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Abstract Spindle epithelial tumor with thymus-like elements (SETTLE) is a rare malignant neoplasm of the thyroid gland which is believed to arise from intrathyroidal thymic tissue. It predominantly affects young adults and children presenting with a thyroid mass of variable duration and rarely occurs in adults. It has a high overall survival with a tendency for delayed metastasis. SETTLE is a biphasic lobulated tumor composed of spindle shaped cells along with glandular formations seen on histopathological examination. Despite its typical morphology it is commonly misdiagnosed on histopathology due to its rarity and overlapping morphology with other close mimics such as a carcinoma, synovial sarcoma and thymoma. Herein we report such a case occurring in a middle aged female presenting with a neck mass. She had an initial diagnosis of metastatic poorly differentiated squamous cell carcinoma possibly with an orophayngeal primary in view of co expression of CK, p40 and p16 on immunohistochemistry. The patient underwent surgical resection with modified neck dissection. On review at our hospital it was diagnosed as SETTLE and she remains disease free after a follow-up period of 1 year. Diligent histopathological examination espoused with a judicious panel of IHC markers in conjunction with clinicoradiological findings forms the mainstay of diagnosis. Diffuse and strong p16 immunoexpression has not been documented or evaluated in literature so far, and needs to be explored for its diagnostic utility in this rare entity.

For medical fraternity, health system and policymakers to undertake effective countermeasures, a comprehensive assessment of the prevalence of anxiety among older adults is required; hence, we conducted a systematic review and meta-analysis using data pooled from surveys across the country. The search strategy was developed using medical subject headings (MeSH) terms and free-text keywords. We searched PubMed and Scopus for articles to report the prevalence of anxiety among older adults. The appraisal tool for cross-sectional studies was used for quality assessment. Prevalence from different studies was pooled together using the inverse variance heterogeneity method. Sensitivity analyses were performed to assess the impact of included studies’ methodological quality on pooled results and to investigate potential causes of heterogeneity. Twenty-three studies were included in this systematic review. The overall pooled estimate of the prevalence of anxiety in older adults was 18.7% (95% CI: 2.4, 38.8). The studies had a high degree of heterogeneity. Publication bias was assessed using a Doi plot which showed an luis furuya-kanamori (LFK) index of 1.21 indicating minor asymmetry. Like other parts of the world, India has witnessed a rise in the number of elderly due to lower fertility rates, higher life expectancies and a shift in illness patterns from communicable to non-communicable. The number of elderly people suffering from diseases of mental health is expected to rise dramatically. This review consolidates the existing evidence to showcase anxiety as an upcoming public health problem requiring due focus from policymakers and health systems.

Advanced Non-Small Cell Lung Carcinoma (NSCLC) patients with ROS1 gene rearrangement have shown significant therapeutic responses to tyrosine kinase inhibitors approved by the US Food and Drug Administration, with approximately 40 fusion partners documented in the existing literature. Our report highlights a novel fusion partner of ROS1 that has demonstrated a conclusive response to the current standard of treatment.

Background: Cervical cancer is a public health problem, and nursing personnel are crucial for successful implementation of low-cost cervical cancer screening approaches in low-resource settings. The following study assessed and compared the knowledge, attitude, and practices regarding cervical cancer and its screening among female nursing staff at different levels of health care facilities in western Rajasthan, India. Methodology: An anonymous pre-validated, structured questionnaire was used as the study tool among 233 female nursing personnel of primary, secondary, and tertiary care health facilities. Multiple logistic regression was performed to determine the association between level of knowledge with level of health care and other demographic variables. Results: The nursing staff of the tertiary care health facility demonstrated significantly higher knowledge compared to those working at primary and secondary levels [adjusted odds ratio (95% confidence interval) 11.01 (3.80–32.40)]. At tertiary care, the practices of the nursing professionals were not found significantly associated with any socio-demographic variable including age, marital status, or level of health care facility. Conclusion: The overall knowledge of cervical cancer was poor, especially among staff nurses at primary and secondary levels of health care. In order to implement a successful population-based screening program in India, it is important to update the nursing curriculum and start in-service trainings at primary and secondary levels of health care facilities.

