Artistic image completion is fundamental for the preservation and restoration of invaluable art paintings and it has experienced significant progress through the implementation of deep learning methodologies. Despite these advancements, challenges persist, even with sophisticated approaches like Generative Adversarial Networks (GANs), particularly in achieving optimal results for high-resolution paintings. Small-scale texture synthesis and the inference of missing information from distant contexts present persistent issues, leading to distortions in lines and unnatural colors, especially in art paintings with complicated structures and textures. Concurrently, patch-based image synthesis has evolved by incorporating global optimization on the image pyramid to enhance structural coherence and details. However, methods relying on gradient-based synthesis encounter obstacles related to directionality, inconsistency, and the heavy computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a groundbreaking approach, integrating Weighted Similarity-Confidence Laplacian Synthesis, to comprehensively address these challenges and advance the field of artistic image completion. This proposal addresses challenges not only in high-resolution artistic image completion but also makes a significant contribution to the broader field of patch-based synthesis by utilizing the Laplacian pyramid for enhanced edge-aware correspondence search. Experimental result confirms the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with complicated details and irregular missing regions.

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

This paper presents the latest developments in microCT, both globally and locally, for supporting the additive manufacturing industry. There are a number of recently developed capabilities which are especially relevant to the non-destructive quality inspection of additive manufactured parts; and also for advanced process optimization. These new capabilities are all locally available but not yet utilized to their full potential, most likely due to a lack of knowledge of these capabilities. The aim of this paper is therefore to fill this gap and provide an overview of these latest capabilities, showcasing numerous local examples.

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.

This study investigated different conditions for the hydrothermal co-liquefaction (co-HTL) of sugarcane bagasse and residual cooking soybean oil, with and without the presence of ethanol as a cosolvent to maximize the bio-crude yield. All co-HTL reactions were carried out in a 300 mL Parr® reactor at temperatures ranging from 200 to 300 °C. Bio-crude yields of around 95 wt.% were obtained at 300 °C using ethanol and water as solvents. The highest biochar yield (16.6 wt.%) was achieved when using only sugarcane bagasse as substrate, without the presence of soybean oil. Bio-crude samples obtained at higher temperatures (280 °C and 300 °C) using ethanol as a hydrogen donor presented higher contents of both free fatty acids and fatty acid ethyl esters. This work presents a promising process to produce high-quality bio-crude using an abundant feedstock (sugarcane bagasse) in the presence of a lipid source which could cause environmental problems if poorly handled.

The objective of this study is to evaluate how two varying amounts of sepiolite-based powder, infused with indigo extract, affect the appearance and durability of a water-based white primer. To examine the influence of this eco-friendly pigment on the coatings' overall appearance, assessments were performed for color, gloss, and surface roughness. Additionally, the coatings were investigated through optical and electron microscopic observations, to evaluate the distribution of the pigment within the polymer matrix. The effect of the pigment on the coating's durability was assessed through accelerated tests, including exposure in a salt spray chamber and a UV-B chamber. These tests aimed to evaluate the emergence of defects and changes in the appearance of the samples over time. Furthermore, the impact of different quantities of sepiolite-based powder on the coating's ability to act as a barrier was assessed using liquid resistance tests and contact angle measurements. These evaluations aimed to understand how the coating responded to various liquids and its surface properties concerning repellency or absorption. In essence, this study underscores the considerable influence of the eco-friendly pigment, demonstrating its capacity to introduce unique color and texture variations to the paint. Moreover, the inclusion of the pigment has enhanced the coating's color stability, its ability to act as a barrier, and overall durability when exposed to harsh environments.

This research explores the enhancement of bio-oil quality through upgrading with the magnetic bimetallic oxide (CuO-Fe3O4) catalysts supported on the activated rice straw biochar (AcB). These catalysts were employed in a supercritical ethanol-based upgrading process. Various characterization techniques, including elemental analysis, XRD, BET, TGA, and SEM, were utilized to characterize the prepared catalyst. The study revealed significant improvements in the physical characteristics and chemical composition of the bio-oil, with higher heating value (HHV) increasing from 21.3 to 32.1 MJ/kg. Esterification and transesterification were identified as key reactions contributing to this improvement. Notably, the pH of bio-oil increased from 4.3 (raw bio-oil) to 5.63 (after upgrading), signifying reduced acidity. The analysis of the bio-oil's chemical composition highlighted a decrease in oxygen content and an increase in carbon and hydrogen content. At the optimum conditions, the application of supercritical ethanol proved to be an efficient method for enhancing the bio-oil's properties. A crucial transformation occurred during the upgrading process and more than 90% % of carboxylic acids were converted into esters, primarily ethyl acetate at the optimal conditions. This study has demonstrated the effective enhancement of raw bio-oil from rice straw through the utilization of carbon-based bimetallic oxide catalysts in a supercritical upgrading procedure.

Metachrony is defined as coordinated asynchronous movement throughout multiple appendages, such as the cilia of cells and swimmerets of crustaceans. We investigate the process of metachronal propulsion, notably used by species of crustaceans and microscopic cells to move through fluid. A rigid crustacean model with paddles moving in symmetric strokes was created to simulate metachronal movement. Coupled with the surrounding fluid domain, we employ the immersed boundary method to analyze fluid-structure interactions. To explore the effect of nonlinear morphology on the efficiency of metachronal propulsion, we generate and simulate a range of crustacean body shapes, from upward curves to downwards curves. We found that the highest propulsion velocity is achieved when the crustacean model morphology follows a downwards curve, specifically a parabola of leading coefficient k=−0.4. This curved morphology results in 4.5% higher velocity compared to the linear model. As k deviates from −0.4, the propulsion velocity decreases with increasing magnitude, forming a concave downward trend. The impact of body shape on propulsion velocity is shown by how the optimal velocity with k=−0.4 is 71.5% larger than the velocity at k=1. Overall, this study suggests that morphology has a significant impact on metachronal propulsion.

Chitin/chitosan and collagen are two of the most important bioactive compounds with applications in the pharmaceutical, veterinary, nutraceutical, cosmetic, biomaterials, and other industries. When extracted from fish and shellfish non-edible parts, by-catches, and invasive species, their use contributes to a more sustainable and circular economy. The present article reviews the scientific knowledge and publication trends along the marine chitin/chitosan and collagen value-chains and assesses how researchers, industry players, and end-users can bridge the gap between scientific understanding and industrial applications. Overall, research on chitin/chitosan remains focused on the compound itself rather than its market applications. Still, chitin/chitosan use is expected to increase in food and biomedical applications, while that of collagen is expected to increase in biomedical, cosmetic, pharmaceutical, and nutritional applications. Sustainable practices, such as the reuse of waste materials, contribute to strengthen both value-chains; the identified weaknesses include the lack of studies considering market trends, social sustainability, and profitability, as well as insufficient examination of intellectual property rights. Government regulations, market demand, consumer preferences, technological advancements, environmental challenges, and legal frameworks play significant roles in shaping both value-chains. Addressing these factors is crucial for seizing opportunities, fostering sustainability, complying with regulations, and maintaining competitiveness in these constantly evolving value-chains.

This work demonstrates that microbial fuel cells (MFCs), optimized for energy recovery, can be used as an effective tool to detect the presence of antibiotics in water-based environments. MFCs directly convert chemical energy of organic matter in electrical energy thanks to electroactive biofilms. The efficiency of the conversion process can be significantly affected by the presence of contaminants acting as toxicants for the biofilm. In the present work, we demonstrate that MFCs can successfully detect the presence of antibiotic residues in water and water-based electrolytes associated to the food industry, especially using honey as a new and more complex electron donor. The effectiveness of MFCs to sense antibiotics is here demonstrated for tetracycline that was added to both water and water/honey electrolytes with a minimum concentration close to 3.5 μg⁄kg. MFCs not only efficiently detect the presence of tetracycline in both the electrolytes, but also recover the same performance after each cycle of exposure, showing to a be very robust and reliable technology for both biosensing and energy recovery.

With the development of bioinspired green solutions for sustainable construction over the past two decades, bio-cementation, which exploits the naturally occurring phenomenon of calcium carbonate precipitation in different environments, has drawn a lot of attention in both building construction and soil stabilization. Various types of microorganisms, along with specific enzymes derived from these microorganisms, have been utilized to harness the benefits of bio-cementation. Different application methods for incorporating this mechanism into the production process of the construction material, as well as a variety of experimental techniques for characterizing the outcomes of bio-cementation, have been developed and tested. Despite the success of bio-cementation as a sustainable method to construction has been demonstrated in a significant body of literature at the laboratory scale, the expansion of this strategy to construction sites and field application remains a pending subject. The issue may be attributed to two primary challenges. Firstly, the complexity of the bio-cementation phenomenon is influenced by a variety of factors. Secondly, the extensive body of literature examines various types of microorganisms under different conditions, leading to a wide range of outcomes. Hence, this study aims to examine the recent advancements in utilizing the most commonly employed microorganism, Sporosarcina Pasteurii, to emphasize the significance of influential factors identified in the literature, discuss the findings that have been brought to light, and outline future research directions toward scaling up the process.

While biologic originators’ patents are expiring, biosimilars are emerging to take their place, offering significant cost savings to healthcare systems. Many challenges still need to be addressed in the clinical practice of inflammatory bowel disease (IBD). A global survey was organized to highlight physicians’ current knowledge and beliefs and to gain insight about their practical management and remaining concerns and obstacles associated with starting a biosimilar, switching from an originator to a biosimilar, or switching from one biosimilar to another (multiple switches and reverse switching). Fifteen physicians with expertise in the field of IBD from 13 countries attended a virtual international consensus meeting to develop practical guidance regarding biosimilar adoption worldwide, considering the survey results. Consensus was reached on 10 statements regarding biosimilar effectiveness, safety, indications and rationale, multiple switches, therapeutic drug monitoring of biosimilars, non-medical switching and future perspectives.

