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Multilevel thresholding image segmentation will subdivide an image into several meaningful regions or objects, which makes the image more informative and easier to analyze. Optimal multilevel thresholding approaches are extensively used for segmentation because they are easy to implement and offer low computational cost. Multilevel thresholding image segmentation is frequently performed using popular methods such as Otsu’s between-class variance and Kapur’s entropy. Numerous researchers have used evolutionary algorithms to identify the best multilevel thresholds based on the above approaches using histogram. This paper uses the Energy Curve (EC) based thresholding method instead of the histogram. Chaotic Bidirectional Smell Agent Optimization with Adaptive Control Strategy (ChBSAOACS), a powerful evolutionary algorithm, is developed and employed in this paper to create and execute an effective method for multilevel thresholding segmentation of breast thermogram images based on energy curves. The proposed algorithm was tested for viability on standard breast thermogram images. All experimental data are examined quantitatively and qualitatively to verify the suggested method’s efficacy. [ABSTRACT FROM AUTHOR]

Co-doping of phosphorus and boron elements into crystalline silicon quantum dot (c-Si QD) is an effective approach for enhancing the photoluminescence (PL) performance. In this paper, we report on the preparation of hydrogenated silicon nitride (SiNx:H) thin films embedded with phosphorus-boron co-doped c-Si QDs via plasma enhanced chemical vapor deposition route. Mixed dilution including hydrogen (H2) and argon (Ar) is applied in the in-situ deposition process for optimizing the deposition process. The P-B co-doped c-Si QD/SiNx:H thin films exhibit a wide range of PL spectra. The emission is greatly improved especially for the short-wavelength light when compared to the SiOx:H thin film containing P-B co-doped c-Si QDs. The effects of H2/Ar flow ratio on the structural and optical characteristics of thin films are systematically investigated through a series of characterizations. Experimental results show that various properties, such as crystallinity, QD size, optical band gap and doping concentrations, are effectively controlled by tuning H2/Ar flow ratio. Based on the red-shift of QCE-related PL peak, the successful P-B co-doping into Si QDs are verified. Finally, a comprehensive discussion has been made to analyze the influence of H2-Ar mixed dilution on the film growth and impurity doping in detail in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we have provided more insights on the relationship between under five morbidity in Nigeria and some background characteristics using a Poisson regression model and the most recent 2018 NDHS data on Acute Respiratory Infection (ARI), diarrhoea and fever. Some of our results are that children 36–47 months old have the highest risk of ARI [OR = 1.45; CI (1.31,1.60)] while children less than 6 months old have the lowest risk of ARI [OR = 0.14; CI (0.11,0.17)]. The prevalence of diarrhoea is generally high among children under 48-59 months old but highest among children 6–11 months old [OR = 4.34; CI (3.69,5.09)]. Compared to children 48–59 months old, children in all other age categories except 24–34 months old have a high risk of fever [OR = 0.95; CI (0.73,1.24)]. ARI is more prevalent among female children [OR = 8.88; CI (8.02,9.82)] while diarrhoea [OR = 21.75; (19.10,24.76)] and fever [OR = 4.78; CI (4.31,5.32)] are more prevalent among male children. Children in urban areas are more likely to suffer ARI [OR = 9.49; CI (8.31,10.85)] while children in rural areas are more likely to suffer both diarrhoea [OR = 21.75; CI (19.10,24.76)] and fever [OR = 4.90; CI (4.26,5.63)]. Children in the South-South have the highest risk of ARI [OR = 4.03; CI (3.65,4.454)] while children in the North Central have the lowest risk of ARI [OR = 1.55; CI (1.38,1.74)] and highest risk of diarrhoea [OR = 3.34; CI (2.30,5.11)]. Children in the Northeast have the highest risk of fever [OR = 1.30; CI (1.14,1.48)]. In the Northcentral region, Kogi state has the highest prevalence of fever [OR = 2.27; CI (1.62,3.17)], while Benue state has the lowest [OR = 0.35; CI (0.20,0.60)]. Children in Abuja state face similar risks of fever and diarrhoea [OR = 0.84; CI (0.55,1.27)], with the risk of diarrhoea in Abuja being comparable to that in Plateau state [OR = 1.57; CI (0.92,2.70)]. Nasarawa state records the highest incidence of diarrhoea in the Northcentral [OR = 5.12; CI (3.03,8.65)], whereas Kogi state reports the lowest [OR = 0.29; CI (0.16,0.53)]. In the Northeast, Borno state has the highest rate of fever [OR = 3.28; CI (2.80,3.84)], and Bauchi state the lowest [OR = 0.38; CI (0.29,0.50)]. In Adamawa state, the risks of fever and diarrhoea are nearly equivalent [OR = 1.17; CI (0.97,1.41)], and the risk of fever there is similar to that in Taraba state [OR = 0.92; CI (0.75,1.12)]. Diarrhoea is most prevalent in Yobe state [OR = 3.17; CI (2.37,4.23)] and least prevalent in Borno state [OR = 0.26; CI (0.20,0.33)]. In the Northwest, the risk of fever is similarly high in Zamfara and Kebbi states [OR = 1.04; CI (0.93,1.17)], with Kastina state showing the lowest risk [OR = 0.39; CI (0.34,0.46)]. Children in Zamfara state experience notably different risks of fever and diarrhoea [OR = 0.07; CI (0.05,0.10)]. Kaduna state reports the highest incidence of diarrhoea [OR = 21.88; CI (15.54,30.82)], while Kano state has the lowest [OR = 2.50; CI (1.73,3.63)]. In the Southeast, Imo state leads in fever incidence [OR = 8.20; CI (5.61,11.98)], while Anambra state has the lowest [OR = 0.40; CI (0.21,0.78)]. In Abia state, the risk of fever is comparable to that in Enugu state [OR = 1.03; CI (0.63,1.71)], but the risks of fever and diarrhoea in Abia differ significantly [OR = 2.67; CI (1.75,4.06)]. Abia state also has the highest diarrhoea rate in the Southeast [OR = 2.67; CI (1.75,4.06)], with Ebonyi state having the lowest [OR = 0.05; CI (0.03,0.09)]. In the South-South region, Bayelsa and Edo states have similar risks of fever [OR = 1.28; CI (0.84,1.95)], with Akwa Ibom state reporting the highest fever rate [OR = 4.62; CI (3.27,6.52)] and Delta state the lowest [OR = 0.08; CI (0.02,0.25)]. Children in Bayelsa state face distinctly different risks of fever and diarrhoea [OR = 0.56; CI (0.34,0.95)]. Rivers state shows the highest incidence of diarrhoea in the South-South [OR = 10.50; CI (4.78,23.06)], while Akwa Ibom state has the lowest [OR = 0.30; CI (0.15,0.57)]. In the Southwest, Lagos and Osun states have similar risks of fever [OR = 1.00; CI (0.59,1.69)], with Ogun state experiencing the highest incidence [OR = 3.47; CI (2.28,5.28)] and Oyo state the lowest [OR = 0.18; CI (0.07,0.46)]. In Lagos state, the risks of fever and diarrhoea are comparable [OR = 0.96; CI (0.57,1.64)], and the risk of diarrhoea is similar to those in Ekiti, Ogun, and Ondo states. Oyo state has the highest diarrhoea rate in the Southwest [OR = 10.99; CI (3.81,31.67)], with Ogun state reporting the lowest [OR = 0.77; CI (0.42,1.42)]. Children of mothers with more than secondary education are significantly less likely to suffer ARI [OR = 0.35; CI (0.29,0.42)], whereas children of mothers without any education run a higher risk of diarrhoea [OR = 2.12; CI (1.89,2.38)] and fever [OR = 2.61; CI (2.34,2.91)]. Our analysis also indicated that household wealth quintile is a significant determinant of morbidity. The results in this paper could help the government and non-governmental agencies to focus and target intervention programs for ARI, diarrhoea and fever on the most vulnerable and risky under five groups and populations in Nigeria. [ABSTRACT FROM AUTHOR]

