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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.

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.

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.

Altres ajuts: Agència per a la Competitivitat de l'Empresa de la Generalitat de Catalunya by FEDER funds, Institució Catalana de Recerca i Estudis Avançats, and ERDF funds In this paper, an organic, fully recyclable and eco-friendly 20-bit inkjet-printed chipless RFID tag is presented. The tag operates in the near field and is implemented by means of chains of resonant elements. The characterization and manufacturing process of the tag, printed with a few layers of a commercial organic ink on conventional paper substrate (DIN A4), are presented, and tag functionality is demonstrated by reading it by means of a custom-designed reader. The tags are read by proximity (through the near field), by displacing them over a resonator-loaded transmission line, and each resonant element (bit) of the tag is interrogated by a harmonic signal tuned to the resonance frequency. The coupling between the reader line and the resonant elements of the tag produce and amplitude modulated (AM) signal containing the identification (ID) code of the tag.

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.

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.

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.

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

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.

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 [...]

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.

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.

An important step in the conservation of old paper documents is the analysis of both the medium and the ink, in an attempt first to determine the extent of deterioration and degradation and then to choose the best preservation and restoration solutions. Our paper focuses on the analysis of three old documents displayed at the ‘Poni-Cernătescu’ Museum in Iaşi City, Romania by optical microscopy (OM), scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM-EDX), micro-FTIR spectroscopy and FT-Raman. Thus, the morphology and chemical composition of the paper media and the type of ink, as well as the cellulose crystallinity index were determined. PCA (Principal Component Analysis) was also used while relying on spectra collected by FTIR spectroscopy. We were able to determine the extent of degradation of the documents by corroborating all these findings.

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.

The paper aimed to investigate the losses on the manufacturing flow of an aesthetic veneer factory, in order to know their value and to take measures to limit or use them efficiently. For the analysis, the oak species (Quercus robur) was taken into account, through 25 analyzed logs. Statistical investigation has used the Minitab 18 program, for a 95% confidence interval. Minimum values of losses were obtained for the sectioning-grooming operations with 0.6% and debarking with 4.9%, and the highest values were obtained when cutting veneers with 15.9% and formatting veneers with 22.8%. Based on the losses on the manufacturing flow of 67.3%, the specific consumption index of 2.75 m3/m3 or 1.84 m3/1000 m2 of veneer was determined when the average thickness of the veneers was 0.67 mm. The paper highlights the methodology and the values resulting from the investigation of the technological losses and of the specific consumption from an aesthetic veneer factory.

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.

In a sustainable society based on circular economy, the use of waste lignocellulosic biomass (LB) as feedstock for biorefineries is a promising solution, since LB is the world’s most abundant renewable and non-edible raw material. LB is available as a by-product from agricultural and forestry processes, and its main components are cellulose, hemicellulose, and lignin. Following suitable physical, enzymatic, and chemical steps, the different fractions can be processed and/or converted to value-added products such as fuels and biochemicals used in several branches of industry through the implementation of the biorefinery concept. Upon hydrolysis, the carbohydrate-rich fraction may comprise several simple sugars (e.g., glucose, xylose, arabinose, and mannose) that can then be fed to fermentation units. Unlike pentoses, glucose and other hexoses are readily processed by microorganisms. Some wild-type and genetically modified bacteria can metabolize xylose through three different main pathways of metabolism: xylose isomerase pathway, oxidoreductase pathway, and non-phosphorylative pathway (including Weimberg and Dahms pathways). Two of the commercially interesting intermediates of these pathways are xylitol and xylonic acid, which can accumulate in the medium either through manipulation of the culture conditions or through genetic modification of the bacteria. This paper provides a state-of-the art perspective regarding the current knowledge on xylose transport and metabolism in bacteria as well as envisaged strategies to further increase xylose conversion into valuable products.

