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In a bid to develop paper bricks as alternative masonry units, unmodified banana fibers (UMBF) and alkaline (1 Molar aqueous sodium hydroxide) modified banana fibers (AMBF), fine sand, and ordinary Portland cement were blended with waste paper pulp. The fibers were introduced in varying proportions of 0, 0.5, 1.0 1.5, 2.0, and 2.5 wt% (by weight of the pulp) and curing was done for 28 and 56 days. Properties such as water and moisture absorption, compressive, flexural, and splitting tensile strengths, thermal conductivity, and specific heat capacity were appraised. The outcome of the examinations carried out revealed that water absorption rose with fiber loading while AMBF reinforced samples absorbed lesser water volume than UMBF reinforced samples; a feat occasioned by alkaline treatment of banana fiber. Moisture absorption increased with paper bricks doped with UMBF, while in the case of AMBF-paper bricks, property value was noted to depreciate with increment in AMBF proportion. Fiber loading resulted in improvement of compressive, flexural, and splitting tensile strengths and it was noted that AMBF reinforced samples performed better. The result of the thermal test showed that incorporation of UMBF led to depreciation in thermal conductivity while AMBF infusion in the bricks initiated increment in value. Opposite behaviour was observed for specific heat capacity as UMBF enhanced heat capacity while AMBF led to depreciation. Experimental trend analysis carried out indicates that curing length and alkaline modification of fiber were effective in maximizing the properties of paperbricks for masonry construction. [ABSTRACT FROM AUTHOR]

The extensive usage and production of copper may lead to toxic effects in organisms due to its accumulation in the environment. Traditional methods for copper detection are time consuming and infeasible for field usage. It is necessary to discover a real-time, rapid and economical method for detecting copper to ensure human health and environmental safety. Here we developed a colorimetric paper strip method and optimized spectrum method for rapid detection of copper ion based on the specific copper chelator bathocuproinedisulfonic acid disodium salt (BCS). Both biological assays and chemical methods verified the specificity of BCS for copper. The optimized reaction conditions were 50 mM Tris–HCl pH 7.4, 200 µM BCS, 1 mM ascorbate and less than 50 µM copper. The detection limit of the copper paper strip test was 0.5 mg/L by direct visual observation and the detection time was less than 1 min. The detection results of grape, peach, apple, spinach and cabbage by the optimized spectrum method were 0.91 μg/g, 0.87 μg/g, 0.19 μg/g, 1.37 μg/g and 0.39 μg/g, respectively. The paper strip assays showed that the copper contents of grape, peach, apple, spinach and cabbage were 0.8 mg/L, 0.9 mg/L, 0.2 mg/L, 1.3 mg/L and 0.5 mg/L, respectively. These results correlated well with those determined by inductively coupled plasma-mass spectrometry (ICP-MS). The visual detection limit of the paper strip based on Cu-BCS-AgNPs was 0.06 mg/L. Our study demonstrates the potential for on-site, rapid and cost-effective copper monitoring of foods and the environment. [ABSTRACT FROM AUTHOR]

The continuous expansion of the construction scale of smart city has reconstructed the urban information pattern. How to maintain the stability of information security while giving full play to the role of information sharing is a practical problem that must be solved for the sustainable development of smart city. Based on the information ecology theory, this paper construct the smart city information security risk evaluation system from six aspects. Then, zGT2FSs is established based on type-2 fuzzy set theory and IAA method, which fully considers the internal and external uncertainty of expert decision-making. According to the calculation results, the key influencing factors of information security risk of smart city are analyzed to provide suggestions and guidance for the formulation of information security control in the process of smart city construction in China. [ABSTRACT FROM AUTHOR]

