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Abstract In this study, zinc oxide (ZnO) nanoparticles were prepared and modified using a wet chemical method with different concentrations of Ag and Cu nanoparticles. The objective was to improve the mechanical, optical, and antibacterial properties of the coated paper by using the prepared pigments. The long-term antimicrobial effects of the coated paper were evaluated over 25 years. The successful synthesis of a hexagonal structure of ZnO nanoparticles decorated with spherical Ag and Cu nanoparticles ranging from 20 to 50 nm was confirmed using X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and transmission electron microscopy (TEM). By increasing the concentrations of Ag and Cu from 0.01% to 1.0%, the mechanical properties of the coated paper were enhanced. The tensile strength reached a maximum of 6.77 kN/m and 7.03 kN/m, elongation increased to 1.69% and 1.70%, tensile energy absorption improved to approximately 77 and 80 J/m2, and burst strength rose to 218 and 219 kPa, respectively. The use of Ag-modified ZnO maintains the optical properties, while Cu-modified ZnO reduces brightness and whiteness without affecting opacity. The antimicrobial inhibition activity was improved with higher silver (Ag) and copper (Cu) content. The formulations containing 1% Ag/ZnO and 1%Cu/ZnO showed long-lasting antibacterial effects against gram-positive Staphylococcus aureus bacteria. Even after 25 years of aging, they maintained inhibition rates of 92.2% and 62.2%, respectively. The molecular docking and GeneMANIA analysis revealed the potential of ZnO, Ag-modified ZnO, and Cu-modified ZnO nanoparticles to disrupt the S. aureus cell wall biosynthesis pathway by targeting the MurA enzyme and associated cell wall synthesis genes.

Abstract Concrete’s widespread use in construction is increasing due to urbanization and population growth. While it offers durability and strength, its production poses environmental challenges. Researchers and global initiatives are working towards more sustainable concrete production methods and effective waste management, particularly for paper waste (WPA) and polyethylene terephthalate waste (PET). This study aims to investigate the effects of waste paper aggregate (WPA) on the flow time, compressive strength, flexural strength, and thermal conductivity of mortars. Furthermore, it explores the potential of polyethylene terephthalate (PET) to enhance the compressive and flexural strengths of these mortars. To conduct this study, the materials were characterized using EDX spectroscopy and SEM analysis. Gwyddion software was employed to conduct a comprehensive analysis of the grain segmentation and microstructural characteristics of the WPA mortars. Subsequently, four distinct mixtures were selected for further investigation. The first mixture involved substituting sand with various percentages of waste paper aggregate (WPA) (1–4%) without PET. The second mixture included WPA (1–4%) and 5% PET. Due to the ineffective plasticizer in the first two mixtures, the third mixture replaced 20% of quarry sand with dune sand (DS) while maintaining the same percentages of WPA and PET as in the previous mixtures. The results indicate that mortars RM1 (0% WPA + 0% PET) and RM2 (0% WPA + 0% PET + 20% DS) demonstrated the best flow time. This suggests that incorporating WPA and PET into the mortar mix can negatively impact its workability, making it more challenging to handle, shape, and apply. Sample PW4, composed of 4% WPA and 0% PET, displayed a significant increase in compressive strength from 46.36 MPa to 49.81 MPa and flexural strength from 10.5 MPa to 11.47 MPa, indicating that incorporating WPA into the mortars can enhance resistance to both compressive and flexural strengths. This same mixture, PW4, demonstrated that the mortars are poor conductors of heat and would be more effective as thermal insulators. Grain segmentation of WPA mortars using Gwyddion software revealed that shorter WPA fibers tend to align with the width, while longer WPA fibers tend to align with the length of the section.

Abstract Expansive soils have a high tendency for volume change in case of fluctuations in moisture content, potentially causing significant damage to light structures, particularly road pavements. This paper investigates the influence of waste paper sludge ( $$\:WPS$$ ) as an alternative sustainable stabilizer on the volume change behavior of expansive road subgrade soils of different origins. For this purpose, $$\:WPS$$ was added to the expansive soils at ratios of 3%, 6%, 9%, 12%, and 15% by dry weight of the soils. A series of Atterberg’s limit, swelling, shrinkage, compaction, and consolidation tests were performed on pure soils and soil specimens with $$\:WPS$$ to attain a comprehensive understanding of the role that $$\:WPS$$ plays in the volume change behavior of expansive soils. The experimental test results showed that the addition of $$\:WPS\:$$ led to a considerable decrease in the plasticity and swell-shrink potentials of subgrade soils. The consolidation settlement of expansive road subgrades was also reduced to some extent with $$\:WPS$$ . Moreover, the statistical analysis of the test data indicated a significant relationship among different swelling-shrinkage parameters. The experimental results presented here suggest that the $$\:WPS$$ may be a cost-effective, environmentally friendly, and sustainable stabilizer to reduce the volume change sensitivity of expansive road subgrade soils.