In this paper, an environmentally friendly inertial motion sensor is investigated, modelled, and characterized as an accelerometer. The sensor is obtained by using bacterial cellulose (BC) as a base biopolymer. BC is then impregnated with ionic liquids. Electrodes are realized by a conducting polymer, in a typical three-layer structure. The sensor works in a cantilever configuration and produces an open voltage signal as the result of a flexing deformation. A model is proposed for the transduction phenomenon. The composite mechano-electric transduction capability is exploited for realizing the accelerometer. Results of the chemical and transduction characterization of the accelerometer are reported. Finally, experimental evidence of the possible nature of the transduction phenomenon is given.

Straw fiber seedling pots are a promising substitute for plastic seedling pots. The mixing mode of straw fiber affects the mechanical properties of the raw material membrane of the seedling pot. To explore the processing technology of making the raw material membrane of the seedling pot with two kinds of plant fibers in a layered manner, the optimal combination of the process parameters of the raw material membrane of the seedling pot without additives in the production process was studied experimentally. Response surface methodology (RSM) was used to analyze the parameters (beating degree of unbleached softwood kraft pulp fiber, beating degree of wheat straw fiber, wheat straw fiber quality percentage content, and film grammage) with regard to the dry tensile index and Z-direction tensile index of the seedling pot body. The optimal process parameter combination with a certain dry tensile index and Z-direction tensile index of seedling pot raw material was obtained by using four factors and five levels of a quadratic regression orthogonal rotation center combination design parameter optimization experiment. The optimal technical parameters were optimized as wheat straw fiber quality percentage content 70%, film grammage 70 g/m2, unbleached softwood kraft pulp fiber beating degree 47–48 °SR, and wheat straw fiber beating degree 65–75 °SR. With the optimal conditions, the dry tensile index of the seedling bowl raw material film was between 21 and 22 N·(m·g−1), and the Z-direction tensile index was greater than 2.1 N·(m·g−1). Using wheat straw fibers and unbleached sulfite wood pulp fibers as raw materials for seedling pots, the raw material membrane of the seedling pots was made in a layered manner. The experimental study proved this feasibility. With this mixing process of raw materials, the straw fiber-based plant fiber seedling pot would meet the demands of a crop nursery after adding chemical additives. The research results provide a theoretical basis and technical support for the manufacture of the raw material membrane of the seedling pot body.

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.

Substitute Ordinary Portland Cement (OPC) by biomass fly ash (BFA) reduce the environmental impact produced by cement-based materials, and at the same time, decreased the economic and environmental burden associated with the landfilling of this waste. This study aims to evaluate the recycling of BFA as supplementary cementitious materials (SCMs) in a commercial screed mortar formulation. Two BFA varieties, both resulting from fluidized bed combustion of forest residues, were used to replace 17, 50, and 67 wt.% of OPC. The influence of simple pre-treatment processes of the BFA, such as sieving and grinding, in the fresh and hardened state properties of the mortars, was evaluated. The BFAs were characterized in terms of chemical (XRF) and mineralogical (XRD) composition, particle size distribution (laser diffraction-COULTER) and morphology (SEM). The prepared formulations were characterized in terms of workability, mass loss upon curing, bulk density, sorptivity (by immersion and capillary), flexural and compressive strength and durability to 25 freeze–thaw cycles. Both of the BFAs are potential SCMs. Substitution of 17 wt.% OPC with BFA complied with the product technical requirements for compressive and flexural strength (10 and 3 MPa, respectively), with the ground and sieved and just sieved BFAs perform slightly better than the as-received BFA.

Biofiltration is a promising wastewater treatment green technology employed to remove various types of pollutants. The efficiency of biofiltration relies on biofilm, and its performance is significantly influenced by various factors such as dissolved oxygen concentration, organic loading rate, hydraulic retention time, temperature, and filter media selection. The existing biofilters utilize conventional media such as gravel, sand, anthracite, and many other composite materials. The material cost of these conventional filter materials is usually higher compared to using organic waste materials as the filter media. However, the utilization of organic materials as biofilter media has not been fully explored and their potential in terms of physicochemical properties to promote biofilm growth is lacking in the literature. Therefore, this review critically discusses the potential of shifting conventional filter media to that of organic in biofiltration wastewater treatment, focusing on filtration efficiency-influenced factors, their comparative filtration performance, advantages, and disadvantages, as well as challenges and prospective areas of organic biofilter development.

