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This review explores circular economy principles in regard to ethanol production from paper-mill sludge. Environmental sustainability and renewability over fossil fuels make second generation ethanol an attractive energy source in a rapidly growing population and consumption world. Paper sludge (PS), a by-product of the pulp and paper (P&P) industry, can no longer be recycled for paper production and is mainly disposed of in landfills. Therefore, it poses a major environmental challenge. However, it has shown potential as a valuable raw material for ethanol production, along with other pulp and paper products, due to its abundant availability and high cellulosic content. This waste-to-energy (WtE) technology for ethanol production is proposed as an alternative, aligning with circular economy concepts to maximize resource efficiency and minimize waste. This review underlines the circular economy aspects of bioethanol production within paper mill sludge management systems. Circular economy principles applied to ethanol production from PS offer a promising avenue for sustainable biofuel development that not only addresses waste management challenges but also enhances the overall environmental performance of biofuel production. Furthermore, economic benefits are described, highlighting the potential for job creation and community development. [ABSTRACT FROM AUTHOR]

In recent years, due to environmental awareness regarding the harmfulness of polymeric materials, there has been a growing interest in using papers, especially those from alternative raw materials. The importance of using all available raw materials is imperative today. Raw materials that were once viewed as waste are now of great importance because they have the ability to replace raw materials that are used irrationally or are lacking. Cereal straw is a lignocellulosic material that could be used in the paper industry and in the production of increasingly prevalent paper packaging. The aim of this research was to analyze the relationships between the qualitative parameters of straw-based printed papers containing 30% agricultural residues (wheat, barley, or triticale). The influence of two qualitative parameters (ink penetration depth and optical ink density) on the print-through was observed using multiple regression analysis on straw-based papers produced at the laboratory level. Throughout the research, 100% recycled wood paper was used as a reference sample. The results of the regression analysis showed that none of the variables individually make a statistically significant contribution to the prediction of the dependent variable in a linear context, that is, they indicate a non-linear interaction between the variables and the specific conditions under which the dependent variable reaches local extremes and changes in the gradient. Considering the results of the regression analysis and the visualization of the relationship, the model was additionally tested with other independent variables (paper type). From the results obtained, it can be concluded that the alternative straw-based paper with 30% barley pulp has the best interaction between ink and paper, which is the most similar to the reference sample in terms of printability, while the alternative straw-based paper with 30% wheat generally differs significantly from the reference paper when all three prints are considered. [ABSTRACT FROM AUTHOR]

Featured Application: Papermaking and printing industry, usability, i.e., legibility of printed text. Invasive alien plant species (IAPS) may cause threats to native biodiversity in ecosystems. Researchers have been investigating all the possible ways that they can be used effectively for other purposes. Since IAPS are capable of forming cellulose fibre nets, in this research, papers were made from three different types of IAPS (Japanese knotweed, giant goldenrod, and black locust). This research examined these IAPS papers and their effectiveness when used as printing substrates. In comparison to commercial office paper, the differences in basic, surface, optical, and microscopic properties were measured. As a widely used technology, inkjet printing was applied. We tested a commonly used sans-serif typeface (which has been established as being more legible than other typefaces in previous research) in three different type sizes (i.e., 8, 10, and 12 pt). According to the results, paper made from IAPS could offer some usable properties and acceptable legibility, especially when printing typefaces with specific attributes, such as moderate counter size, higher x-height, and minimal differences in the letter stroke width, are used. An appropriate typographic tonal density should be achieved in combination with an adequate letter size, e.g., 10 pt type size when a sans-serif typeface is used. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue in the journal Applied Sciences titled "Feature of Review Papers in Nanotechnology and Applied Nanosciences." The special issue aims to gather review papers that highlight recent developments and applications in the field of nanosciences. The seven contributions in this special issue cover a range of topics, including the use of nanocellulose for cultural heritage preservation, protective coatings for metal-based bipolar plates in water electrolysis, carbon dioxide electrocatalytic reduction reactions using transition metal sulfide nanomaterials, plasmonic techniques for detecting micro- and nano-plastics, graphene-based perfect absorbers, nano titanium dioxide for hydrogen production and storage, and X-ray spectroscopy studies of metal-salen complexes. The special issue provides a sample of the diverse applications of nanotechnology and addresses important issues in our polluted and climate-degraded world. [Extracted from the article]

Expansive soil exhibits significant swellings and shrinkages, which may result in severe damage or the collapse of structures built upon it. Calcium-based admixtures, such as lime, are commonly used to improve this problematic soil. However, traditional chemical additions can increase significant environmental stress. This paper proposes a sustainable solution, namely, the use of lignin fiber (LF) from the paper industry to partially replace lime as an amendment for expansive soils. Both the macroscopic and microscopic characteristics of the lignin fiber-treated expansive soil are extensively studied. The results show that the mechanical properties of expansive soil are improved by using lignin fiber alone. Under the condition of an optimal dosage of 8%, the compressive strength of lignin fiber-modified soil can reach 193 kPa, the shear strength is increased by 40% compared with the untreated soil, and the water conductivity is also improved with the increase in dosage. In addition, compared with 2% lime-modified soil, the compressive strength of 8% lignin fiber- and 2% lime composite-treated expansive soil increased by 50%, the cohesion increased by 12%, and the water conductivity decreased significantly. The microstructure analysis shows that at an 8% lignin fiber content, lignin fibers interweave into a network in the soil, which effectively enhances the strength and stability of the improved soil. Simultaneously, the fibers can form bridges across the adjacent micropores, leading to the merging of pores and transforming fine, dispersed micropores into larger, connected macropores. Lime promotes the flocculation of soil particles, forming larger aggregates and thus resulting in larger pores. The addition of fibers exerts an inhibitory effect on the flocculation reaction in the composite-improved soil. In conclusion, lignin fibers are an effective addition used to partially replace calcium admixture for the treatment of expansive soil, which provides a sustainable and environmentally friendly treatment scheme for reducing industrial waste. [ABSTRACT FROM AUTHOR]

