Search Results (15)

Biodegradable waste is commonly treated as a problem to be managed, but it can be a valuable resource when considered within a circular bioeconomy perspective. This article develops a practical and systems-based frame work for integrating biodegradable waste, ranging from municipal food scraps to wastewater biosolids, into valuable resources. It explores real-world strategies for transforming waste into value-added products, including composting, anaerobic digestion, biochemical conversion, and the creation of bio-based materials. The review also highlights key drivers and barriers, including technical, regulatory, and social factors, which shape the feasibility and impact of circular solutions. A visual model illustrates the full cycle, from identifying waste streams to reintegrating recovered resources. The paper also highlights case studies from Toronto, Milan and Brazil as examples of successful implementation. Overall, this paper emphasizes a pragmatic yet regenerative shift toward organic resource recovery aligned with sustainability and decarbonization goals. Full article

The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments. Full article

This work reports the development of novel curcuminoid-based electrochemical sensors for the detection of environmental pollutants from water. In this study, the first set of electrochemical experiments was carried out using curcumin-conjugated multi-walled carbon nanotubes (MWCNT–CM) for 1,4-dioxane detection. The MWCNT–CM/GCE showed good sensitivity (103.25 nA nM⁻¹ cm⁻² in the linear range 1 nM to 1 µM), with LOD of 35.71 pM and LOQ of 108.21 pM. The second set of electrochemical experiments was carried out with bisdemethoxy curcumin analog quantum dots (BDMCAQD) for hydrazine detection. The BDMCAQD/GCE exhibited good sensitivity (74.96 nA nM⁻¹ cm⁻² in the linear range 100 nM to 1 µM), with LOD of 10 nM and LOQ of 44.93 nM. Thus, this work will serve as a reference for the fabrication of metal-free electrochemical sensors using curcuminoids as the redox mediator for the enhanced detection of environmental pollutants. Full article

Diabetes, characterized by elevated blood sugar levels, poses significant health and economic risks, correlating with complications like cardiovascular disease, kidney failure, and blindness. Dipeptidyl peptidase-4 (DPP-4), also referred to as T-cell activation antigen CD26 (EC 3.4.14.5.), plays a crucial role in glucose metabolism and immune function. Inhibiting DPP-4 was anticipated as a potential new therapy for diabetes. Therefore, identification of plant-based natural inhibitors of DPP-4 would help in eradicating diabetes worldwide. Here, for the identification of the potential natural inhibitors of DPP-4, we developed a phytochemicals library consisting of over 6000 phytochemicals detected in 81 medicinal plants that exhibited anti-diabetic potency. The library has been docked against the target proteins, where isorhamnetin, Benzyl 5-Amino-5-deoxy-2,3-O-isopropyl-alpha-D-mannofuranoside (DTXSID90724586), and 5-Oxo-7-[4-(trifluoromethyl) phenyl]-4H,6H,7H-[1,2]thiazolo[4,5-b]pyridine 3-carboxylic acid (CHEMBL3446108) showed binding affinities of −8.5, −8.3, and −8.3 kcal/mol, respectively. These compounds exhibiting strong interactions with DPP-4 active sites (Glu205, Glu206, Tyr547, Trp629, Ser630, Tyr662, His740) were identified. ADME/T and bioactivity predictions affirmed their pharmacological safety. Density functional theory calculations assessed stability and reactivity, while molecular dynamics simulations demonstrated persistent stability. Analyzing parameters like RMSD, RG, RMSF, SASA, H-bonds, MM-PBSA, and FEL confirmed stable protein–ligand compound formation. Principal component analysis provided structural variation insights. Our findings suggest that those compounds might be possible candidates for developing novel inhibitors targeting DPP-4 for treating diabetes. Full article

Fermented foods containing probiotic Leuconostoc mesenteroides 201607 (LM) were used to extract exopolysaccharides. An incomplete understanding exists regarding the immunomodulatory characteristics of exopolysaccharides (EPSs), which are important constituents of bacterial biofilms. In this instance, we examined the immunomodulatory capacity of EPSs from fermented food extracted from L. mesenteroides 201607. Partially purified exopolysaccharide from L. mesenteroides 201607 (PP-LMEPS) consists of glucose (57.1%), rhamnose (29.53%), and galactose (13.36%). The maximum EPS yield was attained after 30 h of incubation at 37 °C and an initial pH of 7.0. When lipopolysaccharide-stimulated RAW264.7 was exposed to PP-LMEPS, the inflammatory cytokines were considerably decreased or elevated dose-dependently. Upon the exposure of lipopolysaccharide-stimulated RAW264.7 cells to PP-LMEPS, a dose-dependent modulation of inflammatory cytokines was observed. This suggests that the extracted EPS possesses immunomodulatory characteristics, as evidenced by a significant decrease or increase in inflammatory cytokine levels. However, further research is warranted to fully elucidate the precise mechanisms and potential therapeutic implications of the immunomodulatory properties of PP-LMEPS. Full article

