Shiraz University of Technology

The increasing demand for real-time carbon emission monitoring in distributed edge environments has highlighted the need for accurate prediction models that consider spatial and temporal context factors. Traditional centralized prediction models face significant challenges in processing large-scale emission data in real-time, especially when dealing with fluctuating data from diverse locations and time intervals. These challenges are further compounded by the need for privacy protection when handling sensitive environmental data. To address these issues, this paper presents a distributed approach to carbon emission prediction in edge environments, incorporating spatial-temporal context factors such as location and time of day. By leveraging locality-sensitive hashing (LSH) techniques, we propose a privacy-preserving model that enables efficient prediction across edge nodes while maintaining user privacy. Experimental results demonstrate the feasibility and effectiveness of the proposed method in accurately predicting carbon emissions, with a significant reduction in computational overhead compared to centralized models. This work provides a practical solution for real-time carbon emission prediction and privacy-preserving environmental monitoring in distributed systems.

Fossil‐based material manufacturing has long been linked to the acceleration of climate change through carbon dioxide emissions. In addition to their negative impact on the environment, the depletion of nonrenewable fossil fuels has led to a global demand for sustainable and environmentally friendly alternatives. This has sparked a surge of academic interest in the past few decades on the manufacture of bio‐based materials as substitutes for fossil‐based materials. As sustainability becomes a global imperative, bioplastics are rapidly emerging as a viable alternative to conventional petroleum‐derived plastics. These materials might be manufactured by using polymers from different bio‐based sources such as plants, animal tissues, or can have a microbial origin. Bioplastics not only offer biodegradability, thereby reducing long‐term environmental impact, but also possess various functional properties that make them suitable for diverse applications, including packaging, agriculture, textiles and pharmaceuticals. This review focuses on new developments in bioplastics regarding their material, processing, and applications. Recent developments in the preparation of bioplastics are reported, highlighting the distinct properties of each type of material according to the polymers of origin. Special attention is given to the film‐forming properties, the barrier functionality, thermal stability, and compatibility with the bioactive compounds, supported by recent empirical findings.

Abstract Biosorption has emerged as a promising and eco-friendly technology with wide applications, attracting considerable interest in recent years. Among various biosorbents, microalgae are particularly noteworthy due to their wide availability, high adsorption efficiency, and cost-effectiveness. In this study, the microalga Chlorella sorokiniana was employed for the bioremoval of acetaminophen from aqueous solutions. The effects of process duration (10-48 h) and initial acetaminophen concentration (10-120 mg/L) on the removal efficiency were evaluated and modeled using response surface methodology (RSM) in Design-Expert software. Optimization of key parameters was performed using the Grey Wolf metaheuristic algorithm. The high determination coefficient (R² = 0.92) indicated strong predictive capability of the model. Using C. sorokiniana, acetaminophen removal efficiency ranged from 13.8% to 99.8%. Analysis results revealed that during the first 12 days, a large proportion of metabolites produced by C. sorokiniana were converted to carbon dioxide. Scanning Electron Microscopy (SEM) images showed that different acetaminophen concentrations induced cellular stress and altered algal cell size. Overall, the findings demonstrate that C. sorokiniana can effectively degrade acetaminophen in contaminated waters, likely utilizing it as an organic carbon source while enhancing its photosynthetic pigments, proteins, and lipids.

