Search

Flax (Linum usitatissimum) is a bast fiber plant known for its long fibers, making it an excellent source of pulp for paper production. In Ethiopia, flax is primarily cultivated for oil, with the residual straw utilized for papermaking. This study focuses on pulping flax straw using the Kraft process and investigates its chemical composition, proximate analysis, and morphological properties. The proximate analysis revealed an ash content of 4.13% and moisture content of 11%. Chemical composition analysis showed cellulose at 51.34%, hemicellulose at 25.20%, lignin at 14.12%, ash at 4.13%, and extractives at 5.21%. The morphological properties included a fiber length of 1.41 mm, diameter of 16.78 μm, lumen width of 9.45 μm, and cell wall thickness of 3.77 μm. Flax straw exhibited an acceptable Runkel ratio (0.8) and flexibility coefficient (56.32), placing it within the range of non-wood fibers. SEM analysis of the pulp's morphology was conducted to assess fiber structure, including the presence of cracks. Pulp quality and length are directly linked to paper strength. Various pulping conditions were studied using a full-factorial design, with optimum conditions being 10% alkaline, 131.74°C, and 120 min of cooking time, yielding a pulp with a Kappa number of 10.45 and a yield of 40.56%. The resulting paper demonstrated standard tensile, tearing, and burst strengths, indicating that flax straw is a promising raw material for paper production. [ABSTRACT FROM AUTHOR]

The pulp and paper industry generates vast quantities of paper sludge, posing significant environmental challenges due to its disposal in landfills or incineration. This study explores the potential of valorizing paper sludge by incorporating it into particleboard production. It aims to optimize sludge content and particle size to enhance board properties—a novel approach to waste management in the wood composites industry. Through systematic variation of sludge content (0–25%) and particle size (< 0.5 to > 2 mm), we assessed the mechanical and physical properties such as internal bond strength (IB), modulus of rupture (MOR), modulus of elasticity (MOE), water absorption (WA), and thickness swelling (TS). The findings indicate that incorporating paper sludge at moderate levels (5–15%) with optimized particle sizes (< 1 mm) significantly improves the mechanical properties of the particleboard, including increased IB, MOR, and MOE while reducing WA and TS. Principal Component Analysis (PCA) further supported these results, revealing that higher-density boards with enhanced mechanical properties absorb less water, highlighting the interrelationship between structural integrity and moisture resistance. The PCA also identified thickness swelling as an independent factor, suggesting that while mechanical properties can be optimized, additional strategies are needed to control swelling. In conclusion, this study demonstrates that up to 15% paper sludge can be effectively used in particleboard production without compromising quality, provided particle size is carefully controlled. This approach not only offers a sustainable solution for managing paper sludge but also contributes to the development of eco-friendly composite materials, aligning with circular economy principles. [ABSTRACT FROM AUTHOR]

In many animals, early-life decisions influence long-term fitness. Mantidflies are spider egg predators; their tiny larvae climb aboard spiders to find eggs, but little is known about how they find spiders. We tested the hypothesis that mantidfly larvae (Dicromantispa sayi) detect and respond to substrate-borne spider cues (e.g., silk and/or excreta). We presented larvae with filter paper exposed to different types of spiders (adult female, adult male, or juvenile Habronattus trimaculatus jumping spiders) versus a no-cue control. Larvae spent more time on filter paper with spider cues. We then tested the hypothesis that mantidflies make finer distinctions between spiders when given direct choices between these cues. Larvae did not discriminate between sexes but spent more time (and exhibited more phoretic behavior) on filter paper with female or male cues compared with juvenile cues. While this suggests that mantidflies actively seek out adult spiders, we also found that adult spiders were more active than juveniles and may have simply deposited more silk and excreta, providing a stronger cue to detect. We discuss these findings in the context of the risks and benefits of different spider hosts, and how early-life spider-searching strategies may shape a mantidfly's long-term fitness. [ABSTRACT FROM AUTHOR]

Nucleic acid amplification testing has great potential for point-of-need diagnostic testing with high detection sensitivity and specificity. Current sample preparation is limited by a tedious workflow requiring multiple steps, reagents and instrumentation, hampering nucleic acid testing at point of need. In this study, we present the use of mixed cellulose ester (MCE) paper for DNA binding by ionic interaction under molecular crowding conditions and fluid transport by wicking. The poly(ethylene) glycol-based (PEG) reagent simultaneously provides the high pH for alkaline lysis and crowding effects for ionic binding of the DNA under high salt conditions. In this study, we introduce Paper-based Abridged Solid-Phase Extraction with Alkaline Poly(ethylene) Glycol Lysis (PASAP). The anionic mixed cellulose ester (MCE) paper is used as solid phase and allows for fluid transport by wicking, eliminating the need for pipetting skills and the use of a magnet to retain beads. Following the release of DNA from the cells due to the lytic activity of the PASAP solution, the DNA binds to the anionic surface of the MCE paper, concentrating at the bottom while the sample matrix is transported towards the top by wicking. The paper was washed by dipping it in 40% isopropanol for 10 s. After air-drying for 30 s, the bottom section of the paper (3 mm × 4 mm) was snapped off using the cap of a PCR tube and immersed in the colourimetric loop-mediated isothermal amplification (cLAMP) solution for direct amplification and colourimetric detection. The total sample processing was completed in 15 min and ready for amplification. cLAMP enabled the detection of 102 CFU/mL of Escherichia coli (E. coli) from culture media and the detection of E. coli in milk < 103 CFU/mL (10 CFU) after incubation at 68 °C for 60 min, demonstrating applicability of the method to complex biological samples. [ABSTRACT FROM AUTHOR]