Background and Objectives: Breast cancer is a significant type of cancer among women worldwide. Studies have reported the anti-carcinogenic activity of Hydrastis Canadensis (Goldenseal) in cancer cell lines. Hydrastis Canadensis could help eliminate toxic substances due to its anti-cancer, anti-inflammatory, and other properties. The design phase includes the identification of potential and effective molecules through modern computational techniques. Objective: This work aims to study Hydrastis Canadensis’s effect in controlling hormone-independent breast cancer through in-silico analysis. Materials and Methods: The preliminary screening of reported phytochemicals includes biomolecular networking. Identifying functionally relevant phytochemicals and the respective target mutations/genes leads to selecting 3D proteins of the desired mutations being considered the target. Interaction studies have been conducted using docking. The kinetic and thermodynamic stability of complexes was studied through molecular dynamic simulation and MM-PBSA/GBSA analysis. Pharmacodynamic and pharmacokinetic features have been predicted. The mechanism-wise screening, functional enrichment, and interactional studies suggest that canadaline and Riboflavin effectively interact with the target proteins. Results: Hydrastis Canadensis has been identified as the effective formulation containing all these constituents. The phytoconstituents; Riboflavin and Canadensis showed good interaction with the targets of hormone-independent breast cancer. The complexes were found to be kinetically and thermodynamically stable. Conclusions: Hydrastis Canadensis has been identified as effective in controlling ‘hormone-independent or basal-like breast cancer’ followed by ‘hormone-dependent breast cancer: Luminal A’ and Luminal B. Full article

Background: We aimed to estimate the economic and clinical impacts of a novel diagnostic test called LIAISON^® MeMed BV^® (LMMBV), which can differentiate bacterial from viral infections, in patients with community-acquired pneumonia (CAP) in emergency departments. Methods: A cost-impact simulation model was developed to investigate the financial consequences of the introduction of LMMBV into the standard of care (SOC) diagnostic process in Italy, Germany, and Spain. Clinical outcomes were expressed as antibiotic patients and days saved, reduced hospital admissions, and shortened hospital length of stay (LOS). Cost savings were evaluated from the perspectives of third-party payers and hospitals. A deterministic sensitivity analysis (DSA) was carried out. Results: LMMBV was associated with a reduction in antibiotic prescriptions, treatment duration, and LOS. Furthermore, the adoption of LMMBV would allow savings per patient up to EUR 364 and EUR 328 for hospitals and EUR 91 and EUR 59 for payers in Italy and Germany, respectively. In Spain, average savings per patient could reach up to EUR 165 for both payers and hospitals. Savings were most sensitive to test accuracy, with DSA confirming the robustness of the results. Conclusions: Combining LMMBV with the current SOC diagnostic process is expected to provide clinical and economic benefits in Italy, Germany, and Spain. Full article

In this study, zinc oxide nanostructures (ZnO NS) were synthesized using Pyrus pyrifolia fruit extract. Biophysical characterization results confirmed that the synthesized materials are crystalline wurtzite ZnO structures. Field emission scanning electron microscopy (FESEM) revealed that the ZnO NS are cubical, and the sizes range 20–80 nm. Transmission electron microscopy (TEM) and XRD results revealed a crystal lattice spacing of 0.23 nm and (101) the crystalline plane on ZnO NS. UV-Visible spectrophotometer results showed an absorbance peak at 373 nm. The ZnO NS demonstrated significant antibacterial activity analyzed by metabolic activity analysis and disc diffusion assay against Escherichia coli and Staphylococcus aureus. FESEM analysis confirmed the bacterial membrane disruption and the release of cytoplasmic contents was studied by electron microscopy analysis. Further, ZnO NS achieved good photocatalytic activity of decolorizing 88% of methylene blue (MB) in 60 min. The dielectric constant and loss of ZnO were found to be 3.19 and 2.80 at 1 kHz, respectively. The research findings from this study could offer new insights for developing potential antibacterial and photocatalytic materials. Full article