This paper explores a new way of designing water supply networks for fire safety using ideas from graph theory, focusing on a method called certified domination. Ensuring a good water supply is crucial for fire safety in communities, this study looks at the rules and problems in Poland for how much water is needed to fight fires in different areas and how this can be achieved at a lowest possible cost. We present a way to plan water supply networks for fire protection as a graph, where each point (node) is a place that needs water, and the lines (links) show where water can go between these points. The main idea is to find the best places to put pumping stations and wells in the network to save money and still meet all the fire safety requirements. Our approach assumes that it costs more to build a pumping station than a well. We use some examples to show how this method can find cost-effective solutions for water supply networks, while ensuring that they meet fire safety requirements and are not too expensive to build. This approach is a new and efficient way to improve the design of water supply networks for fire safety. Key challenges that are solved in this paper are a linear time algorithm finding an optimal solution for networks without cycles and a BLP (Binary Linear Programming) algorithm solving the problem in arbitrary networks. [ABSTRACT FROM AUTHOR]

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health. [ABSTRACT FROM AUTHOR]

The vertical tube indirect evaporative cooler (VTIEC) could be preferred one to air-conditioning buildings if its water film quality could be improved. This paper proposed a new VTIEC with inner grooved tubes to improve the water film quality and obtained its performance by experimental method. The results indicated that inner grooved tubes have positive effects on the water film and performance of the new VTIEC. A low working air enthalpy at inlet can significantly improve the total cooling capacity from evaporation, and then VTIEC performance. In this paper conditions, the velocity ratio of 1.52 of the produced air to the working air is better one condition compared to 1.01 and 2.28. The produced air velocity is positive effect on the cooling capacity per unit area until the produced air velocity of about 4.05 m/s and is negative effect on COP. The maximum unit cooling capacity, wet-bulb efficiency and COP are 307.74W/m2, 81.7% and 44.97.The results will promote the application of the new VTIEC. [ABSTRACT FROM AUTHOR]

The problem of solving the equivalent resistance between two points for resistor networks has important significance in physics. This paper mainly changes and rewrites the formula for calculating the resistance between two points of an unconventional m × n cylindrical resistor network with a zero resistor axis and any two left and right boundaries. To enhance the efficiency of calculating the equivalent resistance between two points, Chebyshev polynomials and hyperbolic cosine functions are employed to represent the new formula. And in the inference process, the famous discrete cosine transform of the third kind (DCT-III) is used to process the matrix. We give the equivalent resistance formula for several special cases, and display them by a three-dimensional graph. Subsequently, the calculation efficiency of the original formula and the rewritten formula are compared. At the end of the paper, a heuristic algorithm suitable for robot path planning on cylindrical environment is proposed. [ABSTRACT FROM AUTHOR]

This paper discusses the design process toward new lumped chaotic systems that originates in higher-order ordinary differential equations commonly used as description of ideal oscillators. In investigated third-order case, two chaotic oscillators were constructed. These systems are dual in the sense of vector field geometry local to fixed points. The existence of robust chaos was proved by both standard routines of numerical analysis and practical measurement. For the fourth-order oscillatory equation, the concept based on interaction between superinductor and supercapacitor was examined in detail. Since both "superelements" are active, the nonlinearity essential to the evolution of chaos is fully passive. It is demonstrated that complex motion is robust and does not represent long transient behavior or numerical artefact. The existence of chaos was verified using standard quantifiers of the flow, such as the largest Lyapunov exponents, recurrence plots, approximate entropy and sensitivity calculation. A good final agreement between theoretical assumptions and practical results will be concluded, on a visual comparison basis. [ABSTRACT FROM AUTHOR]

Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability. [ABSTRACT FROM AUTHOR]

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%. [ABSTRACT FROM AUTHOR]

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, "5+X" was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications. [ABSTRACT FROM AUTHOR]

