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Abstract Medical and surgical treatments for cystic echinococcosis (CE) are challenged by various complications. This study evaluates in vitro protoscolicidal activity of piperine-loaded mesoporous silica nanoparticles (PIP-MSNs) against protoscoleces of Echinococcus granulosus. MSNs were prepared by adding tetraethyl orthosilicate to cetyltrimethylammonium bromide and NaOH, and then loaded with PIP. The mean particle size and hydrodynamic diameter of MSNs were determined at 68 ± 4.5 and 101.4 ± 50.4 nm using transmission electron microscopy and dynamic light scattering, respectively. X-ray diffraction, Fourier-transform infrared analysis, and UV-spectrophotometry confirmed drug loading. Drug loading efficiency and drug loading capacity were calculated at 60% and 18%, respectively. The drug release profile confirmed a 75% PIP release plateau after about 24 h. The cytotoxicity assay showed cell viability > 90% in all concentrations used (≤ 512 µg/mL). E. granulosus protoscoleces were exposed to PIP-MSNs and their viability was assessed using the eosin exclusion test. In a dose-dependent manner (p

High quality cassava flour from low postharvest physiologically deteriorated cassava variety (IITA-TMS-IBA011368) was blended with flour from wheat and Bambara groundnut and analyzed for physicochemical and functional properties to determine their suitability for use in baking industry. DOptima design was used for combination of the flours. Data obtained were subjected to analysis of variance using SPSS 25.0 and significant means were separated using Duncan Multiple Range Tests. Results indicated the flour blends were not significantly (p≥0.05) different in flour lightness (L*), greenness (a*), yellowness (b*) but were significantly different in bulk density, with ranges: 33.32 to 49.90, -1.20 to -3.01, 5.81 to 7.79 and 0.70 to 1.00 g/ml, respectively. The physicochemical properties measured were significantly (p≤0.05) different. The swelling index, water absorption capacity, solubility index, least gelation capacity and oil absorption capacity of the flour blends ranged from 0.71 to 0.91%, 0.19 to 1.24%, 4.23 to 21.67%, 0.17 to 0.30% and 0.92 to 2.27%, respectively. The pasting properties of the flour blends varied significantly (p

ABSTRACT Introduction Long COVID‐19 illness is a severely disabling disease with shortness of breath, weakness and fatigue as leading symptoms, resulting in poor quality of life and substantial delay in return to work. No specific respiratory therapy has been validated for patients with long COVID. The intermittent hypoxia–hyperoxia training (IHHT) is a respiratory therapeutic modality to improve exercise performance via controlled respiratory conditioning. The purpose of the present study is to investigate the therapeutic effect of IHHT on functional and symptomatic recovery of patients with long COVID syndrome. Methods A prospective, controlled, open‐treatment interventional study was conducted in patients with long COVID who were admitted to an inpatient rehabilitation programme. Patients were assigned nonrandomized to receive IHHT in addition to the standardized rehabilitation programme (IHHT group) or standard rehabilitation alone (control group). The IHHT group received supervised sessions of intermittent hypoxic (10–12% O2) and hyperoxic (30–35% O2) breathing three times per week throughout the rehabilitation period. Primary endpoint was improved walking distance in a 6‐min walk test (6MWT) between study groups. Secondary endpoints were change in stair climbing power, dyspnoea (Borg dyspnoea Scale), fatigue assessment scale (FAS) and change in health‐related quality of life (HRQoL) assessed by patient global assessment (PGA), EQ‐5D analogue scale and the MEDIAN Corona Recovery Score (MCRS). Further assessments included maximum handgrip strength, nine hole peg test, timed up‐and‐go, respiratory function and functional ambulation category (FAC), serum analyses and safety of the intervention. Results A total of 145 patients were included in the study (74% female, mean age 53 ± 12 years) and assigned to IHHT (n = 70) or standard care (n = 75). The 6MWT distance improved 2.8‐fold in the IHHT group compared to the control group (91.7 ± 50.1 m vs. 32.6 ± 54.2 m, ANCOVA p