Prior to the long-term utilization of solid oxide fuel cell (SOFC), one of the most remarkable electrochemical energy conversion devices, a variety of difficult experimental validation procedures is required, so it would be time-consuming and steep to predict the applicability of these devices in the future. For numerous years, extensive efforts have been made to develop mathematical models to predict the effects of various characteristics of solid oxide fuel cells (SOFCs) components on their performance (e.g., voltage). Taking advantage of the machine learning (ML) method, however, some issues caused by assumptions and calculation costs in mathematical modeling could be alleviated. This paper presents a machine learning approach to predict the anode-supported SOFCs performance as one of the most promising types of SOFCs based on architectural and operational variables. Accordingly, a dataset was collected from a study about the effects of cell parameters on the output voltage of a Ni-YSZ anode-supported cell. Convolutional machine learning models and multilayer perceptron neural networks were implemented to predict the current-voltage dependency. The resulting neural network model could properly predict, with more than 0.998 R² score, a mean squared error of 9.6 × 10⁻⁵, and mean absolute error of 6 × 10⁻³ (V). Conventional models such as the Gaussian process as one of the most powerful models exhibits a prediction accuracy of 0.996 R² score, 10⁻⁴ mean squared, and 6 × 10⁻³ (V) absolute error. The results showed that the built neural network could predict the effect of cell parameters on current-voltage dependency more accurately than previous mathematical and artificial neural network models. It is noteworthy that this procedure used in this study is general and can be easily applied to other materials datasets. Full article

The objective of the Hurricane Imaging Radiometer (HIRAD) is to produce wide-swath images of hurricane wind and rain fields during a single pass from a high-altitude aircraft. This instrument could be a prototype for the next generation of airborne hurricane remote sensors that operate on NOAA/USAF surveillance flights over named storms and hurricanes. The improved two-dimensional surface wind field measurements provided by the HIRAD approach are crucial to improved forecasts and warnings. For almost a decade, HIRAD has been used in research flights over hurricanes; however, because of various hardware issues, the scientific potential of its measurements has not been fulfilled. This paper presents a reanalysis of HIRAD measurements over Hurricane Gonzalo on 17 October 2014 that demonstrate remarkable results. The basis for this novel approach is to use coincident surface wind speed (WS) and rain rate (RR) measurements from another source to calibrate the HIRAD brightness temperature measurements. As a result, the HIRAD retrievals of WS and RR are in excellent agreement with the accompanying airborne remote sensors and in situ surface wind speed measurements, which validates the HIRAD technique proof of concept. Full article

A worldwide increase in the incidence of fungal infections, emergence of new fungal strains, and antifungal resistance to commercially available antibiotics indicate the need to investigate new treatment options for fungal diseases. Therefore, the interest in exploring the antifungal activity of medicinal plants has now been increased to discover phyto-therapeutics in replacement to conventional antifungal drugs. The study was conducted to explore and identify the mechanism of action of antifungal agents of edible plants, including Cinnamomum zeylanicum, Cinnamomum tamala, Amomum subulatum, Trigonella foenumgraecum, Mentha piperita, Coriandrum sativum, Lactuca sativa, and Brassica oleraceae var. italica. The antifungal potential was assessed via the disc diffusion method and, subsequently, the extracts were assessed for phytochemicals and total antioxidant activity. Potent polyphenols were detected using high-performance liquid chromatography (HPLC) and antifungal mechanism of action was evaluated in silico. Cinnamomum zeylanicum exhibited antifungal activity against all the tested strains while all plant extracts showed antifungal activity against Fusarium solani. Rutin, kaempferol, and quercetin were identified as common polyphenols. In silico studies showed that rutin displayed the greatest affinity with binding pocket of fungal 14-alpha demethylase and nucleoside diphosphokinase with the binding affinity (K_d, −9.4 and −8.9, respectively), as compared to terbinafine. Results indicated that Cinnamomum zeylanicum and Cinnamomum tamala exert their antifungal effect possibly due to kaempferol and rutin, respectively, or possibly by inhibition of nucleoside diphosphokinase (NDK) and 14-alpha demethylase (CYP51), while Amomum subulatum and Trigonella foenum graecum might exhibit antifungal potential due to quercetin. Overall, the study demonstrates that plant-derived products have a high potential to control fungal infections. Full article

