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In an island power system, optimizing energy management is fundamental since there are renewable sources with their limitations. This management includes the allocation and capacity of energy sources to supply the loads. In this context, optimizing losses in the system contributes to improve the efficiency of this management. This paper proposes the losses optimization and energy management in the island power system. The authors propose the Natural Logarithm Particle Swarm Optimization to solve the problem and compare it with the Attractor Point Algorithm and Evolutionary Particle Swarm Optimization. And with that, we also propose a particle initialization for the studied particle-based algorithms to guarantee convergence in radial power systems. This is because the system configuration influences the response of the algorithm convergence. These techniques were applied to the IEEE-34 unbalanced radial island system.

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  Natural Logarithm Particle Swarm Optimization differs from classical PSO in that it does not calculate the velocity of the particles. Therefore, the method considers a cloud of particles with a natural logarithmic trajectory to solve the reduction of losses in a power system with a radial topology.

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  Natural Logarithmic Particle Swarm Optimization uses an initialization equation to minimize the initial estimation process, which is relevant to the convergence process.

In recent years, minimally invasive biopsy techniques have been widely used to generate small tissue samples that require processing in clinical pathology. However, small paraffin-embedded tissues are prone to loss due to their small size. To prevent the loss of small tissues, researchers have employed nonbiological embedding materials for preembedding, but this approach can lead to cumbersome experimental procedures and increase the chances of tissue loss. This study aimed to develop a convenient decellularized embedding material derived from biological membrane tissues to effectively protect small tissues from loss during paraffin embedding. This study decellularized three types of fresh animal-derived membrane tissues and selected the small intestine as the most suitable decellularized raw material through attempts at softening, comparing physical properties, and using tissue as the starting material. Subsequently, small tissues from various tissue sources were embedded, followed by H&E staining, Masson staining, immunofluorescence staining, and immunohistochemical staining. The decellularized material derived from biomembrane tissues (DMBT) developed in this study can reduce the loss of small tissues without the need for preembedding, thereby shortening the embedding process. This provides a new pathological embedding tool for future laboratory and clinical research and work.

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  The fat layer of the pig's small intestine is scraped off, and chemical reagents are used to defat and decellularize it.

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  Chemical reagents are used to soften and make the pig's small intestine transparent, and the decellularized pig's small intestine is dried.

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  DMBT is used for embedding and staining the biological tissue.

The prevalence of diabetic retinopathy (DR) among the geriatric population poses significant challenges for early detection and management. Optical Coherence Tomography Angiography (OCTA) combined with Deep Learning presents a promising avenue for improving diagnostic accuracy in this vulnerable demographic. In this method, we propose an innovative approach utilizing OCTA images and Deep Learning algorithms to detect diabetic retinopathy in geriatric patients. We have collected 262 OCTA scans of 179 elderly individuals, both with and without diabetes, and trained a deep-learning model to classify retinopathy severity levels. Convolutional Neural Network (CNN) models: Inception V3, ResNet-50, ResNet50V2, VggNet-16, VggNet-19, DenseNet121, DenseNet201, EfficientNetV2B0, are trained to extract features and further classify them.

Here we demonstrate:

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  The potential of OCTA and Deep Learning in enhancing geriatric eye care at the very initial stage.

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  The importance of technological advancements in addressing age-related ocular diseases and providing reliable assistance to clinicians for DR classification.

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  The efficacy of this approach in accurately identifying diabetic retinopathy stages, thereby facilitating timely interventions, and preventing vision loss in the elderly population.

Grafting techniques have been successfully adopted to improve resistance to biotic and abiotic stresses, increase yields, fruit quality and study systemic signaling in plants. This technique has not been fully explored in cassava and there is currently no standardized grafting method for this species published especially in Africa. This is the first report on cassava grafting protocol in Africa with valuable advantages including utilizing a cost-effective and environmentally friendly wooden healing chamber. In this study, we describe an optimized cleft grafting protocol for cassava utilizing a wooden healing chamber and outline the step-by-step procedure with optimum conditions to generate a high grafting success rate. Using a top wedge grafting technique with high reproducibility and success rates, we developed a straightforward and robust grafting protocol for cassava (M. esculenta) cultivars. Grafting success was recorded and this protocol produced a high grafting success of 90 % and its reproducibility makes it suitable for mass production thereby addressing the need for efficient cassava propagation. This grafting protocol requires less specialized equipment and expertise making it more accessible to farmers and researchers with limited resources to promote the use of grafting for cassava growth, yield improvement and advanced studies such as systemic long-distance signaling in plants.

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  Optimization of cleft grafting method obtains a high success grafting rate of cassava.

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  A wooden healing chamber provides a controlled environment for graft healing.

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  Promoting cassava grafting; a priority to produce new cultivars and explore breeding research prospects.

Antibiotics are currently recognized as environmental pollutants. In this work, the methods involved in the degradation of a β-lactam antibiotic (i.e., DXC) by treatments based on inorganic peroxides and UVC (e.g., UVC alone, UV-C/H₂O₂, UVC/peroxymonosulfate, and UVC/peroxydisulfate) are presented. The methodology of computational calculations to obtain frontier orbitals and Fukui indices for DXC, and elucidate the reactive moieties on the target substance is also shown. Finally, the direct oxidation by peroxides and UV-C/H₂O₂ action to treat DXC in simulated pharmaceutical wastewater are depicted. The chromatographic and theoretical analyses allowed for determining the degrading performance of inorganic peroxides and UVC-based treatments toward the target pollutant in aqueous samples.