This work proposed an approach to multi-component non-metallic materials, namely regular polygonal paper honeycomb sandwich tubes filled with polyethylene (PE) foam, and focused on the influence of drop impact parameters and structural parameters on the deformation characteristics and energy absorption of the filled tubes. Axial drop impact tests were employed to exert drop impact loading on cross-sections of the specimens with a square drop weight. The results showed that the X-direction filled tube (X-DFT) had a higher crushing strength and yield strength than that of the Y-direction filled tube (Y-DFT). As the tubular cross-section edge number of the filled tube increased, the specific energy absorption (SEA) and specific total efficiency (STE) decreased. Under the same axial drop impact loading conditions, the SEA and STE of the X-DFT were superior to those of the Y-DFT. As the edge number of the tubular cross-section or the tubular length ratio increased, the SEA and STE of the filled honeycomb tube decreased significantly. While the impact energy increased, the SEA and STE of the filled tubes increased approximately linearly. This work investigated the influence of drop-impact and structural parameters on deformation characteristics and energy absorption. It is expected to provide supplementary documentation for the advancement of cushion protection technology and the optimization of product structure. [ABSTRACT FROM AUTHOR]

Firecrackers are a vital element of cultural festivities that happen worldwide. However, the hazardous by-products they emit have a significant impact on environmental pollution, leading to the greenhouse effect and climate change. Aluminium powder serves as the fuel in the traditional flash powder mixture, nitrate of potassium serves as an oxidizing agent, and sulphur acts as the igniter at exact concentrations. The presence of sulfur in the flash powder mixture is critical as it acts as an igniter, contributing to the formation of sulfur dioxide, which can cause environmental harm. We carried out an experiment employing Sargassum wightii brown seaweed powder as a replacement for sulphur at specific amounts to lessen the effects of sulphur in flash powder. We discovered that Sargassum wightii brown seaweed powder may replace up to 50% of the sulphur significance in the flash powder mixture without impairing the flash powder's traditional performance. Our experiments included impact and friction sensitivity, SEM, and FTIR analyses to evaluate the improved flash powder composition. The results revealed that the modified flash powder mixture SP5 and SP10 emits less emission by 12% and 21%, and produces similar noise performance of 108 and 107 dB(A) to the normal flash powder composition (SP), affirming that the SP10 flash powder is a viable alternative. Moreover, in our relentless pursuit to mitigate the detrimental effects on our environment, we have ingeniously introduced a novel product—the Chinese cracker made from vegetable waste paper. Not only does this innovative solution address concerns regarding land pollution, but it also presents a sustainable approach to consumer goods. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices often are combined with scanners as detectors. In this work, different scanning options offered by scanners: resolution, scanning mode, exposure to radiation, colour restoration, and saving format were tested. Moreover, different attempts to mathematical data treatment based on intensities of three channels—Red, Green and Blue, were studied. All measurements presented in this article were conducted for a model dye—bromothymol blue and a model analyte—zinc(II) ion (complexed with xylenol orange in a paper matrix). The article summarizes the scanning options and possibilities of mathematical calculations. Nevertheless, it is suggested that the best option is to use the prior prepared calculation file to paste obtained intensities and compare all presented in this article (and the most frequently used) equations to process intensities and decide which one should be used in the particular analysis. [ABSTRACT FROM AUTHOR]

The creativity of scientists often manifests as localized hot streaks of significant success. Understanding the underlying mechanisms of these influential phases can enhance the effectiveness of support systems and funding allocation, fostering groundbreaking discoveries worthy of accolades. Historically, analyses have suggested that hot streaks occur randomly over time. However, our research, through meticulous examination, reveals that these phases are not flatly distributed but are more frequent at the early and late stages of scientists' careers. Notably, both early and late hot streaks are marked by dense tie collaborations, with the former typically involving close partnerships with particular authors and the latter being characterized by involvement in large-scale projects compared with single-top or ordinary papers. This pattern indicates that mid-career researchers lack both intimate relations and resources to keep big projects, leading to "mid-career pitfall" of consecutive success. This insight holds profound implications for the development of policies and initiatives aimed at bolstering innovative research and discovery. [ABSTRACT FROM AUTHOR]

Anger suppression is important in our daily life, as its failure can sometimes lead to the breaking down of relationships in families. Thus, effective strategies to suppress or neutralise anger have been examined. This study shows that physical disposal of a piece of paper containing one's written thoughts on the cause of a provocative event neutralises anger, while holding the paper did not. In this study, participants wrote brief opinions about social problems and received a handwritten, insulting comment consisting of low evaluations about their composition from a confederate. Then, the participants wrote the cause and their thoughts about the provocative event. Half of the participants (disposal group) disposed of the paper in the trash can (Experiment 1) or in the shredder (Experiment 2), while the other half (retention group) kept it in a file on the desk. All the participants showed an increased subjective rating of anger after receiving the insulting feedback. However, the subjective anger for the disposal group decreased as low as the baseline period, while that of the retention group was still higher than that in the baseline period in both experiments. We propose this method as a powerful and simple way to eliminate anger. [ABSTRACT FROM AUTHOR]

For shaped charge blasting projects in mining, civil engineering, and similar fields, it is proposed to modify the charge structure by combining slotted tubes and shaped charge liners to obtain a new type of charge structure. This aims to achieve directional rock breaking through the focused action of the shaped charge. The influence of different slotted pipe materials on the directional rock-breaking effect of concentrated energy using a new charge structure is explored through theoretical analysis combined with model test study, high-speed camera, stress–strain gauge, and other equipment. A comparison is made between slotted pipes made of aluminum, kraft paper, and PVC, with the cutting width of 2 mm. Based on the characteristics of the cracks formed after blasting, the new charge structure made of aluminum slotted pipe produces a penetrating crack that is almost consistent with the pre-cracking direction. Based on the corresponding characteristics of successively released blasting energy, the guiding and convergence effect of the new charge structure made of aluminum slotted pipe on the explosion energy is greater than that of the new charge structure made of the other two types of slotted pipe material. According to the strain data measured after blasting, the peak arrival time of the strain peak in the direction of the slotted pipe on one side of the shaped hood is shorter than that in the other two directions, and the peak strain is greater than that in the other two directions while having a better energy gathering effect. Based on the findings, the new charge structure with directional energy concentration has a damage reduction effect. Furthermore, the material of aluminum slotted pipe is found to be better than PVC slotted pipe, whereas the material of PVC slotted pipe is better than kraft paper slotted pipe in achieving directional rock breaking. [ABSTRACT FROM AUTHOR]

