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Brewer's spent grains (BSG) offer valuable opportunities for valorization beyond its conventional use as animal feed. Among its components, lignin—a natural polymer with inherent antioxidant properties—holds significant industrial potential. This work investigates the use of microwave-assisted extraction combined with acidic natural deep eutectic solvents (NaDESs) for efficient lignin recovery, evaluating three different NaDES formulations. The results indicate that choline chloride–lactic acid (ChCl-LA), a NaDES with superior thermal stability as confirmed via thermogravimetric analysis (TGA), is an ideal solvent for lignin extraction at 150 °C and 15 min, achieving a balance of high yield and quality. ChCl-LA also demonstrated good solubility and cell disruption capabilities, while microwaves significantly reduced processing time and severity. Under optimal conditions, i.e., 150 °C, 15 min, in the presence of ChCl-LA NaDES, the extracted lignin achieved a purity of up to 79% and demonstrated an IC50 (inhibitory concentration 50%) of approximately 0.022 mg/L, indicating a relatively strong antioxidant activity. Fourier transform infrared (FTIR) and 2D-HSQC NMR (heteronuclear single quantum coherence nuclear magnetic resonance) spectroscopy confirmed the successful isolation and preservation of its structural integrity. This study highlights the potential of BSG as a valuable lignocellulosic resource and underscores the effectiveness of acidic NaDESs combined with microwave extraction for lignin recovery. [ABSTRACT FROM AUTHOR]

This study explores lignin-retaining transparent wood biocomposite production through a lignin-modification process coupled with epoxy resin. The wood's biopolymer structure, which includes cellulose, hemicellulose, and lignin, is reinforced with the resin through impregnation. This impregnation process involves filling the voids and pores within the wood structure with resin. Once the resin cures, it forms a strong bond with the wood fibers, effectively reinforcing the biopolymer matrix and enhancing the mechanical properties of the resulting biocomposite material. This synergy between the natural biopolymer structure of wood and the synthetic resin impregnation is crucial for achieving the desired optical transparency and mechanical performance in transparent wood. Investigating three distinct wood species allows a comprehensive understanding of the relationship between natural and transparent wood biocomposite properties. The findings unveil promising results, such as remarkable light transmittance (up to 95%) for Aspen transparent wood. Moreover, transparent wood sourced from White Spruce demonstrates excellent stiffness (E = 2450 MPa), surpassing the resin's Young's modulus. Also, the resin impregnation enhanced the thermal stability of natural wood. Conversely, transparent wood originating from Larch showcases superior impact resistance. These results reveal a clear correlation between wood characteristics such as density, anatomy, and mechanical properties, and the resulting properties of the transparent wood. [ABSTRACT FROM AUTHOR]

Simple Summary: Skin diseases can be serious, and early detection is key to effective treatment. Unfortunately, the quality of images used to diagnose these diseases often suffers due to interference from hair, making accurate diagnosis challenging. This research introduces a novel technology, the DM–AHR, a self-supervised conditional diffusion model designed specifically to generate clear, hairless images for better skin disease diagnosis. Our work not only presents a new, advanced model that expertly identifies and removes hair from dermoscopic images but also introduces a specialized dataset, DERMAHAIR, to further research and improve diagnostic processes. The enhancements in image quality provided by DM–AHR significantly improve the accuracy of skin disease diagnoses, and it promises to be a valuable tool in medical imaging. Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introduces DM–AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, named DERMAHAIR (DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture of DM–AHR and demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance, DM–AHR holds considerable promise for broader application in medical image enhancement. [ABSTRACT FROM AUTHOR]

Materials for low-permittivity and electrical insulation applications need to be re-engineered to achieve sustainable development. To address this challenge, the proposed study focused on the dielectric and mechanical optimization of 3D-printed cellulose-based composites for electrical insulation applications. Two different fillers, microcrystalline cellulose (MCC) and nanocrystalline cellulose (NCC), were used to create biocomposites and bionanocomposites, respectively, blended into a polylactic acid (PLA) matrix. The effects of infill ratio, printing temperature, and filler content on dielectric and mechanical properties were measured using an incomplete L9 (3^3) factorial design. The findings showed that the infill ratio was the most significant factor influencing the properties tested, directly attributable to the increase in material availability for polarization and mechanical performance. The second most influential factor was the filler content, increasing the polarity of the tested composites and decreasing the toughness of the biocomposites and bionanocomposites. Finally, printing temperature had no significant effect. Results for the biocomposites at a 50% infill ratio, 200 °C printing temperature, and a weight content of MCC of 15% gave a 60% higher tensile-mode stiffness than neat PLA printed under the same conditions, while exhibiting lower dielectric properties than neat PLA printed with a 100% infill ratio. These results pave the way for new lightweight materials for electrical insulation. [ABSTRACT FROM AUTHOR]

This study investigated the influence of fiber loading and maleated polyethylene (MAPE) coupling agent on the structural, thermal, mechanical, morphological properties, and torque rheology of high-density polyethylene (HDPE) reinforced with Posidonia oceanica fiber (POF) composites. HDPE/POF composites, both with and without MAPE, were manufactured using a two-step process: composite pellets extrusion, followed by test samples injection molding with various POF loadings (0, 20, 30, and 40 wt%). HDPE/POF composites reinforced with higher loading of POF (40 wt%) exhibit superior stiffness, better crystallinity, and higher stabilized torque and mechanical energy (Em) compared to other composite formulations. Therefore, varying the POF loading leads to extrusion and injection processing variations. Furthermore, the coupling agent significantly enhances the tensile strength, ductility, impact strength, crystallinity, stabilized torque, and Em of the HDPE/POF composite. This improvement is due to the enhanced interfacial adhesion between the POF and the HDPE matrix with the addition of the MAPE, as supported by the Scanning Electron Microscopy (SEM) micrographs. [ABSTRACT FROM AUTHOR]