This study investigates the influence of the porous structure, on the density and the mechanical behaviour of new foam concretes incorporating hemp shives. A total of eight batches of foam concretes were manufactured and tested, made with a protein-based foam agent, containing cement, ground granulated blast furnace slag and Metakaolin as binder and hemp shives as natural aggregate. The effect of several parameters is studied, elaboration method (direct and preformed), amount of pozzolanic additions (0% and 30%) and incorporation of hemp shives (5 and 15 vol%) on the resulting physical properties, microstructure, porous structure and mechanical behaviour of the concretes. Hemp shives incorporation results in increased concrete porosity and air bubble radius, decreased uniformity and mechanical strength, and lower cohesion with the cement matrix compared to standard concretes.

Polymeric wet-strength agents are important additives used in the paper industry to improve the mechanical properties of paper products, especially when they come in contact with water. These agents play a crucial role in enhancing the durability, strength, and dimensional stability of paper products. The aim of this review is to provide an overview of the different types of wet-strength agents available and their mechanisms of action. We will also discuss the challenges associated with the use of wet-strength agents and the recent advances in the development of more sustainable and environmentally friendly agents. As the demand for more sustainable and durable paper products continues to grow, the use of wet strength agents is expected to increase in the coming years.

Bionic robotic hand can perform many movements similar to human hand. But there is still a significant gap in manipulation between robot and human hand. It is necessary to understand the finger kinematics and motion patterns of human hand to improve the performance of robotic hand. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hand of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a largest dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Besides, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by PIP joint. Regarding the finger sequence, the thumb started to move before four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided kinematic reference for the design of robotic hand and thus contribute to its development.

From the merge of arts and crafts towards a practice for mass production of desirability, consumption and product development in a capitalist economy of scale, design has lost its natural ability of problem comprehension and tension alleviation. The modern world needs the creativity, flexibility, and responsiveness embedded into design practices, mostly when a behavioural change, either individual or organizational, is intended. Still, the informality nature of the field is creating a gap between the study, research, and industrial design practice. Here it is presented the Biology of Creativity Model (BoC) which is a design-by-analogy method that promotes an empowered design practice through analysis of mostly biology reference texts for enhanced creativity and innovation performance in a diverse array of contexts.

Rice is considered as the most important crop for most of the world population. Utilization of seaweed as bio-stimulant can be an alternative way to enhance rice plant growth and productivity, as well as a strategic move to reduce the use of inorganic fertilizer that is harmful to the environment. Seaweed and its derivative products have been widely used as bio-stimulant in the agricultural industry because of their potential use in increasing plant growth and productivity. Auxins, gibberellins, and cytokinin are some of growth regulators found in seaweed extract, as well as macro and micronutrients required for plant growth and development. Several studies have found that seaweed extract has a variety of favorable effects as a plant growth promoter, including early seed germination and establishment, improved nutritional quality, increased yield and crop performance, and increased tolerance to environmental stress. The purpose of this paper is to give a comprehensive overview of the impacts of several seaweed species on seed germination, crop development and production, enhancement of rice plants (Oryza sativa) nutritional quality and the modes of action of seaweed extract includes the chemical components that might be causing plant physiological changes.

Literature revealed the potential of using guayule resin for asphalt cement replacement from the binder’s perspective. However, monitoring guayule resin through binder-aggregate mixture could disclose its performance through field. In this study, designated binders were employed to investigate the applicability of such an innovative replacer through mixture, which were neat asphalt and guayule-based binders (neat guayule, asphalt-rubber-guayule, guayule-rubber binders). Consecutively, field-simulated lab mixtures were prepared to investigate the major distresses. Moisture damage, rutting, fatigue cracking, and thermal cracking resistances were investigated using the modified Lottman (TSR) test, rut test by asphalt pavement analyzer (APA), semi-circular bending (SCB) test, and disk-shaped compact tension (DCT) test, respectively. Additionally, the Hamburg wheel-tracking (HWT) test was employed to evaluate moisture susceptibility and rutting resistance. Outcomes revealed that the neat guayule was susceptible to moisture damage at a 7% air content (V_a) when the TSR test was employed. In contrast, all investigated mixtures yielded perfect performances against moisture susceptibility under the HWT test. Guayule-based mixtures perfectly resisted rutting, as analyzed by the rut test and HWT test. Generally, changing parameters (e.g., V_a, rubber addition, and partial asphalt replacement by guayule and rubber) enhanced the guayule-based mixture resistance to rutting and moisture damage resulting in acceptable performances. Guayule-based mixture had a high fracture toughness at low temperatures, hence fatigue fracture resistance at intermediate temperatures. Neat guayule mixture with or without rubber addition did not entirely resist thermal fracture. However, partial asphalt replacement by guayule and rubber resisted the thermal fracture to a great extent.

Dermal interstitial fluid (ISF) is a novel source of biomarkers that can be considered as an alternative to blood sampling for disease diagnosis and treatment. Nevertheless, in vivo extraction and analysis of ISF are challenging. On the other hand, microneedle (MN) technology can address most of the challenges associated with dermal ISF extraction and is well-suited for long-term, continuous ISF monitoring as well as in situ detection. In this review, we first briefly summarise the different dermal ISF collection methods and compare them with MN methods. Next, we elaborate on the design considerations and biocompatibility of MNs. Subsequently, the fabrication technologies of various MNs used for dermal ISF extraction, including solid MNs, hollow MNs, porous MNs and hydrogel MNs, are thoroughly explained. In addition, different sensing mechanisms of ISF detection will be discussed in detail. Subsequently, we identify the challenges and propose the possible solutions associated with ISF extraction. A detailed investigation is provided for the transport and sampling mechanism of ISF in vivo. Also, the current in vitro skin model integrated with the MN arrays will be discussed. Finally, future directions to develop a point-of-care (POC) device to sample ISF are proposed.

Bacteria a Microscopic organisms are the most inexhaustible and flexible of microorganisms and constitute a huge division of the whole living earthly biomass, certain microorganisms were found to amass metallic components at a high limit Was Known as Bacterial Bio-sorption Due to their little size, capacity to become under controlled conditions, and their Accommodation to an extensive variety of ecological situations; Potent metal bio-sorbents among microorganisms, at low pH esteems, cell divider ligands are protonated and contend essentially with metals for official. With expanding pH, more ligands, such as amino and carboxyl groups, could be exposed, leading to attraction between these negative charges and the metals, and consequently increment bio-sorption onto the cell surface. Starting with Isolation and identification of heavy metal-resistant bacteria from rock Ore. Studying Factors Affecting Uranium Bio-sorption, Optimization of bacterial growth conditions and optimum for metal uptake by free and immobilized bacterial cells and Desorption ratio of uranium ions adsorbed by Coli. /alginate, All this evidence suggest that functions groups Represented in our study are responsible for metal uptake in our bacterial biomass beside change in peaks position which assigned for it's groups confirm bio-sorption of metal ions from waste due to ions charge interaction comparing with immobilized we found increase in no of binding sites indicate that immobilized bacterial have high efficiency for metal up take which also change in peaks position which assigned for its groups confirm bio-sorption of metal ions from waste due to ions charge interaction, Where the high bio-sorption yield obtained by bacteria, the Uranium & heavy metal bioremediation process expects microorganisms to be joined to a strong surface.

Artificial reefs have been deployed in multiple regions of the world for different purposes including habitat restoration and protection, biodiversity and fish stock enhancement, fisheries management and recreation. Artificial reefs can be a valuable tool for ecosystem protection and rehabilitation, helping mitigate the effects of anthropogenic impacts that we face today. However, knowledge on artificial reefs is unevenly distributed worldwide, with some regions having much more quality information available and published (e.g. European Mediterranean Sea area), while others, for instance the North-East Atlantic area, do not. Here, we provide a characterization of purposely built artificial reefs in North-East Atlantic area based on all available literature (i.e. research papers and reports), highlighting the needs and gaps that are vital for establishing future perspectives for artificial reef deployment and research. In the North-East Atlantic area, sixty-one purposely built artificial reefs have been deployed since 1970, mostly between the years 1990-2009, with Spain being the country with the highest number of artificial reefs. The most reported purpose for their deployment is fisheries productivity and habitat/species protection, although, most artificial reefs are multipurpose in order to maximise the benefits of a given financial investment. The majority of artificial reefs were submerged at < 50 m, mainly between 10-20 m of depth. The most used designs were cubic blocks and complex designs made by an array of combined shapes, which mostly consist of concrete (79%). From all the analysed data on artificial reefs, 67% of the cases reported surveys to assess biodiversity after the deployment. However, in 26% of those cases, data was not available. When data was available, only 31% of cases reported long-term biomonitoring surveys (3 years or more). Based upon these findings, we noticed a general lack of scientifically robust data, including records of species and abundance of both fish and invertebrates, as well as macroalgae. Preventing an adequate determination of the best balance between shape, construction material and bio-colonization. Critiques and suggestions are discussed in the light of current available data in order to perform more efficient research, evaluation and functioning of future artificial reefs.

Ca-bentonite (CB) alone and in a mixture with limestone (L), tobacco biochar (TB) and zeolite (Z) on the fixation, geochemical fractions and absorption of Cd and Zn by Chinese cabbage in smelter heavily polluted (S-HP) and smelter low polluted (S-LP) soils were investigated. The results showed that the CB+TB and CB+L+TB treatments significantly immobilized Cd up to 22.03% and 29.68%, respectively, and reduced uptake by Chinese cabbage shoot to 35.98% with CB+Z+L and 61.35% with CB+L in S-HP and S-LP soils compared with the control. The CB+ Z+ L+TB treatment mobilized Cd up to 4.45% and increased absorption in the shoot by 9.85% in S-HP soil. The greatest immobilization of Zn was 53.18% and 58.20% with the CB+Z+L+TB treatment, which reduced Zn uptake in the plant shoot by 9.94% with CB + L and 58.04 with CB+Z+L+TB in S-HP and S-LP soils. The CB+Z+TB and CB+TB treatments mobilized Zn up to 35.40% and 4.80%, respectively, in both soils. Furthermore, the uptake of Zn in plant shoot was observed by 58.96% and 7.82% with application of CB+Z and CB+TB treatments, respectively, in S-HP and S-LP soils. Overall, our results suggest that Ca-bentonite alone and in mixtures with different amendments can be used to reduce the phyto-extraction of Cd and Zn in Zn-smelter polluted soils.

