Multidisciplinary Digital Publishing Institute (Switzerland)
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Effects of Supervised Physiotherapy-Based Exercise on Ovarian Reserve and Spontaneous Pregnancy in Women with Diminished Ovarian Reserve: A Controlled Pilot Study
No full textDiminished ovarian reserve (DOR) is a major cause of female infertility with limited treatment options, and lifestyle interventions such as supervised, structured exercise therapy may support ovarian function. In this pilot study, we evaluated the effect of a supervised, physiotherapy-based exercise program combined with antioxidant supplementation on ovarian reserve markers and spontaneous pregnancy rates in 24 infertile women aged 20–42 years, with body mass index (BMI) 18.5–30 kg/m2, regular menstruation, anti-Müllerian hormone (AMH) < 1.1 ng/mL, and antral follicle count ≥3 measured on days 2–4 of the cycle. Participants were randomized into two groups of 12: Both groups received standardized oral therapy, while the intervention group additionally participated in a three-month supervised, structured exercise therapy programme. Analysis of covariance was used to adjust for baseline differences in AMH and BMI, as groups differed significantly in BMI at baseline. At post-treatment assessment, AMH levels were significantly higher in the intervention group, whereas FSH, LH, estradiol, prolactin, and TSH levels did not change significantly. Spontaneous pregnancies were recorded both during the intervention period and throughout a follow-up period of up to six months. Spontaneous pregnancy occurred in 7 out of 12 participants in the intervention group versus 1 out of 12 in the control group, resulting in four and one live births, respectively. These findings suggest that combining supervised, structured exercise therapy with antioxidant supplementation may enhance ovarian reserve and improve the likelihood of spontaneous pregnancy in women with diminished ovarian reserve
Pedagogical Transformation Using Large Language Models in a Cybersecurity Course
No full textLarge Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning
The Effect of CO2 Laser Treatment on the Composition of Cotton/Polyester/Metal Fabric
No full textThe effect of CO2 laser treatment on the surface composition and properties of a woven fabric (polyester (PET) fiber (59 wt%)/cotton (CO) fiber (31 wt%)/stainless-steel (SS) metal fibers (10 wt%)) was investigated across a range of laser intensities (19.1 × 106 to 615.0 × 106 W/m2). Elemental analysis using wavelength-dispersive X-ray fluorescence (WD-XRF) revealed that for an intensity up to 225.4 × 106 W/m2, the carbon content on the fabric surface increased while the oxygen content decreased, indicating thermally induced surface modification. Fourier transform infrared (FT-IR) spectroscopy confirmed that no new chemical bonds were formed, suggesting that the changes observed were predominantly physical in nature. High-resolution scanning electron microscopy (HR-SEM) showed progressive fiber fusion and surface smoothing with increasing laser intensity, consistent with polyester melting. Tensile testing demonstrated a significant decline in peak load and elongation at peak load with rising laser fluence, indicating mechanical embrittlement. Overall, CO2 laser treatment alters the morphology and elemental composition of the fabric surface without inducing major chemical decomposition, markedly reducing its mechanical strength
GEFA-YOLO: Lightweight Weed Detection with Group-Enhanced Fusion Attention
No full textCotton is an important economic crop, and its weed management directly affects yield and quality. In actual cotton fields, detection accuracy still faces challenges due to the complex types of weeds, variable morphologies, and environmental factors. Most existing models rely on the attention mechanism to improve performance, but channel attention tends to ignore spatial information, while full spatial attention brings high computational costs. Therefore, this paper proposes a grouped enhanced fusion attention mechanism (GEFA), which combines grouped convolution and local spatial attention to reduce complexity and parameter quantity while effectively enhancing feature expression ability. The GEFAY detection model constructed based on GEFA achieves good balance in efficiency, accuracy, and complexity on the CottonWeedDet12, VOC, and COCO datasets. Compared with classic attention methods, this model has the smallest increase in parameters and computational costs while significantly improving accuracy. It is more suitable for deployment on edge devices. The further designed end-to-end intelligent weed detection system and edge device deployment can achieve image detection on local maps and real-time cameras, with good practicality and scalability, providing effective technical support for intelligent visual applications in precision agriculture