License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. Despite the deployment of various super-resolution methodologies, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the quest for satisfactory outcomes in license plate image enhancement persists. This paper introduces "DiffPlate", a novel Diffusion Model specifically tailored for license plate super-resolution. Leveraging the unprecedented capabilities of Diffusion Models in image generation, DiffPlate is meticulously trained on a dataset comprising low-resolution and high-resolution pairs of Saudi license plates, curated for our surveillance application. Our empirical analysis substantiates that DiffPlate markedly eclipses state-of-the-art alternatives such as SwinIR and ESRGAN, evidencing a 26.47% and 37.32% enhancement in Peak Signal-to-Noise Ratio (PSNR) against these benchmarks, respectively. Furthermore, DiffPlate achieves superior performance in terms of Structural Similarity Index (SSIM), with a 4.88% and 16.21% improvement over SwinIR and ESRGAN, respectively. Human evaluative studies further corroborate that images refined by DiffPlate were preferred 92% more frequently compared to those processed by other algorithms. Through DiffPlate, we present a new solution to the license plate super-resolution challenge, demonstrating significant potential for adoption in real-world surveillance systems. [ABSTRACT FROM AUTHOR]

This review addresses the critical issue of a rapidly increasing worldwide waste stream and the need for sustainable management. The paper proposes an integrated transformation toward a next-generation methanization process, which leads not only to treating waste but also to converting it into higher value compounds and greener energy. Although the current and commonly used anaerobic digestion process is useful for biogas production, it presents limitations of resource exploitation and some negative environmental impacts. Focusing on the acidogenic stage in waste stream processing, the paper discusses the recent strategies to enhance the recovery of volatile fatty acids (VFAs). These acids serve as precursors for synthesizing a variety of biochemicals and biofuels, offering higher value products than solely energy recovery and soil fertilizers. Additionally, the importance of recycling the fermentation residues back into the biorefinery process is highlighted. This recycling not only generates additional VFAs but also contributes to generating clean energy, thereby enhancing the overall sustainability and efficiency of the waste management system. Moreover, the review discusses the necessity to integrate life cycle assessment (LCA) and techno-economic analysis (TEA) to evaluate the environmental impacts, sustainability, and processing costs of the proposed biorefinery. [ABSTRACT FROM AUTHOR]

Renewable dielectric materials have attracted the attention of industries and stakeholders, but such materials possess limited properties. This research focused on studying polylactic acid (PLA)/cellulose acetate (CA) blends produced by 3D printing to facilitate their integration into the electrical insulation field. The dielectric findings showed that a blend containing 40% of CA by weight had a dielectric constant of 2.9 and an electrical conductivity of 1.26 × 10−11 S·cm−1 at 100 Hz and 20 °C while exhibiting better mechanical rigidity in the rubbery state than neat PLA. In addition, it was possible to increase the electrical insulating effect by reducing the infill ratio at the cost of reduced mechanical properties. The differential scanning calorimetry, broadband dielectric spectroscopy, and dynamic mechanical analysis results showed that the PLA plasticizer reduced the energy required for PLA relaxations. These preliminary results demonstrated the benefits of using a combination of PLA, CA, and 3D printing for electrical insulation applications. [ABSTRACT FROM AUTHOR]

2-phenylethanol (2-PE) is a valuable aromatic alcohol with diverse applications in cosmetics, food, beverages, and pharmaceutical industries. Currently, 2-PE is produced either through chemical synthesis or by extraction from plant materials. However, both conventional production methods have their own limitations. Therefore, there is a need for more eco-friendly and cost-effective approaches to produce natural 2-PE. Biotechnological routes, particularly microbial fermentations, hold promise for natural 2-PE production, especially when using low-cost substrates. In this study, 2-PE was produced by de novo synthesis via the shikimate pathway, using the yeast Yarrowia lipolytica in a medium composed of sugar beet molasses (SBM) and yeast extract (YE) as carbon and nitrogen sources, respectively. A genetically engineered strain was generated, in which the SUC2 gene was transformed, expressing the invertase enzyme, enabling Y. lipolytica to efficiently utilize SBM as a cost-effective substrate. A central composite design allowed for the optimization of the concentrations of the carbon and nitrogen sources, resulting in approximately 0.71 g(2-PE)/L(culture medium). The results obtained highlight the potential of utilizing SBM as a low-cost substrate for 2-PE production, advancing biotechnological approaches in fragrance synthesis. [ABSTRACT FROM AUTHOR]

Developing novel eco-friendly broad-spectrum visible light photocatalysts for dye removal is one of the urgent problems for water treatment. Here, copper-doped carbon dots (CDs) were reported to be directly fabricated from chromated copper arsenate (CCA) wood waste for the photocatalytic degradation of the methylene blue dye. The properties of the resulting CDs were thoroughly characterized and analyzed, preceding an investigation into the adsorption kinetics of dye degradation. The kinetic study showed that reactant concentration was the rate-limiting factor. The obtained CDs showed a 151 mg/g photocatalytic degradation capacity. Comparing pure CDs to CDs/TiO2 composites, the former demonstrated higher photodegradation efficiency. This superiority can be attributed to the synergistic action of adsorption and photocatalytic degradation working in tandem. This study prepared Cu doped CDs and elucidated the photocatalysis mechanism of methylene blue degradation by CDs. The photodegradation of organic dyes through CDs derived from waste CCA wood emerges as an eco-friendly, facile, and highly efficient method. [ABSTRACT FROM AUTHOR]

