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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.

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.

Woody and agricultural wastes are important fuels in many countries, and have the potential of being even more important in the future. The main problems of plant biomass combustion are low ash melting temperatures and increased emissions. The most widely used treatment to solve the problem of low ash melting point is blending a fuel with an additive. In this work, pellets were produced from wheat straw containing wood sawdust and paper sludge in the following proportions 40:40:20 and 45:45:10 (straw/sawdust/paper sludge). The purpose of this work was to study the influence of sludge and dendromass on the straw pellet parameters and combustion process. The highest calorific value of 15.71 MJ kg&minus, 1 was registered for a sample with a 10% paper sludge concentration. The effectiveness of paper sludge was proved, and the ash melting temperature was increased from 1025 to 1328 &deg, C for the same sample.

Due to several of their characteristics, such as their renewability, sustainability, and eco-friendliness, nanocellulose-based materials are arousing growing interest from researchers in various fields of study and applications. The purpose of this review article is to provide an overall view of the most recent applications of these innovative bio-nanomaterials in the field of cultural heritage. First, an introduction of the different classes of cellulose nanomaterials and their synthesis and characterization methods is presented. After that, many consolidation treatments based on nanocellulose structures for the recovery of degraded and archeological wood, the stabilization of damaged painting canvases, and the deacidification of historical papers are shown in order to underline the advanced potential of nanocellulose for the conservation of artistic heritage and the respect for the environment.

The study of materials has entailed several efforts by materials scientists to gain a deep understanding of their structural, mechanical, chemical, optical, magnetic and electronic properties and engineering applications [...]

This study compares the performance of graph convolutional neural network (GCN) models with conventional natural language processing (NLP) models for classifying scientific literature related to radio frequency electromagnetic field (RF-EMF). Specifically, the study examines two GCN models: BertGCN and the citation-based GCN. The study concludes that the model achieves consistently good performance when the input text is long enough, based on the attention mechanism of BERT. When the input sequence is short, the composition parameterλ, which combines output values of the two subnetworks of BertGCN, plays a crucial role in achieving high classification accuracy. As the value ofλincreases, the classification accuracy also increases. The study also proposes and tests a simplified variant of BertGCN, revealing performance differences among the models under two different data conditions by the existence of keywords. This study has two main contributions: (1) the implementation and testing of a variant of BertGCN and citation-based GCN for document classification tasks related to radio frequency electromagnetic fields publications, and (2) the confirmation of the impact of model conditions, such as the existence of keywords and input sequence length, in the original BertGCN. Although this study focused on a specific domain, our approaches have broader implications that extend beyond scientific publications to general text classification.

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.

Paper production and recycling result in large amounts of recycled paper sludge (RPS) that is currently being disposed of in very costly and unsustainable practices, raising the importance of developing green solutions for waste management. The use of nanocellulose (NC) as the next generation of materials has gained much attention due to its economic potential. However, there are substantial challenges in NC extraction, detection, and quantification methods. In this study, NC was produced from RPS as a means of converting waste into a resource. The process included a short, 30 min ozonation (21 mg O3/g RPS), which allowed a sufficient delignification and facilitated the following hydrolysis step. Among all tested durations, a 4-h hydrolysis with 64% w/w sulfuric acid resulted in the highest NC production. Fluorescent staining by calcofluor white was used for simple and low-cost detection of NC in-situ. Crude NC showed a significant 63% dye uptake of 0.1 ppm acid red 131 within 30 min. Compared to the standard disposal methods of RPS, its utilization for NC production supports the circular economy concept and significantly contributes to the development of cellulose bio-based nanomaterials for water treatment applications.

This paper reports on the manufacture of ZnO nanoparticles (ZnO NPs) from Prosopis juliflora leaf extracts. Various methods of characterization were used, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), and transmission electron microscope TEM. ZnO NPs has a hexagonal wurtzite structure with a preferred orientation of 101 planes, according to XRD. The functional groups found in ZnO NPs isolated from leaves are responsible for the FT-IR peaks that correspond to them. The morphology of the produced nanoparticles is a sphere-like form, as shown in the SEM pictures. TEM examination revealed ZnO NPs with a size of 50–55 nm. These ZnO NPs were used to remediate pollutants in paper mill effluents, and they were able to remove 86% of the organic pollutants from the sample at 0.05 mg/L dose and reduce 89% of the organic pollutants during a 5-h reflex time. Meanwhile, for the photocatalysis of paper mill effluents, it has been noted that COD was removed by 74.30%, 63.23%, and 57.96% for the first, second, and third cycles, respectively.

Applied Sciences has reached a remarkable milestone by publishing its 10th volume in 2020 [...]

