Search Results (189)

This research is focused on exploring the importance of STEM (Science, Technology, Engineering, and Mathematics) education in the development of critical competencies among secondary school students in the 21st century. This was aimed to assess the impact of STEM-based activities on students’ creative and computational thinking skills. A quasi-experimental design that included 77 secondary school students from public and private schools in Bandar Lampung, Indonesia, who participated in STEM interventions for over 5 weeks, was adopted. Data were collected through creative thinking tests and questionnaires on CT and STEM attitudes. The results showed that students who participated in STEM activities exhibited significantly higher creative thinking scores compared to peers taught with the traditional curriculum. Specifically, the experimental group showed a progressive increase in weekly test scores, suggesting that STEM methods improved students’ performance over time. Structural equation modeling (SEM) disclosed strong positive associations between attitudes towards STEM, CT, and creativity. The implications of these results outlined the need to integrate STEM education into curricula to foster essential skills for future challenges. This research contributes to the understanding of effective educational strategies and also advocates for a shift towards more interactive and integrative methods in secondary education to meet the demands of the contemporary workforce. Full article

This study aimed to comprehensively review surgical interventions for ocular surface diseases (OSDs), including dry eye syndrome (DES), exposure keratopathy, Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and ocular graft versus host disease (oGVHD), and to highlight the indications, contraindications, outcomes, and complications of various oculoplastic procedures used in their management. A narrative review was performed based on expert-guided selection of relevant studies retrieved from PubMed, Scopus, and Web of Science. Relevant keywords included “ocular surface disease”, “dry eye syndrome”, “exposure keratopathy”, “thyroid eye disease (TED)”, “neurotrophic keratopathy (NK)”, “Stevens-Johnson syndrome”, “toxic epidermal necrolysis”, “punctal occlusion”, “tarsorrhaphy”, “botulinum toxin”, “eyelid loading”, “retractor weakening”, “corneal neurotization (CN)”, “amniotic membrane transplantation (AMT)”, “conjunctival flap”, “ocular graft versus host disease”, and “salivary gland transplantation (SGT)”. Studies addressing surgical approaches for OSDs were included. In conclusion, surgical options for OSDs offer significant benefits when non-invasive treatments fail. Surgical techniques such as punctal occlusion, eyelid fissure narrowing, AMT, and conjunctival flap procedures help stabilize the ocular surface and alleviate symptoms. Advanced methods like CN and SGT target the underlying pathology in refractory cases such as oGVHD. The outcomes vary depending on the disease severity and surgical approach. Each procedure carries specific risks and requires individualized patient selection. Therefore, a tailored approach based on clinical condition, anatomical involvement, and patient factors is essential to achieve optimal results. Ongoing innovations in reconstructive surgery and regenerative medicine are expected to further improve outcomes for patients with OSDs. Full article

Gamification has emerged as a powerful strategy in digital education, enhancing engagement, motivation, and learning outcomes. However, most research lacks theoretical grounding and often applies multiple and uncontextualized game elements, limiting its impact and replicability. To address these gaps, this study introduces a Systemic Gamification Theory (SGT)—a comprehensive, human-centered model for designing and evaluating inclusive and effective gamified educational environments. Sustained in Education, Human–Computer Interaction, and Psychology, SGT is structured around four core principles, emphasizing the importance of integrating game elements (1—Integration) into cohesive systems that generate emergent outcomes (2—Emergence) aligned synergistically (3—Synergy) with contextual needs (4—Context). The theory supports inclusivity by accounting for individual traits, situational dynamics, spatial settings, and cultural diversity. To operationalize SGT, we developed two tools: i. a set of 10 Heuristics to guide and analyze effective and inclusive gamification; and ii. a Framework for designing and evaluating gamified systems, as well as comparing research methods and outcomes across different contexts. These tools demonstrated how SGT enables robust, adaptive, and equitable gamified learning experiences. By advancing theoretical and practical development, SGT fosters a transformative approach to gamification, enriching multimedia learning through thoughtful system design and reflective evaluation practices. Full article