Air lime mortars with addition of two types of sesame cooking oil were synthesized and cured in two different conditions: laboratory and natural climatic conditions of the Pasargadae and Persepolis World Heritage Sites. For the mortars cured in the laboratory, addition of oils in the mortar induced hydrophobic effects, but it adversely affected on permeability values and carbonation rates. However, the air lime mortars cured in the site conditions demonstrated increment in their hydrophobicity and superficial durability without retarding their setting time and adverse effects on their calcium carbonate formation values, due to various climatic parameters existing in natural outdoor conditions.

Municipalities output large amounts of solid waste into landfills, but degradation slows during the middle and late stages of treatment. Therefore, accelerating the treatment of degradable substances to achieve rapid stabilization, excavation, screening, and reuse would increase landfill utility and reduce economic costs. This article provides an aerobic degradation model for landfilled municipal solid waste that includes two types of biochemical reactions. Using degradable solid-phase organic matter and ammonia nitrogen as limiting substrates, an equation describing degradation over time was obtained. Both aerobic organic matter hydrolysis and the synchronous nitrification and denitrification reaction followed a kinetic, first-order equation. The influences of temperature, water content, oxygen concentration, and carbon to nitrogen ratio on the kinetic reactions were considered. Similarly, the exothermic reaction characteristics were considered. The model was applied to two previously conducted experiments. The results showed that the model can accurately reflect the degradation laws of various substances under aerobic degradation conditions. SDC and ammonia nitrogen were rapidly degraded and reached very low levels in a short time under aerobic conditions. This indicated that aeration has a significant effect on the degradation of aged waste, which can be used in the accelerated stabilization of aged landfills in the future.

Olive oil production represents an agro-industrial activity of vital economic importance for many Mediterranean countries. However, it is associated with the generation of a huge amount of by-products, both in solid and liquid forms, mainly constituted by olive mill wastewater, olive pomace, wood, leaves, and stones. Although for many years olive by-products have only been considered as a relevant environmental issue, in the last decades, numerous studies have deeply described their antioxidant, anti-inflammatory, immunomodulatory, analgesic, antimicrobial, antihypertensive, anticancer, anti-hyperglycemic activities. Therefore, the increasing interest in natural bioactive compounds represents a new challenge for olive mills. Studies have focused on optimizing methods to extract phenols from olive oil by-products for pharmaceutical or cosmetic applications and attempts have been made to describe microorganisms and metabolic activity involved in the treatment of such complex and variable by-products. However, few studies have investigated olive oil by-products in order to produce added-value ingredients and/or preservatives for food industries. This review provides an overview of the prospective of liquid olive oil by-products as a source of high nutritional value compounds to produce new functional additives or ingredients and to explore potential and future research opportunities.

Thermal treatment is the most popular decontamination technique used in the dairy industry to ensure food protection and prolong shelf life. But it also causes nutrient and aroma degradation, non-enzymatic browning, and organoleptic changes of dairy products. Non-thermal solutions, on the other hand, have been extensively explored in a response to rising market demand for more sustainable and safe goods. For a long time, the use of ultraviolet (UV) light in the food industry has held great promise. Irradiation with shortwave UV light has excellent germicidal properties, which can destroy a variety of microbial pathogens (for example bacteria, fungi, molds, yeasts, and viruses), at low maintenance and installation costs with minimal use of energy to preserve food without undesirable effects of heat treatment. The purpose of this review is to update the studies made on the possibilities of UV-C radiation while also addressing the essential processing factors involved in the disinfection. It also sheds light on the promise of UV light-emitting diodes (UV-LEDs) as a microbial inactivation alternative to conventional UV lamps.