The ubiquitous presence of electronic devices demands robust hardware security mechanisms to safeguard sensitive information from threats. This paper presents a physical unclonable function (PUF) circuit based on magnetoresistive random access memory (MRAM). The circuit utilizes inherent characteristics arising from fabrication variations, specifically magnetic tunnel junction (MTJ) cell resistance, to produce corresponding outputs for applied challenges. In contrast to Arbiter PUF, the proposed effectively satisfies the strict avalanche criterion (SAC). Additionally, the grid-like structure of the proposed circuit preserves its resistance against machine learning-based modeling attacks. Various machine learning (ML) attacks employing multilayer perceptron (MLP), linear regression (LR), and support vector machine (SVM) networks are simulated for two-array and four-array architectures. The MLP-attack prediction accuracy was 53.61% for a two-array circuit and 49.87% for a four-array circuit, showcasing robust performance even under the worst-case process variations. In addition, deep learning-based modeling attacks in considerable high dimensions utilizing multiple networks such as convolutional neural network (CNN), recurrent neural network (RNN), MLP, and Larq are used with the accuracy of 50.31%, 50.25%, 50.31%, and 50.31%, respectively. The efficiency of the proposed circuit at the layout level is also investigated for simplified two-array architecture. The simulation results indicate that the proposed circuit offers intra and inter-hamming distance (HD) with a mean of 0.98% and 49.96%, respectively, and a mean diffuseness of 49.09%. [ABSTRACT FROM AUTHOR]

The Fenshui River Bridge is a steel–concrete composite girder bridge with a main span of 90 m. Due to the topographical constraints, the steel box girder was constructed without the use of auxiliary piers, employing incremental launching techniques. This article focuses on the construction technology used for the steel box girder of the Fenshui River Bridge. Firstly, using the influence matrix method, the cable force is determined based on the maximum cantilever state of the structure, with the vertical deformation of the front end of the guide beam and the horizontal deformation of the top of the tower as the control objectives. The unstressed cable length is then calculated based on the mechanical relationship between cable deformation and cable force. A calculation method for adaptive cable force is proposed, which is based on the variation of the stress-free cable length within the adaptive structural system. Next, the finite element analysis method was employed to determine the optimal layout position for the tower. The results indicate that during the incremental launching construction of the steel box girder, the calculated cable forces using the method proposed in this paper are in close agreement with the measured cable forces. At the maximum cantilever state of the structure, the calculated and measured values of the cable force resulted in a percentage difference of 3.96%. The calculated values of deformation and stress in key sections showed a percentage difference of 6.4% for deformation and 6.6% for stress. To maximize the effectiveness of the tower and cable, the tower should be positioned above the bridge pier when the guide beam crosses the maximum span. The findings of this paper can serve as a reference for the construction of similar types of bridges. [ABSTRACT FROM AUTHOR]

Adaptive sampling and situational awareness are key features of modern autonomous underwater vehicles (AUVs) since data quality can be improved while operation time and cost can be reduced. An example for adaptive sampling in the marine environmental context is thermocline detection and tracking. The thermocline as horizontal ocean layer separates warm and cold water and is a key feature in many marine disciplines. For example, it influences the distribution and exchange of nutrients and is a habitat for many organisms. In this paper we use an unscented Kalman Filter (UKF) based extremum seeking control (ESC) to find and follow ocean layers such as the thermocline. Computer simulations and real-world tests show that the method is able to find and track non-trivial real-world ocean layers with sensors subject to hysteresis and delay effects. [ABSTRACT FROM AUTHOR]

Undercover videos have become a popular tool among NGOs to influence public opinion and generate engagement for the NGO's cause. These videos are seen as a powerful and cost-effective way of bringing about social change, as they provide first-hand evidence and generate a strong emotional response among those who see them. In this paper, we empirically assess the impact of undercover videos on support for the cause. We in addition analyze whether the increased engagement among viewers is driven by the negative emotional reactions produced by the video. To do so, we design an online experiment that enables us to estimate both the total and emotion-mediated treatment effects on engagement by randomly exposing participants to an undercover video (of animal abuse) and randomly introducing a cooling-off period. Using a representative sample of the French population (N=3,310), we find that the video successfully increases actions in favor of animals (i.e., donations to NGOs and petitions), but we fail to prove that this effect is due to the presence of primary emotions induced by the video. Last, we investigate whether activists correctly anticipate their undercover videos' (emotional) impact via a prediction study involving activists (exploratory analysis). Protocol registration: This manuscript is a Stage-2 working paper of a Registered Report that received In-Principle-Acceptance from Scientific Reports on November 20th, 2023 [Link to Stage-1]. The Stage-1 that received In-Principal-Acceptance can be found here: https://osf.io/8cg2d. [ABSTRACT FROM AUTHOR]

Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction (ECER) fiscal policies on enhancing carbon emission (CE1) reduction and carbon efficiency (CE2). It further analyzes the mediating role of Green Innovation (GI), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE2, a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE1 reduction and CE2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE1 reduction and the improvement of CE2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals. [ABSTRACT FROM AUTHOR]

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers. [ABSTRACT FROM AUTHOR]

The study of hydrogen concentration distribution law of hydrogen-doped methane pipeline is directly related to the safety and stability of hydrogen-doped methane pipeline network. Based on the theory of fluid dynamics, this paper established a model of hydrogen-doped methane pipeline and simulated the operation and shutdown status of hydrogen-doped methane pipeline by adopting the computational fluid dynamics method and selecting the mixture multiphase model and standard k - ε turbulence model. This paper investigates the hydrogen concentration distribution law in hydrogen-doped methane pipelines as well as the influence law of different hydrogen-doping ratios, operating flow velocities, operating pressures, shutdown time and gas usage on the hydrogen concentration distribution in gas pipeline. The results show that: under the operation condition, there is a weak uneven distribution of hydrogen in the pipeline, the hydrogen-doping ratio, flow velocity, pressure on the hydrogen volume fraction of the change in the 0.9% or less, the effect can be ignored; in the shutdown status, there is a clear stratification phenomenon, the hydrogen-doping ratio increased from 10 to 25%, the change in the volume fraction of hydrogen in the 11.2% or less, a positive correlation; with the extension of the shutdown time to 900s, the pipeline firstly appeared obvious stratification phenomenon in the branch pipe, the thickness of the gas with hydrogen volume fraction above 40% on the upper wall surface of the branch pipe increased to 0.7 mm, and after the shutdown time was extended to 10 h, obvious stratification phenomenon appeared in the main pipeline, and the volume fraction of hydrogen near the top of the main pipe of about 16.5 mm was above 30%, which was positively correlated; In the shutdown status, the shutdown time has the greatest effect on the stratification phenomenon in the pipe, followed by the hydrogen-doping ratio, and the gas usage has the least effect. [ABSTRACT FROM AUTHOR]