BACKGROUND: Among patients who express interest in bariatric surgery, dropout rates from bariatric surgery programs are reported as high as 60%. There is a lack of understanding how we can better support patients to obtain treatment of this serious chronic disease. METHODS: Semi-structured interviews with individuals who dropped out of bariatric surgery programs from three clinical sites were conducted. Transcripts were iteratively analyzed to understand patterns clustering around codes. We mapped these codes to domains of the Theoretical Domains Framework (TDF) which will serve as the basis of future theory-based interventions. RESULTS: Twenty patients who self-identified as 60% female and 85% as non-Hispanic White were included. The results clustered around codes of perceptions of bariatric surgery, reasons for not undergoing surgery, and factors for re-considering surgery. Major drivers of attrition were burden of pre-operative workup requirements, stigma against bariatric surgery, fear of surgery, and anticipated regret. The number and time for requirements led patients to lose their initial optimism about improving health. Perceptions regarding being seen as weak for choosing bariatric surgery, fear of surgery itself, and possible regret over surgery grew as time passed. These drivers mapped to four TDF domains: environmental context and resources, social role and identity, emotion, and beliefs about consequences, respectively. CONCLUSIONS: This study uses the TDF to identify areas of greatest concern for patients to be used for intervention design. This is the first step in understanding how we best support patients who express interest in bariatric surgery achieve their goals and live healthier lives.

Owing to its favorable geographical and climatic conditions, Tunisia offers a great deal of solar potential, making solar energy projects highly feasible. However, to maximize efficiency and return on investment, it is critical to identify the most promising locations to invest in costly solar power plants. Determining the best-suited location for implementing such projects involves various conflicting parameters, including technical, economic, and environmental dimensions. To address the complex nature of the challenges associated with site selection, this study applies an integrated GIS-based MCDM approach to conduct a land suitability analysis to determine the most feasible locations for installing large-scale solar PV power plants in Tunisia. With the aim of achieving maximum power while minimizing costs, the model considers a number of factors, including solar radiation, slope, average cloudy days, land use, soil texture, temperature, orientation, proximity to grid networks, roads, and residential areas, as well as water resources. To this end, we apply the fuzzy analytical hierarchy process (FAHP) to assign weights to the defined criteria and evaluate the potential sites. The findings revealed that 17.6% of the study area is suitable for developing solar PV systems on a large scale. According to the spatial analysis, the most suitable sites covered an area of 5251 km2 (3.31% of the available area) and were densely scattered across the central and southwestern, southeastern, and eastern coastal regions of the country. Furthermore, the technical potential of these sites was estimated at nearly 1060 TWh/yr. An examination of the locations of operational solar farms and planned projects revealed that they were in line with the most suitable sites, which indicated the consistency of the proposed model.

Makalah ini membincangkan dialek Melayu yang dituturkan di Baling, Kedah, Malaysia. Dialek di kawasan ini dipersepsikan oleh penutur lokal sebagai unik dan berbeza berbanding dengan dialek Melayu Kedah yang piawai. Oleh sebab wujud persepsi yang sedemikian, beberapa siri kunjungan lapangan dilakukan ke kawasan Baling bagi mengumpulkan data dialek. Tujuannya adalah untuk menjawab dua pertanyaan asas dalam dialektologi diakronik, iaitu: (i) apakah ciri fonologi dialek di Baling yang menjadikannya berbeza daripada dialek Melayu Kedah yang piawai dan dialek Melayu lain?, dan (ii) apakah hubungan dialek Melayu di Baling dengan dialek yang dibandingkan itu? Bagi menjawab pertanyaan ini, pengkaji menggunakan kaedah temu bual dengan memanfaatkan sebanyak 643 item leksikal dalam daftar kata, termasuklah rakaman perbualan. Data yang telah dipungut itu kemudiannya dianalisis menggunakan kerangka dialektologi diakronik yang memfokuskan pemerian inovasi bersama dan pengekalan, serta perbandingan antara dialek. Pemerian inovasi bersama dan pengekalan dalam aspek fonologi adalah bagi tujuan pengelompokan. Beberapa inovasi dan pengekalan fonologi membuktikan dua perkara. Pertama, dialek Melayu di Baling terpisah daripada cabang dialek Melayu Semenanjung Utara. Dengan erti kata lain, dialek tersebut bukan subdialek bagi dialek Kedah piawai. Kedua, oleh sebab dialek Melayu di Baling terpisah daripada induknya yang sering diuar-uarkan selama ini dan berkongsi persamaan dengan dialek Melayu lain yang terdapat di sekitarnya, dialek tersebutmembentuk cabangnya yang tersendiri. Cabang yang dimaksudkan ini dikenali sebagai cabang dialek Melayu Hulu Semenanjung Utara. Cabang ini terdiri daripada dialek yang dituturkan oleh masyarakat yang tinggal di kawasan Hulu Kedah yang merangkumi Baling, Sik, Kuala Nerang, Padang Terap dan Hulu Perak Utara. Selain linguistik, hubungan akrab dialek dalam cabang ini juga turut diperkukuh dengan bukti sejarah dan sosiologi. Implikasi penemuan makalah ini yang terpenting ialah kewujudan cabang baharu dalam dialek Melayu Semenanjung Utara yang perlu diangkat dalam pengelompokan dialek Melayu Semenanjung secara umum.