(1) Background: Cumin seeds, extracted from the plant Cuminum cyminum, are abundant in phenolic compounds and have been extensively researched for their chemical makeup and biological effects. The objective of this research is to enhance the water extraction of polyphenols through the water bath (WB) technique and to evaluate the antiradical, antibacterial, and anticancer effects of the extract. (2) Methods: Response Surface Methodology was used to find the best parameters to extract polyphenols. Three experimental parameters, time, temperature, and solid-liquid ratio, were tested. The disc diffusion method has been used to determine the antimicrobial activities against Salmonella Typhimurium, Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, and Candida albicans. The antiradical activity was performed using the DPPH method, while total phenolic content was performed using Folin–Ciocalteu. High-Performance Liquid Chromatography (HPLC) was conducted to analyze the phytochemical profile of WB extracts. The anticancer activity of the lyophilized extract was assessed against three cancer cell lines (colon (HT29), lung (A549), and breast (MCF7) cancer cell lines).; (3) Results: The optimal conditions for water extraction were 130 min at 72 °C. The total phenolic compounds yield (14.7 mg GAE/g DM) and antioxidant activity (0.52 mg trolox eq./mL) were obtained using a 1:40 solid–liquid ratio. The primary polyphenols identified were the flavonoids rutin (0.1 ppm) and ellagic acid (3.78 ppm). The extract had no antibacterial or antifungal activities against the microorganisms tested. The extract showed anticancer activity of about 98% against MCF7 (breast cancer cell line), about 81% against HT29 (colon cancer cell line), and 85% against A549 (lung cancer cell line) at high doses. (4) Conclusions: Extraction time and a high solid–liquid ratio had a positive impact on polyphenol recovery and in maintaining their quantity and quality. Furthermore, the optimal aqueous extract exhibited strong antiradical activity reflected by the inhibition of free radicals in addition to a significant specificity against the tested cancer cell lines. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the effects of three warm-up durations on post-exercise oxidative stress biomarkers, as well as recovery from the Wingate test in 14 amateur handball players. These players completed the Wingate test three times: after 5 min, after 10 min, and then after 15 min of warm-up, spread over 2 weeks with an interval of 1 week between each session. At the end of each session of the Wingate test, blood samples were taken: at rest (PR), after warming up (PWU) and immediately at the end of the test (P0), then after 10-min (P10) and after 20-min (P20) of recovery. The measured parameters are the total antioxidant status (TAS), superoxide dismutase (SOD), glutathione reductase (GR), glutathione peroxidase (GPx), malondialdehyde (MDA), protein-bound-carbonyls (PCs) and thiobarbituric acid reactive substances (TBARSs). The main findings revealed that anaerobic exercise induces oxidative stress, as evidenced by changes in antioxidant status and significant increases in concentrations of the majority of oxidative stress indicators (p < 0.05). At P20, plasma GPx, SOD, GR, TBARSs, and PC contents, are lower and significantly different after a 15-min warm-up than after a 10-min or 5-min warm-up. ANOVA showed a significant "warm-up × recovery" effect on plasma GPx, SOD, GR, TBARSs, and PC contents [F(8.104) = 3.61; p < 0.001; η2p = 0.22, F(8.104): from 1.88 to 19.97; η2p: from 0.19 to 0.61: 0.05 < p < 0.001], respectively. We concluded that a 15-min warm-up was more efficient than the other duration in reducing a free radical rise, emphasizing the importance of the warm-up length on plasma oxidative stress biomarkers. [ABSTRACT FROM AUTHOR]

In the intricate field of legal studies, the analysis of court decisions is a cornerstone for the effective functioning of the judicial system. The ability to predict court outcomes helps judges during the decision-making process and equips lawyers with invaluable insights, enhancing their strategic approaches to cases. Despite its significance, the domain of Arabic court analysis remains under-explored. This paper pioneers a comprehensive predictive analysis of Arabic court decisions on a dataset of 10,813 commercial court real cases, leveraging the advanced capabilities of the current state-of-the-art large language models. Through a systematic exploration, we evaluate three prevalent foundational models (LLaMA-7b, JAIS-13b, and GPT-3.5-turbo) and three training paradigms: zero-shot, one-shot, and tailored fine-tuning. In addition, we assess the benefit of summarizing and/or translating the original Arabic input texts. This leads to a spectrum of 14 model variants, for which we offer a granular performance assessment with a series of different metrics (human assessment, GPT evaluation, ROUGE, and BLEU scores). We show that all variants of LLaMA models yield limited performance, whereas GPT-3.5-based models outperform all other models by a wide margin, surpassing the average score of the dedicated Arabic-centric JAIS model by 50%. Furthermore, we show that all scores except human evaluation are inconsistent and unreliable for assessing the performance of large language models on court decision predictions. This study paves the way for future research, bridging the gap between computational linguistics and Arabic legal analytics. [ABSTRACT FROM AUTHOR]

Colorimetric whole-cell biosensors are natural or genetically engineered microorganisms utilized to detect target molecules and ions as indicators of pollutants and biological activity in the environment. Upon detection, within specific concentration ranges which vary depending on the microorganism and its genetic circuitry among other factors, these sensors produce pigments which can be detected with the human eye past certain thresholds and quantified using simple analytical techniques, namely spectrophotometry. These sensors, which can be rendered portable through lyophilization and other methods, provide valuable and reliable substitutes of more demanding analytical ex situ techniques. The insights gained from this review can highlight technological progress in the field and contribute to the identification of potential opportunities afforded by these advancements. [ABSTRACT FROM AUTHOR]

Three-dimensional printing is a prototyping technique that is widely used in various fields, such as the electrical sector, to produce specific dielectric objects. Our study explores the mechanical and dielectric behavior of polylactic acid (PLA) and plasticized cellulose acetate (CA) blends manufactured via Fused Filament Fabrication (FFF). A preliminary optimization of 3D printing parameters showed that a print speed of 30 mm·s−1 and a print temperature of 215 °C provided the best compromise between print quality and processing time. The dielectric properties were very sensitive to the three main parameters (CA content WCA, infill ratio, and layer thickness). A Taguchi L9 3^3 experimental design revealed that the infill ratio and WCA were the main parameters influencing dielectric properties. Increasing the infill ratio and WCA increased the dielectric constant ε′ and electrical conductivity σAC. It would, therefore, be possible to promote the integration of CA in the dielectric domain through 3D printing while counterbalancing its greater polarity by reducing the infill ratio. The dielectric findings are promising for an electrical insulation application. Furthermore, the mechanical findings obtained through dynamic mechanical analysis are discussed. [ABSTRACT FROM AUTHOR]