Fungi are the main contaminants of books and archival documents. In addition to their degrading power, offered by various types of lignolytic and cellulolytic enzymes, they can also hue the surface of the paper through the production of pigments. The fungi on paper release various types of pigments belonging mostly to two chemical groups (polyketides and carotenoids), which cause unpleasant anaesthetic stains. The paper surface can also be hued with several synthetic colors, which are part, for example, of stamps and inks. These synthetic colors could be degraded by lignin-modifying enzymes (LMEs) and also by dye-decolorizing peroxidases (DyPs). Therefore, the mechanism of action of LEMs and DyPs is illustrated. Moreover, we have examined the potentiality of LEMs and DyPs to remove the synthetic stains and also their hypothetical application in order to clean the fungal hues from the paper surface. Our review article, using the enzymatic removal parallelism between fungal and synthetic pigments, would like to show prospective solutions to this arduous problem.

In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.

Paper sludge ash (PSA) typically has a low Si abundance and significant Ca content because of the presence of calcite fillers, which interfere with the zeolitic conversion of PSA. Ca-masking with ethylenediaminetetraacetic acid (EDTA) was used to reduce Ca interference during zeolite synthesis so that a zeolitic product with a high cation exchange capacity (CEC) could be synthesized. Hydroxysodalite, zeolite-P, hydroxycancrinite, tobermorite, and zeolite-A can be synthesized from PSA by an alkali reaction with EDTA. With the addition of EDTA, calcium ions in the solution were trapped by chelation, and the number of zeolitic crystals with low Si/Al (Si/Al = 1), zeolite-A, increased owing to the promotion of the synthesis reaction. A product with a high CEC that has a high zeolite-A content was obtained. The chelating agent can inhibit Ca interference for zeolite synthesis by Ca-masking, and a product with a high zeolite-A content can be obtained from PSA using EDTA.

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.

The introduction of mobile devices such as smartphones provides new opportunities to enhance the teaching–learning process of a foreign language. However, its use can affect not only the students’ type and form of writing but also their ability to achieve the academic competencies targeted. Thus, aspects such as the development of linguistic and communicative competencies could be affected. In this paper, a comparative analysis of two different versions (a paper and a digital version) of the same learning task is carried out in order to analyze the impact of each on the development of students’ linguistic and communicative competencies. The study is conducted with undergraduate students enrolled in a course called German II. Modern Language I (level A1.2, Common European Framework of Reference for Languages). The results obtained illustrate, on the one hand, the advantages of the paper version for developing students’ linguistic competence and, on the other hand, that the digital version, despite facilitating students’ interaction amongst each other, does not seem to be more suitable for developing students’ communicative competence. Future studies will, therefore, focus on identifying those features that might increase the potential of the digital version for fostering the targeted competencies.

The world is accelerating globalization and urbanization; thus, planning plays an irreplaceable role in the macro development, especially in sustainable development. Planning research is important in turning theories of planning into urban practices. This research based on the theory that planning research is crucial, as it represents that planning-related activities improve processes in urbanization. However, the current understanding of the importance of planning research is limited. There is no research on the interaction between planning research and macro national development to support the importance of planning or its research. This study uses 750,000 articles on urban planning fields from WoS from 1950 to the present. Firstly, the study concludes the investment pattern changes of the global planning research. Over the past 70 years, the total number and diversity of countries engaged in planning research have grown rapidly, and developing countries have gradually integrated into the mainstream research community and become the main contributors. Secondly, the investment intensity and characteristics of planning research are consistent with the speed and characteristics of urbanization, which proves that to some extent, the demand of urbanization development drives the investment of planning research. Then, according to the different characteristics of planning research investment in different countries, this paper summarizes the main investment characteristics of major countries, analyzes the development rule behind the investment characteristics, and predicts the interaction with the international political and economic pattern. By analyzing the relationship between planning research intensity and urbanization, it is found that the investment intensity of planning research has a time rule with urbanization and has different interactions in different stages of urbanization development. It was found that the intensity of planning research was strongly correlated with HDI, and the amount of research was crucial.

At the core of the climate crisis lies excessive carbon emissions from the continued use of fossil fuels [...]

Computer-aided design (CAD) systems have advanced to become a critical tool in product design. Nevertheless, they still primarily rely on the traditional mouse and keyboard interface. This limits the naturalness and intuitiveness of the 3D modeling process. Recently, a multimodal human–computer interface (HCI) has been proposed as the next-generation interaction paradigm. Widening the use of a multimodal HCI provides new opportunities for realizing natural interactions in 3D modeling. In this study, we conducted a literature review of a multimodal HCI for CAD to summarize the state-of-the-art research and establish a solid foundation for future research. We explore and categorize the requirements for natural HCIs and discuss paradigms for their implementation in CAD. Following this, factors to evaluate the system performance and user experience of a natural HCI are summarized and analyzed. We conclude by discussing challenges and key research directions for a natural HCI in product design to inspire future studies.

This Special Issue celebrates the publication of 20,000 articles in Applied Sciences [...]