Global education has been transformed by the accelerated development of digital technologies, which has introduced eLearning as a new dimension of teaching and learning. This study examines students' perceptions of eLearning adoption in Nigeria, concentrating on the Open and Distance Learning Centre at Ladoke Akintola University of Technology in Ogbomoso (LODLC). Data from a survey of three hundred and seventy-nine (379) undergraduate students at LODLC about their attitude and perception to the use of e-learning in teaching, learning and research was analysed. Students (n = 286) filled in a web-based closed-question questionnaire through the use of google form. Result showed that students have positive attitude towards eLearning, access to personal computer, smart phones and internet with reliable internet connectivity. Also, students prefer the use of eLearning platform that combines both synchronous and asynchronous learning, where learning materials can be accessed at their own pace. However, technological infrastructure issues, limited internet connectivity, and the need for enhanced digital literacy were identified as potential barriers to the widespread adoption of eLearning in the Nigerian context.The study concluded that eLearning provides an effective way to acquire knowledge and skills, more flexibility and convenience than traditional classroom-based learning as well as provides greater access to educational resources and opportunities. The findings contribute to the ongoing discussion on leveraging digital platforms to improve educational outcomes and provide policymakers and educational institutions with recommendations for addressing the identified challenges and ensuring a more inclusive and effective eLearning experience for students in Nigeria.

The unpredictable nature of renewable energy sources, such as solar and wind, raises concerns about consistent power supply based on a single type. Green hydrogen presents a promising solution to mitigate the environmental effects of fossil fuels and control the unpredictability of renewable energy sources. Yet, developing green hydrogen systems mandates immense financial commitments, vitally stressing the importance of determining the most appropriate locations before constructing them. As such, the primary purpose of this paper was to propose an initial spatial analysis and prioritize the most suitable locations for installing solar-based green hydrogen systems in Tunisia. For this reason, an exhaustive literature survey was conducted to develop a GIS-based MCDM approach, taking into account technical, topography, environmental, and accessibility criteria. Then, EDAS, ARAS, MOORA, and COPRAS techniques were used to rank the most promising sites resulting from the previous stage. The study revealed that sites of high suitability, primarily in the southeastern and southwestern parts of Tunisia, spanned a total area of 1591 km 2 (0.78%). Among such regions, Sfax, Monastir, and Sousse appeared to hold the top ranking as the most suitable locations for solar-based hydrogen. The “most suitable” sites had a hydrogen potential capacity of 9910 Mt per year, with Sfax, Monastir, and Sousse accounting for 14.5% of that capacity. By tapping into this immense potential and embracing the green hydrogen industry, Tunisia has the opportunity to lead in renewable energy adoption. This move could help ensure energy security, reduce dependence on imports, stimulate economic growth, and steer the nation toward a more sustainable and prosperous future.

In the present investigation, the primary objective is to assess the synergistic impact of a novel hybrid nanofluid composed of nano-enhanced phase change material, multi-walled carbon nanotubes, and water. The governing equations are transformed into dimensionless forms for a more generalized analysis. To solve the problem, the Galerkin finite element method is employed, offering a robust numerical approach. A dataset of 1000 records was created by numerically solving the model for various combinations of control parameters. Using the dataset, a neural network was trained to learn the relationship between control parameters and heat transfer rate. The evaluation outcomes are comprehensively illustrated through key parameters, including local and average Nusselt numbers, total entropy generation, contours, and streamlines in the range of 103

Climate change is a significant global environmental issue that has attracted extensive research and debate. Consequently, the concept of sustainability in the context of aluminium encompasses multiple dimensions. Within the discussions surrounding waste and resource management, there is ongoing deliberation about the actual environmental advantages offered by material recycling. The high demand for raw materials has prompted researchers to explore sustainable recycling processes, with a particular focus on processing technologies. A comprehensive examination of these diverse methods is crucial for understanding three main techniques for aluminium forming: conventional recycling (CR), semi-direct recycling (SDR), and direct recycling (DR). Through a systematic review, it becomes evident that SDR and DR, which incorporate a forming process, present substantial environmental, energy, and cost benefits while promoting sustainability awareness in the manufacturing sectors. This extensive review also encompasses nine life cycle analysis (LCA) studies, comprising sixteen scenarios that have employed various methodologies to assess the environmental impact of managing waste aluminium through different approaches: recycling (melting and solid-state), incineration, or landfill. The outcomes of this comprehensive international review provide a more unbiased evaluation of the environmental effects associated with various wastes management systems. The majority of the studies indicate that recycling offers superior environmental solutions and fewer environmental effects compared to alternative waste green forming management options.

