Friction stir spot welding (FSSW) was developed by Mazda Motors and Kawasaki Heavy Industries to join similar and dissimilar materials in a solid state as an economic and environmentally friendly alternative to resistance spot welding. The FSSW technique, however, includes some structural defects imbedded within the weld joint, such as keyhole formation, hook crack, and bond line oxidation, challenging the joint strength. The unique properties of nanomaterials in the reinforcement of metal matrices motivated researchers to enhance the FSSW joints' strength. At different ratios of nano-reinforcement, nanoparticles may agglomerate due to inefficient stirring during welding, forming stress concentration sites and brittle phases affecting tensile and fatigue strength under static and cyclic loading conditions, respectively.

This work investigated how the welding tool pin affects stirring efficiency by controlling the distribution of a nano-reinforcing material within the joint stir zone (SZ) and thus the tensile and fatigue strength of the joints. Sheets of AA6061-T6 of 1.8 mm thickness were used as a base material. In addition, graphene nanoplatelets (GNPs) with a lateral size of 1–10 µm and a thickness of 3–9 nm were used as nano-reinforcements. OM and SEM micrographs of as-welded specimens visualized the GNPs' incorporation into the SZs of the FSSW joints. Moreover, lower formations of scattered GNPs were achieved by the threaded pin tool compared to continuous agglomerations observed when the cylindrical pin tool was used.

Tensile and fatigue test results revealed significant improvements of 30% and 18%−38%, respectively, exhibited by the threaded pin compared to the cylindrical one.

The decomposition of ilmenite slag by ammonium hydroxide was investigated as a potential enhancement to produce high-purity titanium oxide. It was discovered that the powdered ilmenite slag broke down when it was digested in 4 M NH₄OH at 150 °C. Ammonium titanate ((NH₄)₂TiO₃) was created, and this material was easily hydrolysed in hot water to produce high-purity rutile (TiO₂). It can be determined that it is in the rutile phase using X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM-EDX), and other analytical techniques. A flow sheet was created and tested in accordance with the experimental findings. Present study we focus on conversion of waste ilmenite mud produced from VV titanium pigment private limited Thuthukudi. Ilmenite mud generated during production of TiO₂ through H₂SO₄ digestion. Unreacted mass of ilmenite basically comprised of acidic resistive TiO₂ phases such as rutile and anatase. Other mineral mainly silicates (SiO₂, ZrSiO₄). This study is primarily focused on the reutilization of secondary waste generated from TiO₂ production, aiming to recover valuable elements while ensuring the production of zero secondary waste. Ammonia based technology gaining importance due to the factor like high selectivity of transitional ammonia complex, recyclability of ammonia and low temperature reactions. In this we aimed to lower the production cost of TiO₂ from secondary resources

Ilmenite and rutile are major contributors to titanium dioxide production, depleting high-grade deposits in Ti production industries. In the present study, we investigate the possibility of using unreacted ilmenite mud as feed material for production of commercial TiO₂ production routes. Indian ilmenite mud generally consists of a rutile phase and is generally resistant to leaching. The alkali-assisted leaching process converts unreacted TiO₂ phases into easily leachable NaTiO₃ phases. Removal of silica, vanadium, and aluminium is performed as ternndite, vanadite, and aluminite, which yield valuable components for the whole process. In general, a huge quantity of ilmenite mud is dumped or stored near production sites, posing athreat to the environment. Ilmenite mud comprises valuable elements such as titanium, zirconium, and rare earth elements. Recovery and seperation are difficult due to their non-reactive nature with acids. In the present study, we designed an alkali-based process for the conversion of non-leachable TiO2 phases into easily leachable phases such as sodium titanate and sodium silicates. Byproducts such as silica and vanadium were important components for balancing the cost of production. The process involved the optimised removal of free acid through saline treatment, followed by roasting with sodium hydroxide to convert into easily leachable phases such as sodium silicates. The results of XRD, XRF, and SEM characterisation indicate that water-leached products contain 85% TiO2 content with low silica levels that can be used as the feed material in existing sulphate industries.