Topographic maps comprise a rich data source for the processing and analysis of geographic information. However, the management of paper topographic maps is still dominated by an inefficient manual method, and there is no counting equipment for topographic maps that is suitable for narrow map warehouse passages. The existing algorithms relating to paper counting require paper hardness and cannot directly obtain the internal information of topographic maps. To address the above problems, using a map inventory machine transformed from a paddle-type paper counting machine to perform a preliminary count and obtain the video data of the counting progress, an automatic counting algorithm for topographic maps for map management is proposed in this study. First, the periodic feature changes of all the targets and each individual target in the videos are analyzed. Second, a deformable parts model accelerated by fast feature pyramids and by reducing the region of interest is used for the object detection of mechanical wheels. The grayscale variance function is selected to quantify the quality change in the images. Finally, a fusion window counting model is constructed with the adaptive window and fixed window methods. The experimental results show that the average counting accuracy of the model is about 95%, which can verify the initial counting results of the machine and provide high-quality keyframes for the subsequent sheet designation recognition.

Grapevines are one of the most intensely treated crops with a high potential risk to health and biodiversity. Thus, the distribution control of agrochemicals is crucial to obtain a high quality and sustainable product for intensive viticulture. Although the search for systems to reduce the waste of chemical products is consistent in some countries, such as Italy, the machinery used are obsolete. The development of an upgrading system for sprayers can be a good compromise to achieve the pollution reduction without requiring huge investments. Field tests were conducted using a LIDAR-based prototype coupled to a commercial sprayer. This study tested the distribution performance using water-sensitive papers and evaluated the infections of grapevine downy and powdery mildews. The results showed a distribution in the vegetation gaps with a higher frequency in the coverage classes >20% in the standard treatment and 10–15% in the LIDAR treatment. Treatments performed with LiDAR reduced the incidence of downy mildew and severity of powdery mildew. The innovative sprayer reduces the distribution of agrochemicals thanks to the on/off control of the nozzles in the voids of vegetation and, meanwhile, controls vineyard fungal disease, so it can be a good way to meet the sustainability and quality of the production.

The presence of intensely fissured soils is often found to relate to high geotechnical risks, such as landslide risk. This is especially the case of the Southern Apennines, Italy, where slopes formed of intensely fissured clays are frequently affected by landslides. The latter are generally triggered by rainfall infiltration, which takes place through the outcropping, unsaturated clayey soil cover. With the final aim of reducing landslide risk in areas covered by fissured clays, a detailed hydro-mechanical characterisation of these materials is required. While the behaviour of fully saturated fissured clays has been investigated in the last decade, only a few studies dealing with unsaturated, natural fissured clays are reported in the literature. The present paper aims to give a contribution toward filling this gap by extending an investigation campaign started a few years ago on the Paola Doce fissured clay outcropping on the Pisciolo slope (Southern Apennines, Italy). The physical properties of the material and some of its key micro- to meso-structural features are first analysed, the latter also based on Scanning Electron Microscope (SEM) micrographs of an undisturbed sample taken at 1.4 m depth on the Pisciolo slope, which is mainly formed of Paola Doce clay. Subsequently, water retention data of the soil are presented, which were obtained using both high-capacity tensiometers and the filter paper technique. These data were collected not only on undisturbed samples but also while subjecting the same material to drying paths. The results herein reported aim to make a link between the water retention behaviour of the Paola Doce clay sampled at Pisciolo and its fissured structure.

Neutralization with liquid digestate and CO2 was compared herein to adjust the pH of lime-treated corn stover. The effects on the thermophilic (55 °C) anaerobic digestion were also analyzed. Liquid digestate neutralization (LDN) caused a decrease in pH from 10.5 to 7.5 in 60 h and accumulation of acetic/isobutyric acids. The CO2 neutralization (CN) under solid-state conditions reduced the pH from 10.5 to 8.5 in 30 min, which is faster than that of LDN and did not affect the subsequent anaerobic digestion. Biomethane production rate indicates that LDN contributed to the performance of anaerobic digestion, but this was not sufficient to compensate for the loss of total biomethane yield, resulting in a negative net profit (i.e., revenue from increased energy production minus reagent cost). For CN under solid-state conditions, the biomethane production was highest in both liquid- and solid-state anaerobic digestion, and also obtained a net profit of 98.74–100.89 RMB/tonne dry biomass. Therefore, the solid-state condition CN is a more efficient and economic method for adjusting initial pH of lime-treated corn stover.

Metakaolin is reactive and is widely used in the modern concrete industry. This study presents an integrated strength–sustainability evaluation framework, which we employed in the context of metakaolin content in concrete. First, a composite hydration model was employed to calculate reactivity of metakaolin and cement. Furthermore, a hydration-based linear equation was designed to evaluate the compressive strength development of metakaolin composite concrete. The coefficients of the strength evaluation model are constants for different mixtures and ages. Second, the sustainability factors—CO2 emissions, resource consumption, and energy consumption—were determined based on concrete mixtures. Moreover, the sustainability factors normalized for unit strength were obtained based on the ratios of total CO2 emissions, energy consumption, and resource consumption to concrete strength. The results of our analysis showed the following: (1) As the metakaolin content increased, the normalized CO2 emissions and resource consumption decreased, and the normalized energy first decreased and then slightly increased. (2) As the concrete aged from 28 days to three months, the normalized CO2 emissions, resource consumption, and energy consumption decreased. (3) As the water/binder ratio decreased, the normalized CO2 emissions, resource consumption, and energy consumption decreased. Summarily, the proposed integrated strength–sustainability evaluation framework is useful for finding greener metakaolin composite concrete.

