Search Results (1,920)

Mathematical modeling is essential for understanding the complex regulatory pathways governing cell death and survival, including apoptosis, necroptosis, pyroptosis, ferroptosis, autophagy, and immunogenic cell death (ICD)—a functional category comprising diverse morphological types capable of activating immune responses. The growing number of models describing individual signaling pathways poses the challenge of integrating them into a cohesive framework. This review aims to identify common components across existing ordinary differential equation models that could serve as key nodes to merge distinct signaling modalities. Proposed models highlight Bcl-2, Bax, Ca², and p53 as shared regulators linking autophagy and apoptosis. Necroptosis and apoptosis are interconnected via TNF signaling network and modulated by caspase-8, c-FLIP, and NFκB, with RIPK1 acting as a critical hub directing pathway choice. Pyroptosis and apoptosis are co-regulated by NFκB, tBid, and caspases, while ferroptosis is modeled exclusively as an independent process, separate from other forms of cell death. Furthermore, existing models indicate that ICD intersects with necroptosis during oncolytic virotherapy, with pyroptosis in SARS-CoV-2 infection, and with apoptosis in the context of chemotherapy. Although several models address crosstalk between pairs of cell fate decisions, creating comprehensive frameworks that encompass three or more death modes remains an open challenge. Full article

A recently discovered turquoise deposit in the Fangshankou area of Dunhuang, Gansu Province, has been relatively understudied compared to turquoise from other sources due to its short mining history. Currently, no relevant research literature on this deposit has been identified. Therefore, a systematic mineralogical and spectroscopic study of Dunhuang turquoise samples was conducted using conventional gemological testing methods, combined with techniques such as X-ray powder diffraction (XRD), electron probe microanalysis (EPMA), Fourier transform infrared spectroscopy (FTIR), laser Raman spectroscopy, ultraviolet-visible spectroscopy (UV-Vis), and X-ray fluorescence (XRF) mapping. The test results indicate that the turquoise samples from this area have a density ranging from 2.40 to 2.77 g/cm³ and a refractive index between 1.59 and 1.65. The samples generally exhibit a cryptocrystalline structure, with some displaying spherulitic radial and radial fibrous structures. The texture is relatively dense and hard, with particle diameters less than 10 μm. Chemically, the turquoise samples from this region are characterized by high Fe and Si content and relatively low Cu content. Samples contain, in addition to the turquoise mineral, other minerals such as quartz, goethite and alunite, etc. The oxide content ranges are as follows: w(P₂O₅) between 23.83% and 33.66%, w(Al₂O₃) between 26.47% and 33.36%, w(CuO) between 5.26% and 7.91%, w(FeO) between 2.46% and 4.11%, and w(SiO₂) between 0.97% and 10.75%. In the infrared absorption spectra of Dunhuang turquoise, the bands at 3510 cm⁻¹ and 3464 cm⁻¹ are attributed to ν(OH)⁻ stretching vibrations, while the bands near 3308 cm⁻¹ and 3098 cm⁻¹ are assigned to ν(M-H₂O) stretching vibrations. The infrared absorption bands near 1110 cm⁻¹ and 1058 cm⁻¹ are due to v[PO₄]³⁻ stretching vibrations, and the bands near 651 cm⁻¹, 575 cm⁻¹, and 485 cm⁻¹ are attributed to δ[PO₄]³⁻ bending vibrations. A clear correlation exists between the Raman spectral features and the infrared spectra of this turquoise. The hue and chroma of the turquoise from this area are primarily influenced by the mass fractions of Fe³⁺, Cu²⁺, and Fe²⁺, as well as their bonding modes with water molecules. The ultraviolet-visible spectra are attributed to O²⁻–Fe³⁺ charge transfer, the ⁶A₁–⁴E_g + ⁴A₁ transition of Fe³⁺ ions (D⁵ configuration) in hydrated iron ions [Fe(H₂O)₆]³⁺, and the spin-allowed ²E_g–²T_2g transition of Cu²⁺ ions in hydrated copper ions [Cu(H₂O)₄]²⁺. Associated minerals include goethite, alunite, jarosite, and quartz. Fine-grained quartz often exists as secondary micron-sized independent mineral phases, which have a certain impact on the quality of the turquoise. Full article