Egyptian mongoose (Herpestes ichneumon) is a medium-size carnivore that in Europe is restricted to Iberia. The bio-ecology of this species remains to be elucidated in several dimensions, including gut microbiota that is nowadays recognized as a fundamental component of mammals. In this work, we investigated the gut microbiota of this herpestid by single-molecule real-time sequencing of twenty paired male (n=10) and female (n=10) intestinal samples. This culture-independent approach enabled microbial profiling based on 16S rDNA and investigation of taxonomical and functional features. The core gut microbiome of the adult subpopulation was dominated by Firmicutes, Fusobacteria, Actinobacteria, and Proteobacteria. Eight genera were uniquely found in adults and five in non-adults. When comparing gut bacterial communities across sex, four genera were exclusive of females and six uniquely found in males. Despite these compositional distinctions, alpha- and beta-diversity analyses showed no statistically significant differences across sex or between adult and non-adult specimens. However, males presented a significantly higher abundance of amino acid and citrate cycle metabolic pathways, compared to the significant overrepresentation in females of galactose’ metabolic pathways. Adults showed a significantly higher abundance of cationic antimicrobial peptide resistance pathways, while non-adults bared a significant overrepresentation of two-component systems associated with antibiotic synthesis, flagellin production and chemotaxis control. This study adds new insights into mongoose bio-ecology palette, highlighting taxonomical and functional microbiome dissimilarities across sex and age classes, possibly related to primary production resources and life-history traits that impact on behavior, diet and gut ecosystem.

Developments in technology have enabled envisioning the derivation of materials and products from renewable biomass, as an alternative to finite fossil-based resource consumption. Therefore, bioeconomy is regarded as an opportunity for sustainable economic growth. Countries are formulating strategies in accordance with their goals to attain from bioeconomy. Proper measurement, monitoring and reporting of the outcomes of these strategies is crucial for long-term success. This study aims to critically evaluate the national methods used for the measurement, monitoring and reporting of bioeconomy contribution to the total economy. For this purpose, research and surveys have been conducted on selected countries (Argentina, Germany, Malaysia, the Netherlands, South Africa and the United States). The results reveal that the bioeconomy targets set up in the strategies often reflect country’s priorities and comparative advantages. However, comprehensive approaches to measure and monitor bioeconomy progress are frequently lacking. Most countries only measure the contribution to gross domestic product (GDP), turnover and employment of the sectors included in their bioeconomy definition, which may provide an incomplete picture. In addition, this study identifies the mismatch between the targets and measurement methods, as the environmental and social impacts of bioeconomy are often foreseen, but not measured. It is concluded that existing global efforts towards a sustainable bioeconomy monitoring can be strengthened and leveraged to measure progress towards sustainable goals.

This work deals with the numerical investigation of the delivery of potential therapeutic agents through dentinal discs (i.e. a cylindrical segment of the dentinal tissue) towards the dentin-pulp junction. The aim is to assess the main key features (i.e. molecular size, initial concentration, consumption rate, disc porosity and thickness) that affect the delivery of therapeutic substances to the dental pulp and consequently to define the necessary quantitative and qualitative issues related to a specific agent before its potential application in clinical practice. The CFD code used for the computational study is validated with relevant experimental data obtained using micro Laser Induced Fluorescence (μ-LIF) a non-intrusive optical measuring technique. As the phenomenon is diffusion dominated and strongly dependent on the molecular size, the time needed for the concentration of released molecules to attain a required value can be controlled by their initial concentration. Finally, a model is proposed which, given the maximum acceptable time for the drug concentration to attain a required value at the pulpal side of the tissue along with the aforementioned key design parameters, is able to estimate the initial concentration to be imposed and vice versa.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

Conventional plaque assays rely on the use of overlays to restrict viral infection allowing the formation of distinct foci that grow in time as the replication cycle continues leading to counta-ble plaques that are visualized with standard techniques such as crystal violet, neutral red or immunolabeling. This classical approach takes several days until large enough plaques can be visualized and counted with some variation due to subjectivity in plaque recognition. Since plaques are clonal lesions produced by virus-induced cytopathic effect, we applied DNA fluores-cent dyes with differential cell permeability to visualize them by live cell imaging. We could observe different stages of that cytopathic effect corresponding to an early wave of cells with chromatin-condensation followed by a wave of dead cells with membrane permeabilization within plaques generated by different animal viruses. This approach enables an automated plaque identification using image analysis to increase single plaque resolution compared to crystal violet counterstaining and allows its application to plaque tracking and plaque reduction assays to test compounds for both antiviral and cytotoxic activities. This fluorescent real-time plaque assay sums to those next-generation technologies by combining this robust classical method with modern fluorescence microscopy and image analysis approaches for future applica-tions in virology.

X-ray tomography is an effective non-destructive testing method for industrial quality control. Limited angle tomography can be used to reduce the amount of data that needs to be acquired and thus speed up the process. In some industrial applications, however, objects are flat and layered, and laminography is preferred. It can deliver 2D images of the structure of a layered object at a particular depth from very limited data. An image at a particular depth is obtained by summing those parts of the data that contribute to that slice. This produces a sharp image of that slice while superimposing a blurred version of structures present at other depths. In this paper, we investigate an optimal experimental design (OED) problem for laminography that aims to determine the optimal source positions. Not only can this be used to mitigate imaging artifacts, it can also speed up the acquisition process in case where moving the source and detector is time consuming (e.g., in robotic arm imaging systems). We investigate the imaging artifacts in detail through a modified Fourier Slice Theorem. We address the experimental design problem within the Bayesian risk framework using empirical Bayes risk minimization. Finally, we present numerical experiments on simulated data.

Hyperspectral (HS) imaging plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. Hyperspectral image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper investigates the effectiveness of three dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection using HS images. A HS image dataset, encompassing left and right-hand images captured from 100 subjects with varying skin tones was created and annotated using anatomical data to represent vein and non-vein areas within the images. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with The Support Vector Machine binary classification were determined using the Training dataset. The performance of the three dimensionality reduction based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

There is growing recognition of the adverse effects of body image dissatisfaction (BID) and eating disorder (ED) symptoms on adolescent health. The aim of this study was to estimate the prevalence of ED symptoms, BID, and their relationship in adolescents from public schools in Southern Brazil. A total of 782 schoolchildren (male: n=420, female: n=362); age: 15 ± 0,4 years) answered a self-administrated questionnaire to identify sociodemographic data. Children´s Figure Rating Scale was adopted to identify body image and Eating Attitudes Test (EAT-26) was applied to investigate ED symptoms. Inferential statistics and hierarchical model-controlled logistic regression were used for association between variables. Most of the schoolchildren reported being satisfied with their bodies. However, we observed a higher prevalence of dissatisfaction among girls for being overweight and thinness among boys. Female students and students from schools located in the central area of the city showed higher chances of developing ED symptoms, and the absence of symptoms of ED appeared to act as a protective factor against BID in schoolchildren. Results of this study show the need to reflect on these factors that influence the development of ED and non-acceptance of their own body in a population concerned with their physical appearance.

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. Shu et al. classified 9 different types of rock images using a Support Vector Machine (SVM) with the image features extracted autonomously. Through this method, the authors achieved a test accuracy of 96.71%. In this research, Convolutional Neural Networks(CNN) have been used to classify the same set of rock images. Results show that a 3-layer network obtains an average accuracy of 99.60% across 10 trials on the test set. A version of Self-taught Learning was also implemented to prove the generalizability of the features extracted by the CNN. Finally, one model has been chosen to be deployed on a mobile device to demonstrate practicality and portability. The deployed model achieves a perfect classification accuracy on the test set, while taking only 0.068 seconds to make a prediction, equivalent to about 14 frames per second.

The development of unmanned satellite space technology is increasingly willing, the emergence of medium resolution satellites with sensitivity and spectral variants such as Landsat is very effective in observing environmental changes, while the purpose of this study is to monitor the development of built-in land using image transformation techniques, estimating built-in land changes. The research method uses the NDVI image transformation technique, NDBI and Built Up Index, with Landsat satellite image data obtained from USGS. Accuracy sampling is done by purposive sampling with confusion matrix accuracy test technique. The research results were found. developed land for the period 2004 - 2010 with a percentage of 19.25%, for stages 2010 - 2018 with a percentage of 30.25%. The land development was built based on the area of ​​the highest sub-district in the Kubung area in the early period with a percentage of 7.20% then in the second period with a percentage of 32.23%. The quality of the accuracy of the results of image analysis using confusion matrix technique with an image accuracy level in a field sample of 185 with an image accuracy of 86.04%.

The focus of this study is to identify whether resilience and sustainable development can be used as an image for strategic planning of the city marketing. Resilience is about building and planning for future proof the cities. How urban challenges and crisis have the lowest impact and the maximum of bounce back and evolution. Resilience is part of the sustainable development. Thus, it is important for the decision-makers to define the mission on their strategic planning in a holistically way taking into consideration the basic assets of a city, the environment, the economy and the society and how can all of them can be combined to marketing the city and take into consideration the internal and external environment. As the past few years’ city marketing has become an important tool for the urban development. The main goal is to show how city marketing can be applied on a city that tries to be more resilient and more sustainable by using strategic urban planning to set the vision, to identify the challenges and the problematic areas and to set new goals and objectives in order to plan and build to future proof the complexity of an urban system. For answering the questions of this article we use two case studies Rotterdam (Netherlands) and Thessaloniki (Greece), using a literature review and researches conducted alongside with a benchmarking of their resilient strategies as both of the cities are members of the Resilient Cities Network. From a different perspective of resilient thinking, both of the cities have managed to use resilience as a marketing image for further sustainable development.