Due to increased pressure on the availability of wood biomass in the EU and the regulatory attempts to lower CO2 values, where wood-based biomass plays a crucial role in carbon sequestration, the use of cellulose derived from alternative sources is gaining increased interest in the pulp and paper industry as well as in other industries. The processing properties of alternative fiber-based packaging need to be checked in current processing operations with other types of materials and recycling workflows. For example, in the production of folding boxes, after printing other converting properties such as glueability are also of great importance. The adhesive absorption and bonding strength of materials are important as adhesive joints of packaging can interfere with the protective function. In the presented research, three different paperboards produced on a pilot scale from alternative raw material sources were tested. Two paperboards were produced from the alien invasive plants Japanese knotweed and black locust, and one from residual sawdust. The basic paperboard properties were tested regarding paperboard porosity, roughness, z-directional tensile strength, and dynamical behavior regarding liquid interaction (contact angle and liquid penetration dynamic), as water-based adhesives were used in the research. For adhesive joint strength testing, Y- and T-peel adhesion testing was performed on the joint paperboard samples, as still there is no fully standardized method for the evaluation of such fiber-based material properties. The results indicate differences in the penetration dynamics of liquids. This parameter had the highest influence on the peel adhesion strength, while porosity, roughness, and dynamic contact angle were not so significant. Regarding the two adhesive joint tests, the differences in separate materials regarding peel adhesion curves show similar results. However, the Y-peel maximum force values are higher due to the testing setup (in comparison to the T-peel test). The paperboards made from invasive plants showed adhesive joint failures which are more suitable for tamper-proof packaging due to their low surface strength and crack propagation into the fiber structure. [ABSTRACT FROM AUTHOR]

Paperboard boxes represent a sought-after class of packaging products, where the use of recycled fibers offers a cost-effective and environmentally friendly alternative to virgin fibers. The presence of a significant proportion of recycled fibers in the paperboard leads to a number of limitations associated with the deterioration of its characteristics. In this study, the properties of coated and uncoated paperboards containing recycled fibers, wood pulp, and virgin cellulose fibers were investigated using a laboratory sample of paperboard produced from 100% recycled fibers without any chemical additives for comparison. Properties such as smoothness, bulk density, absorbency, and tensile strength, as well as colorimetric characteristics, were determined for the recycled paperboards; for the latter test, charts were printed using inkjet printing and UV-curable inks. Whiteness was calculated by three formulae (CIE, Berger, and Stensby), and all the paperboards had a relatively low whiteness (not exceeding 90 CIE), with a yellowish tint due to recycled fibers and mechanical pulp in the composition. The absence of optical brightening agents in the paperboards was experimentally confirmed. The color gamuts of the paperboards were in direct ratio to their whiteness, with the highest ones demonstrated by the coated paperboards. Color reproduction, according to the tone value increase curves, also depended on the whiteness. Uncoated paperboards demonstrated smaller color shifts than the coated ones, with the greatest increase in tone for yellow color. Coated recycled paperboards are suitable for packaging printing under the same conditions as virgin fiber boards, while uncoated boards are the recommended choice for digital printing with UV inks. [ABSTRACT FROM AUTHOR]

This study evaluated the impact of soil modifiers on seed germination and soil quality. Mixtures of paper mill sludge (PMS) with camellia oilseed cake (CO) and peat moss (P), as well as with oilseed cake of toothache tree (TO) and peat moss (P), showed promising results as biostimulants for seed germination. The addition of PMS increased the germination index, indicating its beneficial effects on seed performance. The pH levels remained optimal, and the electrical conductivity values stayed within acceptable ranges, suggesting minimal hindrance to plant growth. The cation exchange capacity increased with PMS, enhancing nutrient availability. Composted mixtures exhibited increased phosphorus levels, contributing to soil fertility. While the organic matter content decreased after composting, the carbon-to-nitrogen ratio remained balanced. The bulk density increased after composting, potentially improving soil drainage. Overall, the TO-containing mixture demonstrated superior growth rates compared to the P. These findings support the use of PMS-based soil modifiers for enhancing seed germination and improving soil quality. [ABSTRACT FROM AUTHOR]

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research. [ABSTRACT FROM AUTHOR]

Featured Application: Papermaking industry and technology. Innovative applications of vegetal residues. This study examines the potential of four invasive plant species, both arboreal and herbaceous, within the riparian forest of the Umia River in Galicia, a common ecosystem in northern Spain. These invasive species (Arundo donax, Phytolacca americana, Eucalyptus globulus, and Tradescantia fluminensis) were collected and assessed for their suitability as an alternative source of pulp and paper materials for the paper industry to mitigate the environmental impacts associated with conventional cellulose fiber production from harmful monocultures. Cellulosic material from leaves, bark, and/or stems of each of the selected species was isolated from lignin and hemicelluloses through kraft pulping processes. Resulted fibers and pulps were analyzed visually, morphologically, chemically, and mechanically to evaluate their papermaking properties. To compare these properties with those of commercially available pulp, test sheets were concurrently produced using commercial bleached Eucalyptus cellulose. The findings reveal that the employed fibers exhibit promising characteristics for artistic paper production. Regarding the pulp, two refining times were tested in a PFI machine, and the Schopper–Riegler degree was measured. Paper sheets underwent various tests to determine thickness, basis weight, apparent volume, apparent density, permeability, and chemical composition, as well as microscopic optical and morphological properties. The fibers obtained from the waste derived from the removal of invasive exotic species and biodiversity control present a viable and intriguing alternative for decentralized paper production, yielding noteworthy results for the creative sector. This research highlights the potential of harnessing invasive species for sustainable and innovative paper manufacturing practices. [ABSTRACT FROM AUTHOR]