Background/Objectives: Motion sickness is a prevalent neuro-vestibular syndrome that affects individuals across various modes of transport and can significantly impact quality of life and travel safety. This study aimed to assess the prevalence, severity, and associated risk factors of severe dizziness related to motion sickness among adult residents in the Kingdom of Saudi Arabia (KSA), with particular focus on socio-demographic and behavioral determinants. Methods: A cross-sectional survey was conducted among 349 participants recruited primarily from the Riyadh region. A structured questionnaire captured demographic variables, personal health history, and experiences of dizziness and related symptoms during air or metro travel. Chi-square tests and multivariable logistic regression were applied to examine associations between dizziness and potential predictors, with p ≤ 0.05 considered significant. Results: Overall, (23.5%) of respondents reported experiencing severe dizziness during metro travel (82/349). Females were more affected than males (32.1% vs. 15.8%; χ²(1) = 12.06, p = 0.0005, Cramer’s V = 0.186), although this association lost significance in the adjusted model. Height showed a borderline association (p = 0.053). In multivariable analysis, previous similar episodes were the strongest independent predictor of dizziness (aOR 15.63, 95% CI 6.40–38.16, p < 0.001). ANOVA revealed no difference in severity by sex or height (p > 0.7). Conclusions: Motion sickness affects nearly one-quarter of Saudi metro travelers (23.5%) and is predominantly influenced by a history of previous similar episodes rather than demographic factors. These findings underscore the need for targeted preventive strategies, ergonomic vehicle design, and public health education to mitigate the burden of motion sickness in the KSA’s expanding transportation systems. Full article

Brain–Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices by interpreting brain wave patterns associated with specific motor imagery tasks, which are derived from EEG signals. Although BCIs allow applications such as robotic arm control and smart assistive environments, they face major challenges, mainly due to the large variation in EEG characteristics between and within individuals. This variability is caused by low signal-to-noise ratio (SNR) due to both physiological and non-physiological artifacts, which severely affect the detection rate (IDR) in BCIs. Advanced multi-stage signal processing pipelines, including efficient filtering and decomposition techniques, have been developed to address these problems. Additionally, numerous feature engineering techniques have been developed to identify highly discriminative features, mainly to enhance IDRs in BCIs. In this review, several pre-processing techniques, including feature extraction algorithms, are critically evaluated using deep learning techniques. The review comparatively discusses methods such as wavelet-based thresholding and independent component analysis (ICA), including empirical mode decomposition (EMD) and its more sophisticated variants, such as Self-Adaptive Multivariate EMD (SA-MEMD) and Ensemble EMD (EEMD). These methods are examined based on machine learning models using SVM, LDA, and deep learning techniques such as CNNs and PCNNs, highlighting key limitations and findings, including different performance metrics. The paper concludes by outlining future directions. Full article

This research explored the technical feasibility of creating a controlled chemical composition for Fe-Ni alloys using a Directed Energy Deposition (DED) arc metal additive manufacturing (AM) process in its twin wire feed mode. This method employs two independently controlled arc power sources to feed two different wires into a single torch, creating a unified melt pool protected by a single shielding gas nozzle. The research focused on producing Invar 36 (EN 1.3912), a low thermal expansion alloy, by melting and mixing steel and Ni-Fe wires using Cold Metal Transfer-Twin (CMT-Twin) technology. This method enables the fabrication of multi-material components featuring regions with distinct chemical compositions, including functional gradients, with the aim of leveraging the advantageous properties of each individual material. Furthermore, this new manufacturing route offers the possibility to avoid using some alloying elements, such as Nb, an element considered a critical raw material (CRM) in the European Union (EU). Microstructure and mechanical properties were analyzed and compared to commercial Invar 36 obtained by DED-Arc with single wire as well as the effect of the absence of Nb. Results showed that the in situ obtained alloy had 10–20% lower strength but exhibited 10–15% higher elongation compared to the commercial alloy, making it a promising alternative for advanced manufacturing by using this new manufacturing route. Full article