Zero-emission technology for palm oil mill effluent (POME) has led to a breakthrough in the palm oil industry in relation to the goal of sustainable development. However, there are limited resources on how this technology has affected the bacterial community in the receiving river that has previously been polluted with POME final discharge. Thus, the current study assessed the recoverability of the unexplored bacterial community in the receiving water of a constructed river water system post-zero emission of POME final discharge. An artificial river water system was constructed in this study, where the viability status and the composition of the bacterial community were assessed for 15 days using a flow cytometry-based assay and high-throughput sequencing by Illumina MiSeq, respectively. The zero-emission of POME final discharge reduced not only the physicochemical properties and nutrient contents of the receiving water, but also the bacterial cells’ viability from 40.3% to 24.5% and shifted the high nucleic acid (HNA) to low nucleic acid (LNA) content (38.7% to 34.5%). The proposed POME bacterial indicators, Alcaligenaceae and Chromatiaceae were not detectable in the rainwater (control) but were detected in the artificial river water system after the introduction of POME final discharge at the compositions of 1.0–1.3% and 2.2–5.1%, respectively. The implementation of a zero-emission system decreased the composition of Chromatiaceae from 2.2% on day 8 until it was undetectable on day 15, while Alcaligenaceae was continuously reduced from 1.2% to 0.9% within that similar time frame. As indicated by principal coordinate (PCO) analysis, the reductions in biological oxygen demand (BOD5) would further diminish the compositions of these bioindicators. The zero-emission of POME final discharge has demonstrated its efficacy, not only in reducing the polluting properties, but also in the bacterial biodiversity rebound in the affected water system.

This publication presents the technologies of enzymatic biodiesel production in comparison to the conventional methods using acid and base catalysts. Transesterification with conventional catalysts has some disadvantages, and for this reason, alternative methods of biodiesel production have been investigated. These solutions include the replacement of chemical catalysts with biological ones, which show substrate specificity in relation to fats. Replacing chemical with biological catalysts causes elimination of some disadvantages of chemical processes, for instance: high temperatures of reaction, problematic process of glycerol purification, higher alcohol-to-oil molar ratios, and soap formation. Moreover, it causes operational cost reduction and has a positive environmental impact. This is due to the lower temperature of the process, which in turn translates into lower cost of equipment and lower GHG emissions associated with the need to provide less heat to the process. The increase of biofuels’ demand has led to the technology of enzymatic biodiesel production being constantly being developed. This research mainly focuses on the possibility of obtaining cheaper and more effective biocatalysts, as well as increasing the durability of enzyme immobilization on different materials.

Potted plants have been reported to uptake VOCs and help ‘cleaning’ the air. This paper presents the results of a laboratory study in which two species of plants (Peace Lily and Boston Fern) and three kinds of substrates (expanded clay, soil and activated carbon) were tested and monitored on their capacity to deplete formaldehyde and CO2 in a glass chamber. Formaldehyde and CO2 were selected as indicators to evaluate the bio-filtration efficacy of 28 different test conditions; relative humidity (RH) and temperature (T) were monitored during the experiments. To evaluate the efficacy of every test the Clean Air Delivery Rate (CADR) was calculated. Overall, soil had the best performance in removing formaldehyde (~ 0.07–0.16 m3/h), while plants, in particular, were more effective in reducing CO2 concentrations (Peace lily 0.01m3/h) (Boston fern 0.02-0.03m3/h). On average, plants (~ 0.03 m3/h) were as effective as dry expanded clay (0.02–0.04 m3/h) in depleting formaldehyde from the chamber. Regarding air cleaning performance, Boston ferns presented the best performance among the plant species, and the best performing substrate was the soil.

Advanced oxidation technology of persulfate is a new method to degrade wastewater. As the economy progresses and technology develops, increasingly more pollutants produced by the paper industry, printing and dyeing, and the chemical industry are discharged into water, causing irreversible damage to water. Methods and research directions of activation persulfate for wastewater degradation by a variety of iron-based catalysts are reviewed. This review describes the merits and demerits of advanced oxidation techniques for activated persulfate by iron-based catalysts. In order to promote the development of related research work, the problems existing in the current application are analyzed.