In a multi-level quantum system Fano coherences stand for the formation of quantum coherences due to the interaction with the continuum of modes characterizing an incoherent process. In this paper we propose a V-type three-level quantum system on which we certify the presence of genuinely quantum traits underlying the generation of Fano coherences. We do this by determining work conditions that allows for the loss of positivity of the Kirkwood-Dirac quasiprobability distribution of the stochastic energy changes within the discrete system. We also show the existence of nonequilibrium regimes where the generation of Fano coherences leads to a non-negligible excess energy given by the amount of energy that is left over with respect to the energy of the system at the beginning of the transformation. Excess energy is attained provided the initial state of the discrete system is in a superposition of the energy eigenbasis. We conclude the paper by studying the thermodynamic efficiency of the whole process. [ABSTRACT FROM AUTHOR]

We aimed to investigate differences in patients' anxiety and satisfaction between patients undergoing paper-based patient decision aid (PDA) for shared decision-making (SDM) and those receiving computer-based PDA. We retrospectively collected questionnaires before and after SDM. Basic demographic data as well as anxiety, satisfaction, knowledge acquisition, and participation in SDM were recorded. We divided our population into subgroups according to use of paper-based or computer-based PDA. In addition, Pearson correlation analysis was applied to assess the relationships among variables. In total, 304 patients who visited our Division of Nephrology were included in the final analysis. Overall, over half of the patients felt anxiety (n = 217, 71.4%). Near half of the patients felt a reduction in anxiety after SDM (n = 143, 47.0%) and 281 patients (92.4%) were satisfied with the whole process of SDM. When we divided all the patients based on use of paper-based or computer-based PDA, the reduction of anxiety level was greater in the patients who underwent paper-based PDA when compared with that of those who underwent computer-based PDA. However, there was no significant difference in satisfaction between the two groups. Paper-based PDA was as effective as computer-based PDA. Further studies comparing different types of PDA are warranted to fill the knowledge gaps in the literature. [ABSTRACT FROM AUTHOR]

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory forms and flight distances. This makes optimization and understanding of their behavior challenging for humans. By understanding the behavior of paper airplanes and predicting flight behavior, there is a potential to improve the design of aerial vehicles that operate at low Reynolds numbers. By developing a robotic system that can fabricate, test, analyze, and model the flight behavior in an unsupervised fashion, a wide design space can be reliably characterized. We find there are discrete behavioral groups that result in different trajectories: nose dive, glide, and recovery glide. Informed by this characterization we propose a method of using Gaussian mixture models to extract the clusters of the design space that map to these different behaviors. This allows us to solve both the forward and reverse design problem for paper airplanes, and also to perform efficient optimization of the geometry for a given target flight distance. [ABSTRACT FROM AUTHOR]

In this work, we demonstrate an effective anion capturing in an aqueous medium using a highly porous carbon paper decorated with ZnO nanorods. A sol–gel technique was first employed to form a thin and compact seed layer of ZnO nanoparticles on the dense network of carbon fibers in the carbon paper. Subsequently, ZnO nanorods were successfully grown on the pre-seeded carbon papers using inexpensive chemical bath deposition. The prepared porous electrodes were electrochemically investigated for improved charge storage and stability under long-term operational conditions. The results show effective capacitive deionization with a maximum areal capacitance of 2 mF/cm2, an energy consumption of 50 kJ per mole of chlorine ions, and an excellent long-term stability of the fabricated C-ZnO electrodes. The experimental results are supported by COMSOL simulations. Besides the demonstrated capacitive desalination application, our results can directly be used to realize suitable electrodes for energy storage in supercapacitors. [ABSTRACT FROM AUTHOR]

Bloodstream infection (BSI) is a major complication in patients with cancers due to therapy-induced neutropenia and underlying conditions, which increases hospitalization time and mortality rate. Targeted and timely antimicrobial management is crucial to save the patients' lives and reduce the social and economic burdens. Blood culture is a routine clinical diagnostic method of BSI with a long turnaround time, and generally identifies monomicrobial BSI. Thus, polymicrobial BSI often goes undetected although it occurs more frequently in these patients and results in more severe outcomes compared to monomicrobial BSI. In this work, we apply glutaric anhydride, N-hydroxysuccinimide and N,N′-dicyclohexylcarbodiimide to fabricate a functional surface on cellulose filter paper. Targeting three pathogens (Escherichia coli, Saccharomyces cerevisiae, and human cytomegalovirus) commonly occurring in BSI in neutropenic patients, we demonstrate rapid and accurate triplex pathogen DNA detection using the functionalized paper. All three pathogen DNA was identified in 1–5 min with a detection limit of 0.1–0.5 ng/µL. The developed test tool has the potential to provide rapid polymicrobial BSI diagnosis in support of timely, accurate antimicrobial treatment, and could be integrated into an automatic sample-to-result portable equipment. [ABSTRACT FROM AUTHOR]

