Miracle-dz/UniFAR-FTUs huggingface dataset:
Cobalt-Catalyzed Asymmetric Hydrogenation of Enamides: Insights into Mechanisms and Solvent Effects

The mechanistic details of the (PhBPE)Co-catalyzed asymmetric hydrogenation of enamides are investigated using computational and experimental approaches. Four mechanistic possibilities are compared: a direct Co(0)/Co(II) redox path, a metathesis pathway, a nonredox Co(II) mechanism featuring an aza-metallacycle, and a possible enamide–imine tautomerization pathway. The results indicate that the operative mechanism may depend on the type of enamide. Explicit solvent is found to be crucial for the stabilization of transition states and for a proper estimation of the enantiomeric excess. The combined results highlight the complexity of base-metal-catalyzed hydrogenations but do also provide guiding principles for a mechanistic understanding of these systems, where protic substrates can be expected to open up nonredox hydrogenation pathways.
Mechanisms influencing mainstreaming of adaptation in spatial development: case studies in three Dutch municipalities

Despite adopting a mainstreaming approach to spatial adaptation, few cities actually implement it. Why this is so is disputed, as the implementation stage has been under-researched. Inspired by the “mechanism-based” strand, this paper analyses three implementation projects in the Netherlands. Mechanisms are generalizable patterns yielding more plausible explanations for phenomena than “superficial” barriers or drivers. We found one overarching mechanism: the “institutional void” – an absence of policy frameworks with clear norms and agreements on adaptation. It led to three hampering mechanisms (avoidance of administrative responsibility, aversion to innovation and avoidance of private responsibility) and three stimulating mechanisms (window of opportunity exploitation, efficient coupling and policy entrepreneurship). We demonstrate that, although hampering mechanisms constrain the solution space for mainstreaming adaptation, the stimulating mechanisms prove that room remains for committed actors to use the limited available space efficiently.
A Novel Deep-Learning Method with Channel Attention Mechanism for Underwater Target Recognition

The core of underwater acoustic recognition is to extract the spectral features of targets. The running speed and track of the targets usually result in a Doppler shift, which poses significant challenges for recognizing targets with different Doppler frequencies. This paper proposes deep learning with a channel attention mechanism approach for underwater acoustic recognition. It is based on three crucial designs. Feature structures can obtain high-dimensional underwater acoustic data. The feature extraction model is the most important. First, we develop a ResNet to extract the deep abstraction spectral features of the targets. Then, the channel attention mechanism is introduced in the camResNet to enhance the energy of stable spectral features of residual convolution. This is conducive to subtly represent the inherent characteristics of the targets. Moreover, a feature classification approach based on one-dimensional convolution is applied to recognize targets. We evaluate our approach on challenging data containing four kinds of underwater acoustic targets with different working conditions. Our experiments show that the proposed approach achieves the best recognition accuracy (98.2%) compared with the other approaches. Moreover, the proposed approach is better than the ResNet with a widely used channel attention mechanism for data with different working conditions.
Achieving Operational Efficiency through Risk Disclosure