The building of civil engineering structures on some soils requires their stabilisation. Although Portland cement is the most used substance to stabilise soils, it is associated with a lot of environmental concerns. Therefore, it is very pertinent to study more sustainable alternative methodologies to replace the use of cement. Thus, this work analyses the ability of the more sustainable xanthan-like biopolymer, produced by Stenotrophomonas maltophilia Faro439 strain (LabXLG), to reduce the permeability of a sandy soil. Additionally, the effectiveness of this LabXLG is compared with the use of a commercial xanthan gum (XG) and cement for various hydraulic gradients and curing times. The results show that a treatment with either type of XG can be used to replace the cement over the short term (curing time less than 14 days), although a greater level of effectiveness is obtained with the use of the commercial XG, due to its higher level of purity. The soil treatment with LabXLG creates a network of fibres that link the soil particles, while the commercial XG fills the voids with a homogeneous paste.

Natural dyes have been used from ancient times for multiple purposes, most importantly in the field of textile dying. The increasing demand and excessive costs of natural dye extraction engendered the discovery of synthetic dyes from petrochemical compounds. Nowadays, they are dominating the textile market, with nearly 8 × 105 tons produced per year due to their wide range of color pigments and consistent coloration. Textile industries consume huge amounts of water in the dyeing processes, making it hard to treat the enormous quantities of this hazardous wastewater. Thus, they have harmful impacts when discharged in non-treated or partially treated forms in the environment (air, soil, plants and water), causing several human diseases. In the present work we focused on synthetic dyes. We started by studying their classification which depended on the nature of the manufactured fiber (cellulose, protein and synthetic fiber dyes). Then, we mentioned the characteristics of synthetic dyes, however, we focused more on their negative impacts on the ecosystem (soil, plants, water and air) and on humans. Lastly, we discussed the applied physical, chemical and biological strategies solely or in combination for textile dye wastewater treatments. Additionally, we described the newly established nanotechnology which achieves complete discharge decontamination.

The organic fraction of municipal solid waste (OFMSW) is recognized as a suitable substrate for the anaerobic digestion (AD) process and is currently considered a mature technology. A promising strategy to enhance biogas yield and productivity is the co-digestion of OFMSW with other organic biomass, such as green waste (GW), a mixture of leaves, grass, and woody materials originated from private yards and public greenspace management. The main limitation to the use of GW for biogas production is the high percentage of the lignocellulosic fraction, which makes necessary a pretreatment of delignification to dissolve the recalcitrant structure. In this study, a new strategy of sustainable bio-delignification using the white-rot fungi Bjerkandera adusta (BA) in comparison with other chemical pretreatments were investigated. Untreated and treated GW were, respectively, submitted to anaerobic co-digestion with OFMSW. AD processes were carried out in a lab-scale plant for 30 days in thermophilic conditions (55 °C). Biogas cumulative production was increased by about 100% in the case of treated GW compared with that of just OFMSW, from 145 to 289 Nm3 CH4/ton SV, and productivity almost doubled from 145 to 283 Nm3/ton FM * day. The measured average methane content values in the cumulative biogas were 55% from OFMSW and 54% from GW. Moreover, over 95% of the biogas was produced in 20 days, showing the potential opportunity to reduce the AD time.

The aim of the study was to verify two morphological forms (“angel hair” and “scraps”) of xylit as a trickling filter material. The study was carried out on two types of polluted media: septic tank effluent (STE) and seminatural greywater (GW). The basic wastewater quality indicators, namely, chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSS), ammonium nitrogen (NNH4), and total phosphorus (Ptot) were used as the indicators of treatment efficiency. Filtering columns filled with the investigated material acted as conventional trickling filters at a hydraulic load of 376–472 cm3/d during the preliminary stage, 198–245 cm3/d during stage I, and 184–223 cm3/d during stage II. The removal efficiency of the two morphological forms of xylit did not differ significantly. The average efficiencies of treatment were as follows: for COD, over 70, 80, and 85% for preliminary stage, stage I and stage II, respectively, for BOD5, 77–79% (preliminary stage), for TSS, 42% and 70% during the preliminary stage, and 88, 91, and 65% during stage I, for NNH4, 97–99% for stage I and 36–49% for stage II, for Ptot, 51–54% for stage I and 52–56% for stage II. The study demonstrated that xylit was a material highly effective in wastewater quality indicators removal, even during the initial period of its use.