The present study attempted for the first time an investigation on the effect of deep cryogenic treatment in liquid nitrogen (LN) on magnesium–cerium oxide (Mg/2wt.%CeO2) composites containing equal amounts of different length scales (micron and nanosize) cerium oxide (CeO2) particles. The disintegrated melt deposition method was used to synthesize Mg-2CeO2 micro- and nanocomposites, followed by hot extrusion as the secondary processing. Further liquid nitrogen treatment was performed at a cryogenic temperature of −196 °C. The combined effects of cryogenic treatment and reinforcement length scale on physical, mechanical, and thermal behaviors were studied. The results indicate that LN-treated micro- and nanocomposite samples exhibit, in common, a reduction in porosity, similar grain size, and a limited effect on the original texture of the matrix. However, microhardness, 0.2% Compressive Yield Strength (CYS), failure strain, and energy absorbed increased for both micro- and nanocomposite samples. Overall, results clearly indicate the capability of deep cryogenic treatment with LN to positively diversify the properties of both micro- and nanocomposite samples.

Introduction: Popularly, teletherapy (telecobalt/LA) equipment is based on a C-arm gantry system. Recently, a fast O-ring gantry system introduced a medical linear accelerator (LA) to smoothen the workflow of treatment of cancer patients because of the increasing trend of the number of cancer cases over the past few years. This study aimed to analyze the commissioning parameters and validation of the O-ring gantry-based LA for improved radiotherapy techniques. Material and Methods: Three-dimensional (3D) radiation field analyzer (RFA) used to commission HalcyonTM LA. It is used for measuring percent depth dose (PDD), profiles, and output factors. Results: TPS data was validated by comparing it with our measured data. Plans per the TG-119 protocol showed good agreement between treatment planning systems (TPS) calculated and measured doses. For patient-specific, QA plans showed good agreement with gamma evaluation criteria. Conclusion: Commissioning and validation of O-ring gantry system HalcyonTM LA was performed successfully.

Hyperbaric Oxygen Therapy (HBOT) utilizes 100% oxygen at high atmospheric pressure for clinical applications. HBOT has proven to be an effective supplementary treatment for a variety of clinical and pathological disorders. HBOT’s therapeutic results are based on the physiological effects of increased tissue oxygenation, or improved oxygen bioavailability. HBOT’s current indications in illnesses like as wound healing, thermal or radiation burns, and tissue necrosis point to its function in facilitating the regeneration process. Various research has revealed that HBOT plays a function in vascularization, angiogenesis, and collagen production augmentation. Individual regeneration capacity is influenced by both environmental and genetic factors. Furthermore, the regenerating ability of different types of tissues varies, and this ability declines with age. HBOT affects physiological processes at the genetic level by altering gene expression, delaying cell senescence, and assisting in telomere length enhancement. The positive results in a variety of indications, ranging from tissue regeneration to better cognitive function, indicate that it has enormous potential in regenerative and anti-aging therapy.

This paper introduces an innovative methodology to disrupt deep-learning (DL) surveillance systems by implementing an adversarial framework strategy, inducing misclassification in live video objects and extending attacks to real-time models. Focusing on the vulnerability of image-categorization models, the study evaluates the effectiveness of face mask surveillance against adversarial threats. A real-time system, employing the ShuffleNet V1 transfer-learning algorithm, was trained on a Kaggle dataset for face mask detection accuracy. Using a white-box Fast Gradient Sign Method (FGSM) attack with epsilon at 0.13, the study successfully generated adversarial frames, deceiving the face mask detection system and prompting unintended video predictions. The findings highlight the risks posed by adversarial attacks on critical video surveillance systems, specifically those designed for face mask detection. The paper emphasizes the need for proactive measures to safeguard these systems before real-world deployment, crucial for ensuring their robustness and reliability in the face of potential adversarial threats.