Starch production is mainly focused on feedstocks such as corn, wheat and potato in the EU, whereas cassava, rice, and other feedstocks are utilised worldwide. In starch production, a high amount of wastewater is generated, which accumulates from different process steps such as washing, steeping, starch refining, saccharification and derivatisation. Valorisation of these wastewaters can help to improve the environmental impact as well as the economics of starch production. Anaerobic fermentation is a promising approach, and this review gives an overview of the different utilisation concepts outlined in the literature and the state of the technology. Among bioenergy recovery processes, biogas technology is widely applied at the industrial scale, whereas biohydrogen production is used at the research stage. Starch wastewater can also be used for the production of bulk chemicals such as acetone, ethanol, butanol or lactic acids by anaerobic microbes.

Crash fault tolerance describes the capability of a distributed system to maintain its proper function despite the occurrence of crashes or failures in one or more of its components. When a distributed system possesses crash fault tolerance, it can be further fortified to achieve Byzantine fault tolerance. Byzantine fault tolerance empowers a distributed system to establish consensus among participants, even when faced with faulty or malicious behavior. Consensus plays a critical role in various tasks, including determining the accurate value of a shared variable, electing a leader, or validating the integrity of a business transaction. Compared to crash fault tolerance, Byzantine fault tolerance instills greater trust because it enables consensus even in the presence of malicious entities. This paper focuses on the performance evaluation of two blockchain solutions that exhibit Byzantine fault tolerance, in contrast to a blockchain solution that demonstrates crash fault tolerance. Specifically, the paper investigates the additional performance requirements associated with the enhanced trust resulting from Byzantine fault tolerance in e-business trading on both national and transnational scales. We analyze the resources needed to operate a business-to-business/business-to-government (B2B/B2G) compliance framework in two distinct geographic scenarios. The first examines the national scale, using Denmark as an example, which is the eleventh largest European country by GDP. The second scenario considers the scale of the European Union (EU) with its 27 member states (plus the United Kingdom).

Vertical displacement acceleration and the pitch angle record produce the phenomenon of beat vibration when testing a 200 m electro-dynamic suspension (EDS) magnetic levitation (maglev) test vehicle with high-temperature superconducting (HTS) at the CRRC Changchun Railway Vehicles Co., Ltd., where the vehicle is clamped and in planar motion. First, to examine this phenomenon, this paper establishes dynamic equations of the vehicle with three degrees of freedom (DOF), and the levitation force on each superconducting magnet (SCM) is calculated by dynamic circuit theory. Second, the theory vertical equilibrium point is obtained from the average of the levitation force for a different velocity and the magneto-motive force (MMF) of the SCM. Third, this paper decouples SCM levitation forces from each other using MATLAB/SIMULINK, and a multi-body dynamic model with six DOF is developed in SIMPACK. All vertical displacements and acceleration responses, as well as the pitch angle and acceleration response from the simulation, appear to show the phenomenon of beat vibration since there are two closing natural frequencies of approximately 2 Hz and 2.4 Hz. Finally, based on the traversing method considering the influence of the velocity, initial vertical displacement, and the MMF of the SCM, the multi-body dynamic model is frequently utilized to study the response of the mean and amplitude of vertical displacement and that of the pitch angle. The results show that increasing the MMF or velocity could decrease the vertical displacement and pitch angle; the mean vertical displacement is a little larger than the theory equilibrium point; and the amplitude of vertical displacement is small when the initial vertical displacement is near the theory equilibrium point. Both the numerical and experimental results verify the validity of the dynamic circuit model and mechanical model in this paper.

In the research field of the automated process discovery and analysis, the purity of event log datasets ought to be a matter of vital importance to the success of discovering sound and exact process models. Moreover, there exist various types of anomalies that result in the discovery of inaccurate process models from the process enactment event log datasets. A peculiar one out of these anomalies, which is the core challenging issue of this paper, is the temporal activity-sequencing anomaly that critically affects the overall quality of the automated process discovery. This paper explores such event-log anomalies and noises produced by the special type of anomalies inevitably formed in the event-log preprocessing phase of the automated process discovery. More precisely, it implements an algorithmic approach that is able to detect and filter out those anomalies and noises in performing the automated process discovery. The author also carries out a series of experimental analyses by applying the implemented approach to the five datasets of process event logs available in the 4TU Center for Research Data.

The reliability of the machining process is a vital indicator in the manufacturing process of mechanical parts, and directly related to the machining quality of mechanical products. In order to effectively utilize machining process information and machining quality inspection data to evaluate the reliability of the machining process, this paper extends the relevant concepts of information theory to the field of mechanical manufacturing error analysis and establishes a reliability analysis model of the machining process. The model introduces information entropy into the analysis of machining process reliability by defining vertex entropy to measure the uncertainty of the machining quality of each process, and thus realizing the determination of critical processes using a fully quantitative method. Meanwhile, the transfer coefficient defined by mutual information is used to measure the transfer of machining error between two processes. Through combining vertex entropy and the transfer coefficient, the quantitative analysis of machining process reliability is achieved. The model proposed in this paper addresses the issue that the subjective factors of researchers in other related studies have an impact on the analysis results. Finally, the correctness of the proposed model is verified using Monte Carlo simulations.