Manuscript type: Research paper Research aims: This study aims to investigate the effect of risk disclosure and CEO age on the operational efficiency. Design/Methodology/Approach: A quantitative study was undertaken on 130 manufacturing firms listed on the Indonesian Stock Exchange in 2019. Risk disclosure was evaluated through the content analysis, while operational efficiency was measured using the Data Envelopment Analysis (DEA) technique. The data was analysed by utilising weighted least square regression. Research findings: Results show that risk disclosure positively affects operational efficiency. The effect on CEO’s age however, is not substantial. These findings imply that a company becomes more efficient when it discloses more risk, while the CEO’s age has no bearing on the efficiency attained. Risk disclosure operates as a form of risk awareness that demonstrates management’s commitment to resolving the problems. Theoretical contribution/Originality: This study addresses the gap in the literature by empirically assessing how risk disclosure affect firms’ efficiency in developing countries. It also expands on previous literature by considering CEO age as determinant of the efficiency. The findings of the study support the stakeholder theory in which a company must consider its key stakeholders, especially those who encourage it to improve its efficiency, and hence, risk disclosure is required. Practitioner/Policy implications: This research suggests companies to disclose more to improve operational efficiency. It also recommends investors to invest in firms committed to risk disclosure. Research limitation/Implications: In this study, risk disclosure was measured by the number of risks disclosed in the annual report and did not consider other sources. Therefore, future studies should further explore risk disclosure in different media, such as the newspaper, corporate websites, and social medias. Additionally, this study was conducted based on one industry sector, and hence, future studies may incorporate broader range of industries to capture the impact of CEO characteristics. Keywords: CEO Age, Data Envelopment Analysis (DEA), Operational Efficiency, Risk Disclosure JEL Classification: M41
VALUE RELEVANCE: ANÁLISE DOS EFEITOS DA ADOÇÃO DA IFRS 15 – RECEITA DE CONTRATO COM CLIENTES

O International Accounting Standards Board (IASB) e o Financial Accounting Standard Board (FASB) emitiram, em 2014, a norma IFRS 15 (Revenue from Contracts with Customers) que dispõe sobre o reconhecimento das receitas de contratos com os clientes. No Brasil, foi emitido o pronunciamento técnico CPC 47 - Receita de Contrato com Clientes - que passou a vigorar a partir do ano de 2018. Considerando essas informações, a presente pesquisa teve por objetivo analisar o efeito da adoção da IFRS 15 na qualidade da informação contábil apresentada pelas empresas brasileiras do setor de construção civil e construção pesada listadas na B3 - Brasil Bolsa Balcão. A amostra do estudo é composta por empresas de capital aberto listadas na B3, pertencentes ao setor de construção civil e construção pesada, totalizando 20 empresas. O período de observação contemplou os períodos anterior (2014 a 2017) e posterior à vigência obrigatória da IFRS 15 (ano de 2018 e primeiro trimestre de 2019). Para fins desta pesquisa foram considerados os resultados trimestrais, totalizando 400 observações. Os dados foram coletados na base de dados da Economática®, nas Demonstrações Financeiras Padronizadas e no sítio da B3. Para tanto, foi utilizado o modelo de Value Relevance, desenvolvido por Ohlson (1995), com a inclusão de outras variáveis. Para verificar se houve uma associação positiva entre a adoção da IFRS 15 com a relevância da informação contábil, fez-se uso da regressão linear múltipla com dados em painel balanceado. Os resultados encontrados evidenciaram que a relevância da informação contábil nas empresas brasileiras do setor de construção civil e pesada aumentou após a adoção da IFRS 15.
GPI 2.0: performance of upgrades to the Gemini Planet Imager CAL and IFS

The Gemini Planet Imager (GPI) is a facility class instrument for the Gemini Observatory with the primary goal of directly detecting young Jovian planets. After several years of successful operations on sky at Gemini South, GPI is undergoing an upgrade at the University of Notre Dame and is being moved to Gemini North. We present the current performance results, from in-lab testing, for several of the upgraded components to the Integral Field Spectrograph (IFS) and the Calibration Wavefront Sensor (CAL) for GPI 2.0. These upgrades include changes to the IFS dispersion prisms, changes to the pupil viewing cameras, and changes to the low order wavefront sensor. These improvements are designed to improve the magnitude and contrast range of GPI. We describe the alignment of several components, their noise characteristics, and their performance in the GPI environment.
System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis

Background The System Usability Scale (SUS) is a widely used scale that has been used to quantify the usability of many software and hardware products. However, the SUS was not specifically designed to evaluate mobile apps, or in particular digital health apps (DHAs). Objective The aim of this study was to examine whether the widely used SUS distribution for benchmarking (mean 68, SD 12.5) can be used to reliably assess the usability of DHAs. Methods A search of the literature was performed using the ACM Digital Library, IEEE Xplore, CORE, PubMed, and Google Scholar databases to identify SUS scores related to the usability of DHAs for meta-analysis. This study included papers that published the SUS scores of the evaluated DHAs from 2011 to 2021 to get a 10-year representation. In total, 117 SUS scores for 114 DHAs were identified. R Studio and the R programming language were used to model the DHA SUS distribution, with a 1-sample, 2-tailed t test used to compare this distribution with the standard SUS distribution. Results The mean SUS score when all the collected apps were included was 76.64 (SD 15.12); however, this distribution exhibited asymmetrical skewness (–0.52) and was not normally distributed according to Shapiro-Wilk test (P=.002). The mean SUS score for “physical activity” apps was 83.28 (SD 12.39) and drove the skewness. Hence, the mean SUS score for all collected apps excluding “physical activity” apps was 68.05 (SD 14.05). A 1-sample, 2-tailed t test indicated that this health app SUS distribution was not statistically significantly different from the standard SUS distribution (P=.98). Conclusions This study concludes that the SUS and the widely accepted benchmark of a mean SUS score of 68 (SD 12.5) are suitable for evaluating the usability of DHAs. We speculate as to why physical activity apps received higher SUS scores than expected. A template for reporting mean SUS scores to facilitate meta-analysis is proposed, together with future work that could be done to further examine the SUS benchmark scores for DHAs.
Complete mitochondrial DNA sequence of Golden trevally Gnathanodon speciosus (Forsskål, 1775) and the phylogenetic analysis of Carangidae

Abstract In this study, the complete mitochondrial genome of Gnathanodon speciosus was determined. The entire mitochondrial DNA sequence is 16,555 bp in length and consists of 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA) genes, two ribosomal RNA (rRNA) genes, and a control region. Phylogenetic analysis was performed using 13 PCGs showing that G. speciosus belongs to the Carangidae family, and is most closely related to the species in genera Caranx and Megalaspis.
Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.
An Investigation of the Neurotoxic Effects of Malathion, Chlorpyrifos, and Paraquat to Different Brain Regions

Acute or chronic exposures to pesticides have been linked to neurotoxicity and the potential development of neurodegenerative diseases (NDDs). This study aimed to consider the neurotoxicity of three widely utilized pesticides: malathion, chlorpyrifos, and paraquat within the hippocampus (HC), corpus striatum (CS), cerebellum (CER), and cerebral cortex (CC). Neurotoxicity was evaluated at relatively low, medium, and high pesticide dosages. All pesticides inhibited acetylcholinesterase (AChE) and neuropathy target esterase (NTE) in each of the brain regions, but esterase inhibition was greatest in the HC and CS. Each of the pesticides also induced greater disruption to cellular bioenergetics within the HC and CS, and this was monitored via inhibition of mitochondrial complex enzymes I and II, reduced ATP levels, and increased lactate production. Similarly, the HC and CS were more vulnerable to redox stress, with greater inhibition of the antioxidant enzymes catalase and superoxide dismutase and increased lipid peroxidation. All pesticides induced the production of nuclear Nrf2 in a dose-dependent manner. Collectively, these results show that pesticides disrupt cellular bioenergetics and that the HC and CS are more susceptible to pesticide effects than the CER and CC.
Expected Utility Theory on General Affine GARCH Models

ABSTRACT Expected utility theory has produced abundant analytical results in continuous-time finance, but with very little success for discrete-time models. Assuming the underlying asset price follows a general affine GARCH model which allows for non-Gaussian innovations, our work produces an approximate closed-form recursive representation for the optimal strategy under a constant relative risk aversion (CRRA) utility function. We provide conditions for optimality and demonstrate that the optimal wealth is also an affine GARCH. In particular, we fully develop the application to the IG-GARCH model hence accommodating negatively skewed and leptokurtic asset returns. Relying on two popular daily parametric estimations, our numerical analyses give a first window into the impact of the interaction of heteroscedasticity, skewness and kurtosis on optimal portfolio solutions. We find that losses arising from following Gaussian (suboptimal) strategies, or Merton's static solution, can be up to and 5%, respectively, assuming low-risk aversion of the investor and using a five-years time horizon.
Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

