We present a single-step approach for the synthesis of Ti₃C₂ MXene and gold nanoparticle (AuNP) composites via electrostatic self-assembly. A unique challenge was addressed: the electrostatic repulsion between negatively charged Turkevich-synthesized AuNPs and negatively charged MXene sheets. We induced a positive charge on the AuNP surface using cetyltrimethylammonium bromide (CTAB), which enabled effective electrostatic interactions with the negatively charged surface of the MXene sheets. The success of AuNP surface modification with CTAB was confirmed via UV-Vis spectroscopy as well as the resulting MXene-AuNP composite. The as-synthesized MXene had a zeta potential of -34 mV, while the positively charged AuNPs displayed a potential of 25 mV. In contrast, the initial Turkevich-synthesized gold nanoparticles exhibited a charge of -30 mV. This charge alteration enabled the electrostatic self-assembly of the two components and gives a control over NPs count over MXene flakes. Notably, no AuNP peaks were observed at lower AuNP ratios in the composite. However, distinct peaks for both MXene and AuNPs appeared at an optimized ratio, confirming the successful formation of the composite. Scanning Electron Microscopy (SEM) images provided further insights into the successful formation of the composite with clearly visible AuNPs decorating the MXene matrix. Dynamic Light Scattering (DLS) measurements were performed for size analysis. These measurements revealed that MXene had an average size of 650 nm, consistent with literature but with a smaller flake size. AuNP exhibited dimensions of around 40 nm. In conclusion, our single-step synthesis method offers a sustainable platform for synthesizing MXene-AuNP composites with enhanced properties, rendering them suitable for a wide array of applications, notably in bio-sensing and nanomaterial-based technologies.

Photovoltaic (PV) system-generated solar energy has inconsistent and variable properties, which makes controlling electric power distribution and preserving grid stability extremely difficult. A photovoltaic (PV) system's performance is profoundly affected by the amount of sunlight that reaches the solar cell, the season of the year, the ambient temperature, and the humidity of the air. Every renewable energy technology, sadly, has its problems. As a result, the system is unable to function at its highest or best level. To combat the unstable and intermittent performance of solar power output, it is essential to achieve a precise PV system output power. This work introduces a new approach to enhancing accuracy and expanding the time range of very-short-term solar energy forecasting (15 min step ahead) by using multivariate time series inputs in deferent seasons. First, Linear Discriminat Analysis (LDA) is used to select the relevant factors from the mixed meteorological input data. Secondly, two very short-term deep learning prediction models, CNN and LSTM, are used to predict PV power for a shuffled and reduced database of weather inputs. Finally, the predicted output from the two models are combined using classification strategy. The proposed method is applied to one year of real data collected from a solar power plant located in southern Algeria, to demonstrate that this technique can improve the forecasting accuracy compared to other techniques, as determined by statistical analysis involving normalized root mean square error (NRMSE), mean absolute percentage error (MAPE), mean bias error (MBE), and coefficient of determination (R²).

Friction stir spot welding (FSSW) is a solid state welding technique, was developed by Mazda Motor Corporation and Kawasaki Heavy Industries as a novel derivative of the friction stir welding as an alternative to the traditional fusion welding processes. FSSW successively used to join similar and dissimilar metals. Tool rotational speed and dwell time are the most effective FSSW process parameters. In the present investigation, the role of the tool rotational speed and the dwell time in determining the strength of the FSSW joints was studied using AA6061-T6 aluminum alloy sheets with thickness of 1.8 mm as a workpiece material. A classic milling machine was employed to carry out the welding process. Four different values of tools rotational speed with two dwell time values were taken to produce the FSSW lap-shear specimens. Four specimens were performed for each FSSW process condition. three specimens were averaged to evaluate the tensile-shear fracture load, while the remaining specimen was used for the micro-Vickers hardness examination and the microstructure observation. The investigation reported an increase in the joint strength within a certain range of tool rotational speed and dwell time values corresponding to grain refinement in the weld zone. The variation in mechanical properties was attributed to the corresponding frictional heat generation and material flow during the welding process. Strain hardening and dynamic recrystallization determined the grain refinement and the weld nugget hardness. Lower mechanical properties were observed with the excessive frictional heat generation and material flow with the too high speeds and dwell time values.