Contemporary culture presents a substantial obstacle for cyber security experts in the shape of software vulnerabilities, which, if taken advantage of, can jeopardize the Confidentiality, Integrity, and Availability (CIA) of any system. Data-driven and modern threat intelligence tools can enhance cyber security, bolster resilience, and foster innovation across cloud, multi-cloud, and hybrid platforms. As a result, performance evaluation and accuracy verification have become essential for Security Information and Event Management (SIEM) to prevent cyber threats. The SIEM system offers threat intelligence, reporting, and security incident management through the collection and analysis of event logs and other data sources that are specific to events and their context. We propose a hybrid strategy to address threat intelligence, reporting, and security incident management consisting of two layers that utilize a predefined set of characteristics. Here, we use RStudio to assess how well a hybrid intrusion detection system (HIDS) handles the CIC-Bell-DNS-EXF-2021 dataset. Furthermore, we have incorporated our developed model into Multi-Criteria Decision Analysis Methods (MCDM) to enhance the methods’ ability to identify complex DNS exfiltration attacks using machine learning algorithms: RF-AHP (RA), KNN-TOPSIS (KT), GBT-VIKOR (GV), and DT-Entropy-TOPSIS (DET). We consider several factors during the work, including accuracy, absolute error, weighted average recall, weighted average precision, kappa value, logistic loss, and root mean square deviation (RMSD). We use the Machine-Automated Model function to integrate and validate the models. According to the findings, GV has the highest level of accuracy, with a rate of 99.52%, while KT has the lowest level of authenticity, with a rate of 93.65%. Furthermore, these findings illustrate enhanced performance metrics for multiclass classification in comparison to previous approaches.

Abstract Topological slow light exhibits potential to achieve stopped light by virtue of its widely known robust and non-reciprocal behaviours. Conventional approach for achieving topological slow light often involves flat-band engineering without disentangling the underlying physical mechanism. Here, we unveil the presence of counter-propagating waves within valley kink states as the distinctive hallmark of the slow light topological photonic waveguides. These counter-propagating waves, supported by topological vortices along glide-symmetric interface, provide significant flexibility for controlling the slowness of light. We tune the group velocity of light by changing the spatial separation between vortices adjacent to the glide-symmetric interface. We also dynamically control the group delay by introducing a non-Hermitian defect using photoexcitation to adjust the relative strength of the counter-propagating waves. This study introduces active slow light topological photonic device on a silicon chip, opening new horizons for topological photon transport through defects, topological light-matter interactions, nonlinear topological photonics, and topological quantum photonics.

Sentiment analysis has received incremental growth in recent years for emerging applications, including human-robot integration, social platforms monitoring, and decision-support systems. Several neural or transformer model-based solutions have been provided in the field of sentiment analysis that relies on the decision of a single classifier or neural model. These are erroneous to encode contextual information into appropriate dialogues and increase extra computational cost and time. Hence, we proposed a compact and parameter-effective Particle Swarm Optimization-based Ensemble Fusion Voting Model (PSO-EFVM) that exploited the combined properties of four ensemble techniques, namely Adaptive-Boost, Gradient-Boost, Random-Forest, and Extremely-Randomized Tree with Particle Swarm Optimization (PSO)-based hyperparameter selection. The proposed model is investigated on five cross-domain datasets after applying the foremost initialization and feature extraction using Information Gain (IG). It employs adaptive and gradient learning to incorporate the automatic attribute selection with the arbitrary loss function optimization. In short, a generalized two-block composite classifier is designed to perform context compositionality and sentiment classification. A population-based meta-heuristic optimization PSO is applied to each base ensemble learner that calculates weights for the best parameter selection. Comprehensive investigations of different domains reveal the superiority of the proposed PSO-EFVM over established baselines and the latest state-of-the-art models.

