted on top. The DGFSA consists of double-layer patterned graphene and a band-pass frequency selective surface (FSS). Two patterned graphene lossy layers with different square resistances are used, which broaden the electromagnetic (EM) wave absorption bandwidth of the DGFSA, thus greatly reducing the out-band monostatic RCSs of the patch antenna. Meanwhile, due to the quasi-Fabry-Perot (F-P) effect, the gain of the proposed antenna is enhanced by 2.4 dB. Additionally, the low-RCS antenna reduces the monostatic RCS from 1.32 to 17 GHz under y-polarization and from 1.4 to 16.8 GHz under x-polarization, respectively. Furthermore, a decrease in the bistatic RCS is accomplished. Results from simulations and measurements match up nicely, which means the antenna we proposed has a good application on the stealth platform. [ABSTRACT FROM AUTHOR]

The health of young people is crucial for the future and development of a nation. It is the collective responsibility and imperative mission of society to ensure the holistic well-being, both physically and mentally, of young individuals. Therefore, it is essential to thoroughly comprehend the factors that influence their health in order to expedite the exploration of effective solutions. The objective of this study is to comprehend the mechanisms that underlie the correlation between physical exercise behavior and psychological resilience among teenagers, while also examining the mediating role played by social sensitivity and need to belong. So put forward the hypothesis: (1) physical exercise behavior can positively predict the psychological resilience. (2) Social sensitivity and need to belong plays a mediating role between physical exercise behavior and psychological resilience. (3) Social sensitivity and need to belong plays a chain mediating role between physical exercise behavior and psychological resilience. Using the cluster sampling method, a total of 1106 students (with an average age of 15.7 and a standard deviation of 0.598) who met the requirements were surveyed from Shandong Province in China. Standard scales were utilized to assess Physical Exercise Behavior, Psychological Resilience, Social Sensitivity, and Need to Belong. For data analysis, Pearson's correlation analysis and bias-corrected percentile Bootstrap method were sequentially conducted. (1) The present study did not find any significant methodological bias, and the observed correlations between physical exercise behavior, psychological resilience, social sensitivity, and need to belong were all statistically significant; (2) Based on the self-determination theory, this study elucidates the relationship between physical exercise behavior and psychological resilience among teenagers. The findings indicate that physical exercise behavior positively predicts the need to belong and psychological resilience, while negatively predicting social sensitivity. Similarly, social sensitivity negatively predicts the need to belong and psychological resilience. Moreover, the need to belong directly and positively predicts psychological resilience. Importantly, all hypotheses proposed in this paper were empirically supported. (3) The indirect effect of the path mediated by social sensitivity is 0.009, while the indirect effect of the path mediated by need to belong is 0.033. Additionally, the combined indirect effect of both social sensitivity and need to belong as mediating variables is 0.014. (4) The cumulative sum of all these indirect effects amounts to 0.056. Based on the self-determination theory, we propose a chain mediation model, specially, physical exercise behavior can significantly positively predict psychological resilience, among which, social sensitivity and need to belong play a significant mediating role between Physical exercise behavior and psychological resilience. In addition, the adoption of good physical exercise behavior can enhance the psychological resilience of adolescents by diminishing social sensitivity and augmenting the need to belong. [ABSTRACT FROM AUTHOR]

High voltage circuit breakers are one of the core equipment in power system operation, and the voiceprint signals generated during operation contain extremely rich information. This paper proposes a fault identification method for high voltage circuit breakers based on voiceprint information data. Firstly, based on the developed voiceprint information data acquisition device, the voiceprint information of a certain high voltage circuit breaker is obtained; Secondly, an improved S-transform is proposed in the article, which generates an amplitude matrix based on the S-transform of voiceprint information; Then, through the matrix Singular value decomposition method, the fault feature quantity of voiceprint information is extracted from the time–frequency angle, and the diagnosis system of the support vector machine model is established, and the system is trained to realize the fault identification of the high-voltage circuit breaker; Finally, through experimental simulation calculations, it was shown that the accuracy of the proposed fault identification method in different operating conditions reached 92.6%, verifying the good accuracy and robustness of the proposed method and equipment. [ABSTRACT FROM AUTHOR]