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  Treatments based on inorganic peroxides and UVC as useful methods for degrading the β-lactam antibiotic dicloxacillin.

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  Persulfates and UV-C/H2O2 showed high degrading action on the target pharmaceutical.

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  Methodologies based on theoretical calculations for the identification of reactive moieties on the DXC susceptible to radical attacks are presented.

A growing number of studies have investigated how land surface temperature (LST) is influenced by a variety of driving factors; however, little effort has been made to identify the dominant ones. The suggested method used the Upper Awash Basin (UAB), Ethiopia, as an example to explore the spatial heterogeneity and factors affecting LST, which is critical for selecting effective mitigation strategies to manage the thermal environment. The study employed two models: ordinary least squares (OLS) and geographically weighted regression (GWR). The OLS model was first used to capture the overall relationship between LST and some biophysical factors. The GWR was then utilized to investigate the spatial non-stationary relationships between LST and its influencing biophysical factors. Although the method was tested in UAB, Ethiopia, it can be applied in similar agroecosystems, to identify the dominant factors that influence LST and develop site-specific LST mitigation strategies.

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  The OLS and GWR models investigated the spatial heterogeneities of the influencing factors and LST.

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  Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST

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  The models performance was computed using the adjusted coefficient of determination (adj. R²), Akaike Information Criterion (AICc), and residual sum of squares (RSS).

In arid and semi-arid regions where surface water resources are scarce, groundwater is crucial. Accurate mapping of groundwater depth is vital for sustainable management practices. This study evaluated the performance of three spatial interpolation techniques – inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF) – in predicting groundwater depth distribution across Dire Dawa City, Ethiopia. The results demonstrated the superiority of the RBF method, exhibiting the lowest RMSE (3.21 m), MAE (0.16 m), and the highest R² (0.99) compared to IDW and OK. The IDW method emerged as the next best performer (RMSE = 4.68 m, MAE = 0.16 m, R²= 0.97), followed by OK (RMSE = 5.32 m, MAE = 0.42 m, R²= 0.95). The RBF's superior accuracy aligns with findings from other semi-arid regions, underscoring its suitability for data-scarce areas like Dire Dawa. This comparative evaluation provides valuable insights for selecting the optimal interpolation method for groundwater depth mapping, supporting informed decision-making in local water resource management.

The methodological approach comprised:

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  Implementation of three interpolation techniques, namely, inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF), utilizing 56 groundwater depth measurements from locations dispersed throughout the study area.

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  Cross-validation through randomly withholding 20 % of the data for validation purposes.

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  Comparison of the techniques based on statistical measures of accuracy, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²).

The present article provides an improvement in the method to correct indirect strain measurements in triaxial compressive strength tests through axial displacement and hydraulic fluid volume change measurements. The improvement focused on reducing the parameters of the formula proposed for indirect volumetric strain in the original method, thereby facilitating the development of a simpler formula in which the radial strain depends on only two parameters: the initial volume of the rock specimen and the volume changes of the hydraulic fluid for each instant. The comparison between the improvement proposed, and original method resulted in a mean absolute difference of 0.003.

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  This improvement does not depend on the axial strain, unlike the original method, which requires correcting the indirect axial strain measurements before correcting the indirect radial strain measurements.

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  This improvement can be useful for research on the stress-strain behavior of intact rock under laboratory conditions, such as in the study of the post-peak state.

This review critically assesses the determination of low molecular weight volatiles by different methods, providing context for the development of suitable techniques to determine volatile content in plant tissue and soil samples as well as the associated analytical challenges. Although sensitive analytical methods have been reported in recent decades, studies on their application in modern investigative techniques are lacking. Herein, the latest sampling methods in volatile biochemistry, current advancements in the understanding of these analytes, and the significance of these findings for other types of volatiles are summarized. Gas chromatography, high-performance liquid chromatography, ion chromatography, thin-film microextraction, and real-time monitoring techniques are discussed and critically determined. This review concerns the methods most suitable for future research in this area.

In the era of 5 G network advancements, the potential for extremely robust, less-latency, and huge-capacity communication opens up new perspective for multimedia. Steganography enables embedding of sensitive data within multimedia files, making it unreadable to unauthorized third parties. Notably, when using videos as cover, the capacity for data embedding is substantially increased. Recent developments in steganography have largely revolved around modified versions of transform domain techniques. Due to this repetitiveness, it becomes easier for steganalytic tools in detecting concealed data. Addressing this issue, our paper introduces an innovative data embedding approach MARVIS based on the Mellin transform. The superiority of the proposed approach is exhibited using the metrics, MSE, PSNR, and SSIM. MARVIS has achieved PSNR of 50–60 dB and SSIM of 0.9998 for embedding 4 bits of secret data, outperforming other methods that achieve 40 dB for 1 bit. By quadrupling stego capacity, we can embed more secret data per pixel without compromising the integrity of the cover object.

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  MARVIS utilizes phase modulation for data embedding, offering advantages beyond traditional frequency domain techniques which use frequency domain for data embedding.

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  The effectiveness of the proposed data embedding approach is validated through Y-Net, a deep learning-based steganalysis tool.