Unprecedented in the literature, levulinic acid (LA), one of the top value-added intermediates of chemical industry, is obtained from cigarette butts as cellulose feedstock by means of a one-pot hydrothermal process carried out at 200 °C for 2 h and catalysed by phosphoric acid. The protocol avoids the use of more aggressive and toxic H2SO4 and HCl, that are generally employed on several cellulose sources (e.g. sludge paper), thus minimizing corrosion phenomena of plants. Neither chemical pre-treatment of butts nor specific purification procedure of LA are required. Notably, by simply modifying acid catalyst (e.g. using CH3COOH), another top value-added fine chemical such as 5-hydroxymethylfuraldehyde (HMF) is obtained, thus widening the scope of the method. Being cigarette filters a waste available in quantities of megatonnes per year, they represent an unlimited at no cost source of cellulose, thus enabling the up-scale to an industrial level of LA production. [ABSTRACT FROM AUTHOR]

The nasal potential difference test (nPD) is an electrophysiological measurement which is altered in patients and animal models with cystic fibrosis (CF). Because protocols and outcomes vary substantially between laboratories, there are concerns over its validity and precision. We performed a systematic literature review (SR) of the nPD to answer the following review questions: A. Is the nasal potential difference similarly affected in CF patients and animal models?", and B. "Is the nPD in human patients and animal models of CF similarly affected by various changes in the experimental set-up?". The review protocol was preregistered on PROSPERO (CRD42021236047). We searched PubMed and Embase with comprehensive search strings. Two independent reviewers screened all references for inclusion and extracted all data. Included were studies about CF which described in vivo nPD measurements in separate CF and control groups. Risk of bias was assessed, and three meta-analyses were performed. We included 130 references describing nPD values for CF and control subjects, which confirmed substantial variation in the experimental design and nPD outcome between groups. The meta-analyses showed a clear difference in baseline nPD values between CF and control subjects, both in animals and in humans. However, baseline nPD values were, on average, lower in animal than in human studies. Reporting of experimental details was poor for both animal and human studies, and urgently needs to improve to ensure reproducibility of experiments within and between species. [ABSTRACT FROM AUTHOR]

In transportation, roads sometimes have cracks due to overloading and other reasons, which seriously affect driving safety, and it is crucial to identify and fill road cracks in time. Aiming at the defects of existing semantic segmentation models that have degraded the segmentation performance of road crack images and the standard convolution makes it challenging to capture the spatial and channel coupling relationship between pixels. It is difficult to differentiate crack pixels from background pixels in complex backgrounds; this paper proposes a semantic segmentation model for road cracks that combines channel-spatial convolution with the aggregation of frequency features. A new convolutional block is proposed to accurately identify cracked pixels by grouping spatial displacements and convolutional kernel weight dynamization while modeling pixel spatial relationships linked to channel features. To enhance the contrast of crack edges, a frequency domain feature aggregation module is proposed, which uses a simple windowing strategy to solve the problem of mismatch of frequency domain inputs and, at the same time, takes into account the effect of the frequency imaginary part on the features to model the deep frequency features effectively. Finally, a feature refinement module is designed to refine the semantic features to improve the segmentation accuracy. Many experiments have proved that the model proposed in this paper has better performance and more application potential than the current popular general model. [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]

This study introduces an applicable colorimetric sensor array for the detection of pesticides in the vapor phase. The array consisted of six metal nanoparticles spotted on the piece of filter paper. 3D-origami pattern was used for the fabrication of a paper-based sensor to decrease the effect of the nanoparticles leaching after exposure to analytes. Exposure to pesticide aerosols caused changes in the color of the array due to the aggregation of nanoparticles. These changes provided selective responses to thion pesticides such as malathion, parathion, chlorpyrifos, and diazinon. The sensing assay could also differentiate between aliphatic and aromatic thions and discriminate amine-containing compounds from the other studied analytes. These finding results are clearly confirmed by both visual detection and multivariate statistical methods. The proposed sensor was successfully developed for the quantitative measurement of pesticide aerosols at a very low concentration. The limit of detection of this method determined for malathion, parathion, chlorpyrifos and diazinon were 58.0, 103.0, 81.0 and 117.0, respectively. Moreover, the array could be employed to simultaneously analyze four studied pesticides. The statistcal results confirmed that the method has high performance for concurrent detection of thions as a major air pollutant without the interference of other species. [ABSTRACT FROM AUTHOR]