Metagenomics has revolutionized access to genomic information of microorganisms inhabiting the gut of herbivorous animals, circumventing the need for their isolation and cultivation. Exploring these microorganisms for novel hydrolytic enzymes becomes unattainable without utilizing metagenome sequencing. In this study, we harnessed a suite of bioinformatic analyses to discover a novel cellulase-degrading enzyme from the camel rumen metagenome. Among the protein-coding sequences containing cellulase-encoding domains, we identified and subsequently cloned and purified a promising candidate cellulase enzyme, Celcm05-2, to a state of homogeneity. The enzyme belonged to GH5 subfamily 4 and exhibited robust enzymatic activity under acidic pH conditions. It maintained hydrolytic activity under various environmental conditions, including the presence of metal ions, non-ionic surfactant Triton X-100, organic solvents, and varying temperatures. With an optimal temperature of 40 °C, Celcm05-2 showcased remarkable efficiency when deployed on crystalline cellulose (> 3.6 IU/mL), specifically Avicel, thereby positioning it as an attractive candidate for a myriad of biotechnological applications spanning biofuel production, paper and pulp processing, and textile manufacturing. Efficient biodegradation of waste paper pulp residues and the evidence of biopolishing suggested that Celcm05-2 can be used in the bioprocessing of cellulosic craft fabrics in the textile industry. Our findings suggest that the camel rumen microbiome can be mined for novel cellulase enzymes that can find potential applications across diverse biotechnological processes. [ABSTRACT FROM AUTHOR]

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary. This paper proposes three improvement strategies for this type of "information leakage" problem: sliding window decomposition (SW-EMD), single training and multiple decomposition (STMP-EMD), and multiple training and multiple decomposition (MTMP-EMD). They are combined with a bidirectional multiscale temporal convolutional network (MSBTCN), bidirectional long- and short-term memory network (BiLSTM), and attention mechanism (DMAttention), which introduces a dependency matrix based on cosine similarity to be applied to water quality prediction. The experimental results show that the model achieves good performance in the prediction of three water quality indicators (pH, DO and KMnO4), and the accuracies of the three models proposed in this paper are improved by 1.958% and 0.853% in terms of the RMSE and MAPE, respectively, compared with those of the mainstream LSTM models. The key contributions of this study include the following: (1) three methods are proposed to improve the class EMD decomposition, which can effectively solve the problem of "information leakage" that exists in the current models via class EMD decomposition; (2) the CEEMDAN-MSBTCN-BiLSTM-DMAttention model structure is innovated by combining improved class EMD decomposition methods; and (3) the three improved decomposition methods proposed in this paper can effectively solve the problem of "information leakage" and optimize the prediction model at the same time. This study provides an effective experimental method for water quality prediction and can effectively address the problem of "overfitting" models via class EMD decompositions during model training and testing. [ABSTRACT FROM AUTHOR]

To achieve integrated resource utilization of graphite tailings to improve their water-holding capacity, river silt and cow dung powder were added to graphite tailings as organic matter improvers. Improver ratios were designed using 4 g cow dung powder and 20, 30, and 50 g river silt. Soil–water characterization tests were performed using a combined tensiometer and filter paper method based on optimum density measurements. Analysis of the influence of river silt dosing on the soil–water characteristic curves of improved graphite tailing specimens was performed with data fitting using the Van Genuchten model. Here, we investigated the effect of river silt dosing on the internal pore structure and water-holding capacity of modified graphite tailing samples and verified the applicability of the model to graphite tailings. Our results demonstrate that the organic matter improver incorporated into graphite tailings can improve the internal structural compactness of graphite tailings, improving the water holding capacity. With an increase in river silt dosage, the saturated water content is larger, and the residual water content increases and then decreases. When river silt dosage is 30 g, the residual water content is the highest at a value of 3.32%. The van Genuchten model was highly accurate for assessing the graphite tailings. With an increase in river silt doping, the internal pore space first decreased and then increased, and the internal structure gradually became compact and loosened. The internal structure was in the optimal state in the experimental study when the dosage of cow dung powder was 4 g and the dosage of river silt was 30 g. The water holding capacity was optimal at this time. The results of this study provide a theoretical foundation for graphite-tailing-based mine reclamation and play a guiding role in exploring the value of the hydraulic characteristic index parameters when applying graphite tailings engineering. [ABSTRACT FROM AUTHOR]

In many animals, early-life decisions influence long-term fitness. Mantidflies are spider egg predators; their tiny larvae climb aboard spiders to find eggs, but little is known about how they find spiders. We tested the hypothesis that mantidfly larvae (Dicromantispa sayi) detect and respond to substrate-borne spider cues (e.g., silk and/or excreta). We presented larvae with filter paper exposed to different types of spiders (adult female, adult male, or juvenile Habronattus trimaculatus jumping spiders) versus a no-cue control. Larvae spent more time on filter paper with spider cues. We then tested the hypothesis that mantidflies make finer distinctions between spiders when given direct choices between these cues. Larvae did not discriminate between sexes but spent more time (and exhibited more phoretic behavior) on filter paper with female or male cues compared with juvenile cues. While this suggests that mantidflies actively seek out adult spiders, we also found that adult spiders were more active than juveniles and may have simply deposited more silk and excreta, providing a stronger cue to detect. We discuss these findings in the context of the risks and benefits of different spider hosts, and how early-life spider-searching strategies may shape a mantidfly's long-term fitness. [ABSTRACT FROM AUTHOR]

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources. [ABSTRACT FROM AUTHOR]