In the rapidly evolving realm of remote sensing technology, the classification of Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent limitations in hyperspectral imaging, enhancing the accuracy and efficiency of HSI classification remains a critical and much-debated issue. This review study focuses on a key application area in HSI classification: Land Use/Land Cover (LULC). Our study unfolds in fourfold approaches. First, we present a systematic review of LULC hyperspectral image classification, delving into its background and key challenges. Second, we compile and analyze a number of datasets specific to LULC hyperspectral classification, offering a valuable resource. Third, we explore traditional machine learning models and cutting-edge methods in this field, with a particular focus on deep learning, and spectral decomposition techniques. Finally, we comprehensively analyze future developmental trajectories in HSI classification, pinpointing potential research challenges. This review aspires to be a cornerstone resource, enlightening researchers about the current landscape and future prospects of hyperspectral image classification.

Right ventricular failure is a mechanism of hemodynamic collapse in acute aortic dissection. Mostly RV failure happens as a result of coronary malperfusion secondary to compression of right coronary artery ostium by the false lumen of type A aortic dissection or the dissection flap involving this coronary artery. Another mechanism is compression of pulmonary artery and an acute rise of pulmonary pressure below the level of obstruction, which is rarely reported. Herein, we presented an 82-year-old man who was admitted with type A aortic dissection in whom echocardiographic examination revealed right pulmonary artery compression resulting in acute right ventricular failure.

Abstract Developing wind and solar photovoltaics on a large scale requires substantial financial investments, making it crucial to identify the most suitable locations beforehand. To address this issue, a spatial analysis is carried out to determine the most potential sites for hosting large‐scale solar photovoltaic and wind systems in the region of Kasserine, central‐western Tunisia. To this end, an integrated model based on Step‐wise Assessment Ratio Analysis (SWARA), Decision‐Making Trial and Evaluation Laboratory (DEMATEL), and Geographic Information System is proposed. An extensive literature survey is conducted to establish suitability criteria and constraints. The SWARA‐DEMATEL is used to assign weights and capture the interdependencies among the considered criteria, while the raster calculator tool from the ArcGis 10.8 software is utilized to extract the final suitability maps. The obtained results indicate that the region of Kasserine exhibits great solar and wind potential, with areas of 635 and 467 km2 extremely fit for installing solar and wind systems, respectively. Furthermore, 349 km2 are identified as potential locations for hosting solar‐wind hybrid systems. Considering these outcomes, policymakers can take the initiative to accelerate the deployment of these facilities, which would assist the country in achieving its plans by 2030.

We use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The sensors are positioned at the vertices of a symmetrical and evenly spaced 3 × 3 grid. The main electronic card orchestrates their measurement by supplying the required driving current and amplifying and sampling their output in a synchronized manner. A two-dimensional interpolation of the data collected from the nine sensors fails to yield a satisfactory mapping. To address this, we employed the Levenberg–Marquardt Algorithm (LMA) as a deterministic optimization method to estimate the magnetic source’s position and parameters, as well as machine earning (ML) algorithms, which consist of a Fully Connected Neural Network (FCNN). While LMA provided reasonable results, its reliance on a sparse sensor network and initial guesses for variables limited its accuracy. We show that the mapping is significantly improved if the data are processed with an FCNN that undergoes training and testing. Using simulations, we demonstrate that achieving similar improvement without ML would require increasing the number of sensors to more than 50.