Residual Effects of Cover Crop Species, Tillage, and Manure Application on Corn Yield and Soil Nitrogen Dynamics in Organic Management Systems
No full textNitrogen (N) management remains a primary challenge in organic grain systems, particularly in rotations where heavy N-consuming crops, such as corn and wheat, follow one another. Daikon radish (Raphanus sativus L.) is widely adopted for its ability to scavenge residual soil nitrate between cash crops; yet the subsequent availability of scavenged N to the following crop is inconsistent and often negligible. This 4-year field study (2014–2017) at the University of Minnesota Southwest Research and Outreach Center evaluated whether planting daikon radish in polyculture with berseem clover, and either annual oats or winter rye could improve N retention and timed release compared to daikon radish monoculture. Three cover crop treatments were tested across three common organic management systems: no manure with no tillage, manure with tillage, and manure plus shallow tillage incorporation before cover crop seeding. Polycultures, especially those including winter rye, produced significantly more fall biomass (up to 6435 kg ha−1) than daikon radish monoculture (573–1272 kg ha−1). Manure incorporation consistently increased total and daikon radish biomass, as well as the percent living cover. Despite substantial biomass differences, mid-season and fall soil inorganic N, potentially mineralizable N, permanganate-oxidizable C, and enzyme activities showed few consistent treatment effects. Corn grain yield was highest following manure with tillage incorporation but was significantly reduced after the winter rye polyculture in all years, likely due to N immobilization and delayed corn planting caused by late rye termination under wet spring conditions. Results indicate that while polycultures with winter rye maximize biomass and soil cover, they do not reliably enhance N recycling to the subsequent organic corn crop and can reduce yield
Interactive Mixed Reality Simulation Enhances Student Knowledge and Ultrasound Interpretation in Sheep Pregnancy Diagnosis
No full textTransitioning from theoretical learning to practical application remains a significant challenge for students in medical and veterinary science education, particularly in the context of medical imaging and ultrasound interpretation. Traditional lecture-based methods offer limited support for developing the spatial reasoning and technical skills required for ultrasound pregnancy diagnosis. This study evaluates the effectiveness of an interactive mixed reality (MR) training tool, Ewe Scan, delivered through the Apple Vision Pro, compared to traditional lecture-based instruction. Forty-two undergraduate students were randomly assigned to either a lecture-trained or MR-trained group and assessed immediately after training and again after six weeks. Results showed that MR-trained students significantly outperformed their lecture-trained peers in both immediate comprehension and retention over time, particularly in ultrasound interpretation skills. The MR-trained group also reported higher levels of engagement, confidence, and satisfaction with their training experience. These findings suggest that MR-based learning enhances educational outcomes by improving spatial understanding, increasing active engagement, and supporting knowledge retention. Integrating MR simulations into ultrasound education offers a scalable, ethical, and effective alternative to traditional training methods, contributing to advancements in medical imagery education
Tunable Narrow-Linewidth Si3N4 Cascaded Triple-Ring External-Cavity Semiconductor Laser for Coherent Optical Communications
No full textWe propose an external-cavity laser that combines wide tunability with narrow linewidth. The design utilizes a low-loss Si3N4 waveguide and a thermally tuned cascaded triple-ring resonator to enable continuous wavelength tuning. The numerical simulations indicate that the proposed laser exhibits a tuning range of 64 nm with a sub-kHz linewidth, an SMSR of more than 80 dB, an output power of 24 mW and a linewidth of 193 Hz at 1550 nm. Furthermore, we perform comparative system-level simulations using QPSK and 16QAM coherent optical fiber links at 50 Gbaud over 100 km. Under identical conditions, when the laser linewidth is reduced from 1 MHz level to 193 Hz, the BER of 16QAM decreases from 1.5 × 10−3 to 5.3 × 10−5. These results indicate that a narrow linewidth effectively mitigates phase noise degradation in high-order modulation formats. With its narrow linewidth, wide tuning range, high SMSR, and high output power, this laser serves as a promising on-chip light source for high-resolution sensing and coherent optical communications
CNN–BiLSTM–Attention-Based Hybrid-Driven Modeling for Diameter Prediction of Czochralski Silicon Single Crystals