Abstract 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 Electrochemical analysis of glucose monitoring without painful blood collection provides a new noninvasive route for monitoring glucose levels. Thus, in this study, biobased cellulosic papers (methylated and phosphorylated one) based glucose monitoring sensor is developed. To achieve high hydrophilicity, microfibrillated cellulose (MFC) were functionalized using hexokinase mediated phosphorylation (–OH to – $$\text{SO}_{3}^{2-}$$ ). The instinctive increased surface charge density from 36.2 ± 3.4 to 118.4 ± 1.2 µmol/g and decrease contact angle (45°–22°) confirms the increased hydrophilicity of paper. Furthermore, functionalized phos-MFC paper increase the capillary flow of sweat, required low quantity (1 µl) of sweat for accurate analysis of glucose level. Additionally, chemically induced methyl groups (–CH3) make the sensor more barrier to other chemicals. In addition, a multilayer patch design combined with sensor miniaturization was used to lead to an increase in the efficiency of the sweat collection and sensing processes. Besides, this paper sensor integrated with artificial transdermal drug delivery unit (agarose gel as skin) for monitoring glucose levels in sweat. The patch monitoring system increase the accuracy of sensing with fluctuation in sweat vol. (1–4 µl), temperature (20–70 °C), and pH (4.0–7.0). In addition, temperature dependency artificial transdermal delivery (within agarose gel) of drug metformin agrees the measurement accuracy of sensor, called “switch system” without any error. As a result, the reported MFC paper based multi-patch disposable sensing system provides a novel closed-loop solution for the noninvasive sweat-based management of diabetes mellitus.

Abstract 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 ( 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 (

Abstract 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 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

Abstract The paper industry is a major environmental polluter due to paper waste sludge (PWS), often disposed of in hazardous ways. The techniques are employed to disposing of PWS are posing significant environmental hazards and risks to well-being. This study aims to evaluate PWS as a potential replacement for commercial adsorbents like AC and ZEO in treating stabilized leachate. Contact angle analysis of PWS was 92.60°, reveals that PWS to be hydrophobic. Batch adsorption experiments were conducted with parameters set at 200 rpm stirring speed, 120 min contact time, and pH 7. Optimal conditions for COD and NH3–N removal were identified at 120 min contact time, 200 rpm stirring speed, pH 7, and 2.0 g PWS ratio. Removal percentages for COD and NH3–N were 62% and 52%, respectively. Based on the results of the isotherm and kinetic studies, it was observed that the Langmuir and Pseudo second order (PSO) model exhibited greater suitability compared to the Freundlich and Pseudo first order (PFO) model, as indicated by higher values of R-squared (R2). The R-squared of Langmuir for COD and NH3–N were 0.9949 and 0.9919 and for Freundlich model were 0.9855 and 0.9828 respectively. Whereas the R-squared of PFO for COD and NH3–N were 0.9875 and 0.8883 and for PSO were 0.9987 and 0.9909 respectively.

Abstract 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 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 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 Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.

Abstract 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 This article presents the results of an experiment designed to study the impact of temperature on the characteristic parameters of creeping discharges. The insulating interfaces consist of a thermally enhanced cellulose surface immersed in mineral oil, palm kernel oil methyl ester (PKOME) and castor oil methyl ester (COME). The study was carried out under a standard negative lightning impulse voltage (1.2/50 μs). The article also presents the complete algorithms for calculating the maximum extension of the discharges, the ionisation rate and the charge produced by them. The results of the study show that temperature favors the propagation of discharges and the ionisation rate. It was observed that liquids with a higher dielectric constant and high electrical conductivity were more exposed to the impact of temperature. The results show ionisation increments of 0.973%/°C, 1.093%/°C and 1.076%/°C in mineral oil (MO), COME and PKOME respectively. The maximum extension of the discharges shows a linear evolution with the applied voltage and temperature but a non-linear increment with the temperature. As for the charge produced, it shows a constant increment with temperature and voltage in each liquid. These values are (5.839%/°C, 1.977%/kV), (6.047%/°C, 2.082%/kV) and (6.177%/°C, 2.113%/kV) respectively in MO, COME and PKOME.

Abstract This work features a new corona discharge plasma technology for de-inking yellow, blue, and red colors on various papers. This work was developed to minimize the chemical and environmental impacts of de-inking processes. A nonchemical contribution, operating at room temperature and atmospheric pressure, reduces the environmental impact of the process. The deinkability factor (DEMLab) values for all papers are determined with the optimal assessment results provided by a 36-mm variation gap at 2-min (blue) and 10-min (yellow and red) plasma exposure times, followed by applied voltages of 20 kV (yellow), 16 kV (blue), and 20 kV (red). The corona discharge plasma led to 48.58% (yellow printed paper), 64.11% (blue printed paper), and 41.11% (red printed paper) deinkability without altering the physical properties of the paper itself. The change in the tensile strength for the plasma-exposed paper was relatively little, less than 10%, compared to that of common recycling. The tensile strength of the untreated white paper was 5065 ± 487.44 N/mm2, and that of the plasma-treated printed paper was 4593 ± 248.47 N/mm2. It appears that there is little impact on the physicochemical properties of paper induced by the corona plasma treatment during the de-inking process.