Historical paper documents are susceptible to complex degradation processes, including biodeterioration, which can progressively compromise their aesthetic and structural integrity. This study analyses seventeenth century handwritten historical letters stored at the Correr Museum Library in Venice, Italy, exhibiting pronounced signs of biodeterioration. The techniques used encompassed traditional colony isolation on agar plates and proteomics analyses, employing nanoscale liquid chromatography coupled with high-resolution mass spectrometry (nano-LC–MS). Fluorescence microscopy was used for the first time in the historical paper biodeterioration context to supplement the conventional stereoscopic, optical, and scanning electron microscopic imaging techniques. This method enables the visualisation of microorganisms beyond and beneath the paper's surface through their natural intrinsic autofluorescence in a non-invasive and non-destructive way. The results demonstrate a diverse, complex, and abundant microbiota composed of coexisting fungal and bacterial species (Ascomycota, Mucoromycota, Basidiomycota, Proteobacteria, and Actinobacteria), along with mite carcasses, insects, parasites, and possibly protists. Furthermore, this study reveals certain species that were not previously documented in the biodeterioration of historical paper, including human pathogens, such as Histoplasma capsulatum, Brucella, Candida albicans, and species of Aspergillus (A. flavus, A. fumigatus, A. oryzae, A. terreus, A. niger) known to cause infections or produce mycotoxins, posing substantial risk to both artefacts and humans. [ABSTRACT FROM AUTHOR]

The agricultural development in China is currently grappling with the challenges of limited water and soil resources, environmental pollution, and ecosystem degradation. Therefore, improving agricultural green productivity has become the focus of academic attention. Based on the panel data of 30 provinces in China from 2008 to 2020, this paper constructs an index system of agricultural productivity. Subsequently, the super-efficiency SBM model is used to calculate the green productivity of agriculture, and its characteristics are analyzed from the perspectives of temporal and spatial.Through the empirical test of threshold model, it is found that China's agricultural green productivity shows a gradual growth trend from 2008 to 2020, and the growth rate has accelerated significantly in recent years. There is a non-linear relationship between energy structure and agricultural green productivity. Especially in areas with reasonable energy structure, the increase in the proportion of coal consumption will inhibit the improvement of green agricultural productivity. This paper suggests reducing dependence on fossil energy, improving market mechanism and optimizing resource utilization in order to promote green agricultural production in China. This study expands the research perspective of agricultural green productivity, provides new research methods and ideas for related fields, and provides a reasonable energy allocation plan for agricultural production, which helps to improve production efficiency and reduce costs. [ABSTRACT FROM AUTHOR]

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement. [ABSTRACT FROM AUTHOR]

Reflecting sex and gender characteristics in biomedical research is critical to improving health outcomes and reducing adverse effects from medical treatments. This study investigates the impact of sex/gender-specific funding policies and journal editorial standards on the integration of sex/gender analysis in biomedical research publications. Using data from the United States, Canada, the United Kingdom, and other countries between 2000 and 2021, we assessed how these policies influenced research output in the fields of medicine and life sciences. Our findings show that countries with progressive funding policies and journals promoting sex/gender-based reporting have significantly improved research quality and publication rates. This highlights the importance of coordinated policy efforts and editorial practices in advancing integrated sex/gender research. We recommend continued global efforts from policymakers, funding bodies, and journals to embed sex/gender perspectives in scientific inquiry, ensuring more effective and equitable biomedical advancements. [ABSTRACT FROM AUTHOR]

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature. [ABSTRACT FROM AUTHOR]

Identifying the ash accumulation generated on the water-cooled walls of the waste incinerator is essential for the cleanup by the robotic arm. This paper improves a new algorithm based on YOLOv8n, which can identify the ash accumulation position on the water-cooled wall quickly and accurately. Firstly, the multi-scale fusion image enhancement algorithm is used to improve the sharpness and contrast of the image and enrich the details of the image. Secondly, the backbone feature extraction network of YOLOv8n is replaced by Mobilenetv3 network, which reduces the parameters in the model greatly. Finally, this paper improves a new attention mechanism AWGAM (Add Weight Global Attention Mechanism) based on GAM (Global Attention Mechanism), which can better integrate the feature information between different dimensions and improve the learning ability of the model. AWGAM is added to the backbone of the model. The experimental results show that compared with the original YOLOv8n model, the improved YOLOv8n model has 59.9% fewer parameters, 4.4% higher precision, 8.8% higher recall, 3.2% higher mAP50 (mean Average Precision) and 8.8% higher mAP50-95. This model has made remarkable progress on the basis of the original algorithm, and has strong competitiveness compared with other advanced target detection models. The lightweight and high accuracy of ash accumulation detection offered by the proposed model presents promising applications in ash accumulation detection tasks of water-cooled walls. [ABSTRACT FROM AUTHOR]

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explore whether and how urban road transport (URT) development affects haze pollution. One of the innovations of this paper is that URT development is measured by road accessibility with novel digital elevation model datasets, which have been used by few scholars. The endogenous problem caused by revere causality issue in the relationship between URT development and haze pollution is also considered. Based on the panel data of prefecture-level cities of Yangtze River Delta (YRD) region in China from 2011 to 2018, this paper uses long-lagged values of URT development as the instrumental variable, employing the two-stage least squares (2SLS) method. The study shows that URT development leads to an increase of haze pollution. Moreover, mechanism tests based on moderating and mediating models support the finding that decreasing haze pollution resulted from better connection effects, while rising agglomeration effects tend to bring about increasing haze pollution, and the latter effect is larger in magnitude than the former. Current URT development may have long-term negative consequences for livability of YRD cities, and urban decision makers should reconsider the effectiveness of the current road transport investment and construction. [ABSTRACT FROM AUTHOR]

Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction. [ABSTRACT FROM AUTHOR]