More than 70% of the potash fertilizer globally is produced by the froth flotation process, in which 4-dodecylmorpholine (DMP) serves as a reverse flotation agent. As the potash fertilizer production rapidly rises, the increased DMP levels in discharged brine pose a threat to the production of high-value chemicals. In this paper, composite particles of basic magnesium sulfate@TiO2 (BMS@TiO2) were prepared using a simple and mild loading method. These particles were utilized for the adsorption and photocatalytic degradation of DMP in brine. Compared with normal powdered materials, the granular BMS@TiO2 in this study can be easily separated from liquid, and the degradation intermediates will not enter the brine without causing secondary pollution. BMS@TiO2 consists of 5·1·7 phase (5Mg(OH)2·MgSO4·7H2O) whisker clusters embedding 2.3% TiO2. The adsorption equilibrium of DMP on BMS@TiO2 particles was achieved through hydrogen bonding and pore interception with the adsorption capacity of approximately 5 mg g−1 after 6 h. The photodegradation efficiency of DMP adsorbed on BMS@TiO2 reached about 92% within 16 h, which is compared with that of pure TiO2 nanoparticles. Additionally, excellent stability and recyclability of BMS@TiO2 were also observed in five cycle tests of adsorption and photocatalytic degradation of DMP, and the possible photocatalytic degradation pathways and mechanism of DMP are proposed following molecular electrostatic potential analysis. This work provides a sustainable and environmentally friendly approach for eliminating organic micropollutants from water environments. [ABSTRACT FROM AUTHOR]

In view of the current problems of spectrum resource scarcity, return congestion, and insufficient energy utilization in the unmanned aerial vehicle (UAV)-assisted Internet of Vehicles (IoV), this paper investigates the cooperative caching and power control, and proposes a joint optimization method to improve the overall Energy Efficiency (EE). In this method, we first propose a communication establishment threshold to control the V2V communication distance and serve as a joint optimization factor. Then we derive the closed form expressions of offloading ratio and EE of the UAV-assisted IoV, and formulate the optimization problem of maximizing EE. Due to the coupling relationship between caching strategy and transmission power, it is difficult for us to directly solve the optimization problem. Furthermore, we propose an alternating optimization algorithm for solving the optimization problem. Finally, the experimental simulation compare the propose joint optimization method with other existing optimization methods, and the simulation results prove the effectiveness and superiority of the propose joint optimization method. [ABSTRACT FROM AUTHOR]

Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts' performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice. [ABSTRACT FROM AUTHOR]

The challenge of elderly care presents a formidable task, demanding the collective attention of governmental bodies and diverse sectors of society. The integration of Artificial Intelligence (AI) into the research and development of Social Elderly Care Service (ECS) has emerged as a dominant trend, holding substantial importance in the establishment of an efficient ECS system. This study aims to serve as a comprehensive reference for the advancement of China's ECS system, achieved through the harmonious integration of a social ECS system with AI capabilities. This paper introduces the fundamental theory of AI, delving into the intricacies of the greyscale model of AI. Furthermore, it provides an overview of the current landscape of elderly care and elder care institutions, offering scientific data and insights to propel further research on AI development and system construction. Through an analysis of the existing research status, the study identifies prevalent issues within the AI-ECS integration, emphasizing pivotal factors influencing the construction of a robust social ECS system. To address these concerns, the study puts forth specific and viable policy recommendations. Notably, the questionnaire's statistics underscore that 83% of the elderly populace would opt for AI-driven solutions in selecting intelligent products, thereby underscoring the pivotal role of AI within the social ECS system. The challenges facing elderly care systems, including demographic shifts, resource constraints, and evolving societal norms, demand innovative solutions for providing efficient and effective care. This study addresses these challenges by exploring the integration of Artificial Intelligence (AI) into Social Elderly Care Services (ECS) in China. By delving into the theory of AI and assessing the existing research status, the study identifies key issues in AI-ECS integration and proposes viable policy recommendations. Insights from stakeholder surveys further highlight the importance of AI-driven solutions in meeting the needs of the elderly population. [ABSTRACT FROM AUTHOR]

To solve the problems of UAV path planning, such as low search efficiency, uneven path, and inability to adapt to unknown environments, this paper proposes A double-layer optimization A* and dynamic window method for UAV path planning. Firstly, the neighboring node clip-off rule is defined to optimize the node expansion mode of the A* algorithm, and the obstacle coverage model is designed to dynamically adjust the heurizing function of the A* algorithm to improve the path search efficiency. Then, the Bresenham algorithm is adopted for collision detection and critical path nodes are extracted to significantly reduce the number of path turning points. Secondly, a new tracking index is proposed to optimize the evaluation function of the dynamic window method to make the local path fit the global path further. By detecting the dangerous distance, the dynamic adaptive method of evaluation function weight is designed to improve the fixed weight of the dynamic window method. Finally, the key turning point of optimizing the A* algorithm is taken as the temporary target point to improve the DWA algorithm, and the local part follows the global part, and the fusion of the two algorithms is realized. Simulation results show that the proposed method can significantly improve the efficiency and smoothness of mobile robot path planning, enhance the real-time obstacle avoidance and adaptive ability of unknown environments, and better meet the requirements of complex planning tasks. [ABSTRACT FROM AUTHOR]

