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Here's the Greenium eclipsed by market-wide illiquidity in the municipal bond market
No full textThis study examines whether a greenium exists in the municipal bond market. Estimations show that after controlling for bond, market, and issuer characteristics, green bonds are issued with a yield advantage between 15 and 25 basis points. This finding is confirmed by using the Oaxaca-Blinder decomposition while accounting for the differences in the bond, market, and issuer characteristics of green and conventional bonds. Decomposition results still show a yield difference between 10 and 20 basis points, which cannot be explained by any of the known control factors. This unexplained yield differential is the greenium in the municipal bond market. Analyses also show that when the impact of the liquidity premium on the green bond yield is ignored, it is possible to conclude that the greenium is insignificant. The study also models a municipality's decision to issue green instead of conventional bonds and presents findings that municipalities are more likely to issue green bonds after periods of higher market liquidity, larger average greenium, and if they have already issued a green bond in the past. Climate related concerns and awareness in an issuer's state also make a municipality more likely to prefer green bonds over conventional bonds when financing is needed
An improved algorithm exploiting the characteristics of a distance-based preference function to converge to preferred solutions
No full textWe address the problem of choosing the most preferred of a set of alternatives that are defined by multiple criteria. We assume that the decision maker's preferences can be represented by a general class of weighted distance functions that can take a wide variety of forms. We exploit the characteristics of these functions and develop an interactive algorithm that guarantees to find the most preferred alternative of a decision maker whose preferences are consistent with a distance-based function. In contrast with a benchmark algorithm that uses similar preference functions, our algorithm moves through different distance functions effectively to converge to the best alternative quickly. Our experiments on a variety of three- and four-objective problems demonstrate that our algorithm performs well, far outperforming the benchmark algorithm in terms of the required preference information from the decision maker
CANet: ChronoAdaptive network for enhanced long-term time series forecasting under non-stationarity
No full textLong-term time series forecasting plays a pivotal role in various real-world applications. Despite recent advancements and the success of different architectures, forecasting is often challenging due to non-stationary nature of the real-world data, which frequently exhibit distribution shifts and temporal changes in statistical properties like mean and variance over time. Previous studies suggest that this inherent variability complicates forecasting, limiting the performance of many models by leading to loss of non-stationarity and resulting in over-stationarization (Liu et al., 2022c). In order to address this challenge, we introduce a novel architecture, ChoronoAdaptive Network (CANet), inspired by style-transfer techniques. The core of CANet is the Non-stationary Adaptive Normalization module, seamlessly integrating the Style Blending Gate and Adaptive Instance Normalization (AdaIN) (Huang and Belongie, 2017). The Style Blending Gate preserves and reintegrates non-stationary characteristics, such as mean and standard deviation, by blending internal and external statistics, preventing over-stationarization while maintaining essential temporal dependencies. Coupled with AdaIN, which dynamically adapts the model to statistical changes, this approach enhances predictive accuracy under non-stationary conditions. CANet also employs multi-resolution patching to handle short-term fluctuations and long-term trends, along with Fourier analysis-based adaptive thresholding to reduce noise. A Stacked Kronecker Product Layer further optimizes the model's efficiency while maintaining high performance. Extensive experiments on real-world datasets validate CANet's superiority over state-of-the-art methods, achieving a 42 % reduction in MSE and a 22 % reduction in MAE. The source code is publicly available at1
First Fully Pipelined High Throughput FPGA Implementation and GPU Optimization of Wider Variant of AES
No full textIn response to the recent NIST call for a wider variant of the AES algorithm, we developed a fully pipelined, high-throughput FPGA implementation of the 256-bit block size AES, referred to as WAES-256. This design targets both 7th generation and UltraScale+ FPGAs, focusing on maximizing throughput and efficient hardware utilization. Our work supports AES-128, AES-256, and WAES-256, employing composite field arithmetic in the S-box to reduce critical path delay. All AES layers are fully pipelined, enabling multiple levels of parallelism with minimal architectural changes. Our AES-128 implementations achieved the best throughput-per-slice (TPS) ratios reported in the literature for fair comparisons on the same FPGA platforms. For WAES-256, our designs reached 75.73 Gbps on Spartan-7, 72.32 Gbps on Artix-7, 199.46 Gbps on Zynq UltraScale+, and 206.11 Gbps on Kintex UltraScale+. Additionally, our multi-core parallel WAES-256 designs achieved 426.66 Gbps with x2 cores and 742.63 Gbps with x4 cores on the Kintex UltraScale+ platform, demonstrating the scalability of our approach. These results highlight the efficiency and scalability of our architectures, offering high-throughput performance without relying on BRAM (Block Random Access Memory), making them well-suited for next-generation cryptographic applications. Moreover, we optimized WAES-256 on GPUs and achieved performance comparable to the best AES-256 results. For instance, we achieved 3053.5 Gbps WAES-256 encryption in counter mode of operation on an RTX 4090. Our results show that using FPGAs or GPUs as co-processors for WAES-256 render encryption-free and transition from AES-256 to WAES-256 results in no observable slowdowns
An examination of the views of counsellors and clients towards online counselling