Abstract In mountain areas landslides many times endanger safety of transport infrastructures, and these must be stabilized with retaining wall structures. In this paper the validation of a new composite as a backfill material for landslide stabilization with a large scale demo retaining wall is presented. The new composite was made from residues of paper industry, which uses for its production deinking process. New composite was validated with the laboratory tests, construction of small demo sites and at the end with a large demo retaining wall structure with a length of 50 m. It was concluded that the paper sludge ash and the paper sludge are in proportion 70:30, compacted on the optimal water content and maximum dry density, reached sufficient uniaxial compressive and shear strength. However, the composite's hydration processes required the definition of an optimal time between the composite mixing and installation. In 2019, the retaining wall structure from the new composite was successfully built. The large demo structure is an example of the knowledge transfer from the laboratory to the construction site, in which composite and installing technology could be verified.

Abstract It is estimated that 2% of all journal submissions across all disciplines originate from paper mills, both creating significant risk that the body of research that we rely on to progress becomes corrupted, and placing undue burden on the submission process to reject these articles. By understanding how the business of paper mills—the technological approaches that they adopt, as well as the social structures that they require to operate—the research community can be empowered to develop strategies that make it harder, or ideally impossible for them to operate. Most of the contemporary work in paper-mill detection has focused on identifying the signals that have been left behind inside the text or structure of fabricated papers that result from the technological approaches that paper mills employ. As technologies employed by paper mills advance, these signals will become harder to detect. Fabricated papers do not just need text, images, and data however, they also require a fabricated or partially fabricated network of authors. Most ‘authors’ on a fabricated paper have not been associated with the research, but rather are added through a transaction. This lack of deeper connection means that there is a low likelihood that co-authors on fabricated papers will ever appear together on the same paper more than once. This paper constructs a model that encodes some of the key characteristics of this activity in an ‘authorship-for-sale’ network with the aim to create a robust method to detect this type of activity. A characteristic network fingerprint arises from this model that provides a robust statistical approach to the detection of paper-mill networks. The model suggested in this paper detects networks that have a statistically significant overlap with other approaches that principally rely on textual analysis for the detection of fraudulent papers. Researchers connected to networks identified using the methodology outlined in this paper are shown to be connected with 37% of papers identified through the tortured-phrase and clay-feet methods deployed in the Problematic Paper Screener website. Finally, methods to limit the expansion and propagation of these networks is discussed both in technological and social terms.

Abstract Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million people die from diseases caused by water each year, and 1.1 billion people lack access to potable drinking water. Although industrialization, durable infrastructure, and rapid development have increased living standards, the water problem has left humanity defenseless. As different human activities have contaminated these water reserves, according to an estimate, water is the cause of 80% of ailments. As a result, it is necessary to permit enough infrastructure to ensure the security of a reliable supply of potable water. Thus, a real-time WBPCB dataset with 17 features and a proposed IoT-based system to collect data are used in this research to address the issue. The research paper provides a system for predicting diseases and forecasting long-term trends. Classification is performed using Random Forest, XGBoost, and AdaBoost, which have accuracy rates of 99.66%, 99.52%, and 99.64%, respectively. Forecasting is performed using LSTM, which has an MSE value for the pH parameter of 0.1631. The paper introduces TS-SMOTE, a novel hybridized time-series SMOTE data augmentation approach. Additionally, it offers an IoT system that uses H-ANFIS to gather data in real-time and identify attacks.

Abstract Ensuring robust network security is crucial in the context of Software-Defined Networking(SDN). Which, becomes a multi-billion dollar industry, and it’s deployed in many data centers nowadays. The new technology provides network programmability, network centralized control, and a global view of the network. But, unfortunately, it comes with new vulnerabilities, and new attack vectors compared to the traditional network. SDN network cybersecurity became a trending research topic due to the hype of Machine Learning (ML) when a group of Machine Learning(ML) techniques called Deep Learning(DL) started to take shape in the setting of SDN networks. This paper focuses on developing advanced Deep Learning(DL) models to address the inherent new attack vectors. In this paper, we have built and compared two models that can be used for building a complete Intrusion Detection System(IDS) solution, one using a hybrid CNN-LSTM architecture and the other using Transformer encoder-only architecture. We specifically target the SDN controller where it represents a crucial point. We utilized the InSDN dataset for training and testing our models, this dataset captures real-world traffic within the SDN environment. For evaluation, we have used accuracy, precision, recall, and F1 Score. Our experiment results show that the Transformer model with 48 features achieves the highest accuracy at 99.02%, while the CNN-LSTM model achieves 99.01%. We have reduced the features to 6 and 4, which gave us varying impacts on the models’ performance. We have merged 4 poorly represented attacks in one class, which enhanced the accuracy by a significant score. Additionally, we investigate binary classification by merging all attack types into a single class, as a result, the accuracy increased for both models. The CNN-LSTM model achieves the best results with an accuracy of 99.19% for 6 feature sets, this enhances the state-of-the-art results.