Energy storage devices (ESD) which are intended to power electronic devices, used in close contact of human skin, are desirable to be safe and non-toxic. In light of this requirement, Zn based energy storage devices seem to provide a viable pathway as they mostly employ aqueous based electrolytes which are safe and non-toxic in their functioning. Additionally, having a flexible ESD will play a crucial role as it will enable the ESD to conform to the varying shapes and sizes of wearable electronics which they energize. In this work, we have developed an inkjet-printed Zinc ion battery (IPZIB) with planar electrode configuration over bond paper substrate. Zn has been used as the negative electrode, MnO2 is used as the positive electrode with Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as the active binder. Conducting tracks of reduced graphene oxide (rGO) are used to construct the current collector on the paper substrate. The fabricated IPZIB delivered a high discharge capacity of 300.14 mAh g−1 at a current density of 200 mA g−1. The energy density of the IPZIB is observed as 330.15 Wh kg−1 at a power density of 220 W kg−1 and retains an energy density of 94.36 Wh kg−1 at a high power density of 1650 W kg−1. Finally, we have demonstrated the capability of the IPZIB to power a LED at various bending and folding conditions which indicates its potential to be used in the next generation flexible and wearable electronic devices. [ABSTRACT FROM AUTHOR]

Advanced recycling offers a unique opportunity for the circular economy, especially for mixed and contaminated plastics that are difficult to recycle mechanically. However, advanced recycling has barriers such as poor selectivity, contaminant sensitivity, and the need for expensive catalysts. Reported herein is a simple yet scalable methodology for converting mixed polyethylene (high-density and low-density polyethylene recycled polyethylene) into upcycled waxes with up to 94% yield. This high yield was possible by performing the reaction at a mild temperature and was enabled by using inexpensive and reusable table salt. Without table salt, in otherwise identical conditions, the plastic remained essentially undegraded. These upcycled waxes were used as prototypes for applications such as water- and oil-resistant paper, as well as rheology modifiers for plastics. Their performance is similar to that of commercial wax as well as rheology modifiers. A preliminary economic analysis shows that the upcycled waxes obtained by this table salt-catalyzed approach offer three times more revenue than those reported in the literature. This pioneering discovery opens the door for a circular economy of plastics in general and polyolefins in particular. [ABSTRACT FROM AUTHOR]

Pharmacists are expected to demonstrate their expertise in clinical practice and conduct research activities to generate new evidence. However, the factors promoting research activities among pharmacists remain unclear. Therefore, we investigated the research activities of Japanese pharmacists through a questionnaire survey and examined the factors contributing to the promotion of research activities. A web-based questionnaire using Google Forms was disseminated across pharmacists working in community pharmacies, drugstores, hospitals, and clinics. The questionnaire included respondents' backgrounds, research activities, and research environments. Logistic regression analysis was used to examine the factors promoting pharmacists' research activities, with experience in research paper acceptance as the objective variable. In total, 401 responses were included in the analysis. Of the respondents, 54.1% were hospital pharmacists, and 77.1% were pharmacists with > 5 years of pharmacist experience. Furthermore, 50.4% of the pharmacists had presented at conferences, and 22.2% had experience in research paper acceptance. The influential factors were "master's degree or higher," "number of affiliated academic societies," "acquisition of specialists/certified pharmacists," and "daily availability of a consultant for writing research papers." This study revealed the factors contributing to the promotion of research activities among pharmacists. We believe that our findings will help promote research among pharmacists. [ABSTRACT FROM AUTHOR]

As the social organism, Polistes wasps build a communal nest using woody fibers with saliva for sustaining brood and adult population throughout the season. Limited information exists regarding the identification specific plant materials employed in wasp nest building. Thus, we firstly tested if the DNA metabarcoding approach utilizing rbcL and trnL molecular markers could identify the plant species quantitatively and qualitatively inform the mixed-origin woody samples. A threshold of 0.01 proportion of reads was applied for rbcL and trnL molecular markers, while this threshold for median proportion was 0.0025. In assessing taxa richness, the median proportion demonstrated superior performance, exhibiting higher taxa detection power, however, rbcL marker outperformed in quantitative analysis. Subsequently, we applied DNA metabarcoding to identify the plant materials from the nests of two Polistes species, P. mandarinus and P. rothneyi. The results showed that higher preference of Quercus and Robinia as the major nest building materials regardless of the surrounding plant communities, by two wasp species. Material diversity was higher for P. rothneyi than P. mandarinus, which may explain the abundance of this species possibly with heightened adaptive capacities in their nesting behavior. This study demonstrated that DNA metabarcoding could identify the complex nest-building plant materials of paper wasps and provide insights into their ecological interactions in the natural ecosystem. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is crucial for military reconnaissance and remote sensing, but image quality can be affected by various factors, impacting target detection performance. Thus, pre-evaluation of SAR image quality is essential to filter out poor-quality images, optimize resource allocation, and enhance detection accuracy and efficiency. This paper proposes a comprehensive SAR image quality evaluation method combining objective and subjective approaches. Specifically, the processes encompassing the generation of a series of disturbed SAR images on the SAR ship detection dataset (SSDD), the calculation of various objective quality indicators for those images, and the assignment of a subjective quality label to each image through subjective evaluation. Based on the dataset constructed by the above evaluation methods, the IHHO-XGBoost model was developed. This model uses an improved harris hawk optimization (IHHO) algorithm to optimize extreme gradient boosting (XGBoost) hyperparameters. The IHHO algorithm effectively alleviates the problem of getting trapped in local optima by improving the escape energy calculation strategy and integrating the average difference evolution mechanism while maintaining the diversity of the population, showing significant advantages over the traditional HHO algorithm. Comparative experiments demonstrate the model's superiority in SAR image quality evaluation. This study validates the scientificity and practicability of the proposed method, offering new tools for SAR image quality research. [ABSTRACT FROM AUTHOR]

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields. [ABSTRACT FROM AUTHOR]

To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. Initially, the model decomposes raw stock data using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique and reconstructs the components by estimating their frequencies via the extreme point method. This process enhances component stability and mitigates noise interference. Subsequently, an Encoder-Decoder framework equipped with an attention mechanism is employed for precise prediction of the reconstructed components, facilitating more effective extraction and utilization of data features. Furthermore, this paper utilizes an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the model parameters. On the Pudong Bank dataset, through ablation experiments and comparisons with baseline models, various optimization strategies incorporated into the proposed CED-PSO-StockNet model were effectively validated. Compared to the standalone LSTM model, CED-PSO-StockNet achieved a remarkable 45.59% improvement in the R2 metric. To further assess the model's generalization capability, this paper also conducted comparative experiments on the Ping An Bank dataset, and the results underscored the significant advantages of CED-PSO-StockNet in the domain of stock prediction. [ABSTRACT FROM AUTHOR]