Olive leaves are naturally generated as a by-product during olive harvesting and olive oil production. Usually discarded with no specific use, they are a valuable source of bioactive compounds that should not be overlooked. Their valorization must therefore be achieved through the recovery of their polyphenols using an ecological strategy. Conventional extraction is commonly known as an energy- and solvent-consuming process, whereas emerging and innovative extraction technologies, such as ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), pulsed-electric-field-assisted extraction (PEF), high-voltage-electric-discharge-assisted extraction (HVED), supercritical fluid extraction (SFE), infrared-assisted extraction (IAE), and "Intensification of Vaporization by Decompression to the Vacuum" (IVDV), are considered more sustainable and environmentally friendly. The aim of this review is to provide a comprehensive and updated overview of the valorization of olive leaves through both pretreatment and extraction techniques via an analysis of the recovered polyphenols and their potential applications. [ABSTRACT FROM AUTHOR]

The optimization of the machining parameters of strander-canting is the best way to obtain the optimum strand size, a better quality of the cant surface, and lower energy consumption. The effect of the rake angle on the performance of a strander-canter when processing jack pine logs was evaluated. Thirty-nine logs were cut with three rake angles (59°, 64°, and 69°). The counter-knife angle used in this study was 20° for frozen logs and 35° for unfrozen logs. The cutting speed and width were fixed at 25 m/s and 20 mm, respectively. The results showed that the rake angle affected the strand width, strand proportion, and energy requirements to transform the logs under frozen conditions. The rake angle of 64° produced a higher proportion and larger strands with less energy consumption than the rake angle of 59°. However, using a rake angle of 64° produced poorer surface quality. On the other hand, the effect of the rake angle on the processing of unfrozen logs was only noticeable when the rake angle changed to 69°. The proportion of pin chips increased, and the surface quality became poorer as the rake angle changed from 59° to 69°. The rake angle did not affect energy consumption when transforming the logs under unfrozen conditions. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs), constrained by limited resources, demand routing strategies that prioritize energy efficiency. The tactic of cooperative routing, which leverages the broadcast nature of wireless channels, has garnered attention for its capability to amplify routing efficacy. This manuscript introduces a power-conscious routing approach, tailored for resource-restricted WSNs. By exploiting cooperative communications, we introduce an innovative relay node selection technique within clustered networks, aiming to curtail energy usage while safeguarding data dependability. This inventive methodology has been amalgamated into the Routing Protocol for Low-Power and Lossy Networks (RPL), giving rise to the cooperative and efficient routing protocol (CERP). The devised CERP protocol pinpoints and selects the most efficacious relay node, ensuring that packet transmission is both energy-minimal and reliable. Performance evaluations were executed to substantiate the proposed strategy, and its practicality was examined using an Arduino-based sensor node and the Contiki operating system in real-world scenarios. The outcomes affirm the efficacy of the proposed strategy, outshining the standard RPL concerning reliability and energy conservation, enhancing RPL reliability by 10% and energy savings by 18%. This paper is posited to contribute to the evolution of power-conscious routing strategies for WSNs, crucial for prolonging sensor node battery longevity while sustaining dependable communication. [ABSTRACT FROM AUTHOR]

This paper introduces a novel semicircular distribution obtained by applying the quadratic rank transmutation map to the stereographic semicircular exponential distribution, referred to as the transmuted stereographic semicircular exponential distribution (TSSCED). This newly proposed distribution exhibits enhanced flexibility compared to the baseline stereographic semicircular exponential distribution (SSCEXP). We conduct a comprehensive analysis of the model's properties and demonstrate its efficacy in data modeling through the application to a real dataset. [ABSTRACT FROM AUTHOR]

This work presents a highly accurate method for the numerical solution of the advection–diffusion equation of fractional order. In our proposed method, we apply the Laplace transform to handle the time-fractional derivative and utilize the Chebyshev spectral collocation method for spatial discretization. The primary motivation for using the Laplace transform is its ability to avoid the classical time-stepping scheme and overcome the adverse effects of time steps on numerical accuracy and stability. Our method comprises three primary steps: (i) reducing the time-dependent equation to a time-independent equation via the Laplace transform, (ii) employing the Chebyshev spectral collocation method to approximate the solution of the transformed equation, and (iii) numerically inverting the Laplace transform. We discuss the convergence and stability of the method and assess its accuracy and efficiency by solving various problems in two dimensions. [ABSTRACT FROM AUTHOR]

Inflammation is associated with many pathology disorders and the malignant progression of most cancers. Therefore, targeting inflammatory pathways could provide a promising strategy for disease prevention and treatment. In this study, we experimentally investigated the anti-inflammatory effect of CC5 and CC8, two disintegrin isoforms isolated from Cerastes cerastes snake venom, on LPS-stimulated macrophages, both on human THP-1 and mouse RAW264.7 cell adherence and their underlying mechanisms by measuring cytokine release levels and Western blot assay. Equally, both molecules were evaluated on a carrageenan-induced edema rat model. Our findings suggest that CC5 and CC8 were able to reduce adhesion of LPS-stimulated macrophages both on human THP-1 and mouse RAW264.7 cells to fibrinogen and vitronectin through the interaction with the αvβ3 integrin receptor. Moreover, CC5 and CC8 reduced the levels of reactive oxygen species (ROS) mediated by the NF-κB, MAPK and AKT signaling pathways that lead to decreased production of the pro-inflammatory cytokines TNF-α, IL-6 and IL-8 and increased secretion of IL-10 in LPS-stimulated THP-1 and RAW264.7 cells. Interestingly, both molecules potently exhibited an anti-inflammatory effect in vivo by reducing paw swelling in rats. In light of these results, we can propose the CC5 and CC8 disintegrins as interesting tools to design potential candidates against inflammatory-related diseases. [ABSTRACT FROM AUTHOR]