The diagnosis-related group (DRG) system classifies patients into different groups in order to facilitate decisions regarding medical insurance payments. Currently, more than 600 standard DRGs exist in China. Payment details represented by DRG weights must be adjusted during decision-making. After modeling the DRG weight-determining process as a parameter-searching and optimization-solving problem, we propose a stochastic gradient tracking algorithm (SGT) and compare it with a genetic algorithm and sequential quadratic programming. We describe diagnosis-related groups in China using several statistics based on sample data from one city. We explored the influence of the SGT hyperparameters through numerous experiments and demonstrated the robustness of the best SGT hyperparameter combination. Our stochastic gradient tracking algorithm finished the parameter search in only 3.56 min when the insurance payment rate was set at 95%, which is acceptable and desirable. As the main medical insurance payment scheme in China, DRGs require quantitative evidence for policymaking. The optimization algorithm proposed in this study shows a possible scientific decision-making method for use in the DRG system, particularly with regard to DRG weights. Full article

This paper presents an occupant-centric load optimization framework for Smart Green Townhouses (SGTs). A hybrid Long Short-Term Memory and Convolutional Neural Network (LSTM-CNN) model is combined with real-time Internet of Things (IoT) data to predict and optimize energy usage based on occupant behavior and environmental conditions. Multi-Objective Particle Swarm Optimization (MOPSO) is applied to balance energy efficiency, cost reduction, and occupant comfort. This approach enables intelligent control of HVAC systems, lighting, and appliances. The proposed framework is shown to significantly reduce load demand, peak consumption, costs, and carbon emissions while improving thermal comfort and lighting adequacy. These results highlight the potential to provide adaptive solutions for sustainable residential energy management. Full article

Crude oil is a widely recognized, indispensable global and national economic resource. It is significantly susceptible to the boundless fluctuations attributed to various variables. Despite its capacity to sustain the global economic framework, the embedded uncertainties correlated with the crude oil markets present formidable challenges that investors must diligently navigate. In this research, we propose a hybrid machine learning model based on random forest (RF), gated recurrent unit (GRU), conventional neural network (CNN), extreme gradient boosting (XGBoost), functional partial least squares (FPLS), and stacking. This hybrid model facilitates the decision-making process related to the import and export of crude oil in India. The precision and reliability of the different machine learning models utilized in this study were validated through rigorous evaluation using various error metrics, ensuring a thorough assessment of their forecasting capabilities. The conclusive results revealed that the proposed hybrid ensemble model consistently delivered effective and robust predictions compared to the individual models. Full article

Hydrogen offers a promising solution to reduce emissions in the energy sector with the growing need for decarbonisation. Despite its environmental benefits, the use of hydrogen presents significant challenges in storage and transport. Many studies have focused on the different types of hydrogen production and analysed the pros and cons of each technique for different applications. This study focuses on techno-economic analysis of onsite hydrogen production through ammonia decomposition by utilising the heat from exhaust gas generated by hydrogen-fuelled gas turbines. Aspen Plus simulation software and its economic evaluation system are used. The Siemens Energy SGT-400 gas turbine’s parameters are used as the baseline for the hydrogen gas turbine in this study, together with the economic parameters of the capital expenditure (CAPEX) and operating expenditure (OPEX) are considered. The levelised cost of hydrogen (LCOH) is found to be 5.64 USD/kg of hydrogen, which is 10.6% lower than that of the conventional method, where a furnace is used to increase the temperature of ammonia. A major contribution of the LCOH comes from the ammonia feed cost up to 99%. The price of ammonia is found to be the most sensitive parameter of the contribution to LCOH. The findings of this study show that the use of ammonia decomposition via heat recovery for onsite hydrogen production with ammonic recycling is economically viable and highlight the critical need to further reduce the prices of green ammonia and blue ammonia in the future. Full article

