Sida linifolia L., is traditionally used in a number of diseased conditions including the relief of uncomfortable teething and the prevention of malaria. The aim of this study was to investigate the antioxidant and anti-inflammatory properties of Sida linifolia. For the in vitro anti-inflammatory tests, platelet aggregation, albumin denaturation, protease, and phospholipase A₂ were performed. Then for the in vivo studies of the same, egg albumin and carrageenan induced models were employed. The total antioxidant capacity (TAC), 1,1-diphenyl-2-picrylhydrazyl (DPPH), ferric reducing power (FRAP), and nitric oxide (NO) assays were used in the in vitro antioxidant assessment, and the reference standards for the antioxidant tests were butylated hydroxytoluene (BHT), gallic acid, and ascorbic acid, whereas the anti-inflammatory studies employed aspirin and prednisolone as standards. Every parameter was calculated using conventional methods. In the fraction, there were varying concentrations of terpenoids, saponins, steroids, alkaloids, flavonoids, tannins, and other phenols. The EALFSL displayed robust, concentration-dependent anti-inflammatory effects, which were comparable to those of the reference drugs (Aspirin/Prednisolone). The EALFSL fraction's IC₅₀ values ranged from 0.93 to 1.20 mg/ml which was less active than those of BHT (0.30), ascorbic acid (0.32-0.50), and gallic acid (0.47). The outcomes additionally demonstrated that EALFSL has a significant level of concentration-dependent antioxidant activity. These suggest that the ethylacetate leaf fraction of Sida linifolia possessed anti-inflammatory and antioxidant effects which could be attributed to phytochemicals contained in it.

In this paper, we examine the impact of fear in an eco-epidemiological model with predator harvesting and infection in a prey population. The effect of fear on susceptible prey due to infected prey was discussed. A Predator consumes susceptible and infected prey at various rates in the form of a Holling type II Functional response. To examine the positivity and the boundedness of the solutions. The stability of all biologically feasible equilibrium points and the Hopf-bifurcation of the endemic equilibrium of the system are derived. A numerical simulation is performed to support our analytical findings.

The United Nations' Sustainable Development Goals (SDGs) for 2030 encompass a comprehensive framework to address the world's most pressing challenges, aiming to achieve a more sustainable and equitable future for all. Among these goals, Goal 11 specifically targets sustainable cities and communities, emphasising cultural and natural heritage preservation and management. This research paper delves into the significance of SDG 2030 in the context of heritage management, focusing on both Indian and global perspectives. The paper highlights the importance of safeguarding cultural and natural heritage for sustainable development, contributing to social cohesion, economic growth, and environmental conservation. The paper also draws on empirical data and case studies; the research delves into the unique challenges faced by India in managing its diverse and rich heritage. The study examines how India's efforts align with global goals and identifies potential gaps and areas for improvement. Factors such as rapid urbanisation, population growth, and climate change are explored regarding their impact on heritage sites and their management. In analysing the global context, the research assesses various heritage management strategies and best practices from different countries worldwide. Drawing comparisons with India's approach, the paper highlights successful models that can be adapted to address global heritage management challenges. The study concludes with a series of policy recommendations to strengthen the efforts in achieving the SDG 2030 targets related to heritage management. Emphasising collaboration and knowledge-sharing, the paper advocates for a collective approach to conserving and promoting the diverse heritage of nations and the world.

Telecommunication stations, particularly operating in rural areas are usually powered by diesel generators due to the lack of access to the utility grid. However, the growing cost of energy due to the constantly increasing fuel prices and the related greenhouse gas emissions contributing to global warming have driven telecom companies to seek for better energy management solutions. In this paper, we study the economic feasibility of an environmentally friendly power supply system for a rural telecommunication station in the city of Skikda, northeast Algeria. The proposed system is a standalone hybrid PV-Wind system with pre-existing diesel generators and a battery storage system. Different configurations are considered in this study in order to select the most reliable and economically viable solution based on the net present cost (NPC) and the cost of energy (COE) of each configuration. The optimization is performed using HOMER PRO software.

The inherent complexity of the Raman spectra of biomedical samples reflects the intricate molecular composition and intermolecular interactions of these diverse systems. Biomolecules, such as proteins, lipids, nucleic acids, and carbohydrates, contribute Raman-active vibrational modes, adding layers of complexity to the spectra. Moreover, the prevalence of water, a major component in biological samples, introduces broad and overlapping spectral features, presenting challenges in discerning other biomolecular signals.

Unraveling the complexities of biological Raman spectra is essential for bioscience and bioengineering research because it provides insight into cellular processes, disease states, and drug interactions. For the effective analysis of such complex data, robust and cutting-edge software is required that provides sophisticated algorithms for data preprocessing, thereby enhancing signal-to-noise ratio and revealing hidden spectral information. In addition, novel applications of this type may include machine learning algorithms for automated clustering analysis, enabling the identification of biomolecules and their conformational changes in diverse biological specimens.

We present PyFasma, a Python 3 package built around the most popular scientific Python libraries such as numpy, pandas, scipy, seaborn, and scikit-learn that aims to provide Raman spectroscopists with user-friendly interactive tools for the analysis of complex biomedical Raman data.

The package covers an assortment of methods for data preprocessing, including spike removal, cropping, smoothing, baseline correction, and batch-deconvolution of Raman bands, among others. Additionally, PyFasma facilitates Principal Components Analysis (PCA), Partial Least Squares Regression (PLSR), and two-class Discriminant Analysis (PLS-DA). Its robust functionalities and seamless integration with Jupyter notebooks enable scientists to perform in-depth and reproducible analysis of complex biospectroscopic data.