Abstract Big Data Analysis from the worldwide field shows that lost circulation is a common phenomenon in drilling and the success ratio of remedial treatment for lost circulation is low. It is not only because of these complexities of the formation, but also because of the poor performances of the lost-circulation materials (LCM) and technologies. This paper proposes a new method in plugging, which is based on a particular physical method for lost circulation controlling and plugging while drilling. This method is different from these traditional treatments, which only use chemical methods. When pore-permeable and fractured leakage formations are encountered while drilling, We can use this new hydraulic tool, the drilling fluid will have a diffluence at a constant rate from the jet of the hydraulic tool which is installed on the drilling pipe and the fluid laden with particles can be injected into the natural or induced fractures of the borehole wall which may lead to lost circulation. Combined with the corresponding drilling fluids which have been tested in the laboratory, the additives such as ground marble, ground nutshells, and graphite in the drilling fluid are quickly pushed into the fractures because of the differential pressure provide by the hydraulic tool, then form a firm seal at the mouth of the fractures which could isolate fluid pressure in the wellbore and prevent effective pressure communication to interfere the extension of the fracture. What’s more, according to the stress cage theory, when these sealing particles which prop the fracture open, act as wedges to compress the rock around the wellbore, the hoop stress could be increased, so that the wellbore pressure containment (WPC) could be improved. Since the annular flow may have some interference with the side nozzle jet, this paper also numerically simulates the flow field of the annular air section through the fluid simulation software Fluent, and the simulation results show that the interference of the annular flow on the jet is very weak and basically has no effect. In addition, through the oilfield field test, it is known that this new physical plugging method has good plugging effect, and it can obviously improve the pressure-bearing capacity of the formation, which has great popularization value.

Abstract The paper presents comparative laboratory investigations of tension fastener models that attach rails to concrete sleepers. The aim of the paper was to assess the influence of geometric nonconformity of the actual industrial product that meets the PKP Polskie Linie Kolejowe S.A. requirements on the operation of a tension-fastening clamp under stress. Due to the cost and limited possibility of research of the actual industrial product, an additional objective of the research was to validate the usefulness of it in the comparative assessment of the models. In the research, the authors used models of tension fastening clamps manufactured in incremental engineering technology (3D print) on scale 1:2. The properties of the fastening clamps (in their nominal shape) were compared (described in the PKP Polskie Linie Kolejowe S.A. documentation) with the fastening clamps of the actual shape. In the investigation, the authors have confirmed the negative influence of the non-conformance of the shape of the actual fastening clamps with the nominal ones.

Abstract This paper considers uplink and downlink transmissions in a network with radio frequency-powered Internet of Things sensing devices. Unlike prior works, for uplinks, these devices use framed slotted Aloha for channel access. Another key distinction is that it considers uplinks and downlinks scheduling over multiple time slots using only causal information. As a result, the energy level of devices is coupled across time slots, where downlink transmissions in a time slot affect their energy and data transfers in future time slots. To this end, this paper proposes the first learning approach that allows a hybrid access point to optimize its power allocation for downlinks and frame size used for uplinks. Similarly, devices learn to optimize (1) their transmission probability and data slot in each uplink frame, and (2) power split ratio, which determines their harvested energy and data rate. The results show our learning approach achieved an average sum rate that is higher than non-learning approaches that employed Aloha, time division multiple access, and round-robin to schedule downlinks or/and uplinks.

Abstract The gamut and EOTF (Electro-Optical Transfer Function) of a display together determine its color performance. Gamut determines the range of displayable colors, and the EOTF determines the color accuracy and depth. Image reproduction often comprises two parts: the image source and the display terminal. To ensure the universality of different terminals, the image source is generally produced in a standardized gamut standard, while the terminal pursues a wider gamut and more spectacular color performance. As a widely welcomed form of display, WCGDs (Wide Color Gamut Displays) boast stunning color performance. However, certain colors in WCGDs displays are oversaturated, which is unacceptable to the human eye. This paper analyzes the oversaturation problem of WCGDs. Firstly, a display model based on EOTF and gamut parameters is established. Then the color point vector field varies with EOTF is derived and the drift of luminance and chromaticity are analyzed separately. Finally, two strategies are present to optimize the color oversaturation problem of WCGDs based on the analysis conclusions and verify its effectiveness. This paper presents a detailed analysis and possible strategy for color oversaturation in WCGDs, providing a theoretical basis for the accurate color reproduction and efficient color management of WCGDs.

Abstract The welding workshop of metal structural parts is highly energy-consuming. To meet the national low-carbon green demand, this paper focus on the welding workshop scheduling problem in production process with considering the carbon footprints such as equipment energy consumption, welding material consumption and shielding gas consumption. Firstly, a bi-objective low-carbon welding scheduling mathematical model is established with minimizing makespan and carbon emission. Then, an improved Grey Wolf Optimizer (IGWO) with three strategies is designed to solve this multi-objective problem. The grey wolf multi-wandering strategy (first) is proposed to enhance the population diversity. The grey wolf coordinated hunting strategy (second) based on dynamic weights is introduced to improve the convergence of IGWO. A local optimization strategy(third) is designed to improve the post-optimal search performance by adjusting the machine assignment based on the critical path. A welding workshop green scheduling case is designed to verify the model and algorithm proposed in this paper. The minimum completion time and carbon emissions obtained by the IGWO algorithm are 842.14 and 3.85E + 05, respectively. This result is better than that obtained by NSGA-II and GWO.The results show that the model effectively reduce the carbon emissions of the workshop, and the algorithm can effectively solve the model.