This study aimed to assess the fracture resistance and fracture patterns of endodontically treated permanent mandibular molars restored with either preheated or nonpreheated conventional composite resin or short fiber-reinforced composite resin (SFRC). One hundred and twenty mandibular molars with prepared mesio-occluso-distal (MOD) cavities were allocated to six groups: positive control (intact teeth, no restoration, n = 20), negative control (endodontically treated but unrestored, n = 20), and four experimental groups restored with conventional composite, preheated composite, SFRC, or preheated SFRC (n = 20 each). After thermocycling, fracture resistance was tested using a universal testing machine at 0.5 mm/min. Data were analyzed using Jamovi software (Version 2.4.8; The Jamovi Project, Sydney, Australia). Normality was assessed with the Kolmogorov–Smirnov test. Group differences were evaluated using the Kruskal–Wallis test followed by the Dwass–Steel–Critchlow–Fligner post hoc test. The association between fracture modes and fracture strength categories was examined using the chi-square test of independence. A p-value < 0.05 was considered statistically significant. The positive control showed significantly greater fracture strength than all restored groups (p < 0.05). All restored groups had significantly higher fracture resistance than the negative control (p < 0.05), with no significant differences among the restored groups (p > 0.05). A significant association was found between fracture mode and fracture strength (χ²(1) = 6.97, p = 0.008). The preheated SFRC group showed a higher rate of restorable fractures compared to others, suggesting improved clinical reparability with preheating. Full article

To enhance the battery endurance of unmanned aerial vehicles (UAVs), this article addresses key issues in traditional wireless power transfer (WPT) systems. These issues occur during constant current/constant voltage (CC/CV) switching, such as poor stability, high payload, power loss, and charging instability. Accordingly, a WPT system based on topology switching is proposed. First, a lightweight compensation topology based on LCC-Series compensated topology (LCC-S) is designed. A tuning capacitor is incorporated, and two switches regulate the switching of the compensation capacitor to realize CC/CV mode transition. Meanwhile, the impedance matrix model is built to find optimal compensation component values, maximizing energy transfer. To reduce sensitivity to misalignment, a “+” shaped compensation coil is added to the basic 2 × 2 square coil array. It improves magnetic field uniformity and suppresses flux leakage. Experimental results show that the system achieves stable load-independent output. Within horizontal offset [−150, 150] mm and diagonal offset [−150√2, 150√2] mm, it keeps output power over 150 W and efficiency over 70%, with strong anti-misalignment ability. This system effectively solves key challenges such as endurance bottlenecks, complex CC/CV switching, and weak anti-misalignment. It offers a reliable technical solution for efficient charging of autonomous UAVs. Full article

To address the pronounced asymmetry and strong nonlinearity exhibited by the tractor electro-hydraulic hitch system during lifting and lowering operations, this study proposes a distributed parameter identification method based on a dual-mode grey-box modelling approach. Following a mode decomposition strategy, the lifting and lowering processes are regarded as two independent subsystems. Benchmark transfer function models are established for each subsystem through theoretical derivation. Considering the nonlinear characteristics and unmodeled dynamics that cannot be accurately captured by the benchmark model, a long short-term memory (LSTM) neural network compensator is introduced to enhance the model performance. Ultimately, a series-compensated dual-channel grey-box model is established, which effectively integrates mechanistic interpretability with high modelling accuracy. Then, to cope with the high-dimensional and heterogeneous parameter space of the constructed grey-box structure, a distributed parameter identification framework is proposed. This framework employs a staged optimization process that combines the whale optimization algorithm (WOA) with the gradient descent (GD) method to efficiently identify the hybrid parameter set. The identified models are validated through bench experiments. The results show that the proposed grey-box models achieve root mean square errors (RMSEs) of 0.33 mm and 0.48 mm, and mean absolute errors (MAEs) of 0.24 mm and 0.40 mm for the lifting and lowering processes, respectively. Compared with a single transfer function model, the RMSE is reduced by 57.6% and 87.3%, and the MAE is reduced by 59.2% and 87.9%, respectively. The proposed method substantially improves the modelling accuracy of the electro-hydraulic hitch system, providing a reliable foundation for system characterization and the design of high-performance control strategies for tractor electro-hydraulic hitch systems. Full article

Control chart pattern recognition was initially focused on single patterns with the assumption of normal, independent, and identical distribution. In practice, though, these assumptions are rarely valid in manufacturing processes, due to numerous influencing factors and short intervals in data collecting. It is necessary to consider that the inherent disturbance is autocorrelated and that two single patterns appear at the same time. This study presents a novel framework integrating Empirical Mode Decomposition (EMD) and one-dimensional Convolutional Neural Networks (1DCNN) with feature component selection for recognizing concurrent control chart patterns in autocorrelated manufacturing processes. We assume the inherent disturbance follows a first-order autoregressive (AR (1)) process and simulate eleven concurrent patterns. Then, the EMD method decomposes the concurrent pattern into a series of feature components, wherein the correlation coefficient is employed as the index by which to select the two feature components. Finally, the selected feature components and raw data are combined to create a feature vector that acts as the input for the 1DCNN model. The simulation results demonstrate that the proposed model achieves a recognition accuracy of 92.39%, outperforming both the singular spectrum analysis–support vector machine (SSA-SVM) and the singular spectrum analysis–random forest (SSA-RF) methods in terms of accuracy and robustness. Full article