Derived Hench bioactive glass (BaG) containing boron (B) is explored in this work as it plays an important role in bone development and regeneration. B was also found to enhance BaG dissociation. However, it is only possible to incorporate a limited amount of B. To increase the amount of B in BaG, bioactive borosilicate glasses (BaG-Bx) were fabricated based on the use of the solution-gelation process (sol-gel). In this work, a high B content (20 wt.%) in BaG, respecting the conditions of bioactivity and biodegradability required by Hench, was achieved for the first time. The capability of BaG-Bx to form an apatite phase was assessed in vitro by immersion in simulated body fluid (SBF). Then, the chemical structure and the morphological changes in the fabricated BaG-Bx (x = 0, 5, 10 and 20) were studied. The formation of hydroxyapatite (HAp) layer was observed with X-ray diffraction (XRD) and infrared (IR) spectroscopy. The presence of HAp layer was confirmed using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Enhanced bioactivity and chemical stability of BaG-Bx were evaluated with an ion exchange study based on Inductively Coupled Plasma–Optical Emission Spectrometry (ICP-OES) and energy dispersive spectroscopy (EDS). Results indicate that by increasing the concentration of B in BaG-Bx, the crystallization rate and the quality of the newly formed HAp layer on BaG-Bx surfaces can be improved. The presence of B also leads to enhanced degradation of BaGs in SBF. Accordingly, BAG-Bx can be used for bone regeneration, especially in children, because of its faster degradation as compared to B-free glass. [ABSTRACT FROM AUTHOR]

As greenhouse gas emissions are continuously increasing, research is now privileging greener and more sustainable human activities. An attractive strategy in the pursuit of sustainability is the valorization of lignocellulosic biomasses for the production of bioethanol. This approach relies on the bioconversion of wood and agricultural waste, which are abundant globally. They represent considerable sources of fermentable sugars that can be recovered through enzymatic hydrolysis. However, the presence of lignin in wood waste makes it more recalcitrant to enzymatic hydrolysis, and reduces the efficiency of the bioconversion process. Therefore, a pretreatment preceding hydrolysis is highly necessary in order to disrupt the resistant structure of woody biomass. The type and severity of the pretreatment affect the outcomes of the hydrolysis and fermentation steps, just as they strongly influence the overall process costs. Given this context, bioenergy production from this biomass is a promising alternative method of sustainably responding to energy demands while reducing the amounts of waste left in nature. The aim of this review is to thoroughly compare the recent bioconversion processes performed on woody substrates over the past five years, with a focus on thermomechanical pretreatments. Moreover, this review will discuss the outcomes of their separate steps, their impact on the overall process, and their energetic aspects. [ABSTRACT FROM AUTHOR]

Violacein and deoxyviolacein are bis-indole pigments synthesized by a number of microorganisms. The present study describes the biosynthesis of a mixture of violacein and deoxyviolacein using a genetically modified Y. lipolytica strain as a production chassis, the subsequent extraction of the intracellular pigments, and ultimately their purification using column chromatography. The results show that the optimal separation between the pigments occurs using an ethyl acetate/cyclohexane mixture with different ratios, first 65:35 until both pigments were clearly visible and distinguishable, then 40:60 to create a noticeable separation between them and recover the deoxyviolacein, and finally 80:20, which allows the recovery of the violacein. The purified pigments were then analyzed by thin-layer chromatography and nuclear magnetic resonance. [ABSTRACT FROM AUTHOR]

Remote Sensing (RS) images are usually captured at resolutions lower than those required. Deep Learning (DL)-based super-resolution (SR) architectures are typically used to increase the resolution artificially. In this study, we designed a new architecture called TESR (Two-stage approach for Enhancement and super-resolution), leveraging the power of Vision Transformers (ViT) and the Diffusion Model (DM) to increase the resolution of RS images artificially. The first stage is the ViT-based model, which serves to increase resolution. The second stage is an iterative DM pre-trained on a larger dataset, which serves to increase image quality. Every stage is trained separately on the given task using a separate dataset. The self-attention mechanism of the ViT helps the first stage generate global and contextual details. The iterative Diffusion Model helps the second stage enhance the image's quality and generate consistent and harmonic fine details. We found that TESR outperforms state-of-the-art architectures on super-resolution of remote sensing images on the UCMerced benchmark dataset. Considering the PSNR/SSIM metrics, TESR improves SR image quality as compared to state-of-the-art techniques from 34.03/0.9301 to 35.367/0.9449 in the scale ×2. On a scale of ×3, it improves from 29.92/0.8408 to 32.311/0.91143. On a scale of ×4, it improves from 27.77/0.7630 to 31.951/0.90456. We also found that the Charbonnier loss outperformed other loss functions in the training of both stages of TESR. The improvement was by a margin of 21.5%/14.3%, in the PSNR/SSIM, respectively. The source code of TESR is open to the community. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with computer vision capabilities have been widely utilized in several remote sensing applications, such as precision agriculture, environmental monitoring, and surveillance. However, the commercial usage of these UAVs in such applications is mostly performed manually, with humans being responsible for data observation or offline processing after data collection due to the lack of on board AI on edge. Other technical methods rely on the cloud computation offloading of AI applications, where inference is conducted on video streams, which can be unscalable and infeasible due to remote cloud servers' limited connectivity and high latency. To overcome these issues, this paper presents a new approach to using edge computing in drones to enable the processing of extensive AI tasks onboard UAVs for remote sensing. We propose a cloud–edge hybrid system architecture where the edge is responsible for processing AI tasks and the cloud is responsible for data storage, manipulation, and visualization. We designed AERO, a UAV brain system with onboard AI capability using GPU-enabled edge devices. AERO is a novel multi-stage deep learning module that combines object detection (YOLOv4 and YOLOv7) and tracking (DeepSort) with TensorRT accelerators to capture objects of interest with high accuracy and transmit data to the cloud in real time without redundancy. AERO processes the detected objects over multiple consecutive frames to maximize detection accuracy. The experiments show a reduced false positive rate (0.7%), a low percentage of tracking identity switches (1.6%), and an average inference speed of 15.5 FPS on a Jetson Xavier AGX edge device. [ABSTRACT FROM AUTHOR]

Bioremediation is an effective way to remove heavy metals from pollutants. This study investigated the effects of Yarrowia lipolytica (Y. lipolytica) on the bioremediation of chromated copper arsenate (CCA)-treated wood wastes. Copper ions stressed the yeast strains to improve their bioremediation efficiency. A comparison of changes in morphology, chemical composition, and metal content of CCA wood before and after bioremediation was conducted. The amount of arsenic (As), chromium (Cr), and copper (Cu) was quantified by microwave plasma atomic emission spectrometer. The results showed that yeast strains remained on the surface of CCA-treated wood after bioremediation. The morphologies of the strains changed from net to spherical because of the Cu2+ stress. Fourier-transform infrared spectroscopy showed that carboxylic acid groups of wood were released after removing heavy metals. A large amount of oxalic acid was observed when the optical density (OD600nm) was 0.05 on the 21st day. Meanwhile, the highest removal rate of Cu, As, and Cr was 82.8%, 68.3%, and 43.1%, respectively. Furthermore, the Cu removal from CCA-treated wood increased by about 20% after Cu2+ stress. This study showed that it is feasible to remove heavy metals from CCA-treated wood by Y. lipolytica without destroying the wood structure, especially by copper-induced Y. lipolytica. [ABSTRACT FROM AUTHOR]