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%.

Answer selection, as a crucial method for intelligent medical service robots, has become more and more important in natural language processing (NLP). However, there are still some critical issues in the answer selection model. On the one hand, the model lacks semantic understanding of long questions because of noise information in a question–answer (QA) pair. On the other hand, some researchers combine two or more neural network models to improve the quality of answer selection. However, these models focus on the similarity between questions and answers without considering background information. To this end, this paper proposes a novel refined answer selection method, which uses an attentive bidirectional long short-term memory (Bi-LSTM) network and a self-attention mechanism to solve these issues. First of all, this paper constructs the required knowledge-based text as background information and converts the questions and answers from words to vectors, respectively. Furthermore, the self-attention mechanism is adopted to extract the global features from the vectors. Finally, an attentive Bi-LSTM network is designed to address long-distance dependent learning problems and calculate the similarity between the question and answer with consideration of the background knowledge information. To verify the effectiveness of the proposed method, this paper constructs a knowledge-based QA dataset including multiple medical QA pairs and conducts a series of experiments on it. The experimental results reveal that the proposed approach could achieve impressive performance on the answer selection task and reach an accuracy of 71.4%, MAP of 68.8%, and decrease the BLUE indicator to 3.10.

CNC machine tools are a measure of national manufacturing and comprehensive national strength and how to reduce CNC machine errors in modern industry has received much attention from all walks of life. The thermal deformation of the feed axis of CNC machine tools is affected by the machine’s structure, installation, material properties, motion position, working conditions, and environmental temperature. Therefore, adopting a single thermal error model to the compensation implementation needs is difficult under changing motion parameters. In this paper, VMC655H is used as the research object and an embedded system is used as the control core. The model classification method is based on the approximate matching of the motion parameters of the thermal error model. Dynamic loading technology is used to fit multiple thermal error models as a technical means to reduce the impact of the machine feed axis’s thermal deformation on the positioning accuracy during the manufacturing process. The thermal error classification model for machine tool feed axes with variable motion parameters in an embedded environment can classify, identify, and load compensation device models within a limited range of motion parameters. This strategy can determine the current operating environment and compensate for the poor adaptability of a single thermal error model. The study of the actual operation effect of the compensation device under this strategy shows that the compensation strategy proposed in this paper can compensate for the thermal deformation of the ball screw feed axis under variable motion, and the X-axis positioning accuracy of its feed system can be improved by 53.11%. This study provides a new idea for the compensation method for thermogenic errors in machine tools.

In order to maintain a ski resort efficiently, regular inspections of the cableways are essential. However, there are some difficulties in discovering and observing the cable car cableways in the ski resort. This paper proposes a high-precision segmentation and extraction method based on the 3D laser point cloud data collected by airborne lidar to address these problems. In this method, first, an elevation filtering algorithm is used to remove ground points and low-height vegetation, followed by preliminary segmentation of the cableway using the spatial distribution characteristics of the point cloud. The ropeway segmentation and extraction are then completed using the least squares-principal component combination analysis method for parameter fitting. Additionally, we selected three samples of data from the National Alpine Ski Center to be used as test objects. The real value is determined by the number of point clouds manually deducted by CloudCompare. The extraction accuracy is defined as the ratio of the number of point clouds extracted by the algorithm to the number of point clouds manually extracted. While the environmental complexities of the samples differ, the algorithm proposed in this paper is capable of segmenting and extracting cableways with great accuracy, achieving a comprehensive and effective extraction accuracy rate of 90.59%, which is sufficient to meet the project’s requirements.

In this paper, we apply an effective method to evaluate relative tectonic activity by applying several morph-tectonic indices that are useful in evaluating topography and tectonics. These indices include stream length-gradient, asymmetric factor, hypsometric index, hypsometric curves, valley floor width to valley height ratio, drainage basin shape, and mountain front sinuosity. The study region of Wadi Al-Arish in northern Sinai Peninsula in northern Egypt is a natural laboratory to examine relative tectonic activity levels for calculating morpho-tectonic indices of several catchments and sub-catchments rather than an individual catchment. Northern Sinai, comprising the Waid Al-Arish area, is characterized by several large inversion anticline folds. The cumulative results extracted from morpho-tectonic indices ae presented as a new index, namely relative tectonic activity level (RTAL), which we classified into four levels: low, moderate, high, and very high relative tectonic activity. Therefore, the study region provides different levels of relative tectonic activity resulting from fault patterns affecting the northern Sinai inversion forms. The paper examines the concept that regions with various levels of tectonic activity are associated with specific values of RTAL.