Abstract This paper employs two-way fixed effects models, mediation models and spatial panel models to examine the impact of digital economy development on carbon emission intensity in 72 countries from 2013 to 2020 using TIMG index as the proxy variable of digital economic development. The results of this paper show that (1) there is an inverted U-shaped relationship between the development of the digital economy and the carbon emissions in various countries. The development of digital economy first has a positive effect on carbon emissions, and then, its impact turns negative after it reaches a designated inflection point. (2) The results of the mechanism analysis show that the development of the digital economy can reduce carbon emissions by promoting industrial upgrading, while it could have an inverted U-shaped nonlinear effect on carbon emissions through energy efficiency. (3) The development of a country’s digital economy may have a significantly inverted U-shaped spatial spillover effect on neighboring countries. This paper provides reference for countries to formulate carbon emission reduction policies and promote the coordinated development of digital economy and environmental protection.

Abstract Unlike classic audio guides, intelligent audio guides can detect users’ level of attention and help them regain focus. In this paper, we investigate the detection of mind wandering (MW) from eye movements in a use case with a long focus distance. We present a novel MW annotation method for combined audio-visual stimuli and collect annotated MW data for the use case of audio-guided city panorama viewing. In two studies, MW classifiers are trained and validated, which are able to successfully detect MW in a 1-s time window. In study 1 (n = 27), MW classifiers from gaze features with and without eye vergence are trained (area under the curve of at least 0.80). We then re-validate the classifier with unseen data (study 2, n = 31) that are annotated using a memory task and find a positive correlation (repeated measure correlation = 0.49, p < 0.001) between incorrect quiz answering and the percentage of time users spent mind wandering. Overall, this paper contributes significant new knowledge on the detection of MW from gaze for use cases with audio-visual stimuli.

Abstract The rapid advancement of artificial intelligence and robot technology has spurred the proposal and innovation of a coal gangue sorting robot system (CGSRS) paradigm. The time-varying raw coal flow (TVRCF) with multi-scene and full working conditions affects the gangue queue. Configuring the CGSRS scheme correctly is combative. The field environment puts forward higher requirements for the time complexity of the CGSRS multi-task allocation strategy. Therefore, this paper proposes a scheme evaluation method of the CGSRS with time-varying multi-scenario based on deep learning. Firstly, the gangue queue data set of multi-scene and full-condition TVRCF was obtained according to the belt speed, the maximum coal flow, and the uncorrelated nonlinear changes of coal flow and gangue content. The CGSRS scheme is established based on robot number and rule combination, and the multi-task allocation strategy is adjusted to generate the labels of the gangue queue. Then, the RGB sample set is established based on the labels of the gangue queue. The CGSRS scheme evaluation model is trained based on DenseNet. Finally, the CGSRS scheme evaluation method was designed to realize the prediction of a random gangue queue. In this paper, the CGSRS scheme evaluation model, the stability of the solution, and the comparison of methods are carried out. Experimental results show that the solution of the CGSRS scheme evaluation model is accurate and stable. The time complexity is significantly reduced and very stable. The CGSRS scheme evaluation method is applied to the CGSRS multi-task allocation problem, and the stability of the solution is not affected by the data. It is significantly better than the multi-task allocation strategy. The proposed method is the first attempt to apply deep learning to a multi-task allocation problem in CGSRS.

Abstract The goal of the paper is to examine how convolutional neural network (CNN) is used in the immersive world of digital media art. In order to do this, this paper first explains digital media art in the context of smart cities and the use of immersive scenarios. Next, a brief analysis of the Unet network model and deep aggregation structure is provided. Then, a projector, camera, and computer-based immersive projector-camera display system is constructed. Based on the mathematical reflection model of the system, this paper discusses the method of using CNN to solve the photometric compensation of the projection picture. Meanwhile, a Projection Compensation Network (PCN) is designed, and a multi-scale perceptual loss is added to this compensation network, and the content information of the compensated image is improved by calculating the loss of feature maps of different scales. The final network is named Perceptual Loss-Projection Compensation Network (PL-PCN). Experiments are used to confirm the PL-PCN model’s efficacy. The outcomes demonstrate that the SSIM and PSNR of the projected picture compensated by PL-PCN are boosted by 35.8% and 31.6%, respectively. While the RMSE is reduced by 40.9%, demonstrating an improvement in the compensated image’s quality. Additionally, utilizing a CNN makes it possible to do cross-reflection compensation. Additionally, compared to the network without deep polymerization, the PL-PCN with deep polymerization structure boosts the projected image’s SSIM and PSNR by 6% and 7.57%, respectively, and lowers the RMSE by 13.3% This demonstrates that the addition of deep polymerization structure can have a stronger compensatory effect. This paper can offer a theoretical framework for improving the immersive scene quality of digital media art.

Abstract Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer offers the advantage of a shorter culture duration, which reduces exposure to laboratory conditions, potentially enhancing embryonic development within a more conducive uterine environment and improving the likelihood of successful pregnancies. In this paper, we present a novel ResNet-GRU deep-learning model to predict blastocyst formation at 72 HPI. The model considers the time-lapse images from the incubator from Day 0 to Day 3. The model predicts blastocyst formation with a validation accuracy of 93% from the cleavage stage. The sensitivity and specificity are 0.97 and 0.77 respectively. The deep learning model presented in this paper will assist the embryologist in identifying the best embryo to transfer at Day 3, leading to improved patient outcomes and pregnancy rates in ART.