The study's goal was to develop a spore-based paper strip biosensor for detecting ß-lactam antibiotics in milk using the enzyme induction principle. A new spore-based paper strip biosensor has been developed after important operating parameters such as spore volume, substrate volume, exposure time and temperature, and incubation time and temperature were optimised. The limit of detection for various ß-lactam antibiotics, including amoxicillin, penicillin, ampicillin, carbenicillin, cloxacillin, nafcillin, oxacillin, cephalothin, cefalexin, cefoxitin, cefazolin, and cefuroxime, was determined in milk with detection sensitivity of 1 ppb, 2 ppb, 2 ppb, 10 ppb, 10 ppb, 10 ppb, 20 ppb, 10 ppb 1000 ppb, 10 ppb 300 ppb and 100 ppb, respectively. It was also tested with other contaminants such non-ß-lactam antibiotics, pesticides, aflatoxin, heavy metals, and other chemical contaminants, and no interference was found, indicating that the created biosensor had a low rate of false positive and negative results. In comparison to the AOAC-approved CHARM-ROSA ß-lactam strip test, which identified 7 raw milk and zero pasteurised milk samples positive for ß-lactam antibiotics, the sensor was further analysed and verified using 200 raw milk and 105 pasteurised milk samples. This indicates a perfect match between our biosensor and the AOAC-approved CHARM-ROSA ß-lactam strip test. The developed spore-based paper strip biosensors are expected to be useful in the rapid and cost-effective detection of ß-lactam antibiotic residues in milk samples at the dairy farm, reception dock, and production units, respectively. [ABSTRACT FROM AUTHOR]

In the present work, the composite cross-linked were used to consolidate the dyed paper manuscripts. Nanocomposites of mesoporous silica nanoparticle (MPSNP)/polyvinyl alcohol (PVA) and cellulose nanofiber (CNF)/PVA, which have never been used before, have been evaluated for the consolidation process of the dyed paper manuscripts with madder extract. Three concentrations 1%, 3%, and 5% have been prepared. Analysis and investigation methods like scanning electron microscope (SEM), transmission electron microscope (TEM), dynamic light scattering analysis (DLS), X-Ray diffraction Analysis (XRD), atomic force microscope (AFM), Fourier transform infrared spectroscopy (FTIR) and total color difference (ΔE) by spectrophotometer have been used in order to characterize the prepared nano-sized composites and evaluate the treated dyed paper samples before and after the aging process. The results of surface morphology by SEM revealed the effectiveness of MPSNP/PVA core–shell nanocomposite at 5% in the consolidation process, where the improvement of properties of the aged dyed paper samples. The fibers of the treated paper became strong and appeared clearly. The result of ΔE measurements showed that the treated sample with MPSNP/PVA nanocomposite at 5% gave the lowest ΔE (5.22), while, the treated sample with CNF/PVA nanocomposite at 5% gave the highest ΔE value (11.66). Mechanical measurements (tensile strength and elongation) revealed the efficiency of MPSNP/PVA nanocomposite at 5% in the treatment of the aged dyed paper samples. The treated sample with the mentioned material gave tensile strength and elongation values of 84.8 N/nm2 and 1.736%, respectively. In contrast, the treated sample with CNF/PVA nanocomposite at 1% gave the lowest tensile strength and elongation values 38.2 N/nm2, and 1.166%, respectively. FTIR analysis revealed an increase was noticed in the CH2 stretching band (refers to the crystallinity of cellulose), where the intensity of the treated sample with MPSNP/PVA nanocomposite was at a 5% increase compared to the control sample. The FTIR results supported the results of mechanical measurements. The intensity of the CH2 stretching band, which refers to the crystallinity index of cellulose, was increased with the use of MPSNP/PVA nanocomposite at 3% and 5%, which explains the improvement in mechanical properties. This may be due to the nano-mineral particles, which improve the mechanical properties. Additionally, they reduce the effect of accelerated thermal aging on the cellulosic fibers and give them stability. The detailed analysis of analytical methods used for evaluation revealed the novelty of MPSNP/PVA nanocomposite, especially at 5%. It has a potential role in strengthening and improving different properties of the dyed paper manuscripts with madder extract. [ABSTRACT FROM AUTHOR]

With the increasing demand for heat dissipation of engineering vehicles, a dual-cycle cooling system is introduced in this paper to prevent the adverse effects of engineering vehicles' equipment when operating at the overheating temperature. The performance of the new system is analyzed through tests, and the results show that the dual-cycle cooling system can meet the thermal balance requirements of the engineering vehicle during the shovel operation. Compared with the traditional cooling system, the new cooling system improved performance in terms of volume, engine energy consumption and working oil efficiency. The oil consumption of a wheel loader using the dual-cycle cooling system is reduced by 1% per hour, and the temperature of its transmission oil and hydraulic oil is reduced by more than 10 °C. The new cooling system has bright future in energy saving and emission reduction of engineering vehicles. [ABSTRACT FROM AUTHOR]

Printing as a process itself generates many environmental concerns. The paper addresses ink management in terms of environmental issues in the label printing industry, focusing on its environmental implications. The goal is to demonstrate how a proper ink management system impacts overall printing process efficiency and environmental sustainability for printing companies. The paper introduces an empirical approach to managing components for label and packaging production, utilizing automatic ink dispensing systems. The results demonstrate that the proper management of ink dispensing to minimize waste in packaging printing is crucial for optimizing operating print costs, potentially reducing the amount of ink needed to prepare colors by 52% and achieving energy savings of 37%. This approach fulfills the goal of sustainability by addressing environmental, economic, and social concerns. By optimizing ink usage and energy consumption, companies can significantly reduce operating costs and enhance economic performance. Simultaneously, these practices improve product quality, meet consumer demands for sustainable packaging, and create better working conditions for employees. Future directions and practical implications for supporting operational excellence in production are also discussed. [ABSTRACT FROM AUTHOR]

Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus. [ABSTRACT FROM AUTHOR]

This research addresses the issue of the heritage preventive conservation in the perspective of energy sustainability, for contributing to the achievement of the Sustainable Development Goals (SDGs) and towards the EU Green Deal. The study analyses and compares four cases associated with different microclimate thresholds as suggested by the standard EN 16893:2018 (Cases 1–3) and as derived from the outputs of three degradation models for preserving paper, wood, and canvas paintings (Case 4). Weather-based indices (degree and gram days) were calculated to estimate trends in the potential energy demand of collection facilities in three European cities belonging to different Köppen-Geiger climate zones (Cfb, Csa, and Dfb), under recent past (1981–2010) and near/far future climate scenarios (2021–2050 and 2071–2100) from two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The findings suggest that adapting facilities' management strategies to focus on collections preservation can facilitate the achievement of 5 out of 17 SDGs, offering a viable alternative to costly energy retrofits and encouraging the development of shared solutions for similar facilities in the same climate zone. The results can contribute to inform the revision of EN 16893 and to face major challenges such as the preservation of paper collections in southern latitudes. [ABSTRACT FROM AUTHOR]