Nitrogen vacancy defect centers are promising platform for quantum sensing. They have attracted attentions over the last 15 years. Their performance has been demonstrated in laboratory scale using expensive and bulky laser and microwave sources. There are ongoing efforts to make them miniaturized and portable for civilian, aerospace, geophysics and medical applications. We report using off the shelve voltage controlled oscillators as a reliable replacement of conventional microwave sources. They are cheap, small and consume low power. In combination with mixers, one can tune them to drive double quantum (DQ) resonances and compensate effect of thermal and magnetic drifts on sensors’ performance.

In this 21st century, researchers have been exploring different designs, performance characteristics, charging-discharging regions, and regenerative braking aspects of electric vehicles. However, there has been a major gap in the multimodal analysis of the accelerating pedal drive for electric vehicles; therefore, herein, the novel analytical model of a mimicked foot pedaling control of the electric vehicle is developed by cascading five sub-models (i.e., Foot Pedal, Resistive Potentiometer, 555-Timer, Buck-Converter, and the permanent magnet DC Motor) to synthesize the overall 3rd order transfer function of the system. MATLAB is utilized to comprehensively analyze the transient and steady-state characteristics of the developed model by considering the Pedaling force, four different materials (i.e., Aluminum, Brass, Carbon Fiber, Polyamide 6), Potentiometer’s resistance, mechanical and electrical attributes of the motor. Results highlight that the linear pedaling drive is possible by considering Polyamide 6 material at pedaling properties of 0.25 kg Mass and 2.679 Ns/m Damping Coefficient. Furthermore, at a lesser potentiometer track length (around 10 cm) and equivalent inertia of 5 Kgm², the motor generates the regulated angular speed, thereby minimizing the transient characteristics in the accelerating pedal.

Metal oxide nanostructures are widely applied as electrode surface modifiers in modern electroanalytical chemistry. Among them, manganese dioxide nanorods are of interest and has been applied in colorants electroanalysis. An electrode modified with manganese dioxide nanorods dispersed in cationic surfactant hexadecylpyridinium bromide has been developed for the quantification of rosmarinic acid. The application of hexadecylpyridinium bromide as dispersive agent provides stabilization of nanomaterial suspension in water medium. The electrode developed gives an improved response to rosmarinic acid, i.e. cathodic to anodic peak potential separation of 60 mV and 1.7-fold increased redox currents have been observed. Quasi-reversible electrooxidation controlled by surface processes has been confirmed. Differential pulse voltammetry in Britton-Robinson buffer pH 5.0 has been applied for the quantification of rosmarinic acid. Linear dynamic ranges of 0.025-1.0 and 1.0-10 μM with a detection limit of 9.7 nM have been achieved that are significantly improved compared to other electrochemical methods using modified electrodes. The selectivity of the electrode response to rosmarinic acid has been shown in the presence of a 1000-fold excess of inorganic ions, 100-fold excesses of saccharides, and 10-fold excesses of ascorbic and p-coumaric acids, eugenol, carvacrol, and thymol. Other phenolic acids (gallic, ferulic, caffeic) and flavonoids (quercetin, rutin) give an interference effect. The practical application of the electrode developed has been demonstrated on rosemary spices.

This study investigates the vulnerability of Alberta's agricultural sector to extreme weather events, particularly drought, which has historically caused significant financial losses. Accurate downscaling techniques are crucial for obtaining reliable results to identify trends and patterns in hydro-climatological variables. Traditional statistical downscaling methods may be inefficient in downscaling data from multiple sources or complex datasets. As such, deep learning methods, such as Long Short-Term Memory (LSTM), may offer a promising solution. In this study, monthly climatological data spanning 35 years (1979 to 2014) from 17 NCEP grid points in Alberta were downscaled using LSTM to analyze trends and patterns in precipitation in agricultural areas. The Mann-Kendall and Pettitt tests were employed to analyze precipitation patterns and breakpoints. Additionally, the Standardized Precipitation Index (SPI) was used to identify drought severity at different time scales (SPI 3, 6, 12). The results demonstrate that drought occurrences have been observed in some agricultural regions, with rising tendencies in larger areas which southern parts such as Calgary agricultural areas highly prone to severe drought. The findings highlight the importance of developing effective strategies to mitigate the impacts of drought on Alberta's agricultural sector. The LSTM downscaling technique used in this study can be applied to other regions to identify trends and patterns in hydro-climatological variable.

In recent years, downscaling techniques have emerged as practical methods in numerous fields, including climatology trend simulation. Therefore, identifying the optimal regression technique is critical for assessing, simulating, and predicting climate patterns. This paper aims to identify the optimum regression techniques for downscaling among ten commonly used methods in climatology, including SVR, LinearSVR, LASSO, LASSOCV, Elastic Net, Bayesian Ridge, RandomForestRegressor, AdaBoost Regressor, KNeighbors Regressor, and XGBRegressor. Historical data from Can_ESM5 were collected from four synoptic stations based on the Köppen climate classification. To achieve this goal, the data were classified based on the Köppen climate classification system, including A (tropical), B (dry), C (temperate), and D (continental). Additionally, to enhance the performance of downscaling accuracy, eliminate redundant information, and overcome overfitting, Mutual Information (MI) was employed on the Can-ESM5 dataset. The downscaling performance was evaluated using the Coefficient of Determination (DC) and Root Mean Squared Error (RMSE). In conclusion, SVR had superior performance in tropical and dry climates with DC and RMSE values of 0.89, 0.02 °C and 0.93, 0.01 °C, respectively. On the other hand, LassoCV with RandomForestRegressor had the best results in temperate and continental climates with DC and RMSE values of 0.87, 0.04 °C and 0.88, 0.03 °C, respectively. The outstanding performance of the optimum downscaling methods relies on the network structure in consideration of the suitability of those networks with the target variable and climate type.