This paper proposes a visual management scheme of medical things with a color-change radio frequency identification (RFID) tag. The color-change RFID tag employs a specific RFID tag integrated circuit (IC) and a laminated pH-indicating paper. The IC has energy harvesting and switched ground functions, which enable it to generate electricity to the laminated pH-indicating paper. This phenomenon causes electrolysis of NaCl solution absorbed in the laminated pH-indicating paper. Electrolysis generates alkaline matter to change the color of the pH-indicating paper. This paper gives a new and sensitive structure of the laminated pH-indicating paper. The proposed advanced color-change RFID tag with new laminated pH-indicating paper succeeds in changing its color noticeably at a 1 m distance using an RFID reader radiating 1 W radio waves. The color change was observed 3–5 s after starting radio wave irradiation. The results of this experiment also confirm that the changed color can be held for over 24 h. Furthermore, two demonstrations of the visual management system of medical things (patient clothes and sanitizers) are presented. [ABSTRACT FROM AUTHOR]

Although chatbots such as ChatGPT can facilitate cost-effective text generation and editing, factually incorrect responses (hallucinations) limit their utility. This study evaluates one particular type of hallucination: fabricated bibliographic citations that do not represent actual scholarly works. We used ChatGPT-3.5 and ChatGPT-4 to produce short literature reviews on 42 multidisciplinary topics, compiling data on the 636 bibliographic citations (references) found in the 84 papers. We then searched multiple databases and websites to determine the prevalence of fabricated citations, to identify errors in the citations to non-fabricated papers, and to evaluate adherence to APA citation format. Within this set of documents, 55% of the GPT-3.5 citations but just 18% of the GPT-4 citations are fabricated. Likewise, 43% of the real (non-fabricated) GPT-3.5 citations but just 24% of the real GPT-4 citations include substantive citation errors. Although GPT-4 is a major improvement over GPT-3.5, problems remain. [ABSTRACT FROM AUTHOR]

Oyster Farming is one of important fisheries and aquaculture industries in Taiwan. Each year, approximately 4000–5000 tons of discarded bamboo scaffolding (BS) used in oyster farming, are generated, so the treatment and utilization of BS should be taken seriously. This study evaluates the suitability of BS for pulp and papermaking by assessing the chemical compositions, microstructural, and fiber morphology. The pulping properties is investigated by soda pulping. The chemical composition of BS shows the potential for application in pulping. The BS microstructure shows that can enhance pulping reactions, while the fiber morphology indicates the possibility of producing high-strength paper. Through the pulping experiment, it demonstrated that BS is suitable for pulping with lower NaOH dosage and longer digestion time. The condition at 170 °C with 14% NaOH dosage for 90 min digestion has the highest yield. After refining the highest pulping yield BS pulp, it can improve the handsheet strength and bulk of the OCC-BS mixed pulp, which can achieve the strength property required for industrial paper. In summary, BS exhibits the potential for pulping application and produces a better paper strength than OCC pulp, exhibiting the feasibility of enhancing the circular utilization value of BS in Taiwan. [ABSTRACT FROM AUTHOR]