Abstract In order to solve the problems of long reconstruction time and low reconstruction accuracy of compressed sensing reconstruction algorithm in the measurement of transient signals, a deep unfolding network reconstruction method based on scalable sampling is proposed to achieve fast and high-quality reconstruction of transient signal under low number of measurements. Firstly, the measurement process of compressed sensing is embedded into the neural network to realize automatic design and optimization of the observation matrix, which can reduce the number of measurements. Secondly, scalable sampling is introduced into the measurement process of compressed sensing, which can realize the training of data with different sampling ratios in the same model. Finally, a deep unfolding network model is designed to reconstruct the transient signal, which not only realizes the interpretability of the reconstructed network, but also achieves fast and high-quality reconstruction of the transient signal under the low number of measurements. Experimental results show that compared with the traditional compressed sensing reconstruction algorithms, the proposed method can obtain high-quality reconstruction accuracy with lower measurement times, and the reconstruction time is greatly reduced. The algorithm in this paper also obtains good reconstruction results under different sampling ratios, which shows that the method in this paper has good adaptability and effectiveness.

Abstract Good and optimal rheology is a primary requisite for a drilling fluid to achieve better hole-cleaning and barite sag resistance. Conventional thickeners like organophilic clay that provide rheology to invert emulsion fluids degrade with time and thereby fail to maintain sufficient rheology of the drilling fluid. Generally, excess amount of organophilic clay or low gravity solids (LGS) is added to the drilling fluid to enhance rheology. However, addition of excess amount of organoclay or low gravity solids not only increases the cost of drilling but also may severely affect other drilling fluid properties, which will require further treatment. In addition, addition of organoclay or low gravity solids increases the plastic viscosity, decreases the rate of penetration thereby ultimately increasing the cost of drilling. Thus, there was a need to develop a drilling fluid with optimal rheology sufficient to give good hole cleaning and barite sag resistance. This paper describes the formulation of 70pcf, 90pcf and 120pcf organoclay-free invert emulsion drilling fluids formulated with a novel rheology modifier combination of RM1 and RM2. The paper describes the synergistic effect of RM1 and RM2 which is a combination of fatty acid and fatty amine respectively. The fluids were hot rolled at 250°F, 250°F and 350°F respectively and the temperature effect on the rheological and filtration properties were studied. The paper also describes the static aging and contamination studies of 90pcf and 120pcf fluids at 250°F and 350°F respectively. Rheology of the 90pcf invert emulsion fluid was measured across high temperature and high pressure (HTHP) conditions to observe their effects on the fluid properties. 70pcf, 90pcf and 120pcf organoclay-free invert emulsion drilling fluids formulated with the novel rheology modifier combination of RM1 and RM2 showed optimal rheology and low HTHP fluid loss. Static aging studies of 90pcf IEFs at 250°F respectively showed that the fluids are sag-resistant with low top-oil separation. Contamination studies of 90pcf fluid showed that the contaminants have minimal effect on the rheology and filtration properties of the invert emulsion fluid. HTHP rheology of the 90pcf invert emulsion fluid shows consistent rheology across high temperatures and pressures. The paper thus demonstrates the superior performance of the rheology modifier combination to achieve good rheological and filtration properties.

Abstract Blockchain is a decentralized electronic account book that is secure, open, and impervious to manipulation, and records transactions across numerous computers. Internet of Things (IoT) deployed sensors are limited in bandwidth, processing power, and battery life. Energy efficiency is a vital factor and a decisive matter for energy-bound IoT-based networks. This paper provides an idea of the working principle of blockchain technology in IoT-enabled energy systems. It investigates the fundamentals of blockchain technology, clarifying its decentralized nature, cryptographic mechanisms, and consensus algorithms that ensure data immutability and transparency. It explores how blockchain technology can be integrated with various IoT fields for energy efficiency. The paper investigates the blockchain-based energy consumption approaches in IoT qualitatively, and quantitatively with future research directions and challenges. It is seen that the hybrid blockchain model is suitable for energy efficiency in IoT.

Abstract Wide-Field Small Aperture Telescopes (WFSAT) are widely used for surveilling space objects. Due to their wide-field of view (FOV) characteristics, these telescopes can cover a large areas of the sky at once, improving observation efficiency. However, a wide-field optical telescope is highly sensitive to external stray light (such as moonlight and thin clouds), which can significantly reduce the quality of observation data. In severe cases, it can cause the telescope to malfunction and inaccurately position the object. In response to this problem, this paper proposes a model for suppressing stray light in astronomical images based on deep learning: the Pyramid Deformable Large Kernel Attention (PD-LKA) Model. This model expands the receptive field through a pyramid structure, captures multi-scale features, and improves the model’s robustness to various scales of stray light interference. Meanwhile, through the Deformable Large Kernel Attention (D-LKA), the model can more accurately locate and enhance the feature extraction ability in areas affected by stray light interference, thereby better suppressing stray light.Using simulated astronomical image pairs to train the model, the tests achieved a PSNR of up to 32.540 and an SSIM of up to 0.938. Finally, the model is applied to a image sequence with real stray light interference. The restored images undergo astronomical positioning and orbital association processing. The results show that the positioning accuracy of the object is better than 5 arcseconds. This indicates that the model proposed in this paper not only recovers the object and background stars but also effectively preserves their gray values, shapes, and positional information.