The relative proportions of different chemical components in wood tissues is one of the underlying factors that control wood properties. These proportions vary within and between woody tissues, and an accurate description of these variations is critical for parameterizing forest biogeochemical budgets and models. White spruce (Picea glauca (Moench) Voss) spacing intensities trials in the Petawawa Research Forest, Ontario, Canada, were sampled to evaluate variations in carbon (C), nitrogen (N), and hydrogen (H) concentrations between different tissue types, i.e., bark, cambium, knots, earlywood, latewood, and wood. Samples were freeze-dried and oven-dried to test the impact of the drying methods on these chemical elements. Freeze-dried C (51.14) and H (6.18) concentrations were significantly higher than those of oven-dried C (50.55) and H (6.06). Freeze-dried N (0.18) did not differ from oven-dried N (0.17). The spacing intensities impacted C, H, and N, with C content being higher in wider square spacings (4.3 m and 6.1 m), while the reverse was true for N and H, which exhibited higher content in smaller square spacings (1.2 m and 1.8 m). The results of this study also suggested that when it comes to the content of chemical elements, bark and knots should be treated as separate fuel types, whereas other woody tissues can be aggregated. [ABSTRACT FROM AUTHOR]

Peanuts are the seeds of a legume crop grown for nuts and oil production. Peanut allergy has gained significant attention as a public health issue due to its increasing prevalence, high rate of sensitization, severity of the corresponding allergic symptoms, cross-reactivity with other food allergens, and lifelong persistence. Given the importance of peanuts in several sectors, and taking into consideration the criticality of their high allergic potential, strategies aiming at mitigating their allergenicity are urgently needed. In this regard, most of the processing methods used to treat peanuts are categorized as either thermal or thermomechanical techniques. The purpose of this review is to provide the reader with an updated outlook of the peanut's allergens, their mechanisms of action, the processing methods as applied to whole peanuts, as well as a critical insight on their impact on the allergenicity. The methods discussed include boiling, roasting/baking, microwaving, ultrasonication, frying, and high-pressure steaming/autoclaving. Their effectiveness in alleviating the allergenicity, and their capacity in preserving the structural integrity of the treated peanuts, were thoroughly explored. Research data on this matter may open further perspectives for future relevant investigation ultimately aiming at producing hypoallergenic peanuts. [ABSTRACT FROM AUTHOR]

Smart healthcare using the cloud and the Internet of Things (IoT) allows for remote patient monitoring, real-time data collection, improved data security, and cost-effective storage and analysis of healthcare data. This paper proposes an information-centric dissemination scheme (ICDS) for smart healthcare services in smart cities. The proposed scheme addresses the time sensitiveness of healthcare data and aims to ensure consistent dissemination. The ICDS uses decision-tree learning to classify requests based on time-sensitive features, allowing prioritization of access. The scheme also involves segregating sensitive information and distributing digital health data within the classified time to retain time sensitiveness and prioritize access. The learning is then modified for the leaves based on data significance and minimum resources to reduce waiting times and improve availability. [ABSTRACT FROM AUTHOR]

The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore a better version of an image given in a low-quality format. In this study, the efficiency of ViT in image restoration is studied extensively. The ViT architectures are classified for every task of image restoration. Seven image restoration tasks are considered: Image Super-Resolution, Image Denoising, General Image Enhancement, JPEG Compression Artifact Reduction, Image Deblurring, Removing Adverse Weather Conditions, and Image Dehazing. The outcomes, the advantages, the limitations, and the possible areas for future research are detailed. Overall, it is noted that incorporating ViT in the new architectures for image restoration is becoming a rule. This is due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input. Nevertheless, some drawbacks exist, such as the need for more data to show the benefits of ViT over CNN, the increased computational cost due to the complexity of the self-attention block, a more challenging training process, and the lack of interpretability. These drawbacks represent the future research direction that should be targeted to increase the efficiency of ViT in the image restoration domain. [ABSTRACT FROM AUTHOR]

Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates' Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models' accuracy by taking advantage of the temporally redundant information of the video stream's frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022. [ABSTRACT FROM AUTHOR]

An ecofriendly approach was developed for preparing copper-doped carbon dots (CDs) with superior photocatalysis using chromium-copper-arsenic (CCA)-treated wood waste as a precursor. Original wood (W-CDs), CCA-treated wood (C-CDs), and bioremediation CCA wood (Y-CDs) were used as the precursors. The chemical composition and structural, morphological, and optical properties, as well as the photocatalytic ability of the synthesized CDs varied with wood type. The C-CDs and W-CDs had similar characteristics: quasispherical in shape and with a diameter of 2 to 4.5 nm. However, the Y-CDs particles were irregular and stacked together, with a size of 1.5–3 nm. The presence of nitrogen prevented the formation of an aromatic structure for those CDs fabricated from bioremediation CCA wood. The three synthesized CDs showed a broad absorption peak at 260 nm and a weak absorption peak at 320 nm. Proof of the model study for the fabrication of luminescent CDs from CCA wood waste for bioimaging was provided. The degradation rate of CD photocatalytic MB was 97.8% for 30 min. Copper doping gives the CDs electron acceptor properties, improving their photocatalytic efficiency. This study provides novel ways to prepare nanomaterials from decommissioned wood as a nontoxic and low-cost alternative to fluorescent dots. [ABSTRACT FROM AUTHOR]