11 p., [EN] Mine tailing storage represents an important environmental issue. The generation and dispersal of dust from mine tailings can contaminate air and surrounding soils. In addition, metals and soluble salts present in these wastes could pollute groundwater and surface water. The recovery of metals from mine tailings can contribute to minimize the environmental risk and to achieve a circular economy model. The main objective of the present work is to study the use of two carbon-based materials, a commercial activated carbon (AC) and a commercial charcoal (VC) in the leaching of zinc and copper from low-grade tailing waste. Experimental results obtained show that it is possible to achieve the recovery of more than 87 wt% of Zn after 6 h of leaching with different sulfuric acid solutions. The addition of carbon-based materials increases the extraction of Zn at high sulfuric acid concentrations (1 M) from 89% to 99%. The addition of VC significantly increases the extraction of Cu in leaching solution with high sulfuric acid concentration (1 M), from 41 to 61%. Future research will be necessary to optimize the properties of carbon-based materials and their recovery after leaching experiments in order to assess their potential for industrial application, This research was funded by Ministerio de Ciencia e Innovación (Spain), grant number RTI2018-096695-B-C31.

Intensive swine farming causes strong local environmental impacts by generating effluents rich in solids, organic matter, nitrogen, phosphorus, and pathogenic bacteria. Insufficient treatment of hog farm effluents has been reported for common technologies, and vermifiltration is considered a promising treatment alternative that, however, requires additional processes to remove nitrate and phosphorus. This work aimed to study the use of vermifiltration with a downstream hydroponic culture to treat hog farm effluents. A treatment system comprising a vermifilter and a downstream deep-water culture hydroponic unit was built. The treated effluent was reused to dilute raw wastewater. Electrical conductivity, pH, and changes in BOD5, ammonia, nitrite, nitrate, phosphorus, and coliform bacteria were assessed. Plants were monitored throughout the experiment. Electrical conductivity increased due to vermifiltration, pH stayed within a neutral to mild alkaline range. Vermifiltration removed 83% of BOD5, 99% of ammonia and nitrite, and increased nitrate by 11%. Hydroponic treatment removed BOD5 (63%), ammonia (100%), nitrite (66%), nitrate (27%), and phosphorus (47% total and 44% dissolved) from vermifiltered water. Coliforms were reduced by vermifiltration but recovered in the hydroponic unit. Plants showed the ability to grow on vermifiltered wastewater, although requiring nutrient supplementation. Vermifiltration combined with hydroponics is a promising treatment for swine wastewater, although optimization will be needed for a sustainable real-scale implementation.

Residual biomass from various industries represents an important source of valuable compounds, used as raw materials for the production of a wide range of new products and also in various treatment and valorization processes or/and sanitation services, thus responding to the principles of sustainable development, waste recovery, and a green and circular economy. The aim of this work is to make use of residual Bacillus sp. biomass (resulting from a process of removing fatty acids from municipal wastewater) immobilized in alginate that, although it results in large quantities from biotechnological processes, is not reported to be valorized in dye biosorption processes, except in few specific applications. The biosorption potential of residual Bacillus sp. biomass in the reactive Brilliant Red HE-3B textile dye removal from aqueous systems was studied in a fixed-bed column. The effects of various experimental operating parameters, such as bed depth (h), flow rate (Fv), were investigated, and the modeling of experimental data based on Thomas and Yoon–Nelson kinetic models was satisfactorily achieved. The obtained results reconfirm that the studied residual biomass can be also considered as a good biosorbent in dynamic operating system, and can be beneficially used in the treatment of wastewater containing small quantities of organic dyes.