As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency data transmission over long distances without the need for a physical infrastructure. However, the deployment of FSO systems faces significant challenges, such as atmospheric turbulence, weather-induced signal degradation, and alignment issues, all of which can impair performance. This paper offers a comprehensive survey of the enabling technologies, challenges, trends, and future prospects for FSO communication in next-generation networks, while also providing insights into the current mitigation strategies. The survey explores the critical enabling technologies such as adaptive optics, modulation schemes, and error correction codes that are revolutionizing FSO communication and addressing the unique challenges of FSO links. Also, the integration of FSO with radio frequency, millimeter-wave, and Terahertz technologies is explored, emphasizing hybrid solutions that enhance reliability and coverage. Additionally, the paper highlights emerging trends, such as the integration of FSO with artificial intelligence-driven optimization techniques and the growing role of machine learning in enhancing FSO system performance for dynamic environments. By analyzing the current trends and identifying key challenges, this paper emphasizes the prospects of FSO communication in the evolving landscape of 5G and future networks. In this regard, it assesses the potential of FSO to meet the demands for high-speed, low-latency communication and offers insights into its scalability, reliability, and deployment strategies for 5G and beyond. The paper concludes by identifying the open challenges and future research directions critical to realizing the full potential of FSO in next-generation communication systems.

Whether from the pound sterling to the naira or from lower to higher denominations or even from one colour to another, Nigerian currency has gone through changes at different times in its history. Obviously, a product of the prevailing political situations and socio-economic realities, its social implications on Nigerians are tremendous. This paper examines the history and the politics of currency changes in Nigeria up to 2008. The paper explores different policies and programs formulated at different times to stabilize the naira, and accounts for how the prevailing national and global economic conditions strengthened or weakened the currency. The paper argues that beyond serving as a means of exchange, the naira symbolizes the Nigerian national identity, pride and unity, as well marks the state sovereignty.

The increasing demand for sustainable manufacturing has revived the interest in solid-state recycling (SSR) as a promising alternative method for aluminum waste. In this context, chips generated during machining processes constitute a substantial portion of aluminum waste, offering significant potential for recycling and mitigating waste. However, the machining chip morphology significantly impacts the properties of chip-based recycled parts. This review paper examines the current state-of-the-art solid-state recycling methods, focusing on hot forging, extrusion, equal channel angular pressing, friction stir extrusion and field-assisted sintering. It investigates the impact of aluminum chip morphology on the properties of the directly recycled material, emphasizing the chip machining consequence on the final quality of the product. Several studies reported that the strain and operating temperature are the most influential factors in SSR processes, followed by chip size with an average length of less than 4 mm. Yet, the heating time up to 3 h also had a major impact on chip weld strength. The findings highlighted the significance of aluminum chip morphology in improving the quality of recycled material. The properties of direct recycled samples primarily depend on chip weld strength and microstructure. Overall, this study presented a comprehensive overview of the current state of solid-state recycling and emphasized the significance of chip morphology in advancing the recycling process. Consequently, it equips researchers with a valuable resource for developing effective strategies for sustainable recycling of aluminum chips with high quality.

Background: Toxocariasis is a zoonotic disease caused by the larvae of Toxocara canis or Toxocara cati. Patients with schizophrenia may be at higher risk of infection, possibly due to their cognitive and personal self-care impairments. We aimed to assess the association between Toxocara spp. exposure and schizophrenia. Methods: This case-control study was conducted on 109 patients with schizophrenia admitted to Ibn Sina Hospital, Shiraz, Iran, and 104 age- and gender-matched healthy controls from May to September 2021. A questionnaire was obtained and serum samples were tested for IgG antibodies to Toxocara excretory/secretory (TES) antigens using an enzyme-linked immunosorbent assay (ELISA). Results: Anti-Toxocara IgG was detected in 12 schizophrenic patients and 10 control subjects, giving respective seroprevalences of 11.0% (95% confidence interval [95% CI]=5.8–18.4%) and 9.6% (95% CI=4.7–17.0%). Univariate logistic analyses estimated an odds ratio (OR) of 1.16 (95% CI=0.44–3.16); however, it was not statistically significant (P=0.915). Individuals with a history of eating unwashed vegetables or fruits (23.1%, 95% CI=9.0–43.6, crude odds ratio [COR]=3.21, 95% CI=1.13–9.13) and rural residency (19.5%, 95% CI=8.8–34.9, COR=2.74, 95% CI=1.06–7.05) had significantly higher rates of seropositivity using the univariate logistic analyses. After multivariate logistic analyses, the differences were not statistically significant. Conclusion: The toxocariasis seroprevalence among schizophrenic and healthy participants was not significantly different (11% vs. 9.6%). Since the disease severity, onset, and cognitive sequelae are not the same among schizophrenic patients, clinically matched studies with larger samples are required to address the current inconsistency between the studies.