This work proposes a methodology based on Multicriteria Decision Aid (MCDA) and Cluster Analysis to identify ideal locations for the installation of police facilities or vehicle parking and policing around stadiums in Recife, Brazil, during potential violent sports events (criminal occurrences from football supporters or fanbases). K-Means unsupervised clustering algorithm is used to group criminal data into homogeneous clusters based on their characteristics. Each type of criminal occurrence is linked to a single cluster. The optimal location is addressed based on the PROMETHEE method (Preference Ranking Organization Method for Enrichment Evaluation), allowing clusters to be organized into a hierarchy based on the number of facilities (N), average distance (D) from the criminal occurrence to the associated cluster, and the coverage level (C) which is the proportion of crime occurring in a location less than 500m from the associated cluster. Through data analysis on crimes and violence in the region, the study seeks to identify patterns of criminal behaviour and high-risk areas to determine the most strategic location for the police units and enhance the public security decision-making process. The choice for the k parameters ranged from 1 to 30 incorporating all region of analysis, with computational cost of 43 minutes running time using Intel Core i3-3217U (1800GHz and 10 GB RAM). This approach and methodology can be useful to support public security policies in the region and contribute to the reduction of violence around the stadiums. The empirical application can help guide public managers' decisions regarding resource allocation and the implementation of more effective security policies, with the aim of ensuring a safer environment for fans and residents in the areas near the stadiums.

Deep learning and Machine Learning Algorithms has become the most popular way for analyzing and extracting features especially in medical images. And feature extraction made the task much easier. Our aim is to check which feature extraction technique works best for a classifier. We used Ophthalmic Images and applied feature extraction techniques such as Gabor, LBP (Local Binary Pattern), HOG (Histograms of Oriented Gradients), and SIFT (Scale-Invariant Feature Transform), where the obtained feature extraction techniques are passed through classifiers such as RFC (Random Forest classifier), CNN (Convolutional neural network), SVM (Support vector machine), and KNN (K-Nearest Neighbors). Then we compared the performance of each technique and selected which feature extraction technique gives the best performance for a specified classifier. We achieved 94% accuracy for Gabor Feature Extraction technique using CNN Classifier, 92% accuracy for HOG Feature Extraction technique using RFC Classifier, 90% accuracy for LBP Feature Extraction technique using RFC Classifier and we achieved 92% accuracy for SIFT Feature Extraction technique using RFC Classifier.

Nanotechnology has emerged as a pivotal platform in revolutionizing cancer treatment, offering a diverse array of strategies to enhance therapeutic efficacy while minimizing collateral damage to healthy cells. This review paper extensively explores the recent breakthroughs and applications of nanotechnology in the realm of cancer treatment. The unique physicochemical properties of nanoformulations, specifically nanoparticles, enable precise customization for targeted drug delivery, a hallmark feature of effective cancer therapy. Nanoformulations leverage their diminutive size to exploit enhanced permeability and retention within tumour tissues, thereby facilitating the accumulation of therapeutic agents at the tumour site. The utilization of nanocarrier-based formulations showcases their exceptional potential for precise drug delivery, ensuring optimal therapeutic impact. Beyond drug delivery, nanotechnology has fundamentally advanced cancer diagnosis and imaging techniques. The integration of functionalized nanoparticles with contrast agents has empowered the development of highly sensitive imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). This heightened sensitivity allows for the detection of minute tumour masses, real-time monitoring of treatment responses, and the guidance of intricate surgical interventions. Throughout this comprehensive review, we delve into the multifaceted roles of nanomaterials, including nanoparticles, nanocarriers, and nanodevices, as they address pivotal challenges posed by conventional cancer therapies. Amidst our analysis of these advancements, we critically examine the obstacles faced by nanotechnology-based treatments, ranging from potential toxicities to safety considerations.

Healthcare professionals must take special care of wounds to avoid serious complications such as infections, lengthy healing periods, and even amputations. This study aimed to design and manufacture a self-healing bioabsorbable polymeric based wound dressing with anti-bacterial growth and improved wound healing properties. Gel-based mixtures were successfully made-up of 5wt% chitosan with inhibition bacterial growth feature in 5-20wt% polyvinyl alcohol (PVA) called “pure mixture”. It was observed that the mixture of 5wt% chitosan in 10wt% PVA resulted in the most controlled viscosity and appropriate gel-texture for wound healing. The measured viscosities of 5wt% chitosan and 10wt% PVA are 235 and 531 Pa·s, respectively. The microscopic examination confirmed that addition of chitosan into PVA has successfully inhibited the bacterial growth. Another gel-based mixture called “additive mixture” was also investigated using the optimized preparation condition of 5wt% chitosan in 10wt% PVA with incorporation of some traditional herbs in powder form named frankincense, myrrh, and alum stone. Microscopic examination proven that addition of traditional herbs into chitosan/PVA mixture has initiated some bacteria to growth. A comparison of the wound healing performance of pure mixture gel and additive mixture gel was conducted using rats. The pure mixture gel produced a faster healing rate and a lower level of inflammation than the additive mixture gel.