Abstract 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 To ensure the effective implementation of the compulsory green certificate transaction in China, a multi-participant cross-regional green certificate bilateral transaction model considering the relevant technical characteristics of the unit is proposed to solve the problems such as the mismatch between the supply and demand of green certificates and the reduction of the transaction plan by the grid operation constraints in the current green certificate transaction. To mobilize the initiative of participants, the model adopts the bilateral transaction of free negotiation. To balance the interests of transaction participants, the paper analyzes the complex interaction of multi participants in cross-regional bilateral transactions by using cooperative game theory. Simultaneously, the model considers multiple uncertainties among participants and takes into account the correlation of photovoltaic power in multiple periods. Based on the distributed robust theory, the multivariate nonlinear model and mixed-effect model of Bayesian are proposed to construct the fuzzy sets of the probability distribution for wind and photovoltaic power. On this basis, the paper analyzes the strategic behaviors of multi participants affected by uncertainty factors in the cross-regional green certificate bilateral trade through a numerical example, and quantitatively analyzes the sensitivity of each participant’s transaction plan affected by uncertainty factors. The results show that the degree of competition will affect the strategic behavior and income of the participants, and the full cooperation mode can not only avoid the risk of fluctuations in the transaction price and transaction power within the alliance but also stabilize the supply and demand of green certificate transactions.

Abstract Currently, continuous sign language recognition faces challenges such as difficulty in acquiring skeletal data, long training time for Three-Dimensional convolutional neural networks, and easy occlusion and blurring of hands. To address these problems, this paper proposes a continuous sign language recognition method based on target detection and coding sequence. The algorithm uses Dual-branch Shuffle Attention Mechanis-You Only Look Once version X (DSA-YOLOX) detection network to detect the head and hands, and encodes the sign language video according to the partition to achieve the transformation from Three-Dimensional to One-Dimensional; and then uses the proposed Bi-directional Long Short-Term Memory (BiLSTM) hand coding sequence classification model with jointly weighted Fast Dynamic Time Warping (FastDTW) to extract hand coding similarity and features while reducing the number of parameters to achieve the classification and recognition of unequal-length hand coding sequences. From the results of ablation experiments and comparison experiments, all parts of the improvement perform well. The word error rate (WER) of this paper’s method is reduced by 21.26% compared to Dynamic Time Warping-Hidden Markov Model (DTW-HMM) and 11.53% compared to Long Short-Term Memory-A(LSTM-A); the Giga Floating-point Operations Per Second(GFLOPs) of the algorithm are reduced dramatically, which is about 1/13 of the Visual Alignment Constraint(VAC) model and 1/57 of the Spatial-Temporal Multi-Cue(STMC) model; and the algorithm takes better account of the speed and accuracy of sign language recognition.

Abstract 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 Minimally invasive procedures for endovascular interventions involve manual navigation of a guidewire. Endovascular interventions encompassing highly tortuous vessels would benefit from guidewires which exhibit higher dexterity. This paper introduces a version of the COAST (COaxially Aligned STeerable) guidewire system capable of exhibiting higher dexterity. The system presented in this paper consists of three coaxially aligned tubes with a tendon to actuate the middle tube. Furthermore, it is possible to independently rotate the middle tube with respect to the outer tube. This variation enables the guidewire to achieve curvature in different planes while avoiding rotation of the entire structure. We also present the simulated stability of the guidewire with different outer tube geometries and experimentally validate the model. Experimental analysis and modeling of the kinematic behavior of the system is presented. A model to calculate the curvature vs. tendon stroke relationship for the optimal notch geometry is presented with an average RMSE of 0.16 mm. A control strategy addressing the snapping instabilities to ensure reliable operation is discussed. A custom phantom vessel and an aortic arch phantom model were used to demonstrate the ability of the system to safely navigate through tortuous pathways without exhibiting these elastic instabilities.

Abstract 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 (D 1) and detection cost (D 2). 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 Multimodal Conditioned face image generation and face super-resolution are significant areas of research. To achieve optimal results, this paper utilizes diffusion models as the primary engine for these tasks. This paper presents two main contributions: (1) “Speaking the Language of Faces” (SLF): a flexible, modular, fusion-less and architecturally simple multimodal system. (2) A Scalability scheme and a sensitivity analysis which can assist practitioners in system parameter estimation and feature selection. SLF consists of two main components: a feature vector generator (encoder), and an image generator (decoder) utilizing a conditional diffusion model. SLF can accept various inputs, including low-resolution images, speech signals, person attributes (age, gender, ethnicity), or any combination of these. Moreover, Scalability based on conditional scale values is utilized. The implementation of SLF has confirmed its versatility (e.g., speech to face image generation, conditioned face super-resolution). We trained multiple system versions to conduct a sensitivity analysis and to determine the influence of each individual feature on the output image. Consequently, speaker embeddings have proven to be sufficient audio features for our task. It was also found that the effects of audio signals are profound and are more pronounced than those of the low resolution images (8 × 8), whose effects are still significant. The effect of gender, ethnicity and age were found to be moderate. On another note, conditional scale values significantly impact the system’s behavior and performance.