In this study, semi-continuous anaerobic digestion of lignin-rich steam-exploded Ludwigia grandiflora (Lignin = 25.22% ± 4.6% total solids) was performed to understand better the effect of steam explosion on the substrate solubilisation and inhibitors formation during the process. Steam explosion pretreatment was performed at 180 °C for 30 min at a severity factor of 3.8 to enhance the biogas yield of the lignocellulosic biomass. The semi-continuous anaerobic digestion was performed in a continuously stirred tank reactor for 98 days at an initial hydraulic retention time of 30 days and an organic loading rate of 0.9 g-VS L−1 day−1. The performed steam explosion pretreatment caused biomass solubilisation, resulting in enhanced biogas production during the process. During the anaerobic digestion process, the average biogas yield was 265 mL g-VS−1, and the pH throughout the operation was in the optimum range of 6.5–8.2. Due to fluctuations in the biogas yield, the hydraulic retention time and organic loading rate were changed on day 42 (50 days and 0.5 g-VS L−1 day−1) and on day 49 (40 days and 0.7 g-VS L−1 day−1), and 1 M of NaOH was added to the liquid fraction of the steam-exploded L. grandiflora during the latter part of the operation to maintain the stability in the reactor. Therefore, the steam explosion pretreatment helped in the degradation of L. grandiflora by breaking the lignocellulose structure. In addition, changes in the operating conditions of the anaerobic digestion led to an increase in the biogas production towards the end of the process, leading to the stability in the CSTR.

A hybrid anaerobic reactor was operated under the complementary effluents concept to reduce the unbalanced/inhibitory capacity of the provided piggery effluent. Brewery wastewater was chosen to complement piggery effluent (60:40% v/v, respectively). The HRT reduction from 6.7 to 3.0 days allowed the testing of an organic load increase from 4.5 to 10.0 g COD/L·d, which resulted in the almost doubling of biogas production. Biogas volumes (1.2 and 2.1 L/L·d, respectively) associated with its quality (&gt, 77% CH4) revealed that the hybrid anaerobic reactor responded positively to the operational changes and that piggery effluent can be advantageously digested using the brewery wastewater as the complementary effluent. The unit bottom and the packing bed were the main functional sections recognized in the hybrid. At the beginning of anaerobic digestion, bacterial populations belonged mostly to Bacteroidales (33%) and Clostridiales (35%). The process stability and the biogas quality at 3-d HRT were related to a change in the structure composition, since Flavobacteriales (18%), Bacillales (7%), Pseudomonadales (11%) and members of the Alcaligenaceae family (5%) also integrated the microbial communities. An evident change had also occurred in archaeal populations at this phase. Methanosaeta became the dominant genus (95%), confirming that acetoclastic methanogenesis was the main way for methane production.

The conventional crow search (CS) algorithm is a swarm-based metaheuristic algorithm that has fewer parameters, is easy to apply to problems, and is utilized in various fields. However, it has a disadvantage, as it is easy for it to fall into local minima by relying mainly on exploitation to find approximations. Therefore, in this paper, we propose the advanced crow search (ACS) algorithm, which improves the conventional CS algorithm and solves the global optimization problem. The ACS algorithm has three differences from the conventional CS algorithm. First, we propose using dynamic AP (awareness probability) to perform exploration of the global region for the selection of the initial population. Second, we improved the exploitation performance by introducing a formula that probabilistically selects the best crows instead of randomly selecting them. Third, we improved the exploration phase by adding an equation for local search. The ACS algorithm proposed in this paper has improved exploitation and exploration performance over other metaheuristic algorithms in both unimodal and multimodal benchmark functions, and it found the most optimal solutions in five engineering problems.

This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.