Carbon fiber paper (CFP) is one of the most important units of gas diffusion layer (GDL) in proton exchange membrane fuel cells (PEMFCs). The binder used in the wet-laid technique has a significant effect on the properties of CFP. In this work, the polyvinyl butyral/phenol-formaldehyde resin (PVB/PF) composite fibers firstly prepared by a dry spinning method were applied for CFP fabrication to replace traditional binders during the papermaking process and remove the PF impregnation process. In the composite fibers with a mass ratio of 5:5, PF phase with a size of about 2~3 μm evenly distributed in PVB matrix. PVB and PF were miscible to some degree, which was beneficial for their binding effect during hot-press. These composite fibers can successfully bind carbon fibers (CFs) during the papermaking process, and their residual carbon efficiently welded the CFs after heat treatment. The content and length of composite fibers in the mat affected the binding structure among CFs, which influenced the properties of CFP, increased the composite fibers' content and reduced their length, significantly improving the strength of CFP. Therefore, the application of this solid fiber binder could enhance the comprehensive properties of CFP by adjusting the fibers' parameters in the mat and also make the fabrication of CFP more environmentally friendly and low-cost. [ABSTRACT FROM AUTHOR]

A growing research interest currently exists in the use of paper as a building material. This work aims to present the results of a measurement campaign developed on innovative waste paper-based building components. The research was carried out in Southern Italy and used some local by-product aggregates. Three different mixture designs were developed in the laboratory by adding three kinds of biomass to a pulp paper blend: fava bean residues (FB), sawdust powder (SP) and coffee grains (CG) extracted from exhausted chaffs. A physical characterization was carried out measuring the bulk density and bulk porosity. Scanning Electron Microscopy (SEM) analysis of the single aggregates was followed by a microstructure analysis of the final components. Bulk density evaluation showed a range between 200 and 348 kg·m−3. Furthermore, thermal performances were measured; the thermal conductivity of the experimented samples ranged from 0.071 to 0.093 W·m−1·K−1, thus it is possible to classify the tested materials as thermal insulators. Moreover, the acoustic properties were evaluated and tested. The normal incidence sound absorption coefficient was measured by the impedance tube on cylindrical specimens. In general, a different behavior was observed between the upper and lower base of each specimen due to the manufacturing process and the shrinkage caused by the different interactions occurring between the aggregates and the pulp paper waste; for example, the presence of sawdust reduced shrinkage in the final specimens, with consequent smaller physical variations among the two faces. The correlation existing between the manufacturing process and the microstructural properties was also investigated by the estimation of the non-acoustical parameters using the inverse method and taking into account the JCA (Johnson, Champoux and Allard) model as a reference. [ABSTRACT FROM AUTHOR]

This study investigated the effects of wearing AR devices on users' performance and comfort ratings while performing order-picking (OP) tasks. In addition to a picking-by-paper list, two AR devices combined with three order information display designs were examined. Thirty adult participants joined. They searched for and found the boxes in the order list, picked them up, and returned to the origin. The time to complete the task and the number of incorrect boxes picked up were analyzed to assess the performance of the tasks. The subjective ratings of the participants on the comfort rating scale (CRS) and the simulator sickness questionnaire (SSQ) were collected to assess the designs of both the AR devices and the order information displays. It was found that the participants could complete the OP tasks faster when adopting the order map (4.97 ± 1.57 min) or the 3D graph display (4.87 ± 1.50 min) using either one of the AR devices than when using a paper list (6.03 ± 1.28 min). However, they needed more time to complete the OP tasks when wearing both types of AR glasses when the Quick Response (QR) code option was adopted (10.16 ± 4.30 min) than when using a paper list. The QR code scanning and display design using either one of the AR devices guaranteed 100% accuracy but sacrificed efficiency in task completion. The AR device with a binocular display and hand gesture recognition functions had a significantly lower CRS score in the dimensions of attachment and movement (5.6 and 6.3, respectively) than the corresponding dimensions (8.0 and 8.3, respectively) of the other device with a monocular and hand touch input design. There were complaints of eye strain after using both AR devices examined in this study. This implies that these AR devices may not be suitable to wear for extended periods of time. Users should take off the AR device whenever they do not need to view the virtual image to avoid eye strain and other discomfort symptoms. [ABSTRACT FROM AUTHOR]

The global drive to eliminate mercury is stimulating the development of LED lamps for UV curing of coatings. Despite its many advantages, the transition to this system in the furniture industry presents technological issues. This paper aimed to determine the impact of the paint line's individual technological and process parameters on the coating's optical properties, such as colour and gloss of furniture components made of white-coloured light-weight boards. Samples were prepared under the conditions of an industrial paint line using LED and traditional mercury lamps at the rate of 50 m/min. The colour was tested using the CIELAB system on a BYK GARDNER spectrophotometer, while the gloss was measured using the PICO GLOSS meter. Based on the data obtained, the optical parameters were found to be highly stable irrespective of the power drop in the LED lamps. The coordinates L*, b* and gloss G were affected by the amount of topcoat and basecoat, the number of coats, the mercury lamps' power and the panel grinding programs used. The results of the tests showed that high L* and b* values could be achieved by increasing the number of overlays. This solution also ensures less basecoat and topcoat consumption, contributing to large material savings. [ABSTRACT FROM AUTHOR]

Many researchers in the construction field have explored the utilization of augmented reality (AR) and its impact on the industry. Previous studies have shown potential uses for AR in the construction industry. However, a comprehensive critical review exploring the ways in which AR supports synchronized communication is still missing. This paper aims to fill this gap by examining trends identified in the literature and by analyzing both beneficial and challenging attributes. This work was performed by collecting numerous journal and conference papers, using keywords including "augmented reality", "construction", and "synchronous communication". The papers were then categorized based on the reported attributes that were indicated to be challenges or benefits. Throughout the analysis, several benefits were consistently reported, including training, visualization, instantly sharing information, decision making, and intuitive interaction. Similarly, several challenges were consistently reported, such as difficulty in manipulation, unfriendly interface, device discomfort, and sun brightness. Regarding other attributes, such as field of view, cost, safety hazards, and hands-free mode, researchers provided divergent reports regarding whether they were beneficial or detrimental to AR communication. These findings provide valuable guidance for future researchers and practitioners, enabling them to leverage AR for synchronized communication in ways that consistently offer value. [ABSTRACT FROM AUTHOR]