This article provides a comprehensive overview of no reference (NR) metrics for image quality analysis (IQA) and video quality analysis (VQA). We examine 26 independent evaluations of NR metrics (previously published) and analyze 32 NR metrics on six IQA datasets and six VQA datasets (new results). Where NR metric developers claim Pearson correlation values between 0.66 and 0.99, our measurements range from 0.0 to 0.63. None of the NR metrics we analyzed are accurate enough to be deployed by industry. Performance evaluations that indicate otherwise are based on insufficient data and highly inaccurate. We will examine development strategies, tools, datasets, root cause analysis, and our baseline metric for collaboration, Sawatch.
Genome-Wide Association Study of Leaf Rust and Stem Rust Seedling and Adult Resistances in Tetraploid Wheat Accessions Harvested in Kazakhstan

Leaf rust (LR) and stem rust (SR) are diseases increasingly impacting wheat production worldwide. Fungal pathogens producing rust diseases in wheat may cause yield losses of up to 50–60%. One of the most effective methods for preventing such losses is the development of resistant cultivars with high yield potential. This goal can be achieved through complex breeding studies, including the identification of key genetic factors controlling rust disease resistance. The objective of this study was to identify sources of tetraploid wheat resistance to LR and SR races, both at the seedling growth stage in the greenhouse and at the adult plant stage in field experiments, under the conditions of the North Kazakhstan region. A panel consisting of 193 tetraploid wheat accessions was used in a genome-wide association study (GWAS) for the identification of quantitative trait loci (QTLs) associated with LR and SR resistance, using 16,425 polymorphic single-nucleotide polymorphism (SNP) markers in the seedling and adult stages of plant development. The investigated panel consisted of seven tetraploid subspecies (Triticum turgidum ssp. durum, ssp. turanicum, ssp. turgidum, ssp. polonicum, ssp. carthlicum, ssp. dicoccum, and ssp. dicoccoides). The GWAS, based on the phenotypic evaluation of the tetraploid collection’s reaction to the two rust species at the seedling (in the greenhouse) and adult (in the field) stages, revealed 38 QTLs (p < 0.001), comprising 17 for LR resistance and 21 for SR resistance. Ten QTLs were associated with the reaction to LR at the seedling stage, while six QTLs were at the adult plant stage and one QTL was at both the seedling and adult stages. Eleven QTLs were associated with SR response at the seedling stage, while nine QTLs were at the adult plant stage and one QTL was at both the seedling and adult stages. A comparison of these results with previous LR and SR studies indicated that 11 of the 38 QTLs are presumably novel loci. The QTLs identified in this work can potentially be used for marker-assisted selection of tetraploid and hexaploid wheat for the breeding of new LR- and SR-resistant cultivars.
Deep learning Fault Diagnosis in Flight Control System of Carrier-Based Aircraft

As an indispensable part of carrier-based aircraft, the actuator system plays an important role in ensuring the flight safety. Fault detection and diagnosis of actuator are necessary for improving actuator system reliability. Motivated by solving the uncertainty problem in fault diagnosis of actuator system, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a deep stacked autoencoder network-based (DSAEN) deep learning fault diagnosis method for flight control system. The flight parameters of carrier-based aircraft in different fault modes are measured, detected, and diagnosed by the proposed method. Simulated data is used to train the fault diagnosis model, as well as validate the proposed fault diagnosis method. Experimental results show that compared with traditional fault diagnosis methods, such as back propagation neural network (BPNN) algorithm, the proposed method has better robustness and higher accuracy.
Low profile multi-polarization diversity UWB antenna for body centric communications