Origanum dictamnus L. is a medicinal plant known for its rich content in bioactive compounds. The plant cell wall consists of structural polysaccharides such as cellulose, hemicellulose, pectin, along with lignin, proteins and bioactive compounds. These compounds are trapped within the plant cell wall or free in the cytocol of the plant cell. Enzyme-assisted extraction (EAE) is a green technology that relies on the enzymes ability to selectively degrade the plant cell wall, thereby facilitating the release of the bioactive compounds. In the present study, EAE of bioactive compounds from the leaves of Origanum dictamnus L. was applied using the commercial enzyme preparation Pectinex^® Ultra Color (Novozymes). A Taguchi experimental design was employed to determine the optimal EAE conditions. The variables were enzyme loading (50, 100, and 200 U/mg), solid-to-liquid ratio (1, 4, and 7% w/v), and extraction time (1, 3, and 6 h). The responses were total phenolic content (TPC) and total flavonoid content (TFC). TPC was determined using the Folin–Ciocalteu method and TFC with the aluminum chloride method. Kinetic modelling of the extraction process for the optimum extract was carried out using first-order, second-order, Peleg’s, and power law models. EAE achieved the highest TPC yield 153.4 ± 3.4 mg GAE/g DW and TFC yield 81.3 ± 3.7 mg CAE/g DW at 1% w/v, 200 U/mg, and 1h, outperforming the conventional ethanol–water extraction up to 20%. These findings highlight EAE as an efficient technique with strong potential for scale-up and integration into industrial processes for the production of natural bioactive-rich extracts from Origanum Dictamnus L.

The use of photovoltaic energy is critical for supporting the transition to sustainable energy systems and for reducing dependence on fossil fuels. This study provides an analysis and forecast of the monthly electricity production of four 30kW photovoltaic (PV) power plants located in the Southwestern region of Bulgaria. We used five years of data to consider seasonal variations in solar energy production typical of temperate climates, as well as peak summer production and significant declines in winter.

The prediction was carried out using ARIMA algorithms, which are based on time series models. Analysis of the residuals involves applying different statistical approaches such as autocorrelation (ACF) and partial autocorrelation (PACF) for the determination of a suitable model. The reliability of the models was confirmed by calculating confidence intervals and by applying standard precision metrics, which provides a basis for reliable forecasting of future electricity production.

The study demonstrates that ARIMA models can successfully capture seasonal dynamics and long-term trends in photovoltaic production. Building forecasting models provides valuable information for decision-makers, helping them manage capacity, optimize costs, and plan strategically. According to the results, this approach is capable of improving the efficiency and sustainability of small-scale solar installations for business and personal use.

This study presents a comprehensive numerical investigation into the stiffness behavior of a robotic gripper subjected to geometric scaling in three principal dimensions—length, width, and height—using the Finite Element Method (FEM) within ANSYS Workbench 2025 R1. Robotic grippers play a vital role in industrial automation and precision manipulation tasks, where structural stiffness is a critical parameter influencing performance, load capacity, and accuracy. The objective of this research is to explore how changes in geometric dimensions affect the overall stiffness of the gripper, thereby guiding more informed design decisions.

A three-dimensional baseline model of a parallel-jaw robotic gripper was developed and systematically scaled along the three primary axes to evaluate the independent and combined effects of dimensional variation. Numerical simulations were conducted under realistic boundary conditions and loading scenarios to simulate operational use cases. The analysis focused on capturing the trends in stiffness response as a function of scaling, while considering structural integrity and mechanical efficiency.

The study offers valuable insights into how scaling strategies can influence mechanical behavior, providing a foundation for optimizing gripper geometry in future designs. Ongoing and future work aims to extend the methodology to dynamic loading conditions and explore material alternatives for enhanced performance and reduced weight.

Marinesco–Sjögren Syndrome (MSS) is a rare autosomal recessive disorder characterized by cerebellar ataxia, cataracts, myopathy, and intellectual disability. While MSS patients show no macroscopic brain alterations, the neurobiological mechanisms underlying cognitive impairments remain poorly understood. This study investigated cognitive deficits and underlying mechanisms in the woozy mouse model (Sil1^wz), a preclinical MSS model.

Using Open Field Test (OFT) and Light Dark Box Test (LDBT), we observed genotype- and age-dependent decreases in locomotor activity, confirming cerebellar ataxia and muscular deficits. 16-week-old Sil1^wz mice spent more time in the OFT centre, suggesting cognitive impairments rather than anxiety-like behaviour, supported by LDBT results.

Molecular analysis revealed altered gene expression in the prefrontal cortex and hippocampus across multiple neurotransmitter systems. The cholinergic system showed increased Chrna3 expression in both regions, with age-dependent hippocampal changes in transporters and enzymes. The serotonergic system exhibited time-dependent inverse expression of Htr2a and Tph2 in prefrontal cortex, while the dopaminergic system showed increased Ddc and Drd1 expression in hippocampus. Glutamatergic and GABAergic systems displayed significant alterations, particularly increased Grin2b expression.