The finite element (FE) is one of the techniques implemented to predict residual stresses. The selection of the proper FE technique is essential to achieve accurate results. This study investigated the prediction of residual stress distribution in pressed-braked thin-walled cold-formed steel sections using the 3D-Shell finite element (FE) technique. Cold-forming processes produce residual stresses and affect the structural capacity of thin-walled cold-formed steel sections. Previous studies have used and recommended the 3D-Solid FE technique to predict residual stresses; however, limited studies have applied this 3D-Shell FE technique. Therefore, the results showed this study had compared the longitudinal/transverse residual stresses and the neutral axis shift obtained by the 3D-Shell FE technique with the results from the literature using the 3D-Solid FE technique. It is also revealed that there are limitations in the 3D-Shell FE technique for predicting residual stresses.

The optimal conditions of applied factors to reuse Aluminium AA6061 scraps are (450, 500, and 550) ⁰C preheating temperature, (1–15) % Boron Carbide (B4C), and Zirconium (ZrO2) hybrid reinforced particles at 120 min forging time via Hot Forging (HF) process. The response surface methodology (RSM) and machine learning (ML) were established for the optimisations and comparisons towards materials strength structure. The Ultimate Tensile Strength (UTS) strength and Microhardness (MH) were significantly increased by increasing the processed temperature and reinforced particles because of the material dispersion strengthening. The high melting point of particles caused impedance movements of aluminium ceramics dislocations which need higher plastic deformation force and hence increased the material's mechanical and physical properties. But, beyond Al/10 % B4C + 10 % ZrO2 the strength and hardness were decreased due to more particle agglomeration distribution. The optimisation tools of both RSM and ML show high agreement between the reported results of applied parameters towards the materials' strength characterisation. The microstructure analysis of Field Emission Scanning Electron Microscopy (FE-SEM) and Atomic Force Microscope (AFM) provides insights mapping behavioural characterisation supports related to strength and hardness properties. The distribution of different volumes of ceramic particle proportion was highlighted. The environmental impacts were also analysed by employing a life cycle assessment (LCA) to identify energy savings because of its fewer processing steps and produce excellent hybrid materials properties.

The availability of publicly accessible datasets with identifiable facial images is essential for various research and development purposes. However, this wide access also underscores an increasing need for robust privacy protection. Regulations like the General Data Protection Regulation (GDPR) impose strict requirements for safeguarding personal data, yet effectively anonymizing facial images while maintaining their research utility remains a significant challenge. Most current methods use either latent space or feature space for face anonymization. Each has its strengths, but relying on just one can result in incomplete anonymization or less realistic images. This paper introduces a new two-stage anonymization method that combines Deep Face Latent space Anonymization (FLA) with Face Feature space Anonymization (FFA) guided by semantic masks. In the first stage, FLA hides identity by modifying the latent space of facial images. In the second stage, FFA uses semantic masks to preserve important facial features like expressions and head poses while still hiding identity. Extensive experimentation on the CelebA-HQ and LFW datasets demonstrates that our approach achieves strong identity obfuscation while preserving facial attributes, as evidenced by quantitative metrics. Our pipeline generates high-quality facial images that protect identities while preserving non-identifying features of the original images, ensuring the utility of the anonymized images.

The influences of rare earth (RE) samarium (Sm) added contents to ZRE1 (Mg-Zn-Zr) magnesium (Mg) cast alloy over strength properties to be investigated. Sm at 0.5, 1.0, 1.5, and 2.0 wt.% were added separately as an alloying element to ZRE1 alloy. Optical Microscope (OM), X-ray powder diffraction (XRD), and scanning electron microscope Energy-dispersive X-ray (SEM/EDS) were used to investigate the microstructure of alloy, while mechanical properties investigated include Ultimate Tensile Strength (UTS)and Micro-Hardness (MH) tests. The result revealed that as the Sm level reached 1.5 wt.%, the grain size decreased by 20.9 %. Additionally, UTS and Yield Strength (YS) showed improvements of 8.5 % and 7.9 %, respectively, with the addition of 1.5 wt.% of Sm. In addition, elongation and hardness have been improved by 32.3 % and 10.9 % respectively at 1.5 wt.% Sm addition. Mg-Zn-Ce-Sm was formed as a new phase upon the addition of Sm and was detected via XRD analysis. The addition of Sm to the ZRE1 alloy had significant effects on the refinement of the material's microstructure, leading to an increase in its mechanical and physical properties. Therefore, the new ZRE1-RE magnesium alloy was developed.