In this paper, a technique of image watermarking using multiple images as watermarks is presented. The technique is based on transform domain functions including discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) with an image as the host signal i.e. the watermarks will be used as proofs of the authenticity of the host image. The technique is executed by performing multilevel DWT followed by applying DCT and SVD to both the host and watermark. Multiple watermarks are used for the insurance of better security level. The scheme is immune to common image processing operations & some attacks and exhibits PSNR of 108.3781dB, normalized cross correlation (NCC) over 0.99 and normalized correlation (NC) over 0.99.

The aim of the research was getting to know the risk of dependency on physical exercising in individual sportspeople and the relationship with body dissatisfaction and motivation. 225 triathletes, swimmers, cyclists and athletes- with ages going from 18 to 63 years old took part in the research, of which 145 were men (M = 35.57 ±10.46 years) and 80 women (M = 32.83 ±10.31 years). The EDS-R was used to study the dependency on exercising, BSQ to study body dissatisfaction, BREQ-3 to know the motivation of participants and BIAQ to analyse conducts of avoidance to body image. The obtained results show that 8.5% of the subjects had risk of dependency on exercising and that 18.2% tend to have corporal dissatisfaction, without meaningful differences in the kind of sport they practiced. However, there were important differences concerning the dependency on physical exercise (15% vs 4.8%) and body dissatisfaction (31.1% vs 11%) in relation to sex, being the higher percentage referring to women. The introjected regulation and the conduct of food restriction were the predictor variables of the dependency on exercising and corporal dissatisfaction.

Much has been learned about the interaction between myosin and actin through biochemistry, in vitro motility assays and cryo-electron microscopy of F-actin decorated with myosin heads. Comparatively less is known about actin-myosin interactions within the filament lattice of muscle, where myosin heads function as independent force generators and thus most measurements report an average signal from multiple biochemical and mechanical states. All of the 3-D imaging by electron microscopy that has revealed the interplay of the regular array of actin subunits and myosin heads within the filament lattice has been accomplished using the flight muscle of the large waterbug Lethocerus sp. Lethocerus flight muscle possesses a particularly favorable filament arrangement that enables all the myosin cross-bridges contacting the actin filament to be visualized in a thin section. This review covers the history of this effort and the progress toward visualizing the complex set of conformational changes that myosin heads make when binding to actin in several static states as well as fast frozen actively contracting muscle. The efforts have revealed a consistent pattern of changes to the myosin head structures determined by X-ray crystallography needed to explain the structure of the different acto-myosin interactions observed in situ.

At present, business strategies in SMEs (Small and medium enterprises) are crucial for consolidation in highly competitive markets, in achieving a better image and in business profitability. One of the strategies that have the most success and business success are sustainable practices and social responsibility such as: ISO 14001 and ISO 26001. The literature related to sustainable business is based mainly on the theory of resources and capabilities, and in theory based on Stakeholders. These currents state that companies should focus on profitable strategies to ensure significant and long-term results, in order to achieve organizational and financial results for stakeholders. In this work, the sample consists of 215 companies from the commerce, services and industry sectors, located in the southern region of the State of Sonora in Mexico. The objective of the work is to analyze the influence of ISO 14001 and 26001 standards on the image and profitability of SMEs. The statistical analysis of the data has been carried out through the linear regression technique by OLS (Ordinary Least Squares). The findings prove that the ISO 14001 standard is the one that most influences the improvement of the business image and the level of profitability of the SME. In addition, we discovered that ISO 26001 has a partial influence on the image and profitability of the SME.

As the demand for a more sustainable society increases, adopting a sustainable banking approach serves as a competitive advantage for banks that are focused on attaining bank loyalty. This study revolves around understanding the role of sustainable banking practices on bank loyalty, while exploring the mediating effect of corporate image in the relationship between sustainable banking practices and bank loyalty. 511 data derived from customers of the banking sector was adopted for this study. Result from the structural equation modeling shows that sustainable banking practices positively and directly affects bank loyalty and corporate image, corporate image directly and positively affect bank loyalty, and also mediates in the relationship between sustainable banking practices and bank loyalty.

An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two data sets: our institutional DE-CTPA data set D1 comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7,892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism Detection Challenge data set D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak-signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naive approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.

This paper presents novel feature descriptors and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC) stained slides. The first descriptor is called a characteristic curve which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is an LBP feature curve which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation, and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and comparative analysis.

The ubiquitous nature of polymers have led to a widespread demand for sustainable polymers in numerous industrial applications. However, lack of well laid out guidelines, product development pathways and certifications have resulted in a lot of commotions and confusions within the polymer value chain. Herein, a meticulous review is conducted on the topic of polymer sustainability shedding light on the standards, product declarations, biobased-biomass concepts, product carbon footprint etc. It is critical that companies significantly contribute on such sustainability efforts in lieu of market readiness and competitive advantages. Any discussion within the sustainability horizon references a couple of terms/ abbreviations/concepts. In this article, such key terminologies and concepts related to polymer sustainability are reviewed with a holistic outlook on the widespread approaches within the polymer sustainability horizon.

This work aimed to develop an anionic cellulose nanofiber (CNF) bio-adsorbent from palm date tree waste and to investigate its removal efficiency against cationic methylene blue dye from contaminated water. Palm date pulp was first prepared from palm date leaves through acid hydrolysis using H2SO4, followed by hydrolysis in a basic medium using KOH, in which the process completely removed the components of hemicellulose, lignin, and silica. To obtain anionic CNF, the resulting pulp was further treated with H2SO4, followed by centrifugation. Biogel formation of the CNF suspension was promoted by sonication, where its removal efficiency of methylene blue dye was studied as a function of dye concentration, temperature, contact time, and pH value. Two equilibrium models, the Langmuir and Freundlich models were employed, and the Langmuir model was found to be consistent with the experimental data. The highest removal efficiency was obtained at 60oC and was 5mg/gm. Furthermore, several kinetic models were used to gain insight into the adsorption mechanism. The pseudo-second-order model provided an accurate description of the adsorption process, while the Elovich equation suggested a chemical reaction between methylene blue dye and CNF. The study’s findings were in agreement with the pseudo-second-order model, indicating general chemical reactions. The study indicates that CNF adsorbents derived from renewable natural waste sources such as palm date leaves can be effective in removing cationic contaminants such as methylene blue dye.

Chronic kidney disease (CKD) is characterized by a continuous decline in kidney function, often persisting for years or even a lifetime. This study presents a new exploration into the extraction and application of insect chitosan (Tenebrio molitor) targeting hemodialysis (HD). Initially, the potential of insect-derived chitosan as a sustainable and effective alternative to traditional sources (i.e. crustaceans) is demonstrated. Efficient extraction methods were developed (totaling between 6 h and 12 h), producing chitosan. Firstly, chitin was efficiently isolated from T. molitor through deproteinization and demineralization, with yields of approximately 5% (w/w). Subsequently, chitosan was obtained from the extracted chitin resulting in yields of between 65.0 ± 0.8% (w/w) and 79.3 ± 0.8% (w/w). Characterization using Fourier Transform Infrared Spectroscopy (FTIR) confirmed structural similarities with commercial chitosan and degrees of deacetylation in the 73 - 75% range. The bioactive properties of chitosan obtained from T. molitor, including antimicrobial and antioxidant activities, were evaluated. All the microorganisms tested were inhibited, exhibiting minimum lethal concentrations between 2 and 8 mg/mL, confirming the antimicrobial activity of the extracted chitosan. In addition, chitosan showed antioxidant activity in the range of 60 to 65 µmol Trolox equivalent/g, suggesting its viability for various medical applications.This study also developed a chitosan-based membrane (CH - M) culminating in a proof-of-concept phase for hemodialysis. The permeation characteristics of CH-M for urea and albumin were studied in vitro to assess their suitability as HD membranes. Urea was permeable to values of over 70% and albumin was retained.

The aim of this study was to assess the influence of thermocompression conditions on lignocellulosic biomass such as sugarcane bagasse (SCB) in the production of 100% binderless bio-based materials. Five parameters were investigated: pressure applied (7-102 MPa), molding temperature (60-240°C), molding time (5-30 min), fiber/fine particle ratio (0/100-100/0) and moisture content (0-20%). These parameters affected the properties and chemical composition of the materials. The density ranged from 1198 to 1507 kg/m3, the flexural modulus from 0.9 to 6.9 GPa and the flexural strength at breaking point from 6.1 to 43.6 MPa. Water absorption (WA) and thickness swelling (TS) respectively ranged from 21% to 240% and from 9% to 208%. Higher mechanical properties were obtained using SCB with fine particles, low moisture content (4-10%), high temperature (≥ 200°C) and pressure (≥ 68 MPa), while water resistance was improved using more severe thermocompression conditions with the highest temperature (240°C) and time (30 min) or a higher moisture content (≥ 12.5%). Correlations were noted between the mechanical properties and density, while the material obtained with only fine particles had the highest mechanical properties and density. Material obtained with a 30 min molding time had the lowest WA and TS due to internal chemical reorganization followed by hemicellulose hydrolysis into water-soluble extractables.