This paper primarily explores the modeling method of n-level complex helical structures with coal mining machine cables as the research object. The paper first elaborately introduces the modeling method of n-level helix curves based on parametric equations and coordinate transformations, and compensates for the n-level helix curves with corrected pitch, which can obtain more accurate n-level helix curves and improve the accuracy of n-level helix curves modeling. Subsequently, based on this high-precision n-level helix curves modeling method, the paper elaborates on the method of solving pitch and twisting radius of multi-layer helical structure. Calculation scripts were written based on the above methods, which can be used to batch calculate the twisting radius and pitch of each layer structure in multi-layer structures when satisfying the conditions of in-layer tangency, inter-layer tangency, and extrusion deformation, and retain the actual results through logical judgment. Then, based on the above two methods, the paper developed a modeling method for braided structures based on piecewise functions containing fifth-order polynomials, which can effectively avoid the problem of insufficiently dense arrangement of braided lines and easy interference in traditional methods. Finally, a set of modeling tools was developed using C# and Python in Grasshopper to implement the modeling algorithm. Taking the MCPT-1.9/3.3 3120 + 170 + 4 * 10 coal mining machine cable as an example. The cable was modeled using both the method proposed in this paper and the traditional method. Comparative data shows that the method proposed in this paper can reduce errors by 3.31E6 times in the second-level and above helical structures. In addition this paper compares the standard line length, measured line length, and the line length established by the proposed model, showing that the relative errors are both less than 0.1941%. This paper provides a new, systematic, high-precision, and full-process cable modeling method, in which all parameters except the process parameters are accurately solved by equations. It lays a theoretical foundation for the high-precision simulation and intelligent sensing cables, which is of great significance for improving the safety, stability, and efficient development of the coal mining industry. The research results of the paper can not only be applied to the modeling of coal mining machine cables but also can be extended to the modeling of other complex multi-layer helical structures. [ABSTRACT FROM AUTHOR]

As the pillar industry of coastal areas, aquaculture needs artificial intelligence technology to promote economic development. To realize the automation of the aquaculture industry, this paper proposes a new underwater object detection network: WBi-YOLOSF. It realizes the automatic classification and detection of aquatic products, improves the production efficiency of the aquaculture industry, and promotes its economic development. This paper creates an image dataset containing 15 aquatic products to lay the data foundation for model training. In the data preprocessing part, an underwater image enhancement algorithm is proposed to improve the quality of the data set effectively. Aiming at the problem of high false detection rate and missed detection rate of underwater dense small targets, a new data enhancement method was proposed to improve the training set's data quality comprehensively. Inspired by the weighted bidirectional feature pyramid network, this paper proposes a new feature extraction network: AU-BiFPN, which solves the gradient problem caused by the network hierarchy's deepening on enhancing the network's multi-scale feature fusion. The AU-BiFPN network structure is embedded into the YOLO series network framework, significantly improving the basic network's feature extraction and feature fusion ability and dramatically improving the prediction accuracy without affecting the network inference speed. Here, the swarm intelligence algorithm is introduced to optimize the model hyperparameters, accelerating the convergence speed of model training and significantly reducing the computational cost. At the same time, the model's accuracy is improved by a cliff. In addition, the Funnel Activation is introduced in the network's backbone, and the simple, parameter-free attention module is integrated, effectively improving the accuracy and speed of the model prediction. Ablation and comparison experiments show the effectiveness and superiority of the proposed model. Verified by the mean average precision and frame rate evaluation indicators, the experimental results of the WBi-YOLOSF target detection network can reach 0.982 and 203 frames per second, which are 1.4% and five frames per second higher than the network with the second score. In summary, this method can quickly and accurately identify aquatic products, realize real-time target detection of aquatic products, and lay the foundation for developing an aquaculture automation system. [ABSTRACT FROM AUTHOR]

Fintech is disrupting the traditional financial models with the power of technology. Will it solve the problems faced by farmers in starting their own businesses and stimulate the vitality of rural entrepreneurship? In this paper, we focus on the impact of fintech development on the entrepreneurial behavior of farmers in China. First, we theoretically analyze the effects of fintech development on the entrepreneurial behavior of farmers and put forward the corresponding theoretical hypotheses. Second, using data from Baidu search index and the China Household Finance Survey (CHFS), we employ a Probit model to verify our theoretical hypotheses. Our findings suggest that (1) fintech development encourages farmer entrepreneurial behavior, (2) it achieves this by alleviating credit constraints, information constraints, and risk aversion effects, (3) in terms of the type of entrepreneurship, the development of fintech mainly promotes survival entrepreneurship among farmers, while the entrepreneurial effect of fintech is more significant among farmers with low education level, low income level and in the central and western regions. Overall, our study provides timely insights into boosting farmer entrepreneurship in emerging countries such as China by leveraging the potential of fintech. [ABSTRACT FROM AUTHOR]

Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI. [ABSTRACT FROM AUTHOR]

In order to evaluate the beneficiation plant environment in a more scientific and reasonable way, this paper took the workshop environment of the beneficiation plant as the research object. This paper divided the beneficiation plant into 7 evaluation units according to its functions. The evaluation indices are dust, noise, light environment, microclimate, benzene, toluene and xylene. This paper combines the G1 method and the entropy weight method to evaluate the weight of each evaluation index, the element extension model of the concentrator working environment is established by the element analysis method, and the matter element analysis method is used to establish an evaluation index system of a beneficiation plant in East China. The results show that the evaluation level of the breaking workshop and the auxiliary facilities are unqualified, the auxiliary facility is qualified, the culling workshop, culled yard and accessory building are medium, the screening workshop and grinding workshop are good. [ABSTRACT FROM AUTHOR]