SGT1 (the suppressor of the G2 allele of Skp1) functions as an adaptor protein that positively regulates plant defense and developmental processes. It comprises three functional domains: the tetratricopeptide repeat (TPR) domain, Chord SGT1 motif (CS), and SGT1-specific motif (SGS). In this study, we resolved the crystal structure of the Oryza sativa OsSGT1-TPR domain at 1.53 Å resolution. Structural analysis showed that the TPR domain adopts a homo-dimeric architecture stabilized by salt bridges (mediated by K52/R79/R109) and hydrophobic interactions (involving F17). Functional validation through gel filtration chromatography revealed that the disruption of the dimerization interface via F17A/K52A/R79A mutations caused complete dissociation into monomers, establishing the essential role of TPR-mediated oligomerization in maintaining the structural stability of full-length OsSGT1. Yeast two-hybrid assays showed that the dimerization disruption of SGT1 mutants retained the interaction with OsHSP81-2 (an HSP90 ortholog) and OsRAR1, indicating that SGT1 oligomerization serves primarily as a structural stabilizer rather than a prerequisite for partner interaction. Evolutionary analysis through the sequence alignment of plant SGT1 proteins revealed the conservation of the dimerization interface residues. This study provides structural insights into the conserved molecular features of SGT1 proteins and highlights the functional significance of their oligomerization state. Full article

This study investigates the implementation of artificial intelligence (AI) algorithms on resource-constrained edge devices, such as ESP32 and Raspberry Pi, within the context of smart grid (SG) applications. Specifically, it proposes a smart-meter-based system capable of classifying and detecting the Internet of Things (IoT) electronic devices at the extreme edge. The smart meter developed in this work acquires real-time voltage and current signals from connected devices, which are used to train and deploy lightweight machine learning models—Multi-Layer Perceptron (MLP) and K-Nearest Neighbor (KNN)—directly on edge hardware. The proposed system is integrated into the Artificial Intelligence in the Internet of Things for Smart Grids IAIoSGT architecture, which supports edge–cloud processing and real-time decision-making. A literature review highlights the key gaps in the existing approaches, particularly the lack of embedded intelligence for load identification at the edge. The experimental results emphasize the importance of data preprocessing—especially normalization—in optimizing model performance, revealing distinct behavior between MLP and KNN models depending on the platform. The findings confirm the feasibility of performing accurate low-latency classification directly on smart meters, reinforcing the potential of scalable AI-powered energy monitoring systems in SG. Full article

Integrating digital twins (DTs) into smart grid systems within the Internet of Smart Grid Things (IoSGT) ecosystem brings novel opportunities but also security challenges. Specifically, advanced machine learning (ML)-based Deepfake technologies enable adversaries to create highly realistic yet fraudulent DTs, threatening critical infrastructures’ reliability, safety, and integrity. In this paper, we introduce Authenticating Networked Computerized Handling of Representations for Smart Grid security (ANCHOR-Grid), an innovative authentication framework that leverages Electric Network Frequency (ENF) signals as real-world anchors to secure smart grid DTs at the frontier against Deepfake attacks. By capturing distinctive ENF variations from physical grid components and embedding these environmental fingerprints into their digital counterparts, ANCHOR-Grid provides a robust mechanism to ensure the authenticity and trustworthiness of virtual representations. We conducted comprehensive simulations and experiments within a virtual smart grid environment to evaluate ANCHOR-Grid. We crafted both authentic and Deepfake DTs of grid components, with the latter attempting to mimic legitimate behavior but lacking correct ENF signatures. Our results show that ANCHOR-Grid effectively differentiates between authentic and fraudulent DTs, demonstrating its potential as a reliable security layer for smart grid systems operating in the IoSGT ecosystem. In our virtual smart grid simulations, ANCHOR-Grid achieved a detection rate of 99.8% with only 0.2% false positives for Deepfake DTs at a sparse attack rate (1 forged packet per 500 legitimate packets). At a higher attack frequency (1 forged packet per 50 legitimate packets), it maintained a robust 97.5% detection rate with 1.5% false positives. Against replay attacks, it detected 94% of 5 s-old signatures and 98.5% of 120 s-old signatures. Even with 5% injected noise, detection remained at 96.5% (dropping to 88% at 20% noise), and under network latencies from <5 ms to 200 ms, accuracy ranged from 99.9% down to 95%. These results demonstrate ANCHOR-Grid’s high reliability and practical viability for securing smart grid DTs. These findings highlight the importance of integrating real-world environmental data into authentication processes for critical infrastructure and lay the foundation for future research on leveraging physical world cues to secure digital ecosystems. Full article