We use COMSOL Multiphysics software to model optical processes in media and the effects of light propagation in whispering gallery mode resonators (WGMRs). Although plane wave irradiating on resonator does not create resonances, it was shown that they can be obtained when focused tangentially on the perimeter of the resonator. We demonstrate that resonances can be excited in resonators by prism coupling when the incident angle of the light is larger than the angle of total internal reflection. We determined the optimal distance between the prism and the resonator to get the strongest resonances. Bio-sensing principle of WGMRs is shown by demonstrating that the shift of resonances increases with a larger number of "bio-molecules" attached to the surface of WGMR. Surface functionalization and surface roughness were explored.

Given its exceptional attributes, aerogel is viewed as a material with immense potential. Being a natural polymer, cellulose offers the advantage of being both replenishable and capable of breaking down naturally. Cellulose-derived aerogels encompass the replenish ability, biocompatible nature, and ability to degrade naturally inherent in cellulose, along with additional benefits like minimal weight, extensive porousness, and expansive specific surface area. Even with increasing appreciation and acceptance, the undiscovered possibilities of aerogels within the textile sphere continue to be predominantly uninvestigated. In this context, we outline the latest advancements in the study of cellulose aerogel formulation and their diverse impacts on textile formations. Drawing from the latest studies, we reviewed the materials used for the creation of various kinds of cellulose-focused aerogels and their properties, analytical techniques, and multiple functionalities in relation to textiles. This comprehensive analysis extensively covers the diverse strategies employed to enhance the multi-functionality of cellulose-based aerogels in the textile industry. Additionally, we focused on the global market size of bio-derivative aerogels, companies in the industry producing goods, and prospects moving forward.

Platinum group metals (PGMs) high-grade ore tonnage from Merensky reef keeps on depreciating forcing the metallurgical industry to explore beneficiating PGMs from upper group 2 (UG2) ore which has up to 60% content of chromite and from secondary sources. The PGMs are naturally associated with base metal sulphides and the efficacy of the PGMs extraction processes is dependent on the processing of PGMs-bearing base metal sulphides. These processes include concentrating PGMs-bearing base metal sulphides through flotation, oxidation, etc. prior the metal extraction processes. The evolving mineralogy of PGMs ores has necessitated the use of multiple reagents to enhance the efficiency of the concentration process. This has led to an increased amount of inor-ganic chemicals disposal to the environment and the processing of PGMs to be less economic. Bio-technology has been investigated as a potential low cost, ecologically safe substitute for many current minerals processing methods. The metabolites produced by microorganisms have been used for many years with great success in the leaching and flotation of metals from medium and low-grade sulphide minerals. The possible application of microorganism mainly Acidithiobacillus ferrooxidans (A. ferrooxidans) in the metallurgical processing of PGMs-bearing base metal sulphides is systematically reviewed in this paper. The main emphasis is on the use of A. ferrooxidans in bio-oxidation and bioleaching, as well as their potential to substitute inorganic reagents in the flo-tation of PGMs-bearing base metal sulphides from primary and secondary sources

The production of oxygenated bio-additives for traditional fuels represents a key challenge due to the depletion of fossil fuels in the next future and their contribution to environmental pollu-tion. In this context, the present study considers the synthesis of different mixtures of 1-hexanol/hexyl hexanoate produced through the catalytic hydrogenation of hexanoic acid, a car-boxylic acid obtainable from the fermentation of a wide variety of waste biomasses. In particu-lar, crude hexanoic acid deriving from the fermentation of grape pomace, an abundant Italian agrifood waste, has been taken into consideration. The reaction was carried out with the com-mercial 5 wt% Re/γ-Al2O3 catalyst, whose acidity allowed the tuning of the reaction selectivity towards the preferential formation of hexyl hexanoate instead of 1-hexanol. As consequence, the tunable composition of the obtained 1-hexanol/hexyl hexanoate mixtures was leveraged for en-gine applications, thus allowing studying each component influence on the Diesel engine per-formances and verifying their synergistic effects. The engine experimental activity highlighted that both 1-hexanol and hexyl hexanoate, as well as their mixtures, can be used in Diesel engine with a commercial Diesel fuel up to high loadings (20 vol%) without altering engine perfor-mances and significantly lowering soot and CO emissions by more than 40%.

Sustainable bio-economy is considered to be one of the most promising routes towards the transition to a circular and climate-neutral economy. The valorization industry of bio-waste and agri-food by-products represents a key player in bioeconomy. In this article the design and the development of a web platform aiming at promoting synergies enabling the agricultural waste valorization is presented. The platform consists of: (i) the AgriPLaCE Waste Management Database which provides the users with an extended list of potential utilization measures for a variety of agricultural waste streams (ii) the AgriPLaCE Synergies Tool which aims to create synergies between different actors involved in the value chain from agricultural waste production to waste treatment and new valuable products’ exploitation. At the initial stage, the conceptual design of both tools took place by conducting an in-depth analysis for the user needs and services alongside the system architecture. Following, the AgriPLaCE platform development stage took place, with the implementation of all the necessary subsystems. The results showed that multiple potential collaborations can arise by the use of the AgriPLaCE platform, while users can also deepen on alternative and emerging treatment technologies and valuable products that can derive from a wide range of agricultural waste streams.

This paper is part of a study of earthen mixtures for 3D printing of buildings. To meet the ever-growing environmental needs, the focus of the paper is on a particular type of bio-composite for the stabilization of earthen mixtures – the rice husk-lime bio-composite – and on how to enhance its effect on the long-term mechanical properties of the hardened product. Having assumed that the shredding of the vegetable fiber is precisely one of the possible ways to improve the mechanical properties, we compared the results of uniaxial compression tests performed on cubic specimens made with both shredded and raw vegetable fiber, for three curing periods. The results showed that the hardened earthen mixture is not a brittle material in the strict sense, because it exhibits some peculiar behaviors, anomalous for a brittle material. However, being a “designable” material, its properties can be varied with a certain flexibility to get as close as possible to the desired ones. One of the peculiar properties of the hardened earthen mixture deserves further investigation, rather than corrections. This is the vulcanization that occurs (in a completely natural way) in the long term, thanks to the mineralization of the vegetable fiber by carbonation of the lime.

Cryphonectria is a fungal genus associated with economically significant disease of trees. Herein we characterized a novel double-stranded RNA virus from the fungal species Cryphonectria naterciae, a species unexplored as a virus host. De novo assembly of RNA-seq data and Sanger sequencing of RACE (rapid amplification of cDNA ends) clones gave the complete, non-segmented genome (10,164 bp) of the virus termed Cryphonectria naterciae fusagravirus (CnFGV1) that was phylogenetically placed within the previously proposed viral family Fusagraviridae. Of 31 field-collected strains of C. naterciae, 40% tested CnFGV1-positive. Co-cultivation resulted in within-species transmission of CnFGV1 to virus-free strains of C. naterciae. Comparison of the mycelium phenotype and the growth rate of CnFGV1-infected and virus-free isogenic strains revealed frequent sectoring and growth reduction in C. naterciae. Co-culturing also led to cross-species transmission of CnFGV1 to Cryphonectria carpinicola and Cryphonectria radicalis, but not to Cryphonectria parasitica. The virus-infected C. naterciae and the experimentally infected Cryphonectria spp. readily transmitted CnFGV1 through asexual spores to the next generation. CnFGV1 strongly reduced conidiation and in some cases vegetative growth of C. carpinicola, which is involved in the European hornbeam disease. This study is the first report of a fusagravirus in the family Cryphonectriaceae and lays the groundwork for assessing a hypovirulence effect of CnFGV1 against the hornbeam decline in Europe.

Novel bio-inspired materials have gained recently great attention, especially in medical applications. Applying sophisticated design and engineering methods, various polymer-polymer hybrid systems with outstanding performance have been developed in last decades. Hybrid systems composed of bioelastomers and hydrogels are very attractive due to their high biocompatibility and elastic nature for advanced biomaterials used in various medical applications such as drug delivery systems and scaffolds for tissue engineering. Herein, we describe the advances in current state-of-the-art design, properties and applications of polymer-polymer hybrid systems in medical applications. Bio-inspired functionalities, including bioadhesiveness, injectability, antibacterial properties and degradability applicable to advanced drug delivery systems and medical devices will be discussed in a context of future efforts towards development of bioinspired materials.

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. This study reviews the state-of-the-art ML applications in the biofuels’ life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx, CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

We present the design and overall development of an eight degrees of freedom (DOF) based Bioinspired Quadruped Robot (BiQR). The robot is designed with a skeleton made of cedar wood. The wooden skeleton is based on exploring the potential of cedar wood to be a choice for legged robots’ design. With a total weight of 1.19 kg, the robot uses eight servo motors that run the position control. Relying on the inverse kinematics, the control design enables the robot to perform the walk gait-based locomotion in a controlled environment. The robot has two main aspects: 1) the initial wooden skeleton development of the robot showing it to be an acceptable choice for robot design, 2) the robot’s applicability as a low-cost legged platform to test the locomotion in a laboratory or a classroom setting.

How did life begin on Earth? And is there life elsewhere in the Cosmos? Challenging questions, indeed. The series of conferences established by NoR CEL in 2013, addresses these very same questions. The basis for this paper is the summary report of oral presentations that were delivered by NoR CEL’s network members during the 2018 Athens conference and, as such, disseminates the latest research which they have put forward. More in depth material can be found by consulting the contributors referenced papers. Overall, the outcome of this conspectus on the conference demonstrates a case for the existence of “probable chemistry” during the prebiotic epoch.

In this research paper a novel Ultra Violet Photo Catalyst Oxidation (UVPCO) sensor for air and surface sanitization using Common Source (CS) amplifier is presented. The ultra violet photo catalysis is the process in which the highly reactive radicals like H+, OH-and peroxides ions are produced from air in the presence of the ultra violet radiation and photo catalyst. In this process, the free radicals outbreaks the bio aerosols like bacteria, fungus and volatile organic compounds (VOCs) and destroy them. The proposed system is relies on the fast operation of PCS which operates under sub-threshold conditions and reduced computation time. The properties of common source amplifier like very high voltage gain and input output resistance increased the sensitivity as well as stability of the circuit. The system is more user friendly and the outcomes of simulation are fairly in agreement with the theoretical estimation. Keywords: bio aerosol, Photo Catalytic Oxidation (PCO), hydroxyl, hydrogen peroxide, SPICE, surface sanitizer.