No full textThis study, employing Actor-Network Theory (ANT), aims to examine theexperiences, perspectives, and suggestions of clients and counsellorsregarding online counselling (OC). Participants selected by the criterionsampling method for the study were required to have had at least eightsessions of online videoconferencing counselling. Ten clients and tencounsellors participated in the study. Data was collected through onlineinterviews using two similar semi-structured interview forms, one forcounsellors and one for clients. Interpretative PhenomenologicalAnalysis (IPA) was used to analyse the data. The findings werecategorised into common superordinate themes for both counsellorsand clients: the pros and the cons of OC, online therapeutic process,preferences, and suggestions. One additional superordinate theme forcounsellors was pushing the limits of creativity. The study includes adiscussion of the findings obtained from the two participant groups interms of the existing literature.</p
Analyzing design parameters for lithium-ion batteries: A study on specific energy for practical applications
No full textThe continuous improvement of lithium-ion batteries (LIBs) has been marked by a steady rise in energy density, primarily driven by advancements in materials and cell design. Nevertheless, despite notable progress since their inception, achieving the targeted energy density remains a considerable challenge. While much of the research emphasizes the development of novel materials, the core difficulty in commercialization lies in optimizing and integrating these materials into practical systems. Such optimization not only improves compatibility with existing technologies but also offers the potential to enhance the performance of current LIB cells. In this study, fundamental calculations of cell energy density were performed across all battery components to investigate strategies for achieving targeted values. To ensure accuracy, both theoretical and empirical equations reported in the literature were employed. Furthermore, the challenges associated with optimizing individual parameters, alongside recent developments addressing these limitations, are comprehensively discussed
PEDOT:PSS: borophene and P3HT: borophene nanocomposites for multi-gas detection
No full textThe need for low-cost and selective gas sensors is increasing for industrial safety, environmental monitoring, and occupational health; in particular, the reliable monitoring of NO2, NH3, and volatile organic compounds (VOCs) at low ppm levels is a crucial requirement. In this study, nanocomposite films prepared using Poly(2,3-dihydrothieno-1,4-dioxin)-poly(styrenesulfonate) (PEDOT:PSS) and Poly(3-hexylthiophene-2,5-diyl) (P3HT) with borophene were applied to gold interdigitated transducer (IDT) electrodes, and their multi-gas detection performances against NO2, NH3, formaldehyde, ethanol, methanol, acetone, chloroform, and hexane were compared. The PEDOT:PSS:Borophene-based sensor demonstrated particularly high performance against alcohol-based VOCs; For ethanol, sensitivity was 35%/ppm, response time was 21 s, and LOD was 0.62 ppm, while for methanol, sensitivity was 28%/ppm, response time was 23 s, and LOD was 0.75 ppm. The P3HT: Borophene sensor showed a remarkable response to NO2, a strong electron acceptor; sensitivity for NO2 was 71%/ppm, response time was 16 s, and LOD was 0.12 ppm. The findings indicate that borophene addition significantly enhances detection performance, improves selectivity, and lowers detection limits in both conductive polymer structures. Therefore, the developed PEDOT:PSS:Borophene and P3HT: Borophene nanocomposite sensors are considered strong candidates for environmental monitoring of toxic gases and industrial process safety
A Supervised Learning Method for High-Performance Channel State Information Estimation
No full textChannel state information (CSI) is pivotal for assuring high performances of wireless communication systems. In particular, multiple-input multiple-output transmission is only beneficial when CSI is known. A large number of subcarriers are desired in Orthogonal Frequency Division Multiplex (OFDM) systems to boost overall throughput, which makes channel estimation a more challenging task, especially to extract channel features in a more dynamic environment without causing a significant overhead transmission. Conventional least squares-based methods are affected by the noise and interference that inherently exist in the acquired data for processing. We proposed the deep neural network (DNN)-based method to estimate CSI, and one distinguishing characteristic is to adopt a Discrete Fourier Transform (DFT) operation-based method to mitigate the impact of noise before carrying out the DNN procedure; hence, the accuracy of the learning outcome significantly improved. The effectiveness of the proposed scheme is verified with simulations under a variety of propagation scenarios. The proposed method has demonstrated a high performance for channel estimation. It has shown a particular advantage in more dynamic and noisy environments for wireless communications
Consciousness and the measurement problem
No full textThis paper examines two approaches to the measurement problem in quantum mechanics, invoking the concept of consciousness and highlighting the strengths and weaknesses of each. The first approach is the model proposed by David Chalmers and Kelvin McQueen, based on the idea of the Consciousness Collapses Wave Function (CCWF), originally attributed to John von Neumann and Eugene Wigner. The second approach is the phenomenological framework known as the London-Bauer-French (LBF) approach. We contend that significant challenges have been raised against key features of the CCWF model. However, these criticisms require further arguments to effectively undermine its most crucial claim: that the model can be tested to determine whether consciousness collapses the wave function. We will demonstrate that while CCWF offers a mathematically defined collapse mechanism that yields straightforward, experimentally testable predictions about collapse per se, these tests do not, by themselves, establish that consciousness is the cause of collapse. Nevertheless, despite this limitation, their model provides conceptual clarity in addressing the measurement problem, as it utilizes a mathematically defined collapse mechanism that offers a straightforward and testable solution. In contrast, the LBF approach lacks conceptual clarity regarding the measurement problem, as it continues to depend on the standard quantum formalism
Managing plant viral diseases with advanced nanoparticles
No full textThe chapter evaluates both the beneficial and harmful dimensions of using engineered nanoparticles in agriculture.</p