With the high yield of many wells represented by Well JT1 in the Maokou Formation, has catalyzed a surge in exploration activities along the platform margin facies of the Maokou Formation in central Sichuan and further showed the significant exploration potential of the Maokou Formation in the northern slope. However, the fracture cave body of the Maokou Formation exhibits a high degree of development, strong longitudinal and horizontal heterogeneity, large formation pressure differences, and drilling events such as gas kicks and lost circulation occur frequently, which seriously affects the efficient implementation of drilling. Understanding the spatial distribution of the three-pressure in the formation can help better deal with and solve the above problems. Therefore, in order to help the safe, high-quality and rapid drilling of the Maokou Formation in the study area, and enhance the efficiency of oil and gas development, this paper explores the research on the prediction method of the three-pressure of jointing well-seismic data based on the geomechanical experimental data and the actual drilling data. In the process of prediction of pore pressure, this study found that the pore pressure and formation velocity in the study area have an exponential relationship. In order to enhance the applicability of the Filippone's method in the study area and improve the prediction accuracy of pore pressure, the linear relationship between pore pressure and formation velocity in the Filippone's method is modified to an exponential relationship, and a pore pressure prediction model suitable for the work area was established. Based on the Mohr–Coulomb criterion and Huang's model, the prediction models of collapse pressure and fracture pressure applicable to the study area were established, respectively. Then, the elastic parameters were obtained through pre-stack inversion, and the three-pressure bodies were calculated based on the elastic parameter bodies. The results indicate that: (1) The three-pressure prediction method of the jointing well-seismic data in this paper can predict the formation's longitudinal and transverse pressure anomaly zones in advance. (2) The Maokou Formation in the study area is characterized by abnormally high pressure, to balance the pressure of the high-ground formation, high-density drilling fluid is necessary. (3) The prediction results of three-pressure in this paper are highly consistent with the actual drilling engineering events, which verifies the reliability of the three-pressure prediction results presented in this study. The results of the study can provide a basis for decision-making in drilling geological design, such as the determination of drilling fluid density, the evaluation of borehole stability and other engineering problems that require support from three-pressure data. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy. To solve this problem, this paper proposes an improved YOLOv5-based cancer polyp detection method for colorectal cancer. The method is designed with a new structure called P-C3 incorporated into the backbone and neck network of the model to enhance the expression of features. In addition, a contextual feature augmentation module was introduced to the bottom of the backbone network to increase the receptive field for multi-scale feature information and to focus on polyp features by coordinate attention mechanism. The experimental results show that compared with some traditional target detection algorithms, the model proposed in this paper has significant advantages for the detection accuracy of polyp, especially in the recall rate, which largely solves the problem of missed detection of polyps. This study will contribute to improve the polyp/adenoma detection rate of endoscopists in the process of colonoscopy, and also has important significance for the development of clinical work. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

Deep-buried tunnels with weak surrounding rock are frequently encountered issues in traffic engineering. It plays an important role in the excavation process and the project operation. This paper applies the theoretical analysis and laboratory test related to four different conditions in terms of their thickness to determine the mechanical response of deep-buried tunnel lining. Then, the energy dissipative structure theory is employed to explain the experimental results. This paper has made the following achievements: firstly, it is found that the toughness of the secondary lining was found to be often the most important indicator of tunnel safety, with better-toughness linings having higher tensile strength and crack resistance. Secondly, it suggests that the inclusion of steel reinforcement in the concrete lining can effectively improve the secondary lining toughness. Finally, it proves that the more ductile liner had more energy, higher load-carrying capacity, and was better able to maintain the overall stability of the structure. [ABSTRACT FROM AUTHOR]

A one-step analysis method was developed for four types of amino acids using a microfluidic paper-based analytical device fabricated from chromatography filtration paper and laminate films. Aminoacyl-tRNA synthetase was used to detect each amino acid. The obtained laminated paper-based analytical device (LPAD) contained four enzymatic reaction areas. Colorimetric detection was performed based on the molybdenum blue reaction. A model method for the simple, easy, and simultaneous detection of several amino acid concentrations was suggested, in contrast to the conventional methods such as HPLC or LC–MS. The method provided a selective quantification at the ranges of 3.6–100 μM for tryptophan, 10.1–100 μM for glycine, 5.9–100 μM for histidine and 5.6–100 μM for lysine with a detection limit of 1.1 μM, 3.3 μM, 1.9 μM and 1.8 μM, respectively. LPAD fabrication was considerably simple, and the subsequent detection process was easy and required a short period of time (within 15 min). [ABSTRACT FROM AUTHOR]

The wettability of the paper surface is greatly affected by the wettability of the pulp fibers. We conducted this study in order to understand the relationship between the wettability of a single fiber of recycled pulp and the strength of recycled paper, as well as the inter-fiber bonding strength. The contact angle was determined from a series of photographs of the pulp fiber and the water silhouettes at the point of contact. The contact line and profile history were continuously photographed in every 1 s after the initial contact. The recycled softwood kraft pulp fibers were clearly much less hydrophilic than the original fibers, regardless of whether the fibers had been bleached or not. The contact angle of the original chemi-thermomechanical pulp fiber was much higher than that of the original softwood bleached kraft pulp fiber. Furthermore, increased number of recycling decreased the contact angle of the chemi-thermomechanical pulp fiber. The Page equation was used to evaluate the strength contributions of single fiber and fiber–fiber bonding to tensile strength of paper. As a result, an increase in weakness factor of fiber–fiber bonding strength was obtained for the recycled softwood kraft pulp handsheet. On the other hand, the weakness factor of the original chemi-thermomechanical pulp handsheet decreased with recycling. In addition, the weakness factor of fiber–fiber bonding strength and the contact angles of the provided softwood bleached kraft pulp fibers bore a proportional relationship to each other. [ABSTRACT FROM AUTHOR]