We investigated the properties of the low molecular weight thermo-alkali-stable and mercury ion-tolerant xylanase production from Thermomyces dupontii KKU-CLD-E2-3. The xylanase was purified to homogeneity by ammonium sulfate, Sephadex G–100 and DEAE–cellulose column chromatography which resulted 27.92-fold purification specific activity of 56.19 U/mg protein and a recovery yield of 2.01%. The purified xylanase showed a molecular weight of 25 kDa by SDS–PAGE and the partial peptide sequence showed maximum sequence homology to the endo-1,4-β-xylanase. The optimum temperature and pH for its activity were 80 °C and pH 9.0, respectively. Furthermore, the purified xylanase can maintain more than 75% of the original activity in pH range of 7.0–10.0 after incubation at 4 °C for 24 h, and can still maintain more than 70% of original activity after incubating at 70 °C for 90 min. Our purified xylanase was activated by Cu2+ and Hg2+ up to 277% and 235% of initial activity, respectively but inhibited by Co2+, Ag+ and SDS at a concentration of 5 mM. The Km and Vmax values of beechwood xylan were 3.38 mg/mL and 625 µmol/min/mg, respectively. Furthermore, our xylanase had activity specifically to xylan-containing substrates and hydrolyzed beechwood xylan, and the end products mainly were xylotetraose and xylobiose. The results suggested that our purified xylanase has potential to use for pulp bleaching in the pulp and paper industry. [ABSTRACT FROM AUTHOR]

Pencil traces drawn on print papers are shown to function as strain gauges and chemiresistors. Regular graphite/clay pencils can leave traces composed of percolated networks of fine graphite powders, which exhibit reversible resistance changes upon compressive or tensile deflections. Flexible toy pencils can leave traces that are essentially thin films of graphite/polymer composites, which show reversible changes in resistance upon exposure to volatile organic compounds due to absorption/desorption induced swelling/ recovery of the polymer binders. Pencil-on-paper devices are low-cost, extremely simple and rapid to fabricate. They are light, flexible, portable, disposable, and do not generate potentially negative environmental impact during processing and device fabrication. One can envision many other types of pencil drawn paper electronic devices that can take on a great variety of form factors. Hand drawn devices could be useful in resource-limited or emergency situations. They could also lead to new applications integrating art and electronics. [ABSTRACT FROM AUTHOR]

A scientist's choice of research topic affects the impact of their work and future career. While the disparity between nations in scientific information, funding, and facilities has decreased, scientists on the cutting edge of their fields are not evenly distributed across nations. Here, we quantify relative progress in research topics of a nation from the time-series comparison of reference lists from papers, using 71 million published papers from Scopus. We discover a steady leading-following relationship in research topics between Western nations or Asian city-states and others. Furthermore, we find that a nation's share of information-rich scientists in co-authorship networks correlates highly with that nation's progress in research topics. These results indicate that scientists' relationships continue to dominate scientific evolution in the age of open access to information and explain the failure or success of nations' investments in science. [ABSTRACT FROM AUTHOR]

Membrane fouling is the major factor limiting the wider applicability of the membrane-based technologies in water treatment and in separation and purification processes of biorefineries, pulp and paper industry, food industry and other sectors. Endeavors to prevent and minimize fouling requires a deep understanding on the fouling mechanisms and their relative effects. In this study, Brunauer-Emmett-Teller (BET) nitrogen adsorption/desorption technique was applied to get an insight into pore-level membrane fouling phenomena occurring in ultrafiltration of wood-based streams. The fouling of commercial polysulfone and polyethersulfone membranes by black liquor, thermomechanical pulping process water and pressurized hot-water extract was investigated with BET analysis, infrared spectroscopy, contact angle analysis and pure water permeability measurements. Particular emphasis was paid to the applicability of BET for membrane fouling characterization. The formation of a fouling layer was detected as an increase in cumulative pore volumes and pore areas in the meso-pores region. Pore blocking was seen as disappearance of meso-pores and micro-pores. The results indicate that the presented approach of using BET analysis combined with IR spectroscopy can provide complementary information revealing both the structure of fouling layer and the chemical nature of foulants. [ABSTRACT FROM AUTHOR]

The fabrication of electronic circuits on unconventional substrates largely broadens their application areas. For example, green electronics achieved through utilization of biodegradable or recyclable substrates, can mitigate the solid waste problems that arise at the end of their lifespan. Here, we combine screen-printing, high precision laser drilling and thermal evaporation, to fabricate organic field effect transistor (OFET) active-matrix (AM) arrays onto standard printer paper. The devices show a mobility and on/off ratio as high as 0.56 cm2V−1s−1 and 109 respectively. Small electrode overlap gives rise to a cut-off frequency of 39 kHz, which supports that our AM array is suitable for novel practical applications. We demonstrate an 8 × 8 AM light emitting diode (LED) driver with programmable scanning and information display functions. The AM array structure has excellent potential for scaling up. [ABSTRACT FROM AUTHOR]