Pipe conveyors provide an environmentally friendly alternative to open-troughed bulk solids conveyance, particularly for long or complex routing applications. However, the sustainability of this technology is compromised by unstable operations. Complex routing, operational variations, and environmental factors create uneven contact forces, triggering belt rotation. This is a critical failure mode that requires continuous monitoring throughout the conveyor’s lifecycle. Insufficient failure data represents a typical challenge for this application. This study hypothesized technological principles that constitute the minimum requirements for enabling the scaling of industrial applications of belt rotation monitoring. Enabling technologies were adopted to foster innovation, and a physical prototype was implemented to address data scarcity for this failure mode. Using a controller-responder wireless network of ESP32 Industrial Internet of Things devices, we developed a belt-independent measurement system with multiparameter capability. Key criteria for detecting unsafe operational states and a criticality-based approach for determining optimal measuring unit quantities were established. The measurement results demonstrated suitable precision for digitization objectives: overlap angle (3.3107° ± 16.7562°), pipe diameter (+13.3850 ± 7.2114 mm), and overlap length (−26.2750 ± 25.1536 mm), based on 307 samples with a latency of 350.1303 ms. The framework demonstrates potential for industrial deployment with acceptable performance for real-time monitoring. Full article

Future Artemis-class missions to Mars will expose astronauts to prolonged space radiation (SR), sleep disruption, and operational demands requiring greater autonomy, placing decision making and executive function at heightened risk. Both SR and sleep fragmentation (SF) independently impair cognition, yet their combined effects remain poorly understood. Using the Associative Recognition Memory and Interference (ARMIT) task, we assessed cognitive performance in male rats exposed to 10 cGy of Galactic Cosmic Ray simulation (GCRsim), SF, or both. Under well-rested conditions, GCRsim-exposed rats exhibited overt deficits in the C.1.2 stage, performing at chance when reinforcement contingencies shifted, consistent with impaired cognitive flexibility. In contrast, high-performing GCRsim-exposed rats that initially performed comparably to Sham s revealed latent deficits following a single night of SF. Specifically, the SF-induced loss of C.1.3 performance was accompanied by perseverative errors (persistently selecting outdated cues despite negative feedback), reflecting impaired attentional control and decision updating. Sham s maintained stable performance after SF. These findings support a two-hit vulnerability model in which SR primes corticostriatal and frontoparietal networks for collapse under subsequent sleep disruption. Operationally, this suggests that astronauts may display either persistent or stress-induced deficits, with both modes threatening mission success. Identifying mechanisms of such vulnerabilities is essential for countermeasure development. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and communication, as well as by repetitive behaviors, with a rising global prevalence. Concurrently, the use of Assisted Reproductive Technologies (ART) has increased among couples experiencing infertility. This study aimed to compare the frequency of ART-conceived children between those diagnosed with ASD and those with other neurodevelopmental disorders (nASD), and to examine differences in prenatal, perinatal and medical histories of ART- and spontaneously (non-ART)-conceived children within an ASD group. Methods: We retrospectively analyzed data from 507 children with a neurodevelopmental disorders (NDDs) diagnosis, classified into ASD (n = 234) and nASD (n = 273) groups. Subsequent analyses focused on the ASD group, further divided into an ART and non-ART group according to the conception mode. Results: ART-conceived children were more frequent in the ASD group than in the nASD group. Moreover, within ASD, ART was significantly associated with potential risk factors such as twin pregnancy, cesarean delivery, low birth weight and parental age. Logistic Binary Regression confirmed these results, suggesting that ART co-occurs with a cluster of perinatal and familial risk factors. Conclusions: Our results indicate that ART is not an independent causal exposure; however, given the retrospective design and the absence of a general population control group, causal inference cannot be drawn. The observed association with ASD appears to be mediated by perinatal and parental variables. These findings underscore the importance of improving obstetric management and care, and ensuring early developmental monitoring for ART-conceived children. Full article