Oil-impregnated paper condenser transformer bushings are an important part of transformer equipment, and partial discharge (PD) occurred when defects exist on the condenser aluminum foil layers. Firstly, to study the PD process of the oil-paper insulated capacitance graded bushing with the defect of broken aluminum foil, a defective oil-paper bushing discharge sample is constructed to study the PD parameters and capacitance, and to discharge carbonization traces at different voltage levels. Then, in order to verify the process of condenser aluminum foil layer discharge and the space charge variation in the oil-paper insulation system of a sample model, the surface flashovers of a needle-plane discharge model based on the bipolar charge transport model and the hydrodynamic model was built. The simulation, by Transport of Diluted Species physics of COMSOL Multiphysics software, points out the discharge process of aluminum foil electrode caused by space charge action and electric field distortion under an electric field at different voltages. The results of simulation and sample bushing experiments showed that the PD process of the defective condenser foil layer is mainly divided into three stages: tip corona discharge, streamer in oil, and surface flashovers. The voltage amplitude is larger the more electrical branches are discharged and the shorter the discharge time is. The findings of the article have important implications for the discharge of the foil layer inside the oil-paper bushing. [ABSTRACT FROM AUTHOR]

This Special Issue covers the latest research on fatigue, performance, and damage assessments of concrete. Concrete fatigue is known as a phenomenon characterized by the repetition of loads. The fatigue-based performance of concrete may be affected by a combination of diverse loads, mechanical strength properties, and environmental changes. These factors eventually lead to poor concrete performance. In this Special Issue, nine papers were accepted, divided into three subgroups: two papers on fatigue, two papers on performance, and five papers on damage assessment. Unlike articles published in past Special Issues, our Special Issue contains papers that address concrete performance in concrete pavement applications. In particular, six of the articles contained in this issue concentrate on pavement performance and damage assessments, especially in terms of the Pavement Condition Index. Together with a brief introduction, the keywords, research significance and potential impacts of each article are summarized in this Editorial. [ABSTRACT FROM AUTHOR]

Microbial-induced calcite precipitation (MICP) is an innovative technology in civil engineering. However, the high cost of components and the fragility of the treated soil limit its wide use. One of the possible solutions is organic waste incorporation at different stages of the technology. In the present study, we consider the use of spent brewer's yeast (BSY) to produce bacterial inoculates and wastepaper, flax shives and sawdust as reinforcing additives into the soil. We showed that the replacement of expensive components of LB medium by BSY extract increased biomass growth characteristics of Bacillus subtilis K51, B. cereus 4b and Micrococcus luteus 6 strains by 1.4, 1.5 and 1.8 times, respectively, while for B. subtilis 168, they were comparable to LB medium. The urease activities of all strains were not reduced compared to the control. Among the three kinds of cellulose-containing waste, wastepaper incorporation into MICP-treated soil samples led to an increase in compressive strength by 2.1 times and precipitated calcite percentage by almost 1.5 times compared to a sample without additives. Thus, we showed the potential for soil stabilization through MICP using organic waste. [ABSTRACT FROM AUTHOR]

Featured Application: Sialic acid is a critical membrane constituent required to maintain mucosal integrity. This investigation describes the biochemical quantitation of salivary sialic acid as a rapid measure of oral health that corresponds with the clinical status of the patient. The test can be utilized widely including in remote facilities with limited resources. A rapid, low-cost, visual test for sialic acid using commonly available reagents complements the biochemical assessment. The test can be applied for screening large populations, monitoring oral health status, amenable to digital health and can advance patient engagement to manage their oral health. Objective: Sialic acid [SA] represents a critical mucosal membrane component maintaining mucosal integrity. This investigation stratified adult subjects based on clinical parameters of periodontal health to examine salivary sialic acid [SA] as a health measure and develop a corresponding rapid visual chair-side assay. Methods: Adults [n = 90] were enrolled and clinically stratified into healthy [n = 30], gingivitis [n = 29] or periodontitis [n = 31] groups. Saliva from subjects was evaluated for SA using the Ninhydrin method. A novel rapid SA spot test was developed utilizing filter paper discs soaked in a sialidase substrate. Substrate-laden disks were incubated at room temperature with saliva produced a blue color with increasing color intensities due to higher sialidase activity. Subjects were recalled weekly for clinical and salivary assessments. Results: Average baseline salivary SA in healthy, gingivitis and periodontal disease groups were 64, 95 and 102 µg/mL, respectively with significant differences (<0.05). Differences in SA concentrations among control and test groups were maintained throughout the study. Similarly, the differences in the color intensities in the rapid visual chair side spot test were also observed during the entire study period. Conclusions: Increasing levels of salivary SA were observed from healthy to periodontal disease with these differences remaining consistent over the study. These results corresponded with the chair-side visual assay, which is suitable for patient education or monitoring. [ABSTRACT FROM AUTHOR]

Transformers play a crucial role in power systems. In this respect, fault diagnosis and aging state assessment have garnered significant attention from researchers. Herein, accelerated thermal aging and Raman scattering experiments are conducted on oil–paper insulation samples to accurately detect aging states. The samples are categorized into different aging stages based on the polymerization degree of the insulating paper. Principal component analysis (PCA), multi-dimensional scale change method (MDS), and isometric mapping algorithm (Isomap) are employed to extract features from the Raman spectra. Subsequently, the XGBoost strong classifier, optimized through Bayesian hyperparameter optimization (BO-XGBoost), is utilized to distinguish between four and ten states among 175 groups of samples after feature extraction. The subsequent classification results of the three feature-extraction methods are compared. The results indicate that Isoamp, which pertains to the manifold dimension transformation, achieves the highest average discriminative accuracy after feature extraction. The discriminative accuracies for aging states four and ten are 97.0% and 95.1% respectively, demonstrating that Raman spectroscopy manifold dimension transformation enhances the distinctiveness between samples of different aging states in the feature-extraction process. The diagnostic model constructed with Isomap and BO-XGBoost enables accurate discrimination of the aging states of oil–paper insulation. [ABSTRACT FROM AUTHOR]