Metal matrix composites (MMCs) are dynamic and fascinating materials as they can be designed to suit the end property requirements for any present and futuristic application [...]

The deep learning method (DLM) is one way to fix issues in optical nonorthogonal multiple access (O-NOMA) systems that are caused by signals that overlap and interfere with each other. NOMA increases the optical framework's spectrum efficiency, allowing several users to share the same time-frequency resources. However, NOMA-DLM-based detection's complicated interference patterns and variable channel conditions are challenging for conventional detection methods to manage. By utilizing deep neural networks' advantages, these methods are able to overcome these challenges and improve detection performance. An overview of the main features and advantages of DLM detection in massive multiple input and output (M-MIMO) O-NOMA systems is given in this article. It describes the essential elements, such as the training procedure and the network design. In order to process the sent symbols or decode data streams, DLM networks are built to process the incoming signal, power allocation coefficients, and extra information. Gradient descent optimization is used to update the network parameters iteratively while training the network, and a diverse and representative dataset is created. Additionally, the challenges of detecting deep learning in O-NOMA systems are examined. It recognizes that in order to get the best results, significant computational resources, a large amount of training data, and careful model design are required. It looks at and compares the 16 × 16, 32 × 32, and 64 × 64 M-MIMO-NOMA models in terms of bit error rate (BER), complexity, and power spectral density (PSD). The suggested DLM algorithms have been demonstrated to perform better than traditional methods by achieving an excellent BER of 10-3 at 4.1 dB and PSD (−2500) performance with low complexity.

The widespread dissemination of false information across various online platforms has emerged as a matter of paramount concern due to the potential harm it poses to individuals, communities, and entire nations. Substantial efforts are currently underway in the research community to combat this issue. A burgeoning area of study gaining significant traction is the development of fake news identification techniques. However, this field faces formidable challenges primarily stemming from limited resources, including access to comprehensive datasets, computational resources, and evaluation tools. To overcome these challenges, researchers are exploring various methodologies. One promising approach involves the use of feature abstraction and vectorization techniques. In this context, we highly recommend utilizing the Python sci-kit-learn module, which offers many invaluable tools such as the Count Vectorizer and Tiff Vectorizer. These tools enable the efficient handling of text data by converting it into numerical representations, thereby facilitating subsequent analysis. Once the text data is appropriately transformed, the next crucial step involves feature selection. To achieve optimal results, researchers often employ feature selection methods based on misperception matrices. These methods allow for the exploration and selection of the most suitable features, which are essential for achieving the highest accuracy in fake news identification.

The detrimental effects of PM2.5 on the health of urban populations, especially in developing countries, are being carefully addressed by governments worldwide. To mitigate the adverse health effects of air pollution, this paper presents a case study and suggests a three-phase plan for implementing a prediction and control approach in the Thai metro region of Nakhon Ratchasima (NRM). A data-driven assessment of the region's air pollution situation is conducted in the initial phase, which is then followed by an analysis of the health risks related to PM2.5 concentrations. It is therefore imperative to determine which machine learning model has the best capabilities to estimate PM2.5 concentration. With the help of this advanced predictive capability, decision-makers will have an important window of opportunity to put preventive measures in place to safeguard the population from the adverse effects of air pollution. The study showed that there were respiratory diseases in the NRM area because of high concentrations of PM2.5 particles, primarily from vehicle exhaust, which far exceeded Thailand's threshold of 15 μg m−3. A total of 10,379 people, or 16.91% of the affected population, lived in zones with PM2.5 concentrations >38 μg m−3 in 2019. Another 741 people, or 1.21% of the population, lived in zones with PM2.5 values between 36 and 37 μg m−3. The study finds that Extra-Trees regression combined with AdaBoost has the best-predicting capacity for PM2.5 ambient concentration in the NRM area out of the 13 machine-learning regression models examined. This case study presents an additional tool for policymakers to employ in controlling adverse health effects in urban areas.