Radiotherapy commonly utilizes CBCT for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into HU values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow FOV systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent GAN processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for conducting two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1,572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems that are able to help acne patients understand more about their conditions and support doctors in acne diagnosis.

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiment, and needs the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

The decarbonization of the built environment, both in new construction and renovation, is crucial to mitigate its relevant impact on climate change and achieve the Paris agreement goals. This study presents a systematic LCA-based methodology to assess the whole life carbon emissions of buildings, applied to a proposal for the regeneration of one of Milan’s disused railway yards. As an entry for the 2020 Reinventing Cities competition, Scalo Lambrate is a project for a mainly residential neighborhood with a public park. Strategies to reduce carbon emissions deriving both from the operational energy and construction and maintenance were evaluated and their effects compared to a reference scenario over a time horizon of 100 years. The results show that, while the opportunities to reduce carbon emissions during the use phase are somehow limited due to the already stringent performance requirements for new builds, the use of fast-growing biogenic materials for construction materials, even if mixed with more traditional ones, can provide a significant reduction of the global warming potential over the whole life cycle, with a reduction of 70% over the baseline. The remaining emissions can be offset with afforestation initiatives that, however, must be assessed against land use issues.

This work aims to investigate the effect of process temperature and catalyst content by thermochemical degradation of municipal solid waste (MSW) fraction (organic matter + paper + plastic) on the yield of reaction products (bio-oil, biochar, H2O and gas), physicochemical properties and chemical composition of bio-oils, as well as on the morphology and crystalline phases of biochar in laboratory scale. The organic matter, paper and plastic segregated from the gravimetric composition of total waste sample were subjected to the pre-treatments of drying, crushing and sieving. The experiments were carried out at 400, 450 and 475 °C and 1.0 atmosphere, and at 450 °C and 1.0 at-mosphere, using 5.0, 10.0 and 15.0% (wt.) of FCC zeolite, bath mode, using a laborato-ry scale glass reactor. The bio-oil was characterized for acidic value. The chemical functions present in the bio-oil identified by FT-IR and the composition by GC-MS. Biochar was characterized by SEM/EDS and XRD. Thermal pyrolysis of the MSW frac-tion (organic matter + paper + plastic) shows bio-oil yields between 9.44 and 9.24% (wt.), aqueous phase yields between 21.93 and 18.78% (wt.), solid phase yields between 67.97 and 40.34% (wt.) and gas yields between 28.27 and 5.92% (wt.). The yield of bio-oil decreases with increasing process temperature. For the experiments using FCC, the biochar and gas yields increase slightly with the FCC content, while that of bio-oil de-creases and the H2O phase remains constant. The GC-MS of bio-oils identified the presence of hydrocarbons and oxygenates, as well as nitrogen-containing compounds, including amides and amines. The acidity of the bio-oil increased with increasing temperature and with the aid of FCC as a catalyst. It has been identified the presence of hydrocarbons within bio-oil by addition of FCC catalyst due to the deoxygenation of carboxylic acids, followed by decarboxylation and decarbonylation reactions, producing aliphatic and aromatic hy-drocarbons.

The well-being and sociability of individuals have always been part of modernity. The development of new technologies that meet these aspirations is receiving increasing attention. Thus, strengthening the desired objectives of these tools and minimizing their undesirable side effects is the subject of a growing commitment. The present contribution aims, in this context, to evaluate and analyze the desired and undesirable effects of the interaction of electromagnetic fields with living tissues in general. This involves routines reinforcing expected functions as well as those controlling and protecting against undesirable effects. First, the interactions of electromagnetic fields with tissues are analyzed, involving their thermal biological effects of desired and undesired exposures. The roles of blood and sap fluids in bio-affected tissues are then analyzed. Secondly, the governing electromagnetic, bio-heat equations, and their coupled solution are studied. Third, the thermal behavior of tissues and the exposure adverse effects are examined. Next, monitoring and defending of the effects of exposures are discussed. This contribution illustrates and discusses mathematical modeling routines based on a generalized interaction of electromagnetic fields with living tissues.

Studying the distribution and transport dynamics of cations in plants is crucial for understanding their response mechanisms to saline-alkali stress conditions. However, our current understanding of how restoration measures affect cation distribution and transport in plants is surprisingly limited. To address this gap, we conducted a split-plot experiment using Medicago sativa L. cv. “Zhongmu No. 1” to investigate the combined effects of biological and chemical restoration measures—with bio-fertilizer as the primary zone and flue gas desulfurization (FGD) gypsum with humic acid as the secondary zone—on soil properties, plant growth, and the content, distribution, and transport of cations in plants. The results revealed that different restoration strategies decreased soil alkalinity and enhanced plant growth. Significantly, bio-fertilizer exhibited positive effects on plant growth and yield. On the contrary, FGD gypsum with humic acid reduced the soil's pH level, exchangeable sodium percentage, and sodium adsorption ratio, while increasing the contents of K+, Ca2+, and Mg2+ in the soil. The triple combination of FGD gypsum and humic acid with bio-fertilizer reduced Na+ transport in M. sativa by enhancing the selective absorption of beneficial ions, such as K+, Ca2+, and Mg2+ in leaves, and by facilitating the transport of Ca2+ and Mg2+ from roots and stems to the leaves. Therefore, the application of FGD gypsum and humic acid combined with bio-fertilizer has the potential to decrease soil alkalinity and the proportions of water-soluble ions in the soil, ultimately enhancing the translocation of key ionic components to leaves. This, in turn, increases the salt tolerance of plants and promotes their growth. Our results offer new insights into the interactions among measures, soil, and plants in saline-alkali land restoration, providing practical solutions for the restoration of saline-alkali soil.

Green synthesis offers a superior alternative to traditional methods for producing metal and metal oxide nanoparticles. This approach is not only benign and safe but also cost-effective, scalable, and straightforward, operating under ambient conditions. Notable metals and metal oxide nanoparticles, such as manganese oxides, iron oxides, silver, and gold, have been produced using various bio-reductants derived from plant extracts. These biological agents not only expedite the reduction process but also stabilize the nanoparticles, serving dual roles as reducing and capping agents. This review presents the green synthesis of nanoparticles (NPs) obtained from biogenic wastes and plant extracts. The green-synthesized nanostructured MnO2 are evaluated as a potential photocatalyst for water treatment and as an electrode material in lithium-ion batteries and supercapacitors. The green-derived iron oxide nanoparticles are examined as promising antioxidant, anti-inflammatory, and anti-diabetic agents. Additionally, this review discusses the green synthesis of precious metal nanoparticles, specifically silver (Ag NPs) and gold (Au NPs), highlighting their potential medical applications in areas like antiviral treatments and cancer therapy.

Series of bifunctional catalysts, MoS2/Al2O3(70 wt.%)-zeolite (30 wt.%) (zeolite – ZSM-5, ZSM-12, ZSM-22 and silica aluminophosphate SAPO-11), were synthesized for hydroconversion of methyl palmitate (10 wt.% in dodecane) in a trickle-bed reactor. Mo loading was about 7 wt.%. Catalyst and supports were characterized by different physical-chemical methods (HRTEM-EDX, SEM-EDX, XRD, N2 physisorption, FTIR spectroscopy). Hydroprocessing was performed at temperature of 250-350°C, hydrogen pressure 3.0-5.0 MPa, liquid hourly space velocity (LHSV) 36 h-1, H2/feed ratio – 600 Nm3/m3. Complete conversion of oxygen-containing compounds was achieved at temperature 310°C in the presence of MoS2/Al2O3-zeolite catalysts, the selectivity for the conversion of methyl palmitate via the ‘direct’ hydrodeoxygenation (HDO) route was over 85%. The yield of iso-alkanes gradually increases in order: MoS2/Al2O3 &lt; MoS2/Al2O3-ZSM-12 &lt; MoS2/Al2O3-ZSM-5 &lt; MoS2/Al2O3-SAPO-11 &lt; MoS2/Al2O3-ZSM-22. The sample MoS2/Al2O3-ZSM-22 demonstrated the highest yield of iso-alkanes (40%). Hydroisomerization activity of the catalysts was in a good correlation with concentration of Brønsted acid sites of synthesized supports.

Cheese whey, a significant byproduct of the dairy industry, poses a substantial environmental challenge due to its organic pollutant load and large size demands for effective and affordable valorization methods. The abundance of lactose and other organic compounds in whey contributes to its elevated Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD), further straining natural ecosystems. This study aimed to investigate the nutritional potential of whey and its conversion into value-added products: bioethanol and probiotic drinks. To achieve this, Bacillus megaterium and Bacillus subtilis strains, isolated from soil samples in Nepal, were co-cultured with Lactobacillus and Saccharomyces cerevisiae in the whey broth for fermentation. Ethanol distillation was carried out using a Rota-evaporator to maintain the viability of the fermenting organisms within the broth. Moreover, the probiotic criteria of the fermenting strains were extensively examined. Experimental observations revealed a remarkable concentration of 9.2 g/L of ethanol, resulting in an ethanol yield of 0.23 g/L. Extrapolating this rate, it is theoretically possible to produce an annual global bioethanol output of 5 million tons using whey as a sustainable and abundant resource. Furthermore, the study explores novel advancements in the distillation process, demonstrating the successful extraction of ethanol at room temperature. Additionally, the fermentation organisms employed in this research exhibit robust viability, surviving various in-vitro stress-tolerant tests, including exposure to bile salt, pH fluctuations, and gastric juice. These resilient strains also demonstrated long-term stability in fermented whey broth, making them promising candidates for the development of probiotic beverages. This dual approach not only enables the sustainable management of organic waste but also presents an opportunity to create value-added products with positive implications for both the environment and human health.