Monitoring changes in edema-associated intracranial pressure that complicates trauma or surgery would lead to improved outcomes. Implantable pressure sensors have been explored, but these sensors require post-surgical removal, leading to the risk of injury to brain tissue. The use of biodegradable implantable sensors would help to eliminate this risk. Here, we demonstrate a bioactive glass (BaG)-based hydration sensor. Fluorine (CaF2) containing BaG (BaG-F) was produced by adding 5, 10 or 20 wt.% of CaF2 to a BaG matrix using a melting manufacturing technique. The structure, morphology and electrical properties of the resulting constructs were evaluated to understand the physical and electrical behaviors of this BaG-based sensor. Synthesis process for the production of the BaG-F-based sensor was validated by assessing the structural and electrical properties. The structure was observed to be amorphous and dense, the porosity decreased and grain size increased with increasing CaF2 content in the BaG matrix. We demonstrated that this BaG-F chemical composition is highly sensitive to hydration, and that the electrical sensitivity (resistive–capacitive) is induced by hydration and reversed by dehydration. These properties make BaG-F suitable for use as a humidity sensor to monitor brain edema and, consequently, provide an alert for increased intracranial pressure. [ABSTRACT FROM AUTHOR]

Thuja occidentalis L. wood is desirabl, e for wooden structures that require wood density uniformity. Wood density is a wood quality indicator related to numerous morphological, mechanical, physiological, and ecological properties. This study aimed to investigate the regional, site, and tree-to-tree variations of T. occidentalis wood density and growth components through the analysis of X-ray densitometer data. A total of 287 trees were randomly sampled from 11 sites in the Abitibi-Témiscamingue regions in Québec, Canada. The average ring density at breast height was 356 kg m−3, with a small difference between earlywood and latewood (167 kg m−3), indicating a relatively uniform wood. Ring density and width varied significantly between the Abitibi and the Témiscamingue regions, mainly in the juvenile wood. Trees from the Abitibi region showed higher ring density than those in the Témiscamingue region. In contrast, the ring width was higher in the Témiscamingue region. Site, tree, and cambial age significantly (p < 0.001) affected wood density and growth components. However, the largest variation is due to the tree-to-tree variation, accounting for about 15%–31% of the total variation. Compared to ring widths, ring density components showed a considerably smaller tree-to-tree variation and higher variation (7.1%) with cambial age than ring widths (0.6%). Ring width correlated positively and significantly (p < 0.001) with average temperature and annual precipitation, while ring density correlated negatively and significantly (p < 0.001) with average temperature and annual precipitation. [ABSTRACT FROM AUTHOR]

Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications. [ABSTRACT FROM AUTHOR]

The intensification of total phenolic compound (TPC) extraction from blood orange peels was optimized using a novel green infrared-assisted extraction technique (IRAE, Ired-Irrad®) and compared to the conventional extraction using a water bath (WB). Response surface methodology (RSM) allowed for the optimization of ethanol concentration (E), time (t), and temperature (T) in terms of extracted TPC and their antiradical activity, for both WB extraction and IRAE. Using WB extraction, the multiple response optimums as obtained after 4 h at 73 °C and using 79% ethanol/water were 1.67 g GAE/100 g for TPC and 59% as DPPH inhibition percentage. IRAE increased the extraction of TPC by 18% using 52% ethanol/water after less than 1 h at 79 °C. This novel technology has the advantage of being easily scalable for industrial usage. HPLC analysis showed that IRAE enhanced the recovery of gallic acid, resveratrol, quercetin, caffeic acid, and hesperidin. IR extracts exhibited high bioactivity by inhibiting the production of Aflatoxin B1 by 98.9%. [ABSTRACT FROM AUTHOR]

Four Picea glauca (Moench) Voss trees grown at each of four square spacing intensities between trees: 1.2 m, 1.8 m, 4.3 m, and 6.1 m in a plantation established in 1967 in the Petawawa Research Forest, Ontario, Canada (lat. 45.59° N, long. 77.25° W, elev. 168 m) and sampled at four different heights (1.3 m, 4.3 m, 7.3 m, 10.3 m) were used to study the impact of spacing between trees and sampling height on nine wood quality attributes (ring width, ring density, tracheid length, tracheid diameter, latewood proportion, intra-ring density variation, ring area, earlywood width, and latewood width). In the juvenile wood, ring width was wider and ring density higher than in the mature wood. Tracheid length was longer and tracheid diameter wider in the mature wood compared to the juvenile wood. The variation of ring density between the two wood zones was limited, and latewood proportion did not show any difference with wood zone. Sampling height induced variation in more wood quality attributes than did spacing. Except for growth rate, spacing between trees did not significantly impact wood quality attributes. Most of these variations were registered between widely different spacings. [ABSTRACT FROM AUTHOR]

Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20 % . We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Mycotoxins in solid foods and feeds jeopardize the public health of humans and animals and cause food security issues. The inefficacy of most preventive measures to control the production of fungi in foods and feeds during the pre-harvest and post-harvest stages incited interest in the mitigation of these mycotoxins that can be conducted by the application of various chemical, physical, and/or biological treatments. These treatments are implemented separately or through a combination of two or more treatments simultaneously or subsequently. The reduction rates of the methods differ greatly, as do their effect on the organoleptic attributes, nutritional quality, and the environment. This critical review aims at summarizing the latest studies related to the mitigation of mycotoxins in solid foods and feeds. It discusses and evaluates the single and combined mycotoxin reduction treatments, compares their efficiency, elaborates on their advantages and disadvantages, and sheds light on the treated foods or feeds, as well as on their environmental impact. [ABSTRACT FROM AUTHOR]

While world energy demand has certainly decreased with the beginning of the COVID-19 pandemic in 2020, the need has been significantly on the rise since 2021, all as the world's fossil fuel resources are depleting; it is widely accepted that these resources emit greenhouse gases (GHG), which are the leading cause for the climate crisis. The main contributors to global warming are manufacturing, energy, and agriculture. The agricultural sector is composed of diversified and potential mobilizable sources of waste which can become an attractive alternative to fossil fuels for energy production, and thus sequester and use carbon. Therefore, a paradigm shift towards more sustainable energy alternatives, efficient waste management, and new technologies is necessary. One good solution is the energetic valorization of lignocellulosic biomass (LCB) which can also originate from agricultural wastes. The biomass consists of cellulose, hemicellulose, and lignin, which are sources of fermentable sugars that can be used for bioethanol production. However, the recovery of sugars requires the pretreatment of LCB before enzymatic hydrolysis, due to its inaccessible molecular structure. Different pretreatment technologies, including acid and alkaline pretreatments for selected biomasses (such as hemp, rice straw, corn straw, sugarcane bagasse, and wheat straw) are discussed and compared. Therefore, this review highlights the potential of agricultural waste as a renewable resource for energy production. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%. [ABSTRACT FROM AUTHOR]