Insufficiently treated wastes contained within onsite sanitation systems (OSS) commonly used by disadvantaged and developing communities contribute to public and environmental health concerns, calling for practical alternative solutions. At the basic level, an improved understanding of the evolution of chemical and physical constituents under different waste introduction methods and for short-term and long-term operation is needed. While receiving non-dilute waste under mixed, unmixed, toilet paper exclusion, and urine diversion (UD) regimes, self-flushing OSS simulated using anaerobic digesters (ADs) were compared during three operational stages: (1) 0–1 month service for unsheltered encampments; (2) 1–3 month disaster relief scenario; and (3) ≥ 3 months representing refugee camps and long-term household use. Although stratification was found to promote suitable conditions for short-term use of self-flushing toilets, mixing increased beneficial biodegradation of organic constituents. Urine-containing ADs demonstrated a shift from sulfide to ammonia odor accompanied by high pH (> 8) after ~ 240 d. E. coli reduction following elevated nitrogen and dissolved solids levels pointed to decreased pathogen survival in ADs with urine. The benefits of bacterial disinfection, reduction of sulfurous odors, and heightened organics degradation in mixed, urine-containing ADs suggest this format as more desirable for prolonged use of self-flushing OSS over unmixed or urine-diverting formats. [ABSTRACT FROM AUTHOR]

While traditional techniques have long allowed forensic investigators to positively identify fingermarks on documents of interest, understanding the chronological sequence of events that led to their deposition is still seen as a 'holy grail' for forensic examinations. By way of example, the question of whether a mark is above or below printed text is crucial. The work herein reveals that a novel application of a recently established fingermark development technique readily allows such differentiation. The process in question allies forensic gelatin lifters with RECOVER, a development system that hinges on the polymerisation of disulfur dinitride. While the latter was specifically developed in its current form for the retrieval of prints from metal surfaces exposed to extreme conditions or washing, its ability to target surface effects allows for visualisation of surface interactions on forensic gelatin lifts. Crucially, in doing so the order in which the lifted material was originally deposited is also revealed. This, therefore, permits clear elucidation of the order of deposition of printed text and fingermarks—and does so both rapidly and in a non-invasive way. This long sought-after capability has the potential to revolutionise forensic document examinations. [ABSTRACT FROM AUTHOR]

In medical institutions, radiation shielding is an effective strategy to protect medical personnel and patients from exposure. Reducing the weight of the shield worn by medical personnel in the radiation generating area plays a key role in improving their productivity and mobility. In this study, a new lightweight radiation shield was developed by electrospinning a polymer-tungsten composite material to produce nanofibers with a multi-layered thin-film structure similar to that of a morpho butterfly wing. The fabricated shield was in the form of 0.1 mm thick flexible shielding paper. The multi-layer structure of the thin shielding paper was obtained through nanofiber pattern formation via electrospinning a dispersion of tungsten particles. At 0.1 mm thickness, the paper's shielding rate was 64.88% at 60 keV. Furthermore, at 0.3 mm thick and arranged in a laminated structure, the shielding rate was 90.10% and the lead equivalent was 0.296 mmPb. When used as an apron material, the weight can be reduced by 45% compared to existing lead products. In addition, the material is highly processable and can be used to manufacture various flexible products, such as hats, gloves, underwear, and scarves used in medical institutions. [ABSTRACT FROM AUTHOR]

Chirality at the nanoscale has emerged as a key area of interest in materials science and engineering, with significant implications for various fields such as spintronics, photonics, optoelectronics, quantum computing, and biomedicine. With their unique properties such as enantioselective interactions with light and spin-polarized electron transport, chiral nanomaterials are opening a new window of opportunities for the design of advanced functional devices. This editorial provides an overview of the current state of research in chirality in nanomaterials. We also showcase several papers from this collection that exemplify the breadth of current work, offering insights into the future directions of this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Non-thermal plasma (NTP) is a well-known decontamination tool applicable for a wide range of microorganisms and viruses. Since the recent COVID-19 pandemic highlighted the need to decontaminate all daily used items, it is highly desirable to address the applicability of NTP, including its possible harmful effects. To the best of our knowledge, a comprehensive characterization of NTP effects on sensitive materials is still lacking. We investigated the potential damage to common materials of daily use inflicted by air atmospheric NTP generated in Plasmatico v1.0. The materials tested were paper, various metals, and passive and active electronic components modelling sensitive parts of commonly used small electronic devices. The NTP-exposed paper remained fully usable with only slight changes in its properties, such as whitening, pH change, and degree of polymerization. NTP caused mild oxidation of copper, tinned copper, brass, and a very mild oxidation of stainless steel. However, these changes do not affect the normal functionality of these materials. No significant changes were observed for passive electronic components; active components displayed a very slight shift of the measured values observed for the humidity sensor. In conclusion, NTP can be considered a gentle tool suitable for decontamination of various sensitive materials. [ABSTRACT FROM AUTHOR]

Non-thermal plasma (NTP) is a well-known decontamination tool applicable for a wide range of microorganisms and viruses. Since the recent COVID-19 pandemic highlighted the need to decontaminate all daily used items, it is highly desirable to address the applicability of NTP, including its possible harmful effects. To the best of our knowledge, a comprehensive characterization of NTP effects on sensitive materials is still lacking. We investigated the potential damage to common materials of daily use inflicted by air atmospheric NTP generated in Plasmatico v1.0. The materials tested were paper, various metals, and passive and active electronic components modelling sensitive parts of commonly used small electronic devices. The NTP-exposed paper remained fully usable with only slight changes in its properties, such as whitening, pH change, and degree of polymerization. NTP caused mild oxidation of copper, tinned copper, brass, and a very mild oxidation of stainless steel. However, these changes do not affect the normal functionality of these materials. No significant changes were observed for passive electronic components; active components displayed a very slight shift of the measured values observed for the humidity sensor. In conclusion, NTP can be considered a gentle tool suitable for decontamination of various sensitive materials. [ABSTRACT FROM AUTHOR]