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance. [ABSTRACT FROM AUTHOR]

The Internet era is an era of information explosion. By 2022, the global Internet users have reached more than 4 billion, and the social media users have exceeded 3 billion. People face a lot of news content every day, and it is almost impossible to get interesting information by browsing all the news content. Under this background, personalized news recommendation technology has been widely used, but it still needs to be further optimized and improved. In order to better push the news content of interest to different readers, users' satisfaction with major news websites should be further improved. This study proposes a new recommendation algorithm based on deep learning and reinforcement learning. Firstly, the RL algorithm is introduced based on deep learning. Deep learning is excellent in processing large-scale data and complex pattern recognition, but it often faces the challenge of low sample efficiency when it comes to complex decision-making and sequential tasks. While reinforcement learning (RL) emphasizes learning optimization strategies through continuous trial and error through interactive learning with the environment. Compared with deep learning, RL is more suitable for scenes that need long-term decision-making and trial-and-error learning. By feeding back the reward signal of the action, the system can better adapt to the unknown environment and complex tasks, which makes up for the relative shortcomings of deep learning in these aspects. A scenario is applied to an action to solve the sequential decision problem in the news dissemination process. In order to enable the news recommendation system to consider the dynamic changes in users' interest in news content, the Deep Deterministic Policy Gradient algorithm is applied to the news recommendation scenario. Opposing learning complements and combines Deep Q-network with the strategic network. On the basis of fully summarizing and thinking, this paper puts forward the mode of intelligent news dissemination and push. The push process of news communication information based on edge computing technology is proposed. Finally, based on Area Under Curve a Q-Leaning Area Under Curve for RL models is proposed. This indicator can measure the strengths and weaknesses of RL models efficiently and facilitates comparing models and evaluating offline experiments. The results show that the DDPG algorithm improves the click-through rate by 2.586% compared with the conventional recommendation algorithm. It shows that the algorithm designed in this paper has more obvious advantages in accurate recommendation by users. This paper effectively improves the efficiency of news dissemination by optimizing the push mode of intelligent news dissemination. In addition, the paper also deeply studies the innovative application of intelligent edge technology in news communication, which brings new ideas and practices to promote the development of news communication methods. Optimizing the push mode of intelligent news dissemination not only improves the user experience, but also provides strong support for the application of intelligent edge technology in this field, which has important practical application prospects. [ABSTRACT FROM AUTHOR]

With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

A Hydrogen fuel cell (HFC) broad range associated with Internet of Things (IoT) technologies that require slightly less and constant electricity made possible by remote climate monitoring connections. Novelty demonstrates a miniature HFC based on carbon cloth electrodes and sealing elements manufactured via 3D printing. Cobalt (II) Oxide (Co3O4)—reduced Graphene Oxide (rGO) and Platinum (Pt) based nanoparticles are coated over carbon cloth to increase the catalytic activity at the anode and cathode. Hydrogen is produced by using an aluminium foil (Al) that is stored in between the filter paper and through capillary action the sodium hydroxide pellets (NaOH) are applied and reacted with Al foil to produce hydrogen. The single HFC device working surface area of 1 × 1 cm2 effectively generates an open circuit voltage (OCV) of 1.3 V, a current density of 1.602 mA/cm2, and a peak power density of 761 mW/cm2. The fuel cell stability performance is monitored for up to 10 h. The power obtained from the HFC is stored in a supercapacitor and used to supply energy to the IoT component. The module includes a built-in sensor that monitors the temperature, pressure, and humidity. The measured data is then transmitted to a smartphone via Bluetooth. [ABSTRACT FROM AUTHOR]