A growing concern is that as local newspapers disappear, communities lose trusted gatekeepers and develop information voids, creating openings for misinformation to thrive. Previous work has not evaluated whether residents of news deserts have developed different information acquisition habits. We fill this gap by directly comparing information consumption and referral patterns inside and outside of news deserts in a novel dataset of engagement with online media by millions of users on the Edge browser. We find little evidence that those in news deserts consume more low-quality sites or are more likely to be referred to low-quality sites from search engines or social media. We find some evidence that those in news deserts do consume more national news than locations with local media outlets. These results contribute to our understanding of how the loss of local newspapers has impacted online information acquisition. [ABSTRACT FROM AUTHOR]

As research into wave energy converters progresses and new developers enter the field, there arises a growing requirement for a standardized modelling approach. This article presents a novel design and dynamic emulation for a hybrid solar-wind-wave energy converter (SWWEC) which is the combination of three very well-known renewable energies: solar, wind and wave energy. Photovoltaic (PV) panels and vertical axis wind turbine (VAWT) are installed on top of the floating WEC that harness the energies from the sun and wind respectively. The SWWEC is designed with a point absorber capture system. An electrical motor is used to dynamically emulate the performance of the SWWEC under real world conditions to drive the DC generator. The present paper shows the importance and necessity of the required control schemes for the proper control of generator side converters which is present in the offshore marine substation and the most required grid connected onshore converters. The better switching signal generation for the converter control and generated harmonics elimination techniques are also presented in the paper. Outcomes of the present study are discussed and verified. [ABSTRACT FROM AUTHOR]

To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. Initially, the model decomposes raw stock data using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique and reconstructs the components by estimating their frequencies via the extreme point method. This process enhances component stability and mitigates noise interference. Subsequently, an Encoder-Decoder framework equipped with an attention mechanism is employed for precise prediction of the reconstructed components, facilitating more effective extraction and utilization of data features. Furthermore, this paper utilizes an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the model parameters. On the Pudong Bank dataset, through ablation experiments and comparisons with baseline models, various optimization strategies incorporated into the proposed CED-PSO-StockNet model were effectively validated. Compared to the standalone LSTM model, CED-PSO-StockNet achieved a remarkable 45.59% improvement in the R2 metric. To further assess the model's generalization capability, this paper also conducted comparative experiments on the Ping An Bank dataset, and the results underscored the significant advantages of CED-PSO-StockNet in the domain of stock prediction. [ABSTRACT FROM AUTHOR]

In multiple physical fields, the mutual influence among these fields can significantly impact material elastoplasticity. This paper proposes a thermodynamic-based constitutive model that incorporates the mutual influence of multiple physical fields. Rather than treating physical field characteristics as adjustable "parameters" affecting material coefficients, the proposed model employs a thermodynamic dissipation potential derived from the Onsager reciprocity relations, accounting for thermodynamic forces coupling. This dissipation potential ensures that the thermodynamic flow in the stress field is influenced by both stress field and other physical fields thermodynamic forces, which describes the plastic flow under multiple physical fields, while preserving thermodynamic duality. The paper begins with the formulation of a generalized thermodynamic model applicable to diverse materials and types of coupled fields, which is then degraded to a specific model for AA5182-O AlMg alloy under the influence of temperature and strain rate fields coupling. Given the universal applicability of the generalized model, such degradation provides a structured approach framework for developing thermodynamics-based constitutive models. For different materials encountered in practical engineering, new thermodynamic forces can be introduced to describe their unique mechanical properties while preserving the overarching thermodynamics-based model framework, thereby facilitating model scalability. The paper concludes with a validation example, showing that within the Portevin-Le Chatelie (PLC) regime, the plastic flow stress of AA5182-O AlMg alloy decreases with increasing strain rate at low temperatures but increases at high temperatures. The accurate simulation of these distinct strain rate effects crucially relies on integrating the mutual influence of temperature field and strain rate field. [ABSTRACT FROM AUTHOR]

Based on the perspective of green technology innovation, this paper takes listed companies in China's manufacturing industry from 2012 to 2022 as the research sample, and adopts the Driscoll-Kraay standard error method to explore the relationship between intelligent manufacturing and the high-quality development of the manufacturing industry. The results show that intelligent manufacturing can promote high-quality development of manufacturing industry Green technology innovation plays an mediating role in the above relationship. The above findings still hold after considering robustness tests and endogeneity treatments. Heterogeneity analysis indicates the application of intelligent manufacturing in state-owned enterprises, CEO separately enterprises and heavily polluting industries has a stronger role in promoting the development of manufacturing industry. This paper complements the existing research results on intelligent manufacturing and also establishes a realistic path for the high-quality development of China's manufacturing industry. [ABSTRACT FROM AUTHOR]

During the oilfield development process, various factors can cause different types of reservoir damage, leading to reduced oil well production or even shutdown, and decreased water injection capacity in water wells, resulting in significant economic losses for the oilfield. However, formation damage control measures must be based on the quantitative diagnosis of the types and degrees of reservoir damage. This paper establishes a spatiotemporal evolution numerical model for 12 common types of damage during the oil and gas exploration and development process, based on the material balance theory and Fick's diffusion law in reservoir damage processes. This model achieves numerical simulation of the degree of various reservoir damage types in different spatiotemporal domains. The overall damage degree of a specific well's evolution with time and space is further simulated in simple superposition way. The sequential core flow experiment was carried out in the laboratory, and then compared with the calculation results. The accuracy is above 90%. Finally, using field test data, the simulation results show a 95% or higher degree of agreement with the actual field measurements, proving that the reservoir damage spatiotemporal evolution quantitative simulation technology established in this paper has high accuracy and practicality. [ABSTRACT FROM AUTHOR]