This study for the first time reports that insights into microstructure and thermal and compressive responses can be best achieved following exposure to different cryogenic temperatures and that the lowest cryogenic temperature may not always produce the best results. In the present study, a Mg-SiO2 biocompatible and environment-friendly nanocomposite was synthesized by using the Disintegrated Melt Deposition method followed by hot extrusion. Subsequently, it was subjected to four different sub-zero temperatures (−20 °C, −50 °C, −80 °C, and −196 °C). The results reveal the best densification at −80 °C, marginally improved ignition resistance at 50 °C, the best damping response at −80 °C, the best microhardness at −50 °C, and the best compressive response at −20 °C. The results clearly indicate that the cryogenic temperature should be carefully chosen depending on the property that needs to be particularly enhanced governed by the principal requirement of the end application.

Fatigue refers to the progressive and localized structural damage that occurs when a material is subjected to repeated loading and unloading, typically at levels below its ultimate strength. Several failure mechanisms have been observed in practical scenarios, encompassing high-cycle, low-cycle, thermal, surface, corrosion, and fretting fatigue. Fatigue, connected to the failure of numerous engineered products, stands out as a prevalent cause of structural failure in service. Conducting research on the advancement and application of fatigue analysis technologies is crucial because fatigue analysis plays a critical role in determining the service life of components and mitigating the risk of failure. This study compiles data from a wide range of sources and offers a thorough summary of the state of fatigue analysis. It focuses on the effects of different parameters, including hardness, temperature, residual stresses, and hardfacing, on the fatigue life of different materials and their alloys. The fatigue life of alloys is typically high at low temperatures, but it is significantly reduced at high temperatures or under high-stress conditions. One of the main causes of lower fatigue life is residual stress. High-temperature conditions and hardfacing processes cause the development of tensile residual stresses, which in turn decreases fatigue life. But, if the hardness of the material significantly increases due to hardfacing, then the fatigue life also increases. This manuscript focuses on reviewing the research on fatigue-life prediction methods, shortcomings, and recommendations.

Magnesium-based biomaterials have recently been in the research spotlight in the field of biomedical engineering owing to their properties, such as density and biocompatibility that closely align with those of human bone. However, poor strength and rapid degradation impede their application as bone support fixtures. The present research aims to tailor the properties of Mg by using a novel ultrasonic-assisted rheo-squeeze casting approach. To satisfy the demand, pure Mg (Mg), MHA (Mg/5%HA), MZHA (Mg-1%Zn/5%HA/), and MSHA (Mg-1%Sn/5%HA) were fabricated, and various mechanical tests were conducted to assess the composite’s mechanical properties, including its microhardness, tensile strength, compressive strength, flexural strength, and impact strength. The microstructural and fractured morphology of the composites was examined by scanning electron microscopy (SEM), whereas their elemental composition was analyzed by field emission scanning electron microscopy (FESEM) equipped with elemental mapping. Comparing the MZHA, MHA, and pure Mg samples, the mechanical behavior of MSHA is significantly superior. This is due to composites containing Sn that possess finer-grained materials, which act as barriers to dislocation motion while increasing the strength of the materials. From the observed results, there is a significant improvement in the microhardness of MSHA of 64.5% when compared to that of pure Mg, and 42.7% compared to MHA. Furthermore, MSHA composites possess noticeable enhancements in tensile and compression performance of 80.8% and 58.3%, respectively, and 19% and 22.4% compared to MHA. Additionally, the impact and flexural performance of MSHA composites exhibit higher performance (41% and 42%) than pure Mg and 8% and 7% against the MHA composite.