Two single-crystal X-ray structure determinations of 2-aminopyrimidinium hydrogen tri oxofluorophosphate, (C₄H₆N₃)⁺·(HFO₃P)⁻, (I), and bis(2-aminopyrimidinium) trioxofluorophosphate, 2(C₄H₆N₃)⁺·(FO₃P)²⁻, (II), as well as their vibration spectra (FTIR on powder samples and the Raman spectra on unoriented single crystals) with a detailed assignment of vibrational modes are reported. The structure (I) consists of one independent 2-aminopyrimidinium cation and one hydrogen trioxofluorophosphate anion, while (II) consists of two symmetry independent 2-aminopyrimidinium cations and one trioxofluorophosphate anion. In (I), there is an O-H···O hydrogen bond of a moderate strength. A pair of these hydrogen bonds is situated about the symmetry centre and involved in the graph set motif R²₂(8). There are also N-H···O hydrogen bonds of a moderate strength, which are present in both structures while being involved in the graph set motifs R²₂(8), too. In addition, the N-H···O hydrogen bonds form R³₄(10) graph set motifs in (II). The latter motifs form ribbons which propagate parallel to the unit-cell axis a. In both structures, there are present π···π-electron ring interactions into which the primary amine groups are involved. In both structures, there are also present weak C-H···N hydrogen bonds with participation of the non-protonated ring N-atoms. The fluorine participates in the C-H···F hydrogen bonds in both title structures. The P-F distances are normal in both anions. The structure (I) differs from the known structure of 2-aminopyrimidinium hydrogen phosphite, the compositional isomer, though the main hydrogen bonds show similar geometry in both structures. The crystal of (I) was twinned. Full article

The sensitive and efficient detection of ochratoxin A (OTA) is critical for protecting agricultural ecosystems and public health. A dual-mode plasmonic colorimetric/photothermal aptasensor, based on a Mn²⁺-powered DNA walker for mediating gold nanobipyramid (AuNB) growth, is proposed for OTA detection in this study. In sensing the target OTA, the walking DNA (W-DNA) on the magnetic walker probe was independent and then the environment-friendly Mn²⁺ powered the generation of DNAzyme, where abundant thiol-modified DNA (DNA-SH) was produced by autonomous walking. The positively related DNA-SH level could mediate AuNB growth and reflect dual-mode plasmonic signals. Ultrasensitivity is demonstrated with a limit of detection (LOD) value of 48.6 pg mL⁻¹ for colorimetric mode and 37.6 pg mL⁻¹ for photothermal mode. The aptasensor exhibited high specificity (with cross-reactivity values below 6.2% for other analytes) and high reliability for OTA detection. The requisite practicability and accessibility are verified via its application in agricultural byproduct samples. The findings of this study offer an alternative and efficient biosensing pathway for improving detection performance, enabling green, enzyme-free, homogeneous, and dual-mode strategies for monitoring other pollutants. Full article

Prehabilitation programs for abdominal pre-operative patients are increasingly recognized for improving surgical outcomes, reducing post-operative complications, and enhancing recovery. Internet of Things (IoT)-enabled human movement monitoring systems offer promising support in mixed-mode settings that combine clinical supervision with home-based independence. These systems enhance accessibility, reduce pressure on healthcare infrastructure, and address geographical isolation. However, current implementations often lack personalized movement analysis, adaptive intervention mechanisms, and real-time clinical integration, frequently requiring manual oversight and limiting functional outcomes. This review-based paper proposes a conceptual framework informed by the existing literature, integrating Digital Twin (DT) technology, and machine learning/Artificial Intelligence (ML/AI) to enhance IoT-based mixed-mode prehabilitation programs. The framework employs inertial sensors embedded in wearable devices and smartphones to continuously collect movement data during prehabilitation exercises for pre-operative patients. These data are processed at the edge or in the cloud. Advanced ML/AI algorithms classify activity types and intensities with high precision, overcoming limitations of traditional Fast Fourier Transform (FFT)-based recognition methods, such as frequency overlap and amplitude distortion. The Digital Twin continuously monitors IoT behavior and provides timely interventions to fine-tune personalized patient monitoring. It simulates patient-specific movement profiles and supports dynamic, automated adjustments based on real-time analysis. This facilitates adaptive interventions and fosters bidirectional communication between patients and clinicians, enabling dynamic and remote supervision. By combining IoT, Digital Twin, and ML/AI technologies, the proposed framework offers a novel, scalable approach to personalized pre-operative care, addressing current limitations and enhancing outcomes. Full article