Herein, we report the green synthesis of silver nanoparticles (OE-Ag NPs) by ecofriendly green processes using biological molecules of Olea europaea leaf extract. Green synthesized OE-Ag NPs were successfully characterized using different spectroscopic techniques. Antibacterial activity of OE-Ag NPs was assessed against four different bacteriological strains using the dilution serial method. The cytotoxic potential was determined against MCF-7 carcinoma cells using MTT assay in terms of cell viability percentage. Antioxidant properties were evaluated in terms of 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging. Biocompatibility was further examined by incubating the synthesized NPs with hMSC cells for 24 h. The results were demonstrated that synthesized OE-Ag NPs presented excellent log₁₀ reduction in the growth of all the tested bacterial strains, which as statistically equivalent (p > 0.05) to the standard antibiotic drug. Moreover, they also demonstrated excellent cytotoxic efficacy against the MCF-7 carcinoma cells compared to plant lead extract and Com-Ag NPs. Green synthesized OE-Ag NPs appeared more biocompatible to hMSC and 293T cells compared to Com-Ag NPs. Excellent biological results of the OE-Ag NPs might be attributed to the synergetic effect of NPs’ properties and the adsorbed secondary metabolites of plant leaf extract. Hence, this study suggests that synthesized OE-Ag NPs can be a potential contender for their various biological and nutraceutical applications. Moreover, this study will open a new avenue to produce biocompatible nanoparticles with additional biological functionalities from the plants. Full article

Flow measurement in porous media is a challenging subject, especially when it comes to performing a three-dimensional (3D) velocimetry at the micro scale. Volumetric flow measurement techniques such as defocusing and tomographic imaging generally involve rigorous procedures, complex experimental setups, and multi-part data processing procedures. However, detailed knowledge of the flow pattern at the pore and subpore scales is important in interpreting the phenomena that occur inside the porous media and understanding the macro-scale behaviors. In this work, the flow of an oil inside a porous medium is measured at the pore and subpore scales using refractive index matching (RIM) and shadowgraph imaging techniques. At the macro scale, flow is measured using the particle image velocimetry (PIV) method in two dimensions (2D) to confirm the volumetric nature of the flow and obtain the overall flow pattern in the vicinity of the flow entrance and at the far field. At the micro scale, the three-dimensional (3D) flow within an arbitrary volume of the porous medium was quantified using 2D particle-tracking velocimetry (PTV) utilizing the law of conservation of mass. Using the shadowgraphy method and a single camera makes the flow measurement much less complex than the approaches using laser light sheets or multiple cameras with multiple viewing angles. Full article

The airborne Hurricane Imaging Radiometer (HIRAD) was developed to remotely sense hurricane surface wind speed (WS) and rain rate (RR) from a high-altitude aircraft. The approach was to obtain simultaneous brightness temperature measurements over a wide frequency range to independently retrieve the WS and RR. In the absence of rain, the WS retrieval has been robust; however, for moderate to high rain rates, the joint WS/RR retrieval has not been successful. The objective of this paper was to resolve this issue by developing an improved forward radiative transfer model (RTM) for the HIRAD cross-track viewing geometry, with separated upwelling and specularly reflected downwelling atmospheric paths. Furthermore, this paper presents empirical results from an unplanned opportunity that occurred when HIRAD measured brightness temperatures over an intense tropical squall line, which was simultaneously observed by a ground based NEXRAD (Next Generation Weather Radar) radar. The independently derived NEXRAD RR created the simultaneous 3D rain field “surface truth”, which was used as an input to the RTM to generate HIRAD modeled brightness temperatures. This paper presents favorable results of comparisons of theoretical and the simultaneous, collocated HIRAD brightness temperature measurements that validate the accuracy of this new HIRAD RTM. Full article

Measurements of the near surface horizontal wind field in a hurricane with spatial resolution of order 1–10 km are possible using airborne microwave radiometer imagers. An assessment is made of the information content of the measured winds as a function of the spatial resolution of the imager. An existing algorithm is used which estimates the maximum surface air pressure depression in the hurricane eye from the maximum wind speed. High resolution numerical model wind fields from Hurricane Frances 2004 are convolved with various HIRAD antenna spatial filters to observe the impact of the antenna design on the central pressure depression in the eye that can be deduced from it. Full article