加济安泰普大学

Objective This study aims to map the intramuscular nerve distribution of the human fetal latissimus dorsi, identify its motor entry point, and classify its branching patterns using modified Sihler’s staining. Methods This study was conducted on a total of 16 human fetuses (4 males, 12 females; 24.38 ± 4.34 weeks, range: 18–35 weeks) from the archive of the Department of Anatomy. Bilateral latissimus dorsi (LD) were included, yielding a total of 32 muscles. The innervation patterns and intramuscular nerve distributions of the LD were examined using the modified Sihler’s staining technique. To evaluate the intramuscular nerve distribution, the muscle was divided into 15 regions using two oblique and four transverse reference lines. Results The motor entry point was located within the proximal 0–20% interval, within the anterior region as defined by this grid. Analysis of intramuscular nerve distribution demonstrated that nerve branches were present in all specimens (n = 32) within the 0–20%, 20–40%, and 40–60% intervals of the anterior region. In contrast, nerve branch presence was lowest in the 80–100% interval of the middle and superior regions, where branches were observed in only one specimen (3.12%) in each region. Seven branching patterns were identified, with Type I being the most common and Type VII the least common. Patterns III, IV, and VII were observed exclusively in fetuses aged 18–21 weeks, whereas Patterns I, II, V, and VI were observed only in fetuses aged 22–35 weeks. This age-related distribution most likely reflects reduced visualization of fine distal nerve branches in larger specimens rather than a true developmental difference. Conclusion The fetal latissimus dorsi showed a motor entry point located within the proximal 0–20% interval according to the applied zoning framework and seven intramuscular branching patterns observed in this fetal series. These findings provide baseline anatomical data that may contribute to understanding the intramuscular neural organization of the LD and provide an anatomical basis for future studies investigating its potential clinical relevance.

Nickel laterite processing is commonly reviewed route by route, although ore mineralogy, thermal preparation, sulfur transfer, reduction selectivity, metal-carrier choice, and downstream refining are physically coupled. This critical review treats the thermal front end as one interface in an ore-to-product chain. A reproducible OpenAlex update search performed on 01 October 2026 retrieved 148 unique records; 59 advanced from title-and-abstract screening to manual assessment, supplemented by citation chaining and verified DOI metadata. Evidence is graded from concept or model to controlled laboratory, pilot or plant-adjacent, and industrial operating data. The synthesis separates limonitic and saprolitic front ends and places the legacy Caron route between reduction roasting and aqueous refining. It shows that low-carbon claims are controlled by ore grade, moisture, product basis, electricity and hydrogen source, allocation, and refining depth. In mixed-energy modeling, ferronickel and nickel pig iron required 309 and 598 GJ per tonne of contained Ni and emitted 18 and 69 t CO2-eq per tonne of Ni, respectively; these values are boundary-specific rather than universal rankings. A 2025 hydrogen-plasma study produced Ni-enriched ferronickel in 10-g batch tests and projected up to 18% energy savings and 84% lower direct CO2 emissions under renewable-electricity and green-hydrogen assumptions, but continuous operation has not been demonstrated. Kiln sulfur transfer and inhibition remain controlled laboratory evidence and govern operability, emissions, and product sulfur rather than direct decarbonization. Product claims must distinguish mixed hydroxide or sulfide precipitate, ferronickel, nickel pig iron, matte, and battery-grade salts because each carries different impurity and refining burdens. The resulting framework links mineralogy, front-end state, metal carrier, refining duty, residue closure, carbon boundary, and evidence maturity to define testable priorities for lower-carbon nickel supply.

This study uses high-frequency price data to analyze risk connectivity among 15 cryptocurrencies, focusing on moments such as volatility, skewness, kurtosis, and jumps during the pre-COVID-19 era, the COVID-19 epidemic, and Russian-Ukrainian tensions. The results indicate that Ethereum Classic is a major shock transmitter in all periods, and this effect becomes more pronounced during geopolitical crises. In contrast, Stellar, Tezos, and Tron are important shock absorbers, particularly during market volatility. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. For higher-order moments, the findings reveal that Bitcoin, Ethereum, and Dash are significant transmitters of skewness spreads, whereas Dash and Eos are significant transmitters of kurtosis spreads. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. These findings highlight the need for targeted risk management strategies adjusted to cryptocurrency market dynamics.

The increasing recognition of mathematics teacher educators (MTEs) as a distinct field of inquiry has led to calls for systematic, data-driven analyses of thematic developments and research trends. Despite growing interest, no large-scale review has examined the evolution of MTE research. Addressing this gap, this study applies latent Dirichlet allocation (LDA) to identify 17 research themes in 579 Scopus-indexed publications and employs time series analysis (TSA) to track their progression. TSA findings reveal three key phases in MTE research: emergence, expansion, and divergence. While professional growth and pedagogical approach remain central, topics like STEM, task, technology, and social justice are gaining traction, whereas assessment, belief, and knowledge are in decline. By mapping these thematic shifts over time, this study provides a longitudinal perspective on MTE scholarship and highlights directions for future research.

This study examines extreme tail-risk spillovers among energy-intensive cryptocurrencies (Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, Monero), three major green assets (S P Green Bond Index, S P Global Clean Energy Index, S P ESG Leader Index), and two key commodities (Gold, WTI Crude Oil) across quantiles and frequency horizons. Using a quantile–frequency connectedness framework, we evaluate spillover patterns during bullish, bearish, and stable market conditions, with emphasis on the COVID-19 pandemic and Russia–Ukraine conflict. Results show that cryptocurrencies—particularly Bitcoin and Ethereum—are dominant short-term net transmitters during periods of elevated volatility. Conversely, green assets and commodities, especially Gold, consistently act as net receivers, reflecting stability during market stress. Spillovers weaken and relationships normalize under tranquil conditions. While Gold and WTI absorb short-term shocks, they become balanced over longer horizons. Overall, cryptocurrencies exert short-run influence, whereas traditional and green assets demonstrate long-term resilience, offering important implications for portfolio diversification and risk management.