In federated learning, secret sharing is a key technology to maintain the privacy of participants' local models. Moreover, with the rapid development of quantum computers, existing federated learning privacy protection schemes based on secret sharing will no longer be able to guarantee the data security of participants in the post-quantum era. In addition, existing privacy protection methods have the problem of high communication and computational overhead. Although the multi-stage secret sharing scheme proposed by Pilaram et al. is one of the effective solutions to the above problems, existing studies have proven the privacy leakage risk of this scheme. This paper firstly designs a new lattice-based multi-stage secret sharing scheme Improved-Pilaram to solve the security problem, which allows participants to use public vectors to reconstruct different secret values without changing the secret sharing. Based on Improved-Pilaram, this article proposes a post-quantum secure federated learning scheme PQSF. PQSF uses double masking technology to encrypt model parameters and achieves mask reconstruction through secret sharing. Since Improved-Pilaram is multi-stage, participants do not need to update their local secret shares frequently during training. Analysis and experimental results show that the PQSF proposed in this paper reduces the communication complexity between participants and reduces the computational overhead by about 20% compared with existing solutions. [ABSTRACT FROM AUTHOR]

To address the electromechanical coupling and multi-source disturbance problems of the seeker stabilized platform, this paper constructs an electromechanical coupling model of the seeker stabilized platform based on the Lagrange-Maxwell equation. To mitigate the influence of electromechanical coupling on the control performance of the seeker, a super-twisting controller based on a fractional-order terminal sliding mode surface (FOSTSMC) is proposed. Additionally, to handle various disturbances in the system, this paper introduces a method that combines the extended state observer (ESO) with the proposed controller to enhance the system's stability and anti-disturbance performance. The Lyapunov function is designed to prove that the proposed controller can reach a convergence state within finite time. Finally, the proposed control method is compared with PID control, fuzzy PID control, linear sliding mode control, and super spiral control combined with a disturbance observer (DOB). Multiple simulation experiments demonstrate that, under the influence of electromechanical coupling and multi-source disturbance, the FOSTSMC-ESO significantly improves the stability and anti-disturbance performance of the seeker stabilization platform. [ABSTRACT FROM AUTHOR]

China's manufacturing industry needs to change from a high-emission model to one that is more environmentally friendly to reduce air pollution and achieve sustainable growth. Therefore, it is important to decrease manufacturing waste gas emissions by improving green productivity. This paper expands on the theories of green economic growth and sustainable development to present a framework for analysis of manufacturing green productivity at various levels of air pollution. It applies the Metafrontier Malmquist Luenberger index and SBM model to data from 30 Chinese manufacturing sectors from 2012 to 2022 to identify the obstacles to developing green productivity in the manufacturing sectors. The results show that: (1) From 2012 to 2022, the overall waste gas emission efficiency and GTFP of China's manufacturing industry showed an increasing trend. (2) The manufacturing industry with light air pollution had the fastest growth in GTFP, followed by manufacturing with heavy air pollution, and manufacturing with moderate air pollution had the lowest. (3) The primary driver of GTFP growth is technological progress, while technical inefficiency has severely hindered GTFP growth. (4) There is a "Matthew effect" in the green productivity of China's manufacturing industry. High waste gas emission efficiency often accompanies high levels of GTFP, and vice versa. Finally, this paper proposes policy suggestions to improve the green productivity of the manufacturing industry and reduce air pollution. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods. [ABSTRACT FROM AUTHOR]

Comparing with cities, rural communities especially those declining rural communities have become vulnerable to natural disasters owing to their backward socioeconomic conditions. Taking Xun County of China's Henan Province as the study area, the paper aims to evaluate rural community resilience to flood by unveiling the connection between individuals' cognition, follow-up actions and the community resilience. Research results show that: (1) The logic chain exists as individual's cognition to disaster leads to their constructive actions to cope with disaster, which contribute to community resilience. (2) At the cognition dimension, individual's knowledge reserve of disaster prevention and their recognition to local authority are playing an important role in their decision making and follow-up behaviors when disaster occurs. (3) At the action dimension, individual's familiarity with the disaster preparedness, efficient information transmission when disaster occurs and villagers' following order and their unity of action all contribute to community resilience to disaster. The paper proposes ways to improve rural community resilience to disasters based on the research findings. [ABSTRACT FROM AUTHOR]

With the continuous updating and progress of medical equipment, the overdue medical device has problems such as management difficulties, resource waste, and potential security risks. Therefore, this paper used the Kohonen network algorithm to quantitatively evaluate and analyze the surplus value of overdue medical devices. In this paper, the Kohonen network algorithm was used to build a quantitative model of the surplus value of the overdue medical device, and the self-organization characteristics and data-driven learning ability of the Kohonen network were used to predict the surplus value of the equipment more accurately. Support vector machine was used to quantitatively evaluate and predict the surplus value of overdue medical devices, and further optimize the model performance, to provide more accurate and reliable decision support for medical equipment management. The Kohonen network algorithm used in this paper evaluated the correlation between the service life and maintenance cost of eight types of overdue medical devices and quantitatively predicted the surplus value of overdue medical devices with the random forest algorithm. According to the comparison of prediction bias, the maximum deviation between the expected surplus value and the actual surplus value is only 1, and the deviation value by the random forest algorithm is as low as 6, the Kohonen network algorithm in this paper has better prediction performance than the random forest algorithm. In the experiment of comparative analysis and verification by introducing the decision tree algorithm, the average error rate of the Kohonen network algorithm in this paper was only 20.57%, which was far lower than 46.34% of the random forest algorithm and 65.31% of decision tree algorithm. The Kohonen network algorithm used in this paper can effectively quantitatively evaluate and predict the surplus value of overdue medical devices, thus improving the efficiency of medical equipment management, reducing costs, and ensuring patient safety. [ABSTRACT FROM AUTHOR]