Abstract This paper reports on the development of a method for enhanced non-destructive assay (NDA) of radioactive waste using the novel technique neutron-gamma emission tomography (NGET). The technique relies on the detection of correlated fast neutrons and gamma rays emitted in spontaneous or induced fission. It is based on fast organic scintillators and enables sensitive detection and three-dimensional (3D) localization of the fission events. The technique is passive and does not require moving components. In this work, we apply the NGET technique to the category of radioactive waste which is often referred to as historic or legacy waste. This can include mixed wastes encased in shielded containers many decades ago, before the advent of detailed waste description criteria. These low or intermediate level wastes are often associated with lacking, limited or conflicting documentation. This poses a challenge when assigning the waste to the proper disposal route as well as in deciding whether the waste needs to undergo sorting and conditioning to fulfil waste acceptance criteria both with regards to safe interim storage and to its ultimate disposal. Actinides, such as isotopes of uranium and plutonium, with their typically long half-lives and decay chains are of special interest in this regard since they may challenge the long-term safety assessment in repositories predicated on mainly shorter half-life radionuclides if undetected. Accurate identification and localisation of actinides is also important from a safeguards perspective, especially since they are generally difficult to detect and localise by established passive means due to their relatively weak radiation emissions, in particular in shielded containments and in the presence of strong radiation fields from other radioactive materials. In this paper we present findings of measurements on shielded containments of long-lived radioactive waste performed at the Studsvik site in Sweden, as well as measurements on a laboratory assembly simulating a grouted waste drum. Similarities and differences between the novel NGET technique and a commercially available gamma imaging system are also briefly discussed.

Abstract 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 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 Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents’ abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA.

Abstract Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices.

Abstract This paper introduces a Hybrid Plasmonic Nano-Antenna (HPNA) with a gradient-index dielectric flat lens modeled with different materials to enhance and steer the radiation in a particular direction based on a phase shift array. Firstly, the design of hybrid plasmonic Nano-Antenna (NA) is introduced and analyzed considering different horn-shapes such as diamond, hexagonal, circular, rectangular, and square shapes. The commercial software Computer Simulation Technology-Microwave Studio (CST-MWS) is used to analyze the radiation characteristics of the plasmonic NAs at the standard telecommunication wavelength of 1,550 nm. The produced horn-shaped nano-antenna made up from gold cladding with low- and high-index dielectric materials of SiO2 and InGaAs, respectively. The gain of the Square Horn shape Hybrid Plasmonic Nano-Antenna (SHHPNA) achieves the greatest gain with a value of 10.7 dBi at the desired frequency and the return loss reached -18.09 dB due to the wide aperture area for SHHPNA, which results in a narrower beam-width and higher gain. Moreover, by using two different shapes of dielectric flat lens to enhance the antenna’s performance by improving directivity while correspondingly reducing beam-width, the gain is enhanced and reaches 16.7 for SHHPNA with a circular lens and 16.9 for SHHPNA with a rectangular lens compared with the traditional NA that equal to 9.03 dBi. The main lobe for SHHPNA with each lens is more directed, with Side Lobe Level (SLL) and Half Power Beam-Width (HPBW) of -13.1 dB and 16.5° for SHHPNA with a circular lens and -15.1 dB and 15.4° for SHHPNA with a rectangular lens, respectively. In addition, the array configuration was investigated, and the gain was found to be 21 dBi for the single row array of 4×1 and 23.2 dB for the array of 3×3. Moreover, the array of 4×1 and 3×3 with +90° showed gains of 18.6 dBi and 20.7 dBi, respectively, compared to traditional paper with gains of 11.20 dBi and 13.1 dBi.

Abstract The BIS-TMDI is a hybrid control strategy that significantly reduces the seismic response of structures compared to conventional BIS through proper tuning, albeit with the trade-off of increased control forces exerted by the TMDI on the host building structure. However, these potentially significant forces are generally not considered in current TMDI tuning approaches. In this paper, a multi-objective optimization design method is herein developed to determine the compromise between the competing objectives of suppressing earthquake-induced vibrations in buildings and avoiding the generation of excessive control forces of TMDI. The study shows that under non-stationary excitations, the average control force of the TMDI system can be reduced by 16% compared to single optimization constraint, with a maximum reduction of up to 25%. Finally, the optimization design methods of this paper are applied to an 8-story frame as a validation case.

Abstract The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts.

Abstract This article presents the unique research results of the comprehensive analysis of the porous structure of activated carbons obtained from biomass waste materials from the wood industry during activation in an air atmosphere. The porous structure was analysed on the basis of nitrogen and argon adsorption isotherms via complementary multi-method analysis, i.e. the new numerical clustering-based adsorption analysis, the non-local density functional theory and the quenched solid density functional theory methods. The analytical results for the prepared activated carbons were compared with analogous results obtained for commercial activated carbon. On the basis of the conducted studies it has been determined that the new numerical clustering-based adsorption analysis method gives credible and valuable information on the textural properties of activated carbons which are in strict correlation and mutually complement with the results of the analysis with the use of the quenched solid density functional theory method. The research results obtained in this paper, it has also been shown that from waste materials of the wood industry, in a relatively cheap and cleaner production process, it is possible not only to obtain carbonaceous materials almost comparable to commercial activated carbon, but also to manage the waste in accordance with the principles of a closed-loop economy and sustainable development. The paper pays also attention to the often overlooked economic and ecological aspects, which should nevertheless be taken into account when comparing different adsorbents, rather than their textural properties alone.