Background/Objectives: Salivary gland tumors (SGTs) are a rare and histologically heterogeneous group of neoplasms that are challenging to diagnose due to phenotypic heterogeneity and overlapping histomorphological markers. Accurate diagnosis is required for clinical management, particularly in unusual subtypes. The objective of this study was to ascertain whether attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, in combination with enzymatic deglycosylation, would be useful in SGT classification by detecting glycosylation-related metabolic variations. Methods: 155 tissue sections, consisting of 80 SGTs and 75 controls, were analyzed. ATR-FTIR spectroscopy was used to record the mid-infrared (MIR) spectra (4000–400 cm⁻¹) of enzymatically untreated and deglycosylated samples. Spectral data were preprocessed and analyzed by principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Enzymatic deglycosylation focused on sialic acid and fucose residues with α2-3,6,8 neuraminidase, α1-2,4,6 fucosidase O, and α1-3,4 fucosidase. Results: Tumor and control samples were discriminated with an OPLS-DA model, achieving an accuracy of 81.9% (78.7% for controls and 85.0% for tumors), especially in the glycosylation-relevant spectral range (850–1250 cm⁻¹). Classification between benign and malignant tumors was more challenging, with an accuracy of 70.0% (72.5% for benign and 67.5% for malignant cases). Enzymatic deglycosylation resulted in detectable changes in the MIR spectra, confirming the contribution of glycosylation to tumor-specific signatures. Benign vs. malignant tumor discrimination was still poor and was not much enhanced in the sense of incorporating glycosylation-specific regions. Conclusions: ATR-FTIR spectroscopy coupled with enzymatic deglycosylation can distinguish tumor and control tissues based on glycan-associated spectral differences. Application of the technique to benign/malignant SGT discrimination is hampered by spectral overlap and tumor heterogeneity. Further research will be necessary to explore other clustering algorithms and larger and more homogeneous datasets for improved diagnostic accuracy. Full article

Background: The management of salivary gland tumors (SGTs) during pregnancy is a subject that has received scant attention in the medical literature. While treatment recommendations for cancer therapy in pregnancy have been delineated, those for benign tumors remain unspecified. The present inquiry focuses on the number of women of reproductive age with SGTs and the optimal diagnostic and treatment strategies for tumors occurring during pregnancy. Materials and Methods: This was a retrospective multicenter cohort study based on data from the Polish Salivary Network Database, collected between 2018 and 2022. From a total of 2653 patients with salivary gland tumors (SGTs), we identified 1313 women, including 300 of reproductive age (16–42 years). Among them, six cases of SGTs diagnosed during pregnancy were included for detailed analysis. Ethical approval was obtained for this study. Results: Among the 300 women of reproductive age, 285 had benign SGTs and 15 had malignant SGTs. Six tumors were diagnosed during pregnancy: four benign (pleomorphic adenomas) and two malignant (salivary duct carcinoma and mucoepidermoid carcinoma). All benign tumors were monitored during pregnancy and surgically treated postpartum. One malignant tumor was resected postpartum, while the second showed a rapid progression in late pregnancy and required early intervention. Individual case details highlighted the diagnostic and therapeutic complexity in this population. Conclusions: A standard diagnostic protocol, incorporating ultrasounds and a fine-needle aspiration biopsy, is recommended during pregnancy. For cases in which the clinical and imaging characteristics suggest a benign origin, surveillance is proposed. Conversely, surgical resection is recommended for malignant SGTs, irrespective of the gestational stage. The potential for the malignant transformation of benign tumors during pregnancy in young women underscores the necessity for surgical intervention prior to planned conception. Full article