Abiotic stress tolerance is a critical trait in plant breeding programs aimed at developing climate-resilient crop varieties. The accurate identification and selection of stress-tolerant genotypes require comprehensive evaluation using multiple mathematical indices. However, the manual calculation of these indices from large-scale experimental datasets is time-consuming, error-prone, and computationally demanding. Here, we present PTSIonline (Plant Tolerance and Sensitivity Indices online, http://87.107.144.237 ), an integrated web-based computational platform designed to streamline the analysis of abiotic stress tolerance indices in crop breeding research. The platform implements 18 widely recognized stress evaluation indices including Tolerance Index (TOL), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Superiority Index (SI), Abiotic Tolerance Index (ATI), Stress Susceptibility Percentage Index (SSPI), Relative Efficiency Index (REI), Modified Stress Tolerance Indices (K₁STI, K₂STI), Stress Distribution Index (SDI), Drought Index (DI), and Stress Non-Productivity Index (SNPI). PTSIonline features an intuitive user interface that accepts standard experimental data formats and generates comprehensive statistical outputs, visualizations, and genotype rankings within seconds. Comparative analysis demonstrates that PTSIonline provides the most extensive index coverage among available online tools while maintaining computational efficiency suitable for high-throughput phenotyping programs. The platform eliminates computational barriers in stress tolerance research, enabling researchers and plant breeders to rapidly identify superior genotypes from diverse germplasm collections. PTSIonline represents a significant advancement in computational tools for crop improvement under changing environmental conditions.

The aim of this study was the encapsulation of Haematococcus pluvialis containing astaxanthin (HAST) using emulsion prepared with refined olive oil and octenyl succinic anhydride (OSA) modified starch. Thus, emulsions were prepared by different concentrations of OSA-modified starch (5%, 10%, and 15% w/w) and their physicochemical (encapsulation efficacy, size, zeta potential, solubility, and stability), structural, and morphological properties were investigated. According to the results, the zeta potential of emulsions was in the range of -6.73 to -15.10mV and the negative charge of droplets caused electrostatic repulsion and improved the emulsion stability. The emulsion prepared with higher concentration of OSA-modified starch had higher stability (p <0.05). Droplet size of emulsions was in the range of 322.23 ± 6.30 to 191.50 ± 3.91nm, and the smaller droplet size was at a higher concentration of OSA-modified starch (p <0.05). Also, the highest encapsulation efficacy (EE)% was observed by emulsion stabilized with 10% w/w OSA-modified starch (91.13 ± 0.35%) (p < 0.05). Color parameters showed that a higher concentration of OSA-modified starch caused an increase in L*, WI, and ΔE and reduction in a* and b* values. Fourier transform infrared spectroscopy (FTIR) revealed successful encapsulation of astaxanthin. Microcapsules embedded with OSA-modified starch at all concentrations showed uneven surfaces with dented areas but no visible cracks. As the concentration of OSA-modified starch increased, solubility declined. The higher stability was associated with the samples with 10% OSA- modified starch at 4°C and 25°C. This study suggests that emulsion prepared with 10% OSA-modified starch and refined olive oil is a suitable carrier for the encapsulation of hydrophobic compounds.

Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical tool for the observation, the detection and the identification of chemical or biological species at low concentrations due to its high sensitivity, specific fingerprinting capability and real-time detection. However, a key challenge lies in establishing a suitable and reliable measurement protocol to ensure both reproducibility and repeatability when SERS is used as a sensing nanoplatform. In this paper, we propose a specific methodology to investigate the performance of SERS substrates, with a particular focus on their reproducibility and repeatability. Furthermore, we validate our approach on one commercial SERS Hamamatsu substrate from Hamamatsu Photonics by using diluted solution of 4-mercaptobenzoic acid (MBA) at an excitation wavelength of 633 nm. This proposed protocol consists in recording 25 SERS maps equally distributed on the whole surface substrate. For each map, 16 spectra have been acquired and averaged to provide a representative SERS signal. In total, 400 spectra have been collected and analyzed by using the integrated intensities of characteristic MBA bands to determine both reproducibility and repeatability. This approach enables us to quantify signal variations, at the micrometer scale, as well as across the entire substrate. We demonstrated that while the SERS response is highly reproducible locally, it becomes less consistent when evaluated across the full surface. However, the SERS signal is not repeatable at the local scale but it can be repeatable at the whole substrate scale as the average SERS intensity is identical for both SERS measurements. Furthermore, we demonstrated that this method can also be applied to DNA strands thereby demonstrating its effectiveness in evaluating biosensors. Finally, the proposed methodology and protocol can then be used as a standard to precisely evaluate the sensing performances of other substrates.