Despite the development of biorefinery processes, so far this area does not take into account the possibility of coupling the “conventional” composting process with the production of biochemicals. However, net carbon monoxide (CO) production has been observed during bio-waste composting. So far, O2 concentration and temperature have been identified as main variables influencing CO formation. This study aimed to investigate the CO net production during bio-waste composting under controlled laboratory conditions, depending on different aeration rates and temperatures. A series of composting processes were carried out in conditions ranging from ~psychrophilic to thermophilic (T=35, 45, 55, and 65°C) and an aeration rate of 2.7, 3.4, 4.8, and 7.8 L‧h-1. The highest concentrations of CO in each thermal variant was achieved with oxygen deficit (aeration rate 2.7 L‧h-1); additionally, CO levels increased with temperature, reaching ~300 ppm at 65°C. Production of CO in mesophilic and thermophilic conditions draws attention to the biological CO formation by microorganisms capable of producing the CODH enzyme. Further research on the CO production efficiency in these thermal ranges is necessary with the characterization of the microbial community and analysis of the ability of the identified bacteria to produce the CODH enzyme and convert CO from CO2.

Nations of the world have seen unprecedented changes in climate variables in recent decades. But it is unclear to what extent climate change has impacted and will impact food systems in some developing regions, and how policymakers can frame an approach to encouraging adaptation and advancing climate-smart agriculture. Many studies attempting to link agroecology to climate change adaptation do so without understanding the potential of Agroecology not only to mitigate climate change – which is the weak response – but to reverse its impact and ‘climate proof’ our food systems. By modeling the near and far future impacts of climate change on crop production, we showed how climate will impact crop production under two crop production systems (agroecology and non-agroecology production systems). The overarching aim is to derive sustainable development strategies and lessons for policymakers and climate researchers - essential components of environment and Agricultural development. Using case studies from Nigeria, we observed that transitioning to agroecology, even at the farm level also transforms farm designs, thereby affecting their overall food and nutrition status. The result showed that the use of agroecology management practices not only reduces the impact of climate change in the near future but will also lead to increased crop yield in the future. The finding suggests that to feed the over 400 million projected population of Nigeria by 2050, the use of agroecological practices will be a better alternative to the conventional farming methods. To advance the use of agroecological farming methods, governments at every level in Nigeria need to mainstream organic agriculture in national government policies. This is important as it will not only address climate change impacts but also hunger and poverty.

Among the various methods for collecting oil spills and oil products, including from the water surface, one of the most effective is the use of sorbents. In this work, three-component bio-based composite granular adsorbents were produced and studied for oil products pollution collection. A bio-based binder made of peat, devulcanised crumb rubber from used tyres, and part fly ash as cenospheres were used for absorbent production. The structure, surface morphology, porosity, mechanical properties, and sorption kinetics of the obtained samples were studied. Composite hydrophobicity and sorption capacity to oil products such as diesel fuel (DF) and motor oil (MO) were determined. The obtained pellets are characterised by a sufficiently pronounced ability to absorb oil products such as DF. As the amount of CR in the granules increases, the diesel absorption capacity increases significantly. The case of 30-70-0 is almost 3 times higher than the granules from homogenised peat. The increase in q is due to two factors: pronounced surface hydrophobicity of the samples (Θ = 152°) and a heterogeneous porous granule structure. The presence of the cenosphere in the biocomposite reduces its surface hydrophobicity while increasing the diesel absorption capacity. Relatively rapid realisation of the maximum saturation by the MO was noted. In common, the designed absorbent shows up to 0.7 g·g^-1 sorption capacity for MO and up to 1.55 g·g^-1 sorption capacity for diesel. A possible mechanism of DF absorption and the limiting stages of the process approximated for different kinetic models are discussed. The Weber-Morris diffusion model is used to primarily distinguish the limiting effect of external and internal diffusion of adsorbate on the absorption process

RNAi technology is a versatile, effective, safe, and eco-friendly alternative for crop protection. There is plenty of evidence of its use through host-induced gene silencing (HIGS) and spray-induced gene silencing (SIGS) techniques to control viruses, bacteria, fungi, insects, and nematodes. For SIGS, its most significant challenge is achieving stability and avoiding premature degradation of RNAi in the environment or during its absorption by the target organism. One alternative is encapsulation in liposomes, virus-like particles, polyplex nanoparticles, and bioclay, which can be obtained through the recombinant production of RNAi in vectors, transgenesis, and micro/nanoencapsulation. The materials must be safe, biodegradable, and stable in multiple chemical environments, favoring the controlled release of RNAi. Most of the current research on encapsulated RNAi focuses primarily on oral delivery to control insects by silencing essential genes. The regulation of RNAi technology focuses on risk assessment using different approaches; however, this technology has positive economic, environmental, and human health implications for its use in agriculture. The emergence of alternatives combining RNAi gene silencing with the induction of resistance in crops by elicitation and metabolic control is expected, as well as multiple silencing and biotechnological optimization of its large-scale production.

Cotton is a potential and excellent candidate to balance both agricultural production and reme-diation of mercury-contained soil, as its main production fiber hardly involve into food chains. However, there is known rarely about the tolerance and response to Hg environments in cotton. In this study, The biochemical and physiological damages, in response to mercury (Hg), were investigated in upland cotton seedlings. The results on cottonseeds germination, indicated the germination rate were suppressed by high Hg levels, as the decrease of percentage was more than 10% at 1000 µM Hg. Shoots and roots’ growth were significantly inhibited above 10 µM Hg. The inhibitor rates (IR) in fresh weight were close between shoots and roots, whereas that in dry weight the root growth was more obviously influenced by Hg. In comparison of organs, the growth inhibition ranked as root > leaf > stem. The declining of translocation factor (TF) op-posed the Hg level even as low to 0.05 at 50 µM Hg. The assimilation of cotton plants was af-fected negatively by Hg toxicity, as evidenced from the performances on photosynthesis pig-ments (chlorophyll a and b) and gas exchange (Intercellular CO2 concentration (Ci), CO2 assimila-tion rate (Pn) and stomatal conductance (Gs)). Sick phenotypes on leaf surface included small white zone, shrinking and necrosis. Membrane lipid peroxidation and leakage were Hg dose-dependent as indicated by malondialdehyde (MDA) content and relative conductivity (RC) values in leaves and roots. More than 10 µM Hg damaged antioxidant enzyme system in both leaves and roots (P<0.05). Concludingly, 10 µM Hg post negative consequences to upland cotton plants in growth, physiology and biochemistry, whereas high phytotoxicity and damage ap-peared at more than 50 µM Hg concentration.

As part of the efforts to reduce the environmental impacts caused by the aviation sector, the use of bio-based instead of fossil-based materials has been assessed as a possible mitigation option. Natural Fibre Polymer Composites have proven to have a higher environmental performance in the automotive sector and are emerging as an option for weight reduction in aircraft. This study quantifies, though Life Cycle Assessment, the environmental performance of specific flax-based composite panels intended for aircrafts as interior fitting elements (i.e. partition panels, tray tables, baggage compartments) compared to a glass fibre/epoxy composite with a honeycomb core. Through system expansion, the fate of co-products issued from the production of the flax fibre technical textile used as reinforcement in the biocomposite material were considered in the assessment. Results showed that for an application in the aeronautics sector, the weight of the panels is the upmost critical parameter shaping the overall environmental performance of panels. Focusing on the panel production only, the biocomposite panel showed a higher environmental performance in the categories of climate change and marine eutrophication compared to the conventional panel, and the fire suppressant agent was identified as the main contributor to the environmental impacts of the bio-based panel. Yet these gains were negligible when considering the full life cycle of the panels, due to the higher weight (14%) of the bio-based panels; which is linked to the bio-based panel being at a prototype stage.In order to improve the environmental performance of the biocomposite panel and thus reduce its weight, it was shown relevant to optimize geometry of the panel itself, especially its core, so less resin could be used.

Efficient use of agricultural wastes and by-products, basically transforming waste materials into value added products, is considered as pivotal for an effective bioeconomy strategy for the rural development. To this scope, citrus waste management represents a major issue for citrus processors but, at the same time, it embodies a potentially unexploited resource for the rural sustainable development. This study focuses on analyzing the current management of citrus waste in South Italy and identifying the determinants and barriers that may affect an entrepreneur’s choice on the destination of citrus waste. Citrus processors’ preferences about the contract characteristics needed to take part in a co-investment scheme have been investigated. Both analyses are preliminary steps to design an innovative and sustainable citrus by-product supply chain. Results show that the distance between the citrus processors and the citrus by-products plant is one of the main criteria for choosing alternative valorization pathways. Moreover, the required guaranteed capital, the short duration of the contract, and low risk are contract scheme characteristics that improve entrepreneurs’ willingness to co-invest in the development of a citrus waste multifunctional plant.

Lead, known as a metal with high neurotoxicity to children, cadmium, which is a carcinogenic and bioaccumulative contaminant, and arsenic, a class 1 carcinogenic according to the International Agency for Research on Cancer, are toxic elements (TEs) whose relevant route of exposure may be diet. We determined the bio-accessible fraction of lead, cadmium, and arsenic from the diet of preschool children from two day care centers (DCC). A cross-sectional study was conducted with 64 one–four-year-old children from two DCCs where the 24-h duplicate diet samples were collected. The diet samples were analyzed by ICP-MS for lead, cadmium, and arsenic total concentrations (n = 64) and their bio-accessibility were analyzed for a subsample (n = 10). The dietary intake (DI) mean for lead, cadmium, and arsenic were 0.18 ± 0.11 µg kg⁻¹ bw, 0.08 ± 0.04 µg kg⁻¹ bw, and 0.61 ± 0.41 µg kg⁻¹ bw, respectively. All DI calculated for TEs, considering total intake, were found lower than the tolerable limits (TL) (European Union, or World Health Organization, WHO, when applicable) except for one child’s Pb intake. Bio-accessibilities ranged between 0% to 93%, 0% to 103%, and 0% to 69%, for lead, cadmium, and arsenic, respectively. Although DI for TEs has been found lower than TL, these reference values have been recently decreased or withdrawn since it was for lead and arsenic whose TL were withdrawn by WHO.