In China, Hebei Province and Guangdong Province have many pollution-intensive industries; yet Hebei suffers more serious atmospheric or environmental pollution than Guangdong. To explain the phenomenon, this paper chooses the statistical data of 286 prefecture-level cities in the Chinese mainland from 2005 to 2018 and empirically tests the spatial effect and threshold characteristics of atmospheric self-purification capacity on environmental pollution by using the spatial autoregression model (SAR), spatial Durbin model (SDM), and panel threshold model. As evinced, the local atmospheric self-purification capacity has a significant inhibitory effect on environmental pollution, and the absolute value of the direct effect coefficient reaches 1.337. Simultaneously, atmospheric self-purification capacity has a threshold effect on environmental pollution. The threshold value of environmental regulation as a threshold variable is 11.6349. This means that when the level of environmental regulation proves higher than 11.6349, it attenuates the inhibitory effect of the atmospheric self-purification capacity on environmental pollution. As the heterogeneity research unveils, atmospheric self-purification capacities in various regions form a significant correlation with environmental pollution. This paper suggests that local governments should strengthen environmental regulation and construct an inter-regional joint prevention and control system for atmospheric-pollution governance to enhance the atmospheric self-purification capacity.

In this paper, the concept of a distributed wireless system of weigh-in-motion is proposed. A wireless sensor for weigh-in-motion (WIM) based on piezoelectric materials is designed. The corresponding prototype for indoor testing is made according to the design. It aims to realize the main functions of dynamic pressure sensing, charge signal amplification and conversion, wireless signal transmission and reception, etc. The material properties of the mechanical properties and the piezoelectric properties in the analyses are provided in detail. Through the indoor test platform, the feasibility of the wireless sensor for WIM designed in this paper is preliminarily verified, which provides a basic tool for the realization of the distributed self-powered wireless system of WIM in the next step.

This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach.

In this paper, a novel and robust measurement method is proposed for obtaining the geometric errors of rotary tables by using LaserTRACER and the reflectors mounted on the reflector standard fixture. For the machining accuracy, the six-degree-of-freedom (6-DOF) geometric errors of the rotary axes interactively influence the manufacturing quality of the precise workpieces. Therefore, this paper mainly aims to develop a measurement method for identifying the 6-DOF geometric errors of rotary tables without using the external linear axis. Furthermore, the set-up errors of the reflector standard fixture are also considered and identified to reduce the influence of the 6-DOF geometric error measurements. For each rotary table geometric error measurement, the positions of the LaserTRACER as well as the relative distance between the reflectors and the LaserTRACER are measured and obtained for determining the 6-DOF geometric errors of the rotary tables. In addition, the homogeneous transformation matrix (HTM), multilateration method, and least squares method are used for building the mathematical measurement algorithm. Moreover, the experimental verifications are implemented to demonstrate the accuracy of the proposed measurement method. Conclusively, the experiment and simulation verification results clearly delineate that the maximal relative differences in the linear errors and the angular errors of the 6-DOF geometric errors are, at most, 3.25% and 2.30%, respectively.

The remarkable development of the Internet has made our lives very convenient, such as through the ability to instantaneously transmit individual pictures. As a result, cyber-attacks are also being developed and increasing, and the computer/mobile devices we use can become infected with viruses in an instant. Rapid cyber situational awareness is essential to prepare for such cyber-attacks. Accelerating cyber situational awareness requires Cyber Common Operational Pictures, which integrate and contextualize numerous data streams and data points. Therefore, we propose a Cyber Common Operational Pictures framework and criteria for rapid cyber situation awareness. First, the system reaction speed based on the user’s request and the standard for easily recognizing the object shown on the screen are presented. Second, standards and frameworks for five types of visualization screens that can directly recognize and respond to cyber-attacks are presented. Third, we show how a system was constructed based on the proposed framework, as well as the results of an experiment on the response time of each visualization screen. As a result of the experiment, the response speed of the 5 visualization screens was about 0.11 s on average for inquiry (simple) and 1.07 s on average for inquiry (complex). This is consistent with the typical response times of the studies investigated in this paper. If CyCOP is developed in compliance with the framework items (UI, object symbol, object size, response speed) presented in this paper, rapid situational awareness is possible. This research can be used in cyber-attack and defense training in the military field. In the private sector, it can be used in cyber and network control.

Effective log anomaly detection can help operators locate and solve problems quickly, ensure the rapid recovery of the system, and reduce economic losses. However, recent log anomaly detection studies have shown some drawbacks, such as concept drift, noise problems, and fuzzy feature relation extraction, which cause data instability and abnormal misjudgment, leading to significant performance degradation. This paper proposes a multi-feature deep fusion of an unstable log anomaly detection model (MDFULog) for the above problems. The MDFULog model uses a novel log resolution method to eliminate the dynamic interference caused by noise. This paper proposes a feature enhancement mechanism that fully uses the correlation between semantic information, time information, and sequence features to detect various types of log exceptions. The introduced semantic feature extraction model based on Bert preserves the semantics of log messages and maps them to log vectors, effectively eliminating worker randomness and noise injection caused by log template updates. An Informer anomaly detection classification model is proposed to extract practical information from a global perspective and predict outliers quickly and accurately. Experiments were conducted on HDFS, OpenStack, and unstable datasets, showing that the anomaly detection method in this paper performs significantly better than available algorithms.