Abstract A low-profile multi-polarized MIMO ultra-wideband antenna suitable for body-centric communications is reported. MIMO configuration is achieved using the symmetric heptagonal monopole (SHM) antenna, a mirrored SHM antenna, an asymmetric ruby-shaped monopole (ARSM) antenna, and a mirrored ARSM antenna placed over the modified C-shaped ground plane (MCGP). The proposed antenna elements are designed to achieve resonance for the entire ultra-wideband frequency range. The SHM antenna is coupled with a co-planar waveguide to produce linear polarization. An ARSM antenna generates circular polarization utilizing an asymmetric geometry both in the radiator and a truncated ground plane. Both the SHM antenna and an ARSM antenna are aligned face-to-face over an MCGP plane and good isolation >15 dB is realized between the antenna elements. The gain and total efficiency for linear polarized and the circularly polarized antennas are 3.05–4.8 dBi/48–66% and from 2.8 to 5 dBic/52 to 70.5%, respectively. The specific absorption rate values are evaluated to understand their suitability for WBAN communications.
Causes and Nature of Educational Neglect in German Research

The relevance of the study is conditioned by the need to find out the causes of pedagogical neglect of adolescents in the context of German experience and identify possible solutions to this problem. The purpose of the study is to highlight the main views of German researchers on the diagnosis of pedagogical neglect and its further correction. From a methodological standpoint, the study is based on the content analysis method, which is one of the tools for conducting desk research. The studies by K. Lissmann, W. Von Humboldt, R.D. Precht, P. Wisner, G. Kindler were considered. The main problems that ensure educational neglect in Germany have been identified: insufficient funding, lack of highly qualified staff, misdirection of skills and qualities of students, low level of social security, etc. An important factor is the quality of relationships between teenagers and other people (parents, teachers, friends), as well as their parents with them. Recommendations were outlined for solving the above-mentioned problematic issues, which is the practical significance of the study. In particular, it is necessary to: increase funding to ensure the organisation of the educational process, increase the number of quotas in teachers’ universities, develop a new educational programme that will meet European standards, but at the same time not harm the local education system, ensure social protection of all participants in the educational process
Air Pollution Trend and Variation during a Mega Festival of Firecrackers (Diwali) in Context to COVID-19 Pandemic

The present study was conducted in Lucknow city to assess the impact of firecracker burning during Diwali, from 2 November 2021–6 November 2021 including the pre and post-Diwali days. The concentrations of PM10, PM2.5, SO2, NO2, CO, O3, benzene and toluene, were monitored from the Central Pollution Control Board site on an hourly basis. The Air Quality Index was also recorded for PM10, PM2.5, SO2 and NO2. A questionnaire survey was done with 51 doctors to know the reported complaints post-Diwali. On Diwali night the PM2.5 value reached 262 µg m−3 around 22:00 hours and the maximum value (900 µg m−3) was obtained on 5 November, reported from the Central School monitoring station. From Gomti Nagar highest PM2.5 value obtained on Diwali day was 538 µg m−3 at 23:00 hours reaching 519 µg m−3 post-Diwali. Areas belonging to the old part of the city witnessed higher variations as PM2.5 crossed 900 µg m−3, in Lalbagh and Talkatora areas. The multivariate analysis showed that on Diwali night there was an increase of 204, 386, 344 and 341 in the PM2.5 concentration reported from Gomtinagar, Central School, Talkatora and Lalbagh stations, showing that firecracker burning resulted in a significant increase in air pollution. The Toluene/Benzene ratio was mostly more than 1 indicating that toluene and benzene may be emitted from other sources as well including the mobile sources. Around 50–75% rise was seen in the number of patients post-Diwali. 57.1% of the reported cases had respiratory issues, followed by allergic reactions. The data obtained from Lalbagh, Talkatora and Central School showed that although the values remained high, a decreasing trend was seen in the AQI compared to previous years which is a good sign and may be attributed to public awareness and the ongoing pandemic making people conscious.