Selective assembly is a manufacturing method that matches and assembles pairs of parts in a manner that offsets the machining errors of these parts. In the production of products requiring high precision and efficient mass production, flow production and search-based selective assembly must be combined for market competitiveness; however, this method increases computational costs and generates many surplus parts. Therefore, research should aim to minimize surplus parts in search-based selective assembly at a low computational cost to suit flow production systems. In this paper, we propose the density-based prioritization (DBP) algorithm, which minimizes surplus parts in the search-based selective assembly of flow production systems. In addition, a method of varying the assembly tolerance is developed and incorporated into DBP to increase its process capability. The proposed algorithm requires an assembly facility to prepare parts with as many different sizes as possible. This paper confirms that DBP reduces computational costs and surplus parts while enhancing process capability.

Breast cancer is one of the most common malignancies that threaten women’s health. Ultrasound testing is a widespread technique employed for the early detection of tumors. However, after receiving the paper ultrasound report, most patients often have to wait for several days to receive the diagnosis results, which can increase their psychological burden and may cause treatment delay. Based on deep learning, this study designed a computer-aided diagnostic system that directly classifies benign and malignant tumors in breast ultrasound images on paper reports taken by patients, helping them obtain auxiliary diagnostic results as soon as possible. In order to segment and denoise ultrasound report images of patients, this paper proposes a breast ultrasound report generation method, which mainly includes a segmentation model, a rotating classification model and a generative model. With this method, multiple high-quality individual breast ultrasound images can be obtained from a single ultrasound report photo, improving the performance of the breast ultrasound image classification model. In order to utilize high-quality breast ultrasound images and improve classification performance, this paper proposed a breast ultrasound report classification model that includes a feature extraction module, a channel attention module and a classification module. The accuracy of the model reached 89.31%, recall rate reached 88.65%, specificity reached 89.57%, F1 score reached 89.42% and AUC reached 94.53% when input images contained noise. The method proposed in this article is more suitable for practical application scenarios and it can quickly and accurately assist patients in obtaining the benign and malignant classification results of ultrasound reports.

The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy, provides significant benefits in clinical practice by improving the efficiency and accuracy of diagnosis. Deep learning techniques show remarkable success in analyzing these videos by automating tasks such as classification, detection, and segmentation. In this paper, we review the application of deep learning techniques for analyzing medical diagnostic videos, with a focus on ultrasound and endoscopy. The methodology for selecting the papers consists of two major steps. First, we selected around 350 papers based on the relevance of their titles to our topic. Second, we chose the research articles that focus on deep learning and medical diagnostic videos based on our inclusion and exclusion criteria. We found that convolutional neural networks (CNNs) and long short-term memory (LSTM) are the two most commonly used models that achieve good results in analyzing different types of medical videos. We also found various limitations and open challenges. We highlight the limitations and open challenges in this field, such as labeling and preprocessing of medical videos, class imbalance, and time complexity, as well as incorporating expert knowledge, k-shot learning, live feedback from experts, and medical history with video data. Our review can encourage collaborative research with domain experts and patients to improve the diagnosis of diseases from medical videos.

In order to accurately identify the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries, this paper proposes an SOH estimation algorithm for lithium-ion batteries based on stream learning and LightGBM. To address the problem of inconsistent data length, which makes it difficult to establish the state mapping relationship between degraded data and health state, the health factors in this paper are extracted from capacity degradation features, entropy features, and correlation coefficient features. Then, the landmark isometric mapping (L-ISOMAP) manifold learning algorithm is used to dimensionally reduce the input feature set and map the high-dimensional features to the low-dimensional space to solve the dimensional explosion problem. Finally, a LightGBM prediction model is developed to perform SOH prediction on different datasets, and the superiority of the multidimensional model is evaluated. The experimental results show that the goodness-of-fit is 0.98 and above, and the MSE values are below 4 × 10−4. Comparing several prediction models, the LightGBM model has the best performance and better results in several indexes, such as MSE and RMSE. Under different working conditions, the proposed model in this paper has a goodness-of-fit of more than 0.98 in dataset B, which proves that the proposed model has a strong generalization ability.