Correlation analysis revealed complex relationships between gene expression and behaviour, with opposite trends between brain regions. Prefrontal cortex cholinergic and GABAergic systems correlated positively with locomotor parameters but negatively with immobility, while hippocampal patterns were inverse.

This study provides the first comprehensive characterization of cognitive deficits in the MSS mouse model, highlighting disrupted excitatory–inhibitory neurotransmission balance in key brain regions. These findings offer insights into pathophysiological mechanisms and potential therapeutic targets for MSS cognitive impairments.

Mediterranean cyclones play a vital role in shaping rainfall patterns and intensities across North African countries, especially along their Mediterranean coastlines. These cyclonic systems are most active during the winter months and serve as a primary driver of seasonal precipitation in otherwise arid and semi-arid regions. While such rainfall can alleviate drought conditions and replenish essential water resources, it also poses significant risks. When these systems intensify, they can lead to hazardous weather events, including flash floods, infrastructure damage, and agricultural disruption. A recent example is Mediterranean Cyclone Daniel, which struck Libya in September 2023, bringing unprecedented rainfall that resulted in catastrophic flooding, widespread destruction, and tragic loss of life. In this study, we analyze several Mediterranean cyclones that have caused extreme precipitation events over North Africa to better understand their characteristics and associated impacts. Specifically, we examine the structural evolution of these cyclones, detecting and tracking cyclonic systems, including their spatial extent, lifespan, and the synoptic-scale pressure systems that accompany them. To achieve this, we utilize ERA5 reanalysis data. This integrated approach allows us to identify key meteorological patterns and mechanisms that contribute to extreme rainfall events in the region, providing insights essential for improving future forecasting and climate resilience strategies.

Injection molding is a key manufacturing process widely used in industries such as automotive, medical devices, consumer goods, and electronics due to its ability to produce complex, high-precision plastic parts at large volumes and low cost. This study aims to investigate the influence of model dimensions on thermal behavior in injection-molded parts, using a geometric scaling approach. The objective is to understand how changes in part size affect the transient temperature of the injected part during the cooling phase.

Numerical simulations were performed in 2D using the finite element method (FEM) software ANSYS Workbench 2025 R1. Several models with different geometric scales were analyzed under equivalent boundary and initial conditions. The results show that larger models tend to retain heat for longer periods, while smaller models cool more rapidly, leading to differences in temperature gradients and potential internal stresses. Well-defined linear relationships could be established between the model dimensions and the temperature evolution in the part, indicating a predictable and scalable thermal response.

These findings suggest that scaling laws can be effectively used to estimate thermal performance in molds of varying sizes without the need for exhaustive simulation. Future work will focus on developing analytical models for the prediction of the influence of the dimensions of the model on the temperature of the injected part.

Accurate prediction of heavy rainfall events poses a significant challenge in numerical weather prediction (NWP) due to the complex processes in the atmosphere, its dynamic behavior, the parameterization of microphysical processes, and model uncertainties. In recent decades, Egypt has experienced extreme precipitation events, particularly along its northern coast, some of which exceeded expected intensity levels. This study aims to enhance the prediction of extreme rainfall events over Egypt using the ICTP regional climate model version 5 (RegCM5). We tested various convective schemes within the following models: Emanuel (1991), Grell (1993) with the Fritsch–Chappell (1980) cumulus closure scheme, Kain–Fritsch (1990), and Tiedtke (1996), to identify the optimal schemes that capture the different rainfall cases. For our experiments, we utilized the ERA5 and FNL reanalysis datasets as the initial and boundary conditions (ICBC) for RegCM5. All simulations were conducted with a spatial resolution of 5 km. To verify our results, we compared the simulated rainfall with the measurements from four ground stations in Alexandria (El-Nouzha, Abu-Qir, Borg El-Arab, and Ras El-Tin), in addition to the precipitation data from the ERA5 reanalysis, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), GPM, and the PERSIANN-Cloud Classification System (PERSIANN-CCS). Based on comparisons between the simulated precipitation from RegCM5 and data from the four rain gauges, the Kain–Fritsch scheme demonstrated both high performance and low mean bias error (MBE), followed closely by the Grell scheme.