The paper-and-pencil Rey–Osterrieth Complex Figure (ROCF) copy task has been extensively used to assess visuo-constructional skills in children and adults. The scoring systems utilized in clinical practice provide an integrated evaluation of the drawing process, without differentiating between its visuo-constructional, organizational, and motor components. Here, a tablet-based ROCF copy task capable of providing a quantitative assessment of the drawing process, differentiating between visuo-constructional, organizational, and motor skills, is trialed in 94 healthy children, between 7 and 11 years of age. Through previously validated algorithms, 12 indices of performance in the ROCF copy task were obtained for each child. Principal component analysis of the 12 indices identified spatial, procedural, and kinematic components as distinct dimensions of the drawing process. A composite score for each dimension was determined, and correlation analysis between composite scores and conventional paper-and-pencil measures of visuo-constructional, procedural, and motor skills performed. The results obtained confirmed that the constructional, organizational, and motor dimensions underlie complex figure drawing in children; and that each dimension can be measured by a unique composite score. In addition, the composite scores here obtained from children were compared with previsions results from adults, offering a novel insight into how the interplay between the three dimensions of drawing evolves with age. [ABSTRACT FROM AUTHOR]

This paper addresses the current existence of attribute reduction algorithms for incomplete hybrid decision-making systems, including low attribute reduction efficiency, low classification accuracy and lack of consideration of unlabeled data types. To address these issues, this paper first redefines the weakly labeled relative neighborhood discernibility degree and develops a non-dynamic attribute reduction algorithm. In addition, this paper proposes an incremental update mechanism for weakly tagged relative neighborhood discernibility degree and introduces a new dynamic attribute reduction algorithm for increasing the set of objects based on it. Meanwhile, this paper also compares and analyses the improved algorithm proposed in this study with two existing attribute reduction algorithms using 8 data sets in the UCI database. The results show that the dynamic attribute reduction algorithm proposed in this paper achieves higher attribute reduction efficiency and classification accuracy, which further validates the effectiveness of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields. [ABSTRACT FROM AUTHOR]

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented. [ABSTRACT FROM AUTHOR]

Quantum computing has already demonstrated great computational potential across multiple domains and has received more and more attention. However, due to the connectivity limitations of Noisy Intermediate-Scale Quantum (NISQ) devices, most of the quantum algorithms cannot be directly executed without the help of inserting SWAP gates. Nevertheless, more SWAP gates lead to a longer execution time and, inevitably, lower fidelity of the algorithm. To this end, this paper proposes an optimized qubit mapping algorithm based on a dynamic look-ahead strategy to minimize the number of SWAP gates inserted. Firstly, a heuristic algorithm is proposed based on maximizing physical qubit connectivity to generate the optimal initial qubit mapping, which reduces the need for logical qubit shifts during subsequent SWAP gate insertion. Secondly, in the form of directed acyclic graphs, we identify quantum gates that violate the constraints of physical coupling and insert SWAP gates to remap qubits, thereby overcoming the limitations of qubit interactions. Finally, the optimal SWAP gate insertion strategy is built by comparing the cost of different SWAP gate insertion strategies through a multi-window look-ahead strategy to reduce the number of SWAP gates inserted. The experimental results show that the strategy in this paper decreases the number of SWAP gate insertions and significantly reduces the depth of quantum circuits when performing qubit mapping compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Industrially produced copy paper is presented as a novel optical material for narrow-band stimulated emission. Fluorescent brightening agents (FBA) such as Calcofluor White provide enough gain to exceed the threshold for amplified spontaneous emission. By applying an additional dye such as Coumarin 307, or simply a highlighter pen, the emission line can be shifted from blue (~440 nm), towards turquoise (>480 nm), up to orange (590 nm) which can be useful for spectroscopic applications. These properties are demonstrated in two papers, a commercial copy paper and a FBA-stained calligraphy paper. [ABSTRACT FROM AUTHOR]

