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Exploring the role of growth hormone secretagogue receptor 1A (GHSR 1A) and γ-melanocyte stimulating hormone (γ-MSH) in obesity regulation
No full textBayesian uncertainty quantification in temperature simulation of borehole heat exchanger fields for geothermal energy supply
No full textAccurate temperature prediction is crucial for optimizing the performance of borehole heat exchanger (BHE) fields. This study introduces an efficient Bayesian approach for improving the forecast of temperature changes in the ground caused by the operation of BHEs. The framework addresses the complexities of multi-layer subsurface structures and groundwater flow. By utilizing an affine invariant ensemble sampler, the framework estimates the distribution of key parameters, including heat extraction rate, thermal conductivity, and Darcy velocity. Validation of the proposed methodology is conducted through a synthetic case involving four active and one inactive BHE over five years, using monthly temperature changes around BHEs from a detailed numerical model as a reference. The moving finite line source model with anisotropy is employed as the forward model for efficient temperature approximations. Applying the proposed methodology at a monthly resolution for less than three years reduces uncertainty in long-term predictions by over 90%. Additionally, it enhances the applicability of the employed analytical forward model in real field conditions. Thus, this advancement offers a robust tool for stochastic prediction of thermal behavior and decision-making in BHE systems, particularly in scenarios with complex subsurface conditions and limited prior knowledge
Ellipsometric study of TlInS<sub>2</sub> chalcogenide crystals: exploring their potential in nonlinear optics and optoelectronics
No full textTlInS2 exhibits remarkable optical properties, making the compound strong candidate for next-generation optoelectronic and nonlinear optical (NLO) applications. In this study, linear and NLO characteristics of TlInS2 crystal were systematically investigated using spectroscopic ellipsometry. The optical band gap was determined to be 2.41 eV, confirming its indirect nature, which plays a crucial role in tailoring its optoelectronic behavior. The dispersion energy and single effective oscillator energy were found to be 23.2 and 4.69 eV, respectively, based on the Wemple-DiDomenico model. The zero-frequency refractive index was calculated as 2.44, corresponding to a dielectric constant of 5.94. Using the linear refractive index, the nonlinear refractive index and first- and third-order susceptibilities were calculated, indicating a potentially strong NLO response. Additionally, the high oscillator strength and optical moments suggest its potential for efficient light-matter interactions. These results indicate that TlInS2 is a promising material for optical modulators and nonlinear frequency conversion
Goal-Oriented Communication for Real-time Inference Over Networks with Two-way Delay
No full textYapay zekâ alanındaki gelişmeler, endüstriyel robotik ve otonom araçlar gibi teknolojilerin hayata geçirilmesinde önemli bir rol oynamıştır. Ancak bu akıllı modeller, genellikle uzaktan toplanan verilere ihtiyaç duymakta olup, doğru verilerin zamanında iletilmesini sağlayabilecek bir ağ altyapısı gerektirir. Bu motivasyondan yola çıkarak, bu tezde akıllı bir modelin, bir hedef sinyalin gerçek zamanlı değerini uzaktaki bir kaynaktan iletilen veri örnekleriyle tahmin ettiği bir senaryo ele alınmaktadır. Bu sistemde çizelgeleyici (scheduler), i) iletilecek örneklerin yaşını, ii) iletim zamanlarını ve iii) her paketin uzunluğunu (yani her pakette kaç örnek bulunduğunu) belirler. Belirli bir paket uzunluğu için tahmin kalitesinin Bilgi Yaşı’na (Age of Information - AoI) olan bağımlılığı genel bir ilişkiyle modellenmiştir. Önceki çalışmalar ya i.i.d. (bağımsız ve özdeş dağılımlı) iletim gecikmeleri ve anlık geri besleme varsayımında bulunmuş, ya da yalnızca verinin yaşlandıkça çıkarım/tahmin performansının kötüleştiği sınırlı durumları ele almıştır. Buna karşılık, bizim formülasyonumuz performansın yaşa bağlılığı açısından monoton olmayan durumları da kapsamakta ve hem ileri hem de geri besleme bağlantılarında Markov gecikme süreçlerini dikkate almaktadır. Bu problem, sonsuz ufuklu ortalama maliyetli bir Yarı-Markov Karar Süreci (Semi-Markov Decision Process) olarak modellenmiştir. İlk olarak, sabit paket uzunluğu için (i) ve (ii)’yi belirleyen kapalı formda bir çözüm elde ediyor ve bu sabit uzunluk değerini ayrıca optimize ediyoruz. Daha sonra, değişken paket uzunluğu durumunda, dinamik programlama çözümünün hesaplama karmaşıklığını önemli ölçüde azaltan endeks tabanlı bir eşik politikası öneriyoruz. Simülasyon sonuçları, hedef odaklı zamanlayıcımızın, birim uzunluklu paketlerle yaş tabanlı zamanlamaya kıyasla çıkarım hatasını altı kata kadar azaltabildiğini göstermektedir.The rise of artificial intelligence (AI) has played a key role in enabling technologies such as industrial robotics and self-driving vehicles. However, these intelligent models often rely on data collected remotely, requiring the network to deliver the relevant data in a timely fashion. Motivated by this, this thesis studies a setting where an intelligent model infers the real-time value of a target signal using data samples transmitted from a remote source. The scheduler decides on i) the age of the samples to be transmitted, ii) the transmission times, and iii) the length of each packet (i.e., the number of samples contained in each transmission). The dependence of inference quality on the Age of Information (AoI) for a given packet length is modeled by a general relationship. Previous work assumed i.i.d. transmission delays with immediate feedback or were restricted to the case where inference performance degrades as the input data ages. In contrast, our formulation captures non-monotonic age dependence and covers Markovian delay processes on both the forward and feedback links. We model this problem as an infinite-horizon average-cost Semi-Markov Decision Process. First, we derive a closed-form solution that decides on (i) and (ii) for a constant packet length whose value is separately optimized. Then, we propose an index-based threshold policy for the variable packet length case, significantly reducing the computational complexity of the dynamic programming solution. Simulation results demonstrate that our goal-oriented scheduler reduces inference error by up to a factor of six compared to age-based scheduling with unit-length packets.</p