Magnesium-based multicomponent alloys with different compositions, namely Mg60Al20Zn5Cu10Mn5 (Mg60 alloy), Mg70Al15Zn5Cu5Mn5 (Mg70 alloy), and Mg80Al5Cu5Mn5Zn5 (Mg 80) alloys, were prepared using the disintegrated melt deposition technique. The DMD technique is a distinctive method that merges the benefits from gravity die casting and spray forming. This approach facilitates high solidification rates, process yields, and reduced metal wastage, resulting in materials with a fine microstructure and minimal porosity. Their potential as biodegradable materials was assessed through corrosion in different simulated body fluids (SBFs), microstructure, and cytotoxicity tests. It was observed that the Mg60 alloy exhibited low corrosion rates (~× 10−5 mm/year) in all SBF solutions, with a minor amount of corrosive products, and cracks were observed. This can be attributed to the formation of the Mg32(AlZn)49 phase and to its stability due to Mg(OH)2 film, leading to excellent corrosion resistance when compared to the Mg70 and M80 alloys. Conversely, the Mg80 alloy exhibited high corrosion rates, along with more surface degradation and cracks, due to active intermetallic phases, such as Al6Mn, Al2CuMg, and Al2Cu phases. The order of corrosion resistance for the Mg alloy was found to be ASS > HBSS > ABP > PBS. Further, in vitro cytotoxicity studies were carried out using MDA-MB-231 tumor cells. By comparing all three alloys, in terms of proliferation and vitality, the Mg80 alloy emerged as a promising material for implants, with potential antitumor activity.

Herein, ZrO2 and Si + ZrO2 composite coatings on AZ91D alloys are deposited at a constant voltage of 8 kV and 1 Å/s deposition rate using the electron beam physical vapor deposition (EBPVD) method. Further, the samples are examined for surface morphology, phase analysis, adhesion, corrosion, and antibacterial properties, as per ASTM standards. The adhesion strength of the composite (Si + ZrO2) coating nominally dropped (9%) compared to the ZrO2 coating even when the coating thickness increased by 18%. However, the composite (Si + ZrO2) coating improved wettability because silanol promotes hydrogen bonding with water molecules, which elevates the surface energy of the silica and increases its hydrophilic nature. Further, increased wettability and surface roughness have the potential to improve cell adhesion and proliferation. The corrosion potential (Ecorr) values of the coated samples exhibited a positive shift in the potentiodynamic polarization curve, indicating a substantial increase in their corrosion resistance in the artificial blood plasma (ABP) electrolyte. Similarly, SEM images of both coated corroded samples are less affected in the ABP solution, indicating that the coating mitigated heavy cracks and micropores, protecting them from corrosion. The Si + ZrO2 coatings exhibited exceptional performance in preventing bacterial infiltration by Staphylococcus aureus, thus inhibiting the subsequent formation of biofilms. In addition, these coatings demonstrate improved vitality among fibroblast cells, enabling better cellular spreading and proliferation.

Multidisciplinary teams decrease the likelihood of adverse pregnancy outcomes in high-risk pregnant cardiac patients. We present the case of a patient with complex congenital heart disease and a mechanical mitral valve, whose treatment included warfarin until delivery despite the discovery of placental hematomas. A multidisciplinary approach mitigated both maternal and fetal adverse pregnancy outcomes.

The second-phase particles in magnesium alloys could affect the mechanical properties of the material significantly. In this work, 3D finite element models with explicit incorporation of second-phase particles are established. The simulations are calibrated with the experimental results of the Mg-1Gd alloy. The influences of factors, such as the particle distribution, size, and orientation of cylindrical particles, on precipitation hardening are investigated in detail. Three interface conditions between particles and the matrix—perfect bonding and high- and low-strength bonding—are studied at the same time. The interface conditions are shown to exert a stronger influence on precipitation hardening compared to the factors of particle distribution and size. In contrast, the influence of the orientation of cylindrical particles at grain boundaries outweighs the effect of interface property. When second-phase particles are relatively large and all located at grain boundaries, the hardening effect can be improved, and the magnesium alloy shows relatively high flow stress. However, the high hardening effect from the second-phase particles could result in high local stress concentration and possible early failure or low ductility of Mg alloys.