Background/Objectives: Compared to single gene testing (SGT), multigene panel testing (MGPT) improves pathogenic variants (PVs) detection. However, MGPT yields complex results, including secondary findings, heterozygous PVs in recessive genes, low-penetrance PVs, and variants of uncertain significance. We reported our mono-institutional experience of germline testing in breast cancer (BC), comparing SGT and MGPT. Methods: We retrospectively analyzed clinical and molecular data from 1084 BC patients: 308 underwent SGT (BRCA1/BRCA2) and 776 MGPT (for 28 cancer-related genes). We compared these approaches regarding the genetic classification of the findings (positive, uncertain, uninformative) and their impact on clinical management (primary findings (PFs); complex and inconclusive results). Additionally, we described clinical features supporting one approach over the other and focused on copy number variation (CNV) frequency in non-BRCA genes. Results: We found ≥1 PV in 165 patients (165/1084 = 15.2%), including 91 in BRCA1/BRCA2 (91/1084 = 8.4%), with 42 identified by SGT (42/308 = 13.6%) and 49 by MGPT (49/776 = 6.3%). MGPT detected PVs in non-BRCA genes in 74 patients (74/776 = 9.5%), including 40 PFs. Overall, MGPT identified 89 PFs (89/776 = 11.5%). We observed complex results in 21 patients (21/308 = 6.8%) with SGT and in 300 (300/776 = 38.7%) with MGPT. Compared to MGPT, SGT detected a similar percentage of PFs (13.6% vs. 11.5%) but a significantly reduced percentage of complex results (6.8% vs. 38.7%) (p < 0.001). Triple-negative BCs prevailed in BRCA1 carriers, while ER-positive BCs were more prevalent in ATM/CHEK2 carriers. Concerning non-BRCA genes, MGPT detected CNVs in PALB2, representing 20% of PVs in this gene. Conclusions: Although MGPT increases hereditary BC detection, its complexity requires clear guidelines for optimal clinical management and strategies for merging the benefits of SGT and MGPT. Full article

The systematic optimization approach highlights the potential of powder metallurgy and GNP reinforcement to enhance the mechanical properties of AZ31 magnesium alloys, making them suitable for lightweight structural applications. The present study employs the Taguchi approach to optimize the processing parameters of powder metallurgy for the fabrication of AZ31/graphene nanoplatelet (1.75 wt.%GNP) composites. The process parameters are varied at three levels, i.e., compaction pressure (250 MPa, 300 MPa, and 350 MPa), sintering temperature (500 °C, 550 °C, and 600 °C), and sintering time (45 min, 60 min, and 75 min) using an L9 orthogonal array. The impact of these parameters on microhardness and compressive strength was analyzed using a signal-to-noise (SN) ratio and an analysis of variance (ANOVA) approach. The results indicate that compaction pressure significantly influences both microhardness (72.99%) and compressive strength (68.38%), followed by sintering temperature and sintering time. Optimal parameter combinations (350 MPa, 600 °C, and 60 min) yielded maximum microhardness (108.5 Hv) and compressive strength (452.2 MPa). Regression models demonstrated strong predictive capabilities with R² values exceeding 85%. This study underscores the importance of efficient parameter optimization to achieve enhanced material properties in a cost-effective manner. Full article

Background and Clinical Significance: Molecular testing plays a crucial role in lung cancer diagnosis and management. While single-gene tests (SGTs) remain an important diagnostic tool, developments in novel methods such as next generation sequencing (NGS) provide a more precise mutational profile and enable the targeted treatment of a larger scope of mutation-driven cancers. Case presentation: We present a case of a patient with a rare EGFR variant lung adenocarcinoma, who was misdiagnosed using a SGT. The initial treatment with immunotherapy was unsuccessful. Conclusions: The patient could have benefited if NGS had been performed instead of traditional real-time polymerase chain reaction (RT-PCR) and if adequate tyrosine kinase inhibitor treatment was initiated at the time of diagnosis. Full article