This paper investigates the design and tuning of a Broadband Dispersion Compensating Modified Honeycomb Lattice Photonic Crystal Fiber (MHL-PCF) with outstanding features such as strong birefringence and nonlinearity. The proposed PCF for y polarization exhibits a negative dispersion coefficient of -263.9 ps/(nm-km) at 1.55 µm operating frequency and a high negative dispersion of -652.9 ps/(nm.km) when air filled fraction (d_c/Λ) grows from 0.35 to 0.65. Because it is a polarization maintaining fiber, it also exhibits birefringence. At 1.55 µm operating frequency, the suggested fiber exhibits 1.482×10^-2 birefringence. The suggested MHL-PCF has a high nonlinear coefficient of 34.68W^-1km^-1 at the same operating frequency. Numerical aperture is also investigated for MHL-PCF as it influences their light-guiding capabilities, light-coupling efficiency, mode control, dispersion qualities, and sensitivity in sensing applications. The numerical aperture of the proposed MHL-PCF at 1550 nm is 0.4175, demonstrating excellent light-coupling property. The purpose of this research is to satisfy the growing need for improved optical communication systems capable of managing high data rates across long transmission distances. The suggested MHL-PCF structure has distinct features that make it an attractive choice for dispersion correction and nonlinear optical applications.

Coffee Robusta (Coffea canephora) increased its total production by 73.5% during the first quarter of 2023. In this study, twenty (20) samples each of healthy and infected coffee leaf were measured for the transmittance properties in the UV-Vis and NIR regions. Coffee Leaf Miner (CLM) infected leaves were identified based on the translucent patches on the plant foliage. Results showed that a healthy coffee leaf has a mean transmittance of 41.53 μW for the NIR region while for the infected leaves, the mean transmittance is 47.06 μW. Healthy coffee Robusta leaves showed significant difference in their transmittance properties compared to infected coffee Robusta leaves in the UV (r = -0.15, p =0.021, F = 5.8, t = -0.2.86), visible (r = -0.15, p = 0.018, F = 6.11, t = -2.88), and NIR (r = -0.14, p = 0.027, F = 5.28, t = -2.99) regions. A CLM index was introduced based on the intensity ratio of green and red wavelengths. I535/575 showed positive correlation with the estimated chlorophyll-a concentration for healthy (r = 0.94, p = 0.227) and infected (r = 0.56, p = 0.622) leaves. This method leads to the development of portable sensors for early detection of CLM in plants.

Olive oil industry is an important sector within the agro-food industries in the Mediterranean countries but constitutes a major environmental problem regarding the disposal of its wastewaters. Olive mill wastewater (OMW) is a turbid, dark colored, foul-smelling and acidic effluent. It presents a low biodegradability due to its antibacterial activity, given by the phenolic content¹. Bioremediation through microalgae is an interesting option, since it is an environmentally friendly process, as wastewaters can be used as cheap nutrient sources for microalgal biomass production that could be a source of stored chemical bond energy, especially into lipids, carbohydrates and proteins². The main objective of this work was to evaluate the potential for bioremediation of two OMW with different origin - olives washing (OW-OMW) and olive oil extraction (OMW) - by microalgae. It were used three species of green microalgae, Chlorella vulgaris, Chlorella protothecoides and Scenedesmus obliquus and the cyanobacterium Arthrospira maxima. Due to the complexity of the effluents, it was necessary a dilution of media: 5% for OMW and 50% for OW-OMW. The best removals of chemical oxygen demand (COD) removal were 61.6% for OMW and 67.9% for OW-OMW in cultures of C. protothecoides and C. vulgaris, respectively. Significant removals of phosphorus (P-PO₄) were only verified with Arthrospira (67.0% for OMW and 36.0% for washing wastewater). Regarding nitrates (N-NO₃), very satisfactory removal rates were obtained with all microalgae (around 80 %). Due to difficult biological degradation, the removal of polyphenols did not exceed 40 % for both OMWs. Although microalgae can grow in these OMWs and show potential for its bioremediation, further studies will not be feasible if this effluent is not subjected to a primary treatment, since its toxicity causes cellular death after 4 days.

Acknowledgements

Authors acknowledge the OBTain project (NORTE-01-0145-FEDER-000084), co-financed by the European Regional Development Fund (ERDF) through NORTE 2020, and FCT for the financial support to CQVR (UIDB/00616/2020).

References

1. Amor, C., Marchão, L., Lucas, M. S. & Peres, J. A. Application of Advanced Oxidation Processes for the Treatment of Recalcitrant Agro-Industrial Wastewater: A Review. Water 11, 205 (2019).
2. Marchão, L. et al. Microalgae and immobilized TiO2/UV-A LEDs as a sustainable alternative for winery wastewater treatment. Water Res. 203, 117464 (2021).