In recent years, the number of applications of virtual reality (VR) for the Autism spectrum disorder (ASD) population has increased and has become one of the most suitable tools to address the psychological needs of these individuals. The present scoping review aims to provide a literature mapping of experimental studies that have used immersive and semi-immersive VR for assessments or interventions specifically addressing high-functioning autism. A total of 23 papers were included and analyzed following PRISMA guidelines. The identified studies concerned social skills (11 papers), eye gaze and joint attention (3 papers), motor learning (3 papers), job training (2 papers), and other aims or rationales (4 papers). The evidence shows that, despite the intellectual potential of high-functioning ASD individuals, little research has been conducted to provide interventions that offer concrete training to improve their adaptive functioning. In addition, the percentage of individuals below 18 years of age is representative of half of the included studies, so aiming future studies at the early stages of development might be an asset in preparing the next generation of young adults to cope with age-related challenges, as early assessments and interventions are more likely to produce major long-term effects. [ABSTRACT FROM AUTHOR]

The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships' single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper's algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution's efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines. [ABSTRACT FROM AUTHOR]

In existing highlight removal methods, research on highlights on metal surfaces is relatively limited. Therefore, this paper proposes a new, simple, effective method for removing highlights from metal surfaces, which can better restore image details. Additionally, the approach presented in this paper is highly effective for highlight removal in everyday real-world highlight scenarios. Specifically, we first separate the image's illumination space based on the Retinex model and generate a highlight mask using the mean plus standard deviation method. Then, based on the mask, we transform the original image and the image at the corresponding mask position to the V channel of the HSV space, achieving the effective elimination of highlights. To enhance the details of the restored image, this paper introduces a method involving adaptive Laplacian sharpening operators and gradient fusion for detail enhancement at highlight removal positions. Finally, a highlight-free image with well-preserved details is obtained. In the experimental phase, we validate the proposed method using real welding seam highlight datasets and real-world highlight datasets. Compared with the existing methods, the proposed method achieves high-quality qualitative and quantitative evaluation. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Applied Sciences titled "Biomechanics and Human Motion Analysis." The special issue includes twenty papers that cover a wide range of topics related to the analysis of human motion and biomechanics. The studies involve both healthy subjects and patients with joint diseases, and they utilize musculoskeletal modeling to support their investigations. The papers were contributed by researchers from different countries and continents, highlighting the global nature of this field of study. The special issue aims to promote the integration of biomechanical analysis and instruments into motion analysis and to demonstrate the potential of modern techniques in addressing complex biomechanical problems in human body dynamics. The contributions cover a range of topics related to biomechanics and motion analysis, including the assessment of patellofemoral pain, shoulder kinematics in overhead sporting motions, tibiofemoral kinematics, military crawl locomotion, instability during stepping, upper body posture recognition, optimal control strategies during obstacle crossing, scoring of human body-balance ability, center of mass motion during gait, kinematic analysis of basketball shots, hidden Markov models in movement pattern identification, smart phone-based motion capture, gait analysis in personalized high tibial osteotomy, walking without an anterior cruciate ligament, crouch gait analysis, spine, pelvis, and hip kinematics, hamstring torque measurements, modeling musculoskeletal dynamics during gait, comparison of joint axis estimation approaches, and balanced foot dorsiflex [Extracted from the article]

This document is a summary of a special issue in the journal Applied Sciences titled "Artificial Intelligence Technology in Medical Image Analysis." The special issue explores the applications of artificial intelligence (AI) in medical imaging and its impact on diagnostic and therapeutic processes. The use of AI-powered tools in image interpretation has shown exceptional capabilities in detecting and diagnosing medical conditions from imaging data, particularly in radiology. AI also contributes to improving image quality, automating routine tasks, and streamlining healthcare workflows. However, challenges such as data privacy, ethics, and regulatory frameworks need to be addressed for responsible implementation. The special issue includes several research papers that present advancements in automated medical decision support, age estimation, quality assurance, orthotic insole recommendation, tumor identification, thalamus segmentation, medical image classification, hyperparameter optimization, lung disease classification, and thoracic cavity segmentation. These papers demonstrate the potential of AI in improving accuracy, efficiency, and personalized treatment in medical image analysis. The integration of AI into healthcare requires collaboration between AI researchers, healthcare professionals, and regulatory bodies to ensure responsible and effective deployment. The future of AI in medical image analysis holds promise for improved diagnostic accuracy, early disease detection, and personalized treatment strategies. [Extracted from the article]

Under the premise of resource constraint, it is crucial to identify the product innovation opportunities contained in failed patents through external search in order to compensate for the shortcomings of enterprises' own technology. Due to the cost of patent research and development and the risk of infringement, this paper constructs a product innovation identification path that integrates the FOS and BERTopic model from the perspective of invalid patents. The path consists of three stages, including the identification of the problem to be solved by the product based on functional analysis, the extraction of the subject matter elements based on the core failed patent technology, and the generation and evaluation of innovative solutions based on TRIZ theory and the best- worst method (BWM). Finally, the feasibility of the path constructed in this paper is verified by taking a slurry pump as an example. The application results show that the product innovation identification path constructed in this paper can provide theoretical support for enterprises to carry out technological innovation activities efficiently. [ABSTRACT FROM AUTHOR]

Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric analysis within the research domain dedicated to the utilization of agent-based modeling (ABM) in the field of transportation. By employing specific keywords related to both agent-based modeling and transportation, we have identified and extracted 1016 scholarly papers from the ISI Web of Science database, spanning the period from 2002 to 2023. Through the application of bibliometric methods, we have systematically examined key contributors, affiliations of academic institutions, influential publications, and renowned journals within this domain. Our analysis reveals a consistent and robust growth in scholarly interest pertaining to agent-based modeling in the field of transportation throughout the considered period. Notably, within approximately four decades of ABM's application in transportation, a distinct upward trajectory began in 2008, culminating in the year 2021. The entire considered period witnessed a remarkable surge in paper production, characterized by an annual growth rate of 21.67%. Furthermore, employing an n-gram analysis, we have delineated and discussed the principal areas within transportation that have progressively benefited from the advancements in agent-based modeling. Prominently, the domains of air transport and road transport have exhibited substantial development over time, while the implications of climate change have emerged as a persistent concern throughout the entire study period. [ABSTRACT FROM AUTHOR]

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. [ABSTRACT FROM AUTHOR]

The early detection of cognitive impairment is essential in order to initiate interventions and guarantee access to healthcare services. Digital solutions are emerging in the literature as an alternative approach to cognitive screening. Our primary goal is to synthesize the evidence on digital solutions' diagnostic ability to screen for cognitive impairment and their accuracy. A secondary goal is to distinguish whether the ability to screen for cognitive impairment varies as a function of the type of digital solution: paper-based or innovative digital solutions. A systematic review and meta-analysis of digital solutions' diagnostic accuracy were conducted, including 25 studies. Digital solutions presented a variable diagnostic accuracy range. Innovative digital solutions offered at least 0.78 of sensitivity but showed lower specificity levels than the other subgroup. Paper-based digital solutions revealed at least 0.72 of specificity, but sensitivity started at 0.49. Most digital solutions do not demand the presence of a trained professional and include an automatic digital screening system and scoring, which can enhance cognitive screening and monitoring. Digital solutions can potentially be used for cognitive screening in the community and clinical practice, but more investigation is needed for an evidence-based decision. A careful assessment of the accuracy levels and quality of evidence of each digital solution is recommended. [ABSTRACT FROM AUTHOR]

Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a multiscale permutation entropy and adaptive wavelet thresholding (ICEEMDAN-MPE-AWT) denoising method and an SE-ResNeXt50 transfer learning model are proposed. Initially, the vibration signal is denoised by ICEEMDAN-MPE-AWT, the denoised vibration signal is then converted into a Gram angle field (GAF) diagram, and then the parameters are transferred by the fine-tuning transfer learning strategy. Finally, a GAF diagram is input into the model for training to achieve fault extraction and classification. In this paper, the open gear dataset of Southeast University is used for experimental research. The experimental results show that when using the ICEEMDAN-MPE-AWT and when the signal-to-noise ratio (SNR) of the experimental data is −4 dB, the average accuracy of the GASF+TSE-ResNeXt50 and the GASF+TSE-ResNeXt18 can reach 98.8% and 97.5%, respectively. When the SNR is 6 dB, the accuracy of the above two models reaches 100% and 99.3%, respectively. Moreover, when compared to alternative approaches, the noise reduction method in this paper can better remove noise interference so that the model can better extract fault features. Therefore, the method proposed in this article shows significant improvement in noise reduction and fault classification accuracy compared to other methods. [ABSTRACT FROM AUTHOR]

Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper's integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering. This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. This research paper's main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award, hence saving time and cost. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices. [ABSTRACT FROM AUTHOR]

With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper. [ABSTRACT FROM AUTHOR]

The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment. [ABSTRACT FROM AUTHOR]

Supply chain and logistics management is of tremendous importance for multinational organizations. Logistics Service Providers (LSPs) provide logistics services and smooth logistics operations between suppliers, manufacturers, distributors, and customers. This paper uses a Systematic Literature Review (SLR) to identify the current trends and future developments of LSPs and the underlying (smart) logistics operations connected to the concept of lifecycle management. An SLR review was conducted to identify relevant research papers in the areas of LSPs and logistics lifecycle management. Out of 288 papers analyzed, 81 were identified as highly appropriate for in-depth analysis. The LSP Lifecycle Model (LSLM) was then developed by combining logistics service characteristics and the lifecycle management concept, including Product Lifecycle Management (PLM), Service Lifecycle Management (SLM), and Product Service System (PSS). The LSLM consists of three phases: The Beginning of Life (BOL), the Middle of Life (MOL), and the End of Life (EOL). The LSLM is characterized by three phases, eight criteria, and seventeen sub-criteria. This paper aims to fulfil customer requirements through a product or service in the whole lifecycle of the logistics service provider. The findings further present an adaptable LSLM by focusing on various logistics services and integrating sustainability factors to meet market trends. Logistics cost factors can also be used to evaluate logistics services in the MOL stage. The EOL shows the trend of risk management, evaluation, and decomposition, which is determined by new or re-designed logistics products and services. [ABSTRACT FROM AUTHOR]

In the field of Chinese-named entity recognition, recent research has sparked new interest by combining lexical features with character-based methods. Although this vocabulary enhancement method provides a new perspective, it faces two main challenges: firstly, using character-by-character matching can easily lead to conflicts during the vocabulary matching process. Although existing solutions attempt to alleviate this problem by obtaining semantic information about words, they still lack sufficient temporal sequential or global information acquisition; secondly, due to the limitations of dictionaries, there may be words in a sentence that do not match the dictionary. In this situation, existing vocabulary enhancement methods cannot effectively play a role. To address these issues, this paper proposes a method based on lexical information and spatial features. This method carefully considers the neighborhood and overlap relationships of characters in vocabulary and establishes global bidirectional semantic and temporal sequential information to effectively address the impact of conflicting vocabulary and character fusion on entity segmentation. Secondly, the attention score matrix extracted by the point-by-point convolutional network captures the local spatial relationship between characters without fused vocabulary information and characters with fused vocabulary information, aiming to compensate for information loss and strengthen spatial connections. The comparison results with the baseline model show that the SISF method proposed in this paper improves the F1 metric by 0.72%, 3.12%, 1.07%, and 0.37% on the Resume, Weibo, Ontonotes 4.0, and MSRA datasets, respectively. [ABSTRACT FROM AUTHOR]

Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools assist pathologists to obtain a faster and more accurate cancer diagnosis. Stain variation between hospitals hampers the performance of CBHIR tools. This paper explores the effects of color normalization (CN) in a recently proposed CBHIR approach to tackle this issue. In this paper, three different CN techniques were used on the CAMELYON17 (CAM17) data set, which is a breast cancer data set. CAM17 consists of images taken using different staining protocols and scanners in five hospitals. Our experiments reveal that a proper CN technique, which can transfer the color version into the most similar median values, has a positive impact on the retrieval performance of the proposed CBHIR framework. According to the obtained results, using CN as a pre-processing step can improve the accuracy of the proposed CBHIR framework to 97% (a 14 % increase), compared to working with the original images. [ABSTRACT FROM AUTHOR]

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively. [ABSTRACT FROM AUTHOR]

Collaborative localization is a technique that utilizes the exchange of information between multiple sensors or devices to improve localization accuracy and robustness. It has a wide range of applications in autonomous driving and unmanned aerial vehicles (UAVs). In the field of UAVs, collaborative localization can help UAVs perform autonomous navigation and mission execution in complex environments. However, when GNSS is not available, it becomes challenging to position the UAV swarm relative to each other. This is because the swarm loses its perception and constraint of the position relationship between each member. Consequently, the swarm faces the problem of the serious drift of relative accuracy for an extended period. Furthermore, when the environment is faced with complex obstruction challenges or a camera with low texture scenes, noise can make it more difficult to solve the relative position relationship between drones, and a single UAV may lose positioning capability. To solve these specific problems, this paper studies a swarm co-operative localization method in a GNSS-denied environment with loud noise interference. In this paper, we proposed a method that utilizes a distributed scheme based on an incremental smoothing and mapping (iSAM) algorithm for state estimation. It incorporates new anchor-free topological constraints to prevent positioning failures and significantly improve the system's robustness. Additionally, a new switching function is applied in front of each factor of the loss function, which adjusts the switches in real time in response to the input information to improve observably the accuracy of the system. A novel co-operative incremental smoothing and mapping (CI-SAM) method is proposed and the method does not require a complete relative position measurement, which reduces the need for vehicle measurement equipment configuration. The effectiveness of the method is verified by simulation. [ABSTRACT FROM AUTHOR]

Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher's work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the same name. In order to handle this issue, we propose a name disambiguation scheme for researchers with the same name based on heterogeneous academic sites. The proposed scheme collects and integrates research results from these varied academic sites, focusing on attributes crucial for disambiguation. It then employs clustering techniques to identify individuals who share the same name. Additionally, we implement the proposed rule-based algorithm name disambiguation method and the existing deep learning-based identification method. This approach allows for the selection of the most accurate disambiguation scheme, taking into account the metadata available in the academic sites, using a multi-classifier approach. We consider various researchers' achievements and metadata of articles registered in various academic search sites. The proposed scheme showed an exceptionally high F1-measure value of 0.99. In this paper, we propose a multi-classifier that executes the most appropriate disambiguation scheme depending on the inputted metadata. The proposed multi-classifier shows the high F1-measure value of 0.67. [ABSTRACT FROM AUTHOR]

Featured Application: One of the main technological challenges within Circular Economy strategies is to minimize the environmental impact of plastic packaging. In this regard, the use of PHBV films to coat paper sheets represents a highly sustainable strategy to produce food packaging multilayer structures with improved mechanical and barrier properties. This work reports on the development and performance evaluation of newly developed paper sheets coated, on both sides, with thin films of biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) for applications of food packaging. For this, PHBV/paper/PHBV multilayers were first prepared by the thermo-sealing technique, optimizing the process variables of temperature and time. Thereafter, the multilayer sheets were characterized in terms of their morphological, optical, thermal, mechanical, and barrier properties and compared with equivalent paper structures double coated with high-barrier multilayer films of petrochemical polymers. The results indicated that the double coatings of PHBV successfully improved the mechanical resistance and ductility, protected from moisture, and also reduced the aroma and oxygen permeances of paper, having a minimal effect on its optical and thermal properties. Finally, the compostability of the resultant multilayer sheets was analyzed, confirming that the presence of the PHBV coatings slightly delayed the aerobic biodegradation and disintegration of paper. [ABSTRACT FROM AUTHOR]

Arsenic is a metalloid frequently found in contaminated sites, especially in the soil. In this regard, soil contamination has attracted researchers' attention because it affects soil suction, which is essential in unsaturated soil mechanics. Due to its reliability and low cost, the filter paper method is frequently used to evaluate soil suction. However, it is laborious and time consuming. As an alternative, different mathematical models have been developed to estimate natural soil's suction. The objective of this study was to elucidate if Fuentes' model (using fractal, Mualem, and Burdine restrictions) can be used to estimate the soil-water retention curve of an arsenic-contaminated soil by comparing it with the filter paper method data already reported. The results showed that under natural conditions, Fuentes' model provided similar results to those obtained by the filter paper method. The model also suggested modification of the soil's structure, observed by the increase in the soil's particle diameter after contamination. However, Fuentes' model was observed to overestimate the suction values for contaminated soils. This observation highlights the necessity to use a soil stabilization method to control the soil's volume variations. The data also showed that Fuentes' model overestimated the hydraulic conductivity function of the contaminated soil. Hence, the retention potential does not reflect the behavior of the contaminated soils and can induce misinterpretation of contaminant transport evaluation in soil. Nevertheless, further analyses should be performed to investigate the direct applicability of this model to soils contaminated with other substances. [ABSTRACT FROM AUTHOR]