Collective behaviors of social insects are often regulated by pheromones. In subterranean termites, some workers forage for and exploit decaying wood for new food resources while forming tunnels from their nest. Colonizing new food resources requires workers to build and disinfect tunnels and chambers inside the nest and ingest decaying wood; therefore subterranean termite colonies should have mechanisms to establish and maintain groups of workers to perform these functions. Recently, an aggregation pheromone was identified in workers of the termite Reticulitermes speratus, which induces quick attraction of nestmate workers and prolonged aggregation to the site of attraction. In this study, we extended this work to another species of Reticulitermes and identified a worker aggregation pheromone in the termite R. virginicus. GC–MS analysis and bioassays demonstrated that this pheromone consists of 3-octanone, 3-octanol, and palmitic acid and shows a colony-specific, dose-dependent attractant response but not an arrestant response. Furthermore, these pheromone components were most likely emitted from the surface of the body. This suggests that aggregation pheromone composition and function differ significantly among termite species, even within the same genus. This study advances our understanding of the regulatory mechanisms of termite aggregation behavior. [ABSTRACT FROM AUTHOR]

As research into wave energy converters progresses and new developers enter the field, there arises a growing requirement for a standardized modelling approach. This article presents a novel design and dynamic emulation for a hybrid solar-wind-wave energy converter (SWWEC) which is the combination of three very well-known renewable energies: solar, wind and wave energy. Photovoltaic (PV) panels and vertical axis wind turbine (VAWT) are installed on top of the floating WEC that harness the energies from the sun and wind respectively. The SWWEC is designed with a point absorber capture system. An electrical motor is used to dynamically emulate the performance of the SWWEC under real world conditions to drive the DC generator. The present paper shows the importance and necessity of the required control schemes for the proper control of generator side converters which is present in the offshore marine substation and the most required grid connected onshore converters. The better switching signal generation for the converter control and generated harmonics elimination techniques are also presented in the paper. Outcomes of the present study are discussed and verified. [ABSTRACT FROM AUTHOR]

The existing deep estimation networks often overlook the issue of computational efficiency while pursuing high accuracy. This paper proposes a lightweight self-supervised network that combines convolutional neural networks (CNN) and Transformers as the feature extraction and encoding layers for images, enabling the network to capture both local geometric and global semantic features for depth estimation. First, depth-separable convolution is used to construct a dilated convolution residual module based on a shallow network to improve the shallow CNN feature extraction receptive field. In the transformer, a multidepth separable convolution head transposed attention module is proposed to reduce the computational burden of spatial self-attention. In the feedforward network, a two-step gating mechanism is proposed to improve the nonlinear representation ability of the feedforward network. Finally, the CNN and transformer are integrated to implement a depth estimation network with a local-global context interaction function. Compared with other lightweight models, this model has fewer model parameters and higher estimation accuracy. It also has better generalizability for different outdoor datasets. Additionally, the inference speed can reach 87 FPS, achieving better real-time performance and accounting for both inference speed and estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles. Simulation experiments are conducted using Matlab to demonstrate the effectiveness of the improved algorithm. Moreover, the performance of the hybrid algorithm is compared with five mainstream algorithms in a real simulated port environment, and the final results show the significant enhancement of this paper’s algorithm in several key performance metrics. Thus, this study provides a feasible strategy for improved path planning efficiency for AGV in the port environment. [ABSTRACT FROM AUTHOR]

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet's robust feature extraction capabilities with ConvNeXt's advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study's methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models. [ABSTRACT FROM AUTHOR]

To address the problem of dense crowd face detection in complex environments, this paper proposes a face detection model named Deep and Compact Face Detection (DCFD), which adopts an improved lightweight EfficientNetV2 network to replace the backbone network of RetinaFace. A large kernel attention mechanism is introduced to address the face detection task more accurately. The backbone network, an improved efficient channel attention (ECA) mechanism, is added to further improve the algorithm performance. The feature fusion module is an improved neural architecture search feature pyramid network (NAS-FPN) that significantly improves the face detection accuracy in different scenes. To balance the training process of positive and negative samples, we use the focus loss function to replace the traditional cross-entropy loss function. In different environments, the DCFD algorithm has shown efficient face detection performance. This algorithm provides not only a feasible and effective solution for solving the problem of face detection in dense groups but also an important basis for improving the accuracy of face detection models in practical applications. [ABSTRACT FROM AUTHOR]

With the wave of artificial intelligence sweeping the world in recent years, UAVs is widely used in various fields. UAV path planning has attracted much attention from scientists as an essential part of UAV work. In order to design an efficient and reasonable 3D UAV path planning program, recent researchers have invented and improved many algorithms. This paper proposes an elite RIME algorithm for 3D UAV path planning. First, we propose an elite reverse learning population selection strategy based on piecewise mapping to enhance the population diversity of the algorithm for better exploration. Second, this paper proposes a stochastic factor-controlled elite pool exploration strategy so that the algorithm is difficult to enter the local optimum and can better explore the global optimum. Then, this paper proposes a hard frost puncture exploitation strategy based on the sine–cosine function so that the algorithm can find the global optimum faster during the exploitation process. Meanwhile, in order to test the performance of the algorithm proposed in this paper, we compare it with 13 other intelligent optimization algorithms that are classical and popular nowadays on 52 test functions in three test sets, CEC2017, CEC2020, and CEC2022, and obtain competitive results. Finally, we applied it to the 3D UAV path planning problem in three different terrain scenarios, and the ELRIME algorithm achieved good results in all of them. Especially in the 7-peak model, the ELRIME algorithm improves the performance of the RIME algorithm by a factor of two. In the 9-peak model, the average value aspect also reduce the cost by 91 compared to the RIME algorithm, and more importantly, it has the smallest fluctuation in 30 runs, which is among the most stable of all the compared algorithms. In the 12-peak model, its stability is also significantly enhanced, and in terms of worst-case cost, it improves the cost by 340 compared to RIME. [ABSTRACT FROM AUTHOR]