No full textHigh-precision prediction of the crystal diameter during the growth of electronic-grade silicon single crystals is a critical step for the fabrication of high-quality single crystals. However, the process features high-temperature operation, strong nonlinearities, significant time-delay dynamics, and external disturbances, which limit the accuracy of conventional mechanism-based models. In this study, mechanism-based models denote physics-informed heat-transfer and geometric models that relate heater power and pulling rate to diameter evolution. To address this challenge, this paper proposes a hybrid deep learning model combining a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and self-attention to improve diameter prediction during the shoulder-formation and constant-diameter stages. The proposed model leverages the CNN to extract localized spatial features from multi-source sensor data, employs the BiLSTM to capture temporal dependencies inherent to the crystal growth process, and utilizes the self-attention mechanism to dynamically highlight critical feature information, thereby substantially enhancing the model’s capacity to represent complex industrial operating conditions. Experiments on operational production data collected from an industrial Czochralski (Cz) furnace, model TDR-180, demonstrate improved prediction accuracy and robustness over mechanism-based and single data-driven baselines, supporting practical process control and production optimization
Animal Models of Aortic Aneurysm and Dissection: A Comparative Guide for Mechanism, Therapeutic Testing, and Translational Readouts
No full textAortic aneurysms and dissections are devastating vascular diseases with high mortality, yet no pharmacological therapy has proven effective in halting growth or preventing rupture. Surgical and endovascular repair remain the only treatment options for advanced disease. Animal models have been indispensable in defining mechanisms and testing candidate therapies, but the diversity of protocols, strain-dependent variability, and heterogeneous endpoints complicate interpretation and translation. This review provides an update focused on how to match models to specific research questions. We critically compare commonly used abdominal aortic aneurysm (AAA) models (angiotensin II ± hyperlipidemia, elastase, calcium chloride, β-aminopropionitrile BAPN hybrids, and mineralocorticoid agonist/fludrocortisone models) with thoracic aortopathy and dissection models (BAPN alone or with AngII, genetic models including Marfan and smooth muscle contractile mutations, and AngII + TGF-β blockade). We highlight practical considerations on segment specificity, rupture incidence, lipid dependence, comorbidities, and outcome measurement, with emphasis on rigor and reporting standards. A translational thread on platelet–intraluminal thrombus biology, including the emerging biomarker and therapeutic targets such as glycoprotein VI (GPVI), is integrated across models. We offer a decision grid and rigor checklist to harmonize model use, enhance reproducibility, and accelerate translation
Suvarṇabhūmi Convergence Area: Humans, Animals, Artefacts
No full textIn this study, we investigate the Suvarṇabhūmi area, corresponding to central–southern Mainland Southeast Asia. We test the hypothesis that this region, located to the south of the Himalayan foothills, can be characterised as a convergence zone in which diverse entities involving humans, animals, and artefacts have significantly diverged from their related counterparts outside the area. We argue that this process of convergence was facilitated by the Maritime Silk Road trade networks, which were particularly active between the 3rd century BCE and the 9th century CE. Comparative data are derived from multiple scientific disciplines, including linguistic typology, onomastics, epigraphy, archaeology, and evolutionary biology. This includes typological features of language, toponyms, inscriptions, glass bead chemistry and related material culture, and phylogenetic data from patterns of endemism to illustrate parallel convergence scenarios observed for each data type. The results reveal recurring patterns of convergence. Linguistic, technological, and biological entities tend to diverge from their original forms and realign with predominant regional types when entering the Suvarṇabhūmi area. The spread of Indic and Sinitic linguistic and cultural elements, the adaptation and development of Brāhmī scripts into distinct local forms, the secondary manufacturing of glass beads, and unique genetic lineages in mammals, amphibians, reptiles, fish, and plants all point to the region’s role as a dynamic interaction sphere. We argue that Suvarṇabhūmi functions as an ecological system, in which trajectories of convergence are notable across a number of individual aspects of cultural and biological diversity. Altogether, these components have contributed to shaping the region’s distinctive natural and cultural history