A magnesium-based metal matrix composite, Mg-5Se-2Zn-2SiO2, was synthesized using the Disintegrated Melt Deposition (DMD) method followed by hot extrusion. Elemental analysis revealed that the material experienced selenium loss which was attributed to the evaporation of selenium at high temperatures. Superior damping characteristics were exhibited while retaining similar Young’s modulus, and significant grain refinement also resulted in decisively superior mechanical properties such as hardness (32% increase), fracture strain (39% increase), as well as yield and ultimate compressive strength (157% and 54% increase, respectively). These were a consequence of SiO2 addition as well as presence of Mg2Si (and MgSe) intermetallic phases which were detected by X-ray characterization. Furthermore, while the material had lower corrosion resistance than pure magnesium, it retained acceptable corrosion resistance as well as structural integrity after the full immersion duration of 28 days. Overall, the material exhibits promising potential for applications in the biomedical field, especially in development of smaller and lighter implants where mechanical properties are paramount, with key lessons learned for the synthesis of Mg-materials containing selenium for the future.

The current study focused on developing a biodegradable implant composite material that could work in a multitude of applications. The fabricated composite showcases a porous matrix of Mg–hydroxyapatite developed through the spacer-holder technique. The composite was incorporated with a natural medicinal plant, i.e., Aloe barbadensis miller, commonly known as the Aloe vera plant. The final composite was enveloped under a thin layer of PLA to work as an encapsulated drug as well as a composite material for implant applications. Further, the mechanical and microstructural properties were analyzed along with corrosion analysis through the weight loss method and pH change. The experiments showed an improvement in the corrosion rate when tested under cell culture medium. The antibacterial rates were experimented with under different aloe vera concentrations against Gram-positive B. subtilis and Gram-negative E. coli, and finally, a minimum inhibitory value was formulated for further experimentations. Hemocompatibility and surface wettability tests were also performed, which revealed improved surface hydrophilicity with a reduced hemolysis rate. An in vitro cell viability analysis was performed against the MG63 osteoblast cell line to indicate the cytotoxicity and cytocompatibility of the samples. This research proposed a novel composite material that provides antibacterial and non-toxic properties and retains its strength under a physiological environment.

In this study, the Mg-15Se binary system was, for the first time, investigated and synthesized using the powder metallurgy (PM) method, including microwave sintering and hot extrusion. The resulting material was shown to possess visible pores with a porosity of 2.91%, higher than other Mg materials synthesized using this method in the literature. Despite this, the material not only exhibited a comparable corrosion response with pure Mg but also a significantly superior mechanical response (76% greater damping capacity, 57% increase in hardness, and increases of 21%, 50%, and 51% for compressive yield strength, ultimate compressive strength, and fracture strain, respectively). Thus, this not only opens the door for future work concerning the addition of medicinal Se to nutritional Mg element and the optimization of process parameters but also could potentially be making inroads into the biomedical field with the use of selenium as a biomedical-oriented alloying element.

Magnesium and its composites have been used in various applications owing to their high specific strength properties and low density. However, the application is limited to room-temperature conditions owing to the lack of research available on the ability of magnesium alloys to perform in sub-zero conditions. The present study attempted, for the first time, the effects of two cryogenic temperatures (−20 °C/253 K and −196 °C/77 K) on the physical, thermal, and mechanical properties of a Mg/2wt.%CeO2 nanocomposite. The materials were synthesized using the disintegrated melt deposition method followed by hot extrusion. The results revealed that the shallow cryogenically treated (refrigerated at −20 °C) samples display a reduction in porosity, lower ignition resistance, similar microhardness, compressive yield, and ultimate strength and failure strain when compared to deep cryogenically treated samples in liquid nitrogen at −196 °C. Although deep cryogenically treated samples showed an overall edge, the extent of the increase in properties may not be justified, as samples exposed at −20 °C display very similar mechanical properties, thus reducing the overall cost of the cryogenic process. The results were compared with the data available in the open literature, and the mechanisms behind the improvement of the properties were evaluated.