The acoustic characteristics and spatial features of the world’s only surviving Italianate Gordonia-style miniature theatre, one of the smallest theatres in the world, have inspired the author to analyse the acoustic behaviour of the Monte Castello di Vibio theatre, also called “Teatro della Concordia”. In this paper, the geometric and architectural features of this historical and unique performing art space were first reproduced, considering that these features are essential factors affecting acoustic characteristics. Subsequently, the acoustic measurements were taken throughout the stall and inside some selected boxes, and their main parameters were acoustically characterised according to ISO 3382-1. Lastly, the main acoustic parameters of the Monte Castello di Vibio theatre were compared to those of the 1763 theatre in Bologna, which is also a miniature theatre of similar size. The aim is to explore the main influences on the acoustic parameters of miniature theatres, and the results show that the plan layout of the theatre and interior decoration are the main factors influencing the acoustic characteristics rather than volume. Preserving the acoustic features of this unique heritage building is also seen as one of the goals of this paper.

This paper presents a dual active bridge DC/DC converter used as an AC current compensator in a hybrid energy storage application. The AC current in the DC link appears when a three-phase, four-wire inverter operates with unbalanced output currents—for example, when trying to compensate for grid voltage unbalance. This AC current has adverse effects on the operation of the electrochemical energy storage, and it should be compensated. To achieve this, a compensator is introduced into the DC link circuit of the inverter—a DC/DC converter with a capacitor bank. The DC/DC converter is responsible for compensating the AC pulsation by creating its own pulsation with the opposite phase. In the paper, the genesis of this pulsation is explained, and a compensation circuit is proposed along with a sensorless compensation algorithm. The algorithm is based on symmetrical decomposition and is used to generate a reference signal for the compensator. The numerical analysis of the algorithm is presented, and the operation of the compensator is verified on the laboratory bench.

Academic societies and funding bodies that conduct peer reviews need to select the best reviewers in each field to ensure publication quality. Conventional approaches for reviewer selection focus on evaluating expertise based on research relevance by subject or discipline. An improved perceiving conflict of interest (CoI) reviewer recommendation process that combines the five expertise indices and graph analysis techniques is proposed in this paper. This approach collects metadata from the academic database and extracts candidates based on research field similarities utilizing text mining; then, the candidate scores are calculated and ranked through a professionalism index-based analysis. The highly connected subgraphs (HCS) algorithm is used to cluster similar researchers based on their association or intimacy in the researcher network. The proposed method is evaluated using root mean square error (RMSE) indicators for matching the field of publication and research fields of the recommended experts using keywords of papers published in Korean journals over the past five years. The results show that the system configures a group of Top-K reviewers with an RMSE 0.76. The proposed method can be applied to the academic society and national research management system to realize fair and efficient screening and management.

This paper analyzes the connection between supply chain management maturity (SCMM) and business performance in light of the balanced scorecard (BSC) framework. The goal is to explore the relationship between SCMM and business performance from the financial and customer, innovation and learning, and internal processes perspectives. Industry characteristics (technological dynamism and the level of state support) are examined to determine their moderating effects. The survey was carried out on a sample of organizations from Bosnia and Herzegovina to test if the BSC approach can be a relevant framework for assessing the effects of SCMM on performance, and whether, as in many countries’ political legacies, the role of the government is significant in this relation. PLS-SEM was used to test the proposed hypotheses. The obtained research results confirm a positive relationship between SCMM and business performance from the BSC perspective. This relation is strengthened when an organization operates in an industry with higher technological dynamism. Interestingly, the results confirm that the level of state support does not influence the contribution of SCMM to business performance. This paper provides a more comprehensive view of the role of SCMM and an additional understanding of its contribution to multiple perspectives of business performance. Furthermore, the relevance of industry characteristics for SCMM and business performance has been illustrated by testing the moderation effect of technological dynamism and the level of state support.

Mobile games continue to gain popularity, and their revenues are increasing accordingly. However, due to the inherent constraints of small screen sizes and restrictions of computing, it has been considered challenging to simulate the complex gameplay of soccer games. To this end, this paper aims to design and develop a simplified version of a five vs. five hyper-casual futsal game with only three player positions: goalkeeper, striker, and defender. It also tests a demo game to verify whether it is possible to implement an AI agent−player for each position to machine-learn and to run on a mobile device. A demo game with an AI agent−player was simulated using both PPO and SAC algorithms, and the feasibility and stability of the algorithms were compared. The results showed that each AI agent−player achieved the assigned objectives for each position and successfully machine-learned. When the algorithms were compared, the SAC algorithm showed a more stable state than the PPO algorithm when SAC directed the gameplay and interactive AI techniques. This paper shows the great potential of the application of machine-learned AI agent−players for soccer simulators on mobile platforms.

Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method.