When phenomena in quantum mechanics are interpreted from the perspective of bio-psychology, wave function collapse from several to a single eigenstate must be plausibly explained. Quantum mechanics requires a context, yet the context of an observer is rarely considered. On the other hand, in bio-psychology, the observer context is examined to explain superposition and collapse by different mental functions used in everyday life. Three mental functions are described, one of which is responsible for observation, and the others for conservation and treatment of information in mental representation. Whereas observation produces information with certainty, the subsequent processes result in uncertain potentiality. In order to encompass uncertainty, multiple possibilities are simultaneously considered in mental superposition, one of which should represent the unknown future outcome. During observation, all suggested potentialities necessarily collapse to one real outcome. The collapse of superposition does not occur in observable physical reality, but in its mental representation. Some physical principles—such as superposition, infinity and nothingness before the Big Bang—are pure phenomena of mental representation, which will always remain unverifiable by observation. This argument proves that mental representation brought about by the observer context participates in the production of mental models for the best approximation of physical reality.

We present a generalized state-space model formulation particularly motivated by an online scheduling perspective. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings.

Our study focused on investigating the impact of Instagram use on body satisfaction and self-esteem among participants aged 20 to 40 years (N = 95) through a cross-sectional survey design in which the Rosenberg Self-Esteem Scale, the Body Shape Questionnaire (BSQ), and ad hoc designed questions were administered. The results highlight the influence of Instagram on body dissatisfaction (p = .005) but not on self-esteem (p = .211). Gender was not found to be an influential factor (p = .173). However, it was found that greater body satisfaction correlates with higher self-esteem and vice versa (p < .001). Finally, we concluded that the number of hours users in the sample spend on Instagram is related to greater body dissatisfaction but not to lower self-esteem.

A free-fall baited camera lander was launched for the first time on the Cosmonaut Sea shelf of East Antarctica at the depth of 694 m during the 38th Chinese National Antarctic Research Expedition (CHINARE) in 2022. We identified 31 unique taxa (23 of invertebrates and 8 of fishes) belonging to 8 phyla from 2403 pictures and 40 videos. The Antarctic jonasfish (Notolepis coatsi) was the most frequently observed fish taxa. Ten species of vulnerable marine ecosystem (VME) taxa were observed, accounting for 32% of all species. The maximum number (MaxN) of Natatolana meridionalis individuals per image frame was ten, and they were attracted to the bait. The macrobenthic community type was sessile suspension feeders with associated fauna (SSFA), which was shaped by the muddy substrata with scattered rocks. Rocks served as the best habitats for sessile fauna. The study reveals the megafauna community and their habitat by image survey in the Cosmonaut Sea for the first time. It helped us obtain Antarctic biodiversity baselines and monitoring data for future’s ecosystem health assessment and better protection.

Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate postharvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the `Roghani` Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was fine-tuned by testing multiple configurations of well-known CNN layers. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods, as well as those based on neural networks. Furthermore, we will explore the artifacts associated with the identification of modified photos and present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all of the above, the final outcome of this study is to provide a comprehensive perspective on the abilities and constraints to detect images for which an inpainting object removal method was applied.

The study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, the study combines causal attention modules with residual connections, proposing the Causal Residual Attention Module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also widens the receptive field. It effectively utilizes global features, particularly capturing vertically correlated features prominently present in subgraphs of candidates. This significantly enhances its capability to model pulsar data.In the experiments, the model is trained and validated using the HTRU1 dataset. The study compares the average negative log-likelihood score with baseline models like GMM, STM, and PixelCNN. The results demonstrate the superior performance of the our model over other models. Finally, the study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.

Many university female students are concerned about their bodies. Body image perception has become a public health issue globally. This study aimed to explore factors contributing to body image dissatisfaction among female students at the University of Venda. The study was qualitative in nature and employed exploratory research design. A sample of 10 female students enrolled at the University of Venda were identified using convenience sampling method. A pre-tested, semi-structured interview guide was used to collect data and thematic content analysis technique was used to analyse the collected data. The findings of the study showed that body comparison, societal beauty standards, social media, and body shaming by family and friends were the main factors contributing to student’s body image dissatisfaction. The findings further revealed that lack of self-confidence, stress, avoidance, anxiety and depressive symptoms were the challenges faced by students with body image dissatisfaction. Acceptance. Self-care, and healthy diet were identified as coping strategies to help deal with the challenges of student’s body image dissatisfaction. Conclusively, students should be encouraged to seek professional help timeously, to help navigate their body image concerns to avoid decline in their daily functioning.

In this study, we explore the potential of using a straightforward neural network inspired by the retina model to efficiently restore low-light images. The retina model imitates the neurophysiological principles and dynamics of various optical neurons. Our proposed neural network model reduces the computational overhead compared to traditional signal-processing models while achieving results similar to complex deep learning models from a subjective perceptual perspective. By directly simulating retinal neuron functionalities with neural networks, we not only avoid manual parameter optimization but also lay the groundwork for constructing artificial versions of specific neurobiological organizations.

Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. The model’s ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. Following are the main 5 DR stages: none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all 5 stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an Enhanced Super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement; augmentation techniques are then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.

In the field of forensic imaging, it is important to be able to extract a “camera fingerprint” from one or a small set of images known to have been taken by the same camera (image sensor). Ideally, that fingerprint would be used to identify an individual source camera. Camera fingerprint is based on certain kind of random noise present in all image sensors that is due to manufacturing imperfections and thus unique and impossible to avoid. PRNU (Photo-Response Non-Uniformity) has become the most widely used method for SCI (Source Camera Identification). In this paper, we design a set of “attacks” to a PRNU based SCI system and we measure the success of each method. We understand an attack method as any processing that alters minimally image quality and that is designed to fool PRNU detectors (or, generalizing, any camera fingerprint detector). The PRNU based SCI system was taken from an outstanding reference that is publicly available.

Depth maps produced by LiDAR based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the traditional approaches focus on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have sub-divided the literature into two major categories; traditional approaches and backbone-based approaches. The latter is further sub-divided into two-branch, and spatial propagation approaches. The two-branch approaches still have a sub-category named guided-kernel approaches. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review and detail different state-of-the art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.

Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. There is no survey to conclude those emerging researches yet. This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. Finally, we give a brief conclusion about this survey and explore some open problems.

Microscopes based on dielectric mesoscale particles, using the effect of a photonic jet or terajet in the terahertz range, are a promising tool for overcoming the diffraction limit. However, the image they generate has limited contrast, which limits the application of this method. In this letter, we demonstrate that it is possible to increase the contrast of an image based on dielectric mesoscale particles that provide the formation of photonic hooks. In this case, the illumination of the object is carried out by an oblique incidence of subwavelength terajet, which significantly (more than 2 times) increases the contrast of the image.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

The concept of destination image is closely related to the brand image of the destination. A good image is a step in branding the destination. The image of the destination can be a primary, sec-ondary or global one, the latter incorporating the first two. The sustainability of a positive image of the destination is based on both a positive secondary image and a positive global image. The purpose of this research is to analyze separately the two types of images for a given tourist des-tination that has registered in recent years a remarkable increase in the number of visitors. The research is based on a questionnaire-based survey of a sample of 607 people. The collected data were processed with SPSS and the results show significant differences between the two types of images (secondary image and global image), a dangerous situation in the medium and long term for destination management. The nuances in the perception of the image of the destination on the two types of respondents (who experienced respectively who did not experience the destination) can be explained by the aggressive strategy of promoting the tourist destination, but inefficient strategy for younger age groups. The study allows the formulation of conclusions and measures to correct the situation.

Within the spectrum of a natural image, the amplitude of modulation decreases with spatial frequency. The speed of such an amplitude decrease, or the amplitude spectrum slope, of an image affects the perceived aesthetic value. Additionally, a human observer would consider a symmetric image more appealing than they do an asymmetric one. We investigated how these two factors jointly affect aesthetic preferences by manipulating both the amplitude spectrum slope and the symmetric level of images to assess their effects on aesthetic preference on a 6-point Likert scale. Our results showed that the preference ratings increased with the symmetry level but had an inverted U-shape relation to amplitude spectrum slope. In addition, a strong interaction existed between symmetry level and amplitude spectrum slope on preference rating, in that symmetry can amplify the amplitude spectrum slope’s effects. Such effects can be described by a quadratic function of the spectrum slope. That is, preference is an inverted U-shape function of spectrum slope whose intercept is determined by the number of symmetry axis. In addition, the interaction between the two factors is manifested as the modulation depth of the quadratic function.

With the widespread use of mobile phones, increasing use of mobile applications in different areas of life have gained a large place. Between the reasons of the preference of the rapid increasing number of apps in the application markets of the different platforms, the aesthetic appereance of the icons that apps have perhaps the most important one. In this study, the visual symmetrical side of the icons of the most downloaded apss which are developed for preparing to Public Servant Exam in Turkey is emphasized. Two different types of data are obtained with working on the icons with image processing technique by using Mathworks Matlab program and survey method which is applied on Korkut Ata University students. By comparing the obtained data with the binary logistic regression method, it was determined that the visual symmetry in the apps’ icons partially contributed to the aesthetic appreciation.