We present a rapid (<10 s), cost-effective, unique single-step method for fabricating paper-based devices without necessitating any expensive instrumentation, simply by deploying correction pens that are otherwise commonly used for masking typos in printed or written matters. The marked regions formed by deposits from the correction pen demonstrate ubiquitous flow resistances to typical aqueous solutions and organic solvents in the transverse direction, resulting in a preferential bulk flow along the axial direction of the paper channels 'fabricated' in the process. Considering the simplicity and cost-effectiveness of this platform, it is deemed to be ideal for (bio) chemical sensing and point-of-care diagnostics in resource-limited settings. [ABSTRACT FROM AUTHOR]

Worldwide, tropospheric ozone (O3) is a potential threat to wood production, but our understanding of O3 economic impacts on forests is still limited. To overcome this issue, we developed an approach for integrating O3 risk modelling and economic estimates, by using the Italian forests as a case study. Results suggested a significant impact of O3 expressed in terms of stomatal flux with an hourly threshold of uptake (Y = 1 nmol O3 m−2 leaf area s−1 to represent the detoxification capacity of trees), i.e. POD1. In 2005, the annual POD1 averaged over Italy was 20.4 mmol m−2 and the consequent potential damage ranged from 790.90 M€ to 2.85 B€ of capital value (i.e. 255–869 € ha−1, on average) depending on the interest rate. The annual damage ranged from 31.6 to 57.1 M€ (i.e. 10–17 € ha−1 per year, on average). There was also a 1.1% reduction in the profitable forest areas, i.e. with a positive Forest Expectation Value (FEV), with significant declines of the annual national wood production of firewood (− 7.5%), timber pole (− 7.4%), roundwood (− 5.0%) and paper mill (− 4.8%). Results were significantly different in the different Italian regions. We recommend our combined approach for further studies under different economic and phytoclimatic conditions. [ABSTRACT FROM AUTHOR]

Paper has been pursued as an interesting substrate material for sensors in applications such as microfluidics, bio-sensing of analytes and printed microelectronics. It offers advantages of being inexpensive, lightweight, environmentally friendly and easy to use. However, currently available paper-based mechanical sensors suffer from inadequate range and accuracy. Here, using the principle of supercapacitive sensing, we fabricate force sensors from paper with ultra-high sensitivity and unprecedented configurability. The high sensitivity comes from the sensitive dependence of a supercapacitor’s response on the contact area between a deformable electrolyte and a pair of electrodes. As a key component, we develop highly deformable electrolytes by coating ionic gel on paper substrates which can be cut and shaped into complex three-dimensional geometries. Paper dissolves in the ionic gel after determining the shape of the electrolytes, leaving behind transparent electrolytes with micro-structured fissures responsible for their high deformability. Exploiting this simple paper-based fabrication process, we construct diverse sensors of different configurations that can measure not just force but also its normal and shear components. The new sensors have range and sensitivity several orders of magnitude higher than traditional MEMS capacitive sensors, in spite of their being easily fabricated from paper with no cleanroom facilities. [ABSTRACT FROM AUTHOR]

On the development of flexible electronics, a highly flexible nonvolatile memory, which is an important circuit component for the portability, is necessary. However, the flexibility of existing nonvolatile memory has been limited, e.g. the smallest radius into which can be bent has been millimeters range, due to the difficulty in maintaining memory properties while bending. Here we propose the ultra flexible resistive nonvolatile memory using Ag-decorated cellulose nanofiber paper (CNP). The Ag-decorated CNP devices showed the stable nonvolatile memory effects with 6 orders of ON/OFF resistance ratio and the small standard deviation of switching voltage distribution. The memory performance of CNP devices can be maintained without any degradation when being bent down to the radius of 350 mm, which is the smallest value compared to those of existing any flexible nonvolatile memories. Thus the present device using abundant and mechanically flexible CNP offers a highly flexible nonvolatile memory for portable flexible electronics. [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]

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]