The article presents a new technique for analyzing phenomena occurring during the measurement of the strength properties of paper in the conditions of compression of the tested samples with forces acting in the paper plane. The technique is based on collecting data on the current distance of the clamps holding the tested sample and the force exerted on the sample using a universal testing machine and on the simultaneous recording of image sequence of the sample during the measurement. Next, the resulting images are subjected to processing and analysis, the purpose of which is to extract information about the shape of the sample edge in all phases of the measurement. Its advantage is the ability to determine the deflection arrow of the sample and describe its shape using the selected function given by the analytical parametric formula. It will be helpful in further research on the development of an analytical model describing the phenomena occurring during paper compression, and a method to determine the mechanism of paper destruction and the corresponding maximum force that destroys a paper sample. [ABSTRACT FROM AUTHOR]

The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources, such as web-pages, blogs, emails, social media, and articles, one of the most common tasks in NLP is the classification of a text corpus. This is important in many institutions for planning, decision-making, and creating archives of their projects. Many algorithms exist to automate text classification tasks but the most intriguing of them is that which also learns these tasks automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables a reduction in the curse of dimensionality in text classification. Experiments with the model were carried out on the Twitter US Airline dataset, the Conference Paper dataset, and the SMS Spam dataset, outperforming baseline models with 98.40%, 89.90%, and 99.26% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this paper gives a system framework for production capacity prediction in discrete manufacturing workshops based on digital twins. Then, a mathematical model is described for discrete manufacturing workshop production capacity under multiple disturbance factors. Furthermore, an innovative production capacity prediction method, using the "digital twin + Long-Short-Term Memory Network (LSTM) algorithm", is presented. Finally, a discrete manufacturing workshop twin platform is deployed using a commemorative disk custom production line as the prototype platform. The verification shows that the proposed method can achieve a prediction accuracy rate of 91.8% for production line capacity. By integrating the optimization feedback function of the digital twin system into the production process control, this paper enables an accurate perception of the current state and future changes in the production system, effectively evaluating the production capacity and delivery date of discrete manufacturing workshops. [ABSTRACT FROM AUTHOR]

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to research and develop maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) with partial module shading and sudden changes in solar irradiance. Modified cat swarm optimization (MCSO) was adopted to track the global maximum power point (GMPP) of the PVMA. Upon a sudden changes in solar irradiance or when certain modules in the PVMA were shaded, the maximum power point (MPP) of the PVMA will change accordingly, and multiple peak values may appear on the power–voltage (P-V) characteristic curve. Therefore, if the tracking pace is constant, the time required to track the MPP might extend, and under certain circumstances, the GMPP might not be tracked, as only the local maximum power point (LMPP) can be tracked. To prevent this problem, a maximum power point tracker based on MCSO is proposed in this paper in order to adjust the tracking pace along with the slope of the P-V characteristic curve and the inertia weight of the iteration formula. The initial voltage for tracking commencement was set to 0.8 times the voltage at the maximum power point of the PVMA under standard test conditions. Firstly, MATLAB 2022a was used to construct the four-series, three-parallel PVMA model under zero shading and partial shading. The feedback of PVMA voltage and current was obtained, where the GMPP was tracked with MCSO. From the simulation results, it was proven that, under different shading percentages and sudden changes in solar irradiance for partial modules in the PVMA, the MCSO proposed in this paper provided better tracking speed, dynamic response, and steady performance compared to the conventional CSO. [ABSTRACT FROM AUTHOR]

The real-time operational safety of in-service bridges has received wide attention in recent years. By fully utilizing the health monitoring data of bridges, a structural abnormal pattern detection method based on data mining can be established to effectively ensure the safety of in-service bridges. This paper takes a large-span arch bridge as the research object, analyzes the time-based variation of the main monitoring data of the structure, establishes Lasso regression models for load characteristic indicators and vertical bending fundamental frequency of the structure under different time scales, and uses the residuals of the Lasso model to indicate the structural state and identify abnormal patterns. Firstly, the monitoring data of bridge structural temperature, girder end displacement, and girder acceleration were analyzed, and the interrelationships were studied to extract characteristic parameters of structural load characteristics and structural frequency. Then, the time-varying patterns of structural response were analyzed, and Lasso regression models and their regression variables were discussed based on monitoring data under two different time scales: daily cycle and annual cycle. The abnormal pattern detection method for bridge structures was developed. Finally, the effectiveness of this method was verified by taking the bridge deck pavement replacement as the abnormal pattern. The research results indicate that the proposed bridge structure abnormal pattern detection method based on Lasso regression can effectively monitor changes in the state of the bridge, and the residual dispersion of the model established on the annual cycle scale is relatively smaller than that on the daily cycle scale, resulting in better abnormal detection performance. [ABSTRACT FROM AUTHOR]

Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification. [ABSTRACT FROM AUTHOR]

Tomb murals are different from cave temple murals and temple murals, as they are underground cultural relics, their painting style is unique, solemn, and austere, and the performance image is characterised by simple colours, low contrast, and fewer survivors. During the digital restoration process, it is important to have sufficient reference samples to ensure the accuracy of the restoration. In addition, the style of mural paintings in the tombs varies greatly from other styles of murals and types of word paintings. Therefore, learning the unique artistic style of tomb murals, providing stylistically consistent training samples for digital restoration, and overcoming the problems of dim lighting and complex surface granularity of tomb murals are all necessary for research. This paper proposes a generative adversarial network algorithm that separates and fuses style features to enhance the generative network's ability to acquire image information. The algorithm extracts underlying and surface style feature details of the image to be tested and conducts fusion generation experiments. The generative network's parsing layer modifies the input noise tensor and optimises the corresponding weights to prevent misalignment between drawing lines and fresco cracks. Finally, to optimise the fresco generation effect, we add the corresponding loss function in the discriminator. The tomb murals dataset was established for experiments and tests, and quantitatively and qualitatively analysed with other style migration models, and SSIM, FID, LPIPS and NIQE were used as evaluation indexes. The results were 0.97, 269.579, 0.425 and 3.250, respectively, and the effect of style migration of this paper's method was significantly higher than that of the control group model. [ABSTRACT FROM AUTHOR]