Ecological processes shaping abundant and rare nirK- and nirS-type denitrifying bacteria in Taihu lake sediments
No full textPurpose: Denitrifying bacteria perform a crucial ecological process regulating nitrogen cycling and greenhouse gas emissions. However, the community assembly mechanisms of nitric oxide producing denitrifying bacteria, particularly abundant and rare communities, are poorly understood. Materials and methods: In this study, nirK- and nirS marker genes were amplified following 16S rRNA sequencing, with the aim to identify the mechanisms shaping abundant and rare nirK- and nirS-type denitrifying bacteria in Taihu Lake sediments, particularly regarding the heterogeneity of a wide range of environmental factors. Results: Taihu Lake sediment showed low-pollution levels of heavy metals, but with significant differences of high nutrient status. The phylogenetic diversity of nirK- and nirS-type denitrifying bacteria, and beta-diversity of abundant and rare communities were affected by a combined stressor of heavy metals and nutrients. Our results revealed that rare denitrifying bacteria had significantly higher homogeneity of dispersal than that of abundant taxa. We found heterogeneous selection was the main process governing both abundant and rare nirK-type denitrifying bacteria, while the abundant nirS-type denitrifying bacteria were mainly governed by the dispersal limitation. Complex network interactions between abundant and rare nirK- and nirS-type denitrifying bacteria were detected, further the Mantel test showed strong significant correlations between keystone denitrifying bacteria with environmental factors. Conclusion: This study provides insights into the effects of nutrient pollution on denitrifying bacteria in lake sediment, and also enhances our understanding of community assembly mechanisms of abundant and rare nirK- and nirS-type denitrifying bacteria in lake ecosystems
Greenness and capital investment decisions: Evidence from NYSE firms
No full textThis study explores the impact of greenness on real capital investment on all New York Stock Exchange (NYSE) firms between 2002 and 2021. We measure the greenness of a firm by adjusting the environmental component of its environmental, social, and governance (ESG) score for industry and market effects. The relationship between greenness and investment is examined using two different methodologies. The dynamic panel regression results show that green firms invest more, regardless of how greenness is defined. The quantile regression results imply that companies that have lower levels of capital investment tend to invest more when they are greener, compared to companies that have higher levels of capital investment. The findings of the study are consistent with Pastor, Stambaugh, and Taylor's (2021) prediction that the market will become greener over time because greener firms have higher levels of capital investment compared to brown firms
ERP/SAP ACCEPTANCE IN ORGANIZATIONS: A TAM- BASED MODEL GROUNDED IN SAP IS-U PRACTICES IN THE ENERGY SECTOR
No full textEvaluation of globally gridded precipitation data and satellite-based terrestrial water storage products using hydrological drought recovery time
No full textAccurate precipitation observations are crucial for understanding meteorological and hydrological processes. Most precipitation products rely on station-based observations, either directly or for bias-corrected satellite retrievals. To validate these station-based precipitation products, additional independent data sources are necessary. This study aims to assess the performance of the Global Precipitation Climatology Centre (GPCC) Full Data Monthly Product v2022 and Global Precipitation Climatology Project (GPCP) v3.2 Monthly Analysis Product by estimating the hydrological drought recovery time (DRT) from precipitation and the terrestrial water storage anomaly (TWSA) acquired from satellite gravimetry. This study also evaluates the drought monitoring performance of G3P and JPL mascon total water storage (TWS) monthly solutions from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. The current study employed two methods to estimate DRT and evaluated the consistency of DRT estimates by calculating the time difference in DRT values derived from the two methods. Globally and across all climate zones, GPCC and GPCP showed comparable performance in hydrological applications with no significant differences in the mean DRT estimates. For the TWS products, DRT estimates using JPL mascon were, on average, 2.6 months longer than those using G3P. However, G3P showed approximately 5.0 % higher consistency than JPL mascon globally and across each climate zone, suggesting its better suitability for more precise drought-related analyses. These findings indicate that G3P outperforms JPL mascon in aligning with precipitation products and offers better consistency in DRT estimation. These results provide valuable insights into the accuracy of precipitation and TWSA products by utilizing hydrological drought characteristics, enhancing our understanding of meteorological and hydrological processes