High-quality development plays a crucial role in China’s economic progress in the new era. It represents a new concept of advancement and mirrors the increasing aspirations of the populace for an improved standard of living. In this context, the role of artificial intelligence (AI) in promoting sustainable development cannot be overemphasized. This paper explores how AI technologies can drive the transition to a green, low-carbon and circular economy. We have established an index system to measure the development level of the artificial intelligence industry and the high-quality development of the economy, which is relevant to the current state of the artificial intelligence industry and the advancement of the economy. Panel data from 2008 to 2017 has been utilized for this purpose. Global principal component analysis method and entropy value method are employed in the evaluation. Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, we clarify that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era. [ABSTRACT FROM AUTHOR]

Stitching of microscopic images is a technique used to combine multiple overlapping images (tiles) from biological samples with a limited field of view and high resolution to create a whole slide image. Image stitching involves two main steps: pairwise registration and global alignment. Most of the computational load and the accuracy of the stitching algorithm depend on the pairwise registration method. Therefore, choosing an efficient, accurate, robust, and fast pairwise registration method is crucial in the whole slide imaging technique. This paper presents a detailed comparative analysis of different pairwise registration techniques in terms of execution time and quality. These techniques included feature-based methods such as Harris, Shi-Thomasi, FAST, ORB, BRISK, SURF, SIFT, KAZE, MSER, and deep learning-based SuperPoint features. Additionally, region-based methods were analyzed, which were based on the normalized cross-correlation (NCC) and the combination of phase correlation and NCC. Investigations have been conducted on microscopy images from different modalities such as bright-field, phase-contrast, and fluorescence. The feature-based methods were highly robust to uneven illumination in tiles. Moreover, some features were found to be more accurate and faster than region-based methods, with the SURF features identified as the most effective technique. This study provides valuable insights into the selection of the most efficient and accurate pairwise registration method for creating whole slide images, which is essential for the advancement of computational pathology and biology. [ABSTRACT FROM AUTHOR]

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time. [ABSTRACT FROM AUTHOR]

Until now, the genus Robsonomyia was represented by two extant species: R. reducta Matile & Vockeroth, 1980 from North America and R. sciaraeformis (Okada, 1939) from Asia. This paper presents the first fossil members of the genus Robsonomyia, which is also the first record from Europe. Two new fossil species from Baltic amber are described: R. baltica Pełczyńska, Krzemiński & Blagoderov, sp. nov. and R. henningseni Pełczyńska, Krzemiński & Blagoderov, sp. nov.. The presence of fossil Robsonomyia spp. on the European continent suggests Holarctic distribution of the genus in the past. We also discuss possible pathways of its intercontinental dispersion. [ABSTRACT FROM AUTHOR]

Deforestation in the tropics remains a significant global challenge linked to carbon emissions and biodiversity loss. Agriculture, forestry, wildfires, and urbanization have been repeatedly identified as main drivers of tropical deforestation. Understanding the underlying mechanisms behind these direct causes is crucial to navigate the multiple tradeoffs between competing forest uses, such as food and biomass production (SDG 2), climate action (SDG 13), and life on land (SDG 15). This paper develops and implements a global-scale empirical approach to quantify two key factors affecting land use decisions at tropical forest frontiers: agricultural commodity prices and national governance. It relies on data covering the period 2004–2015 from multiple public sources, aggregated to countries and agro-ecological zones. Our analysis confirms the persistent influence of commodity prices on agricultural land expansion, especially in forest-abundant regions. Economic and environmental governance quality co-determines processes of expansion and contraction of agricultural land in the tropics, yet at much smaller magnitudes than other drivers. We derive land supply elasticities for direct use in standard economic impact assessment models and demonstrate that our results make a difference in a Computable General Equilibrium framework. [ABSTRACT FROM AUTHOR]

One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening. [ABSTRACT FROM AUTHOR]