In the present study, we assess tyrosol derivatives bearing 3,5-disubstituted isoxazoles and 1,4-disubstituted triazoles for their ability to inhibit the proliferation of K562 cells derived from leukemia as well as primary chronic myeloid leukemia (CML) cells obtained from the peripheral blood of 15 CML patients including 10 patients with untreated chronic phase and 5 patients with resistance against imatinib or multiple TKI. Our results showed that most derivatives displayed significant anti-proliferative activity against K562 cells in a dose-dependent manner. Among them, compounds 3d and 4a exhibited greater potent anticancer activity with respective IC50 values of 16 and 18 µg/mL (45 µM and 61 µM). Interestingly, compound 3d inhibited CML cell proliferation not only in newly diagnosed but also in imatinib-resistant patients. We demonstrated that the anti-proliferative effect of this compound is mediated by a pro-apoptotic activity by promoting oxidative stress and modulating the activity of the Akt, p38 MAPK and Erk 1/2 pathways. In conclusion, our data highlight the potential of this class of derivative as a novel promising therapeutic agent for CML therapy. [ABSTRACT FROM AUTHOR]

The flame retardancy of wood–polymer composites significantly affects their potential applications. Thus, multilayered wood flour/high-density polyethylene (HDPE)/polycarbonate (PC) composites were prepared via thermocompression to improve the fire retardancy of wood–polymer composites in this paper. Thermal degradation behavior, flame retardancy, and flexural strengths of the resulting composites were investigated using a thermogravimetric analysis, cone calorimetry, and mechanical testing machine, respectively. Results revealed that the boric acid treatment reduced the heat release rate and total heat release of the wood flour/HDPE composites and increased their mass of residues. However, boric acid reduced the flexural strength of the resulting composites. The combustion test indicated that PC cap layers suppressed the combustion of the resulting composites via the formation of carbon layers. Adding PC layers reduced heat release and increased the flexural strength of the resulting composites. Finally, the failure mode of the multilayered wood flour/HDPE/PC composites in the three-point flexural test was simulated by finite element analysis. [ABSTRACT FROM AUTHOR]

Chromated copper arsenate-treated (cca) wood disposal faces environmental restrictions due to its toxicity, heavy metal leaching in storage sites, and greenhouse gas emissions during incineration. Thus, finding new management methods for this contaminated wood at the end of life is crucial. This study evaluated the effect of pyrolysis temperature (300, 400, and 500 °C), particle size, biochar yield, and the behavior of arsenic (As), chromium (Cr), and copper (Cu) during treated-wood pyrolysis. The highest biochar yield was obtained at 300 °C for fine particles. The biochar retention of heavy metals decreased with increasing pyrolysis temperature. At 300 °C, the highest biochar As, Cr, and Cu retentions were 76, 91, and 83%. At 500 °C, biochar only retained 43% of the As. Additionally, heavy metal leaching from the biochar exceeded the Environmental Protection Agency's (EPA) maximum concentration limit of 5 mg/L. High-density polyethylene encapsulation of contaminated biochar reduced the leaching of As, Cr, and Cu by 96, 95, and 91%, respectively. Thus, combining pyrolysis and plastic encapsulation to produce a composite material could be a solution for reducing waste (conversion of CCA-wood into biochar) and for the safe disposal of contaminated wood. [ABSTRACT FROM AUTHOR]

With Vehicle-to-Vehicle (V2V) communication capability, vehicle platoon on the highway helps to reduce traffic congestion. However, the dynamic nature of vehicles imposes challenges on the V2V-based platoon management. In this paper, by considering the characteristics of a Vehicular Ad-hoc Network (VANET), a microscopic platoon management scheme is proposed to deal with three basic dynamic platoon maneuvers, namely merging, splitting, and speed-change. The congestion detection feature of VANET is used as a scale for platoon merging, splitting, and speed selection. Real-time congestion is detected if the number of vehicles in a given road segment exceeds the occupancy rate or the time headway is less than the thresholds. In the proposed platoon management scheme, platoon maintenance is triggered in congestion detection. Finally, a VANET-based platoon platform is built by using Network Simulator Version 2 (NS2) network simulation to assess the performance over some real road traces generated by Simulation of Urban MObility (SUMO). It is shown that V2V-based dynamic vehicle platoon management provides an inexpensive technique to cope with the dynamic platoon management requirement. [ABSTRACT FROM AUTHOR]

Incorporating biodegradable reinforcement, such as wood particles, into rigid polyurethane foams (RPUFs) is among the alternatives to reduce their environmental impact. This study aims to assess the effect of different wood particles as reinforcement in RPUFs. Reinforced rigid polyurethane foams are synthesized with milled wood particles of various forms and sizes and commercial polyol and isocyanate. The effect of fiber treatments and mechanical stirring on foams' properties is also studied. Additional tests on polyisocyanurate foams (PIR) were undertaken to assess the effect of reinforcement on their properties. Mechanical properties are measured to investigate the impact of wood particle reinforcement on the foam. Confocal microscopy and Fourier-transform infrared spectroscopy (FTIR) showed the interaction between the wood fibers and the matrix. Despite the adhesion observed for some fibers, most of the cell walls of RPUFs were punctured by the rigid wood fibers, which explained the decrease in the compressive strength of the composites for manually mixed foams. Mechanical stirring proved to be an efficient method to enhance the reinforcement power of untreated fibers. RPUF foams' properties showed similar changes when untreated wood flour was introduced to the formula, increasing compressive strength significantly. [ABSTRACT FROM AUTHOR]