Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived attachment history predicts psychedelic experiences: A naturalistic study

Emerging research indicates that psychedelics may have therapeutic potential by fostering meaningful experiences that act as “inflection points” in people's narratives of personal development. However, psychedelic research has largely failed to address pertinent developmental considerations. We investigated whether attachment-related variables were associated with psychedelic experiences and whether psychedelic experiences moderated expected links between perceived attachment history and current adult attachment orientations.We administered an online survey to an international Jewish sample (N = 185) with psychedelic experience. The survey included measures about recollection of attachment interactions with parents (perceived attachment history), adult attachment orientations (anxiety, avoidance), and psychedelic phenomenology (mystical experiences, challenging experiences, emotional breakthrough, ego dissolution, sensed presence) associated with respondents' most memorable psychedelic experiences.Perceptions of an insecure attachment history were positively linked to all measures of psychedelic phenomenology (r's = 0.19–32, p's mostly < 0.01). In contrast, adult attachment orientations were unrelated to psychedelic phenomenology. Also, psychedelic phenomenology mostly did not moderate the links observed between perceptions of an insecure attachment history and adult attachment orientations.Our findings suggest that perceptions of early attachment experiences may be relevant to psychedelic phenomenology. However, subjective experiences associated with naturalistic psychedelic use do not typically attenuate links between a perceived insecure attachment history and attachment insecurity at present.
Transient Offset in 14C After the Carrington Event Recorded by Polar Tree Rings

The Carrington event of 1859 has been the strongest solar flare in the observational history. It plays a crucial role in shedding light on the frequency and impacts of the past and future Solar Energetic Particle (SEP) events on human societies. We address the impact of the Carrington event by measuring tree‐ring 14C with multiple replications from high‐latitude locations around the event and by comparing them with mid‐latitude measurements. A transient offset in 14C following the event is observed with high statistical significance. Our state‐of‐the‐art 14C production and transport model does not reproduce the observational finding, suggesting features beyond present understanding. Particularly, our observation would require partially fast transport of 14C between the stratosphere and troposphere at high latitudes. The observation is consistent with the previous findings with the SEP events of 774 and 993 CE for which faster integration of 14C into tree rings is observed at high latitudes.
Research on Blockchain-Enabled Smart Grid for Anti-Theft Electricity Securing Peer-to-Peer Transactions in Modern Grids

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain’s core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower–upper decomposition (LUD), lower–upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users’ “honesty coefficients,” providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods.
Chapter 8.A: Food and Agricultural Data Base

One of the main features of the GTAP Data Base is a detailed sectoral classification of agricultural and food products. Twelve sectors within agriculture and eight sectors within the area of food, beverages, and tobacco are identified. This however creates a problem in data collection since not all countries’ input-output (I-O) statistics provide such fine sectoral detail.
Multisensor, Microseismic Observations of a Hurricane Transit Near the ALOHA Cabled Observatory

The generation of microseisms is investigated at the ALOHA Cabled Observatory (ACO) north of Oahu during the close passage of Hurricane Lester in September 2016. Sensors include a seafloor ALOHA pressure gauge at ACO, KIP seismic data on Oahu, and nearby wave buoys. Examination of frequency‐direction spectra from wave buoys and numerical wave model outputs confirms two separate microseism generation processes: At frequencies <0.225 Hz, the microseisms are generated by swells from Hurricane Lester and Typhoon LionRock traveling in opposite directions in the vicinity of ALOHA. At higher frequencies >0.225 Hz, microseisms are dominated by waves originating from Hurricane Lester. The cross‐over frequency (0.225 Hz) occurs where the ocean wave group velocity matches the Hurricane storm track speed. Correcting for impedance, the spectrogram for energy at ALOHA closely correlates with KIP. When opposing swells meet at a distance from the Hurricane Lester and ACO, the resulting microseisms also spread geometrically in propagation to ALOHA and KIP, effectively equivalent to 1/R2. At the microseism peak, 4 September, the dominant motions of KIP are observed with retrograde particle motion characteristic of Rayleigh modes, in both the radial‐vertical and transverse‐vertical sagittal planes at distances of ≲400 km from the eye. Otherwise, the energy on the transverse component is comparable to the radial component. We hypothesize that the observed transverse energy arises locally: (1) from the extended microseism source region near ACO and (2) and from scattering by dipping structure and anisotropy embedded in the crust during emplacement at the Pacific‐Farallon ridge.
COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring

While no longer a public health emergency of international concern, COVID-19 remains an established and ongoing global health threat. As the global population continues to face significant negative impacts of the pandemic, there has been an increased usage of point-of-care ultrasound (POCUS) imaging as a low-cost, portable, and effective modality of choice in the COVID-19 clinical workflow. A major barrier to the widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians who can interpret POCUS examinations, leading to considerable interest in artificial intelligence-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networks for COVID-19 screening using POCUS is the heterogeneity in the types of probes used to capture ultrasound images (e.g., convex vs. linear probes), which can lead to very different visual appearances. In this study, we propose an analytic framework for COVID-19 assessment able to consume ultrasound images captured by linear and convex probes. We analyze the impact of leveraging extended linear-convex ultrasound augmentation learning on producing enhanced deep neural networks for COVID-19 assessment, where we conduct data augmentation on convex probe data alongside linear probe data that have been transformed to better resemble convex probe data. The proposed explainable framework, called COVID-Net L2C-ULTRA, employs an efficient deep columnar anti-aliased convolutional neural network designed via a machine-driven design exploration strategy. Our experimental results confirm that the proposed extended linear–convex ultrasound augmentation learning significantly increases performance, with a gain of 3.9% in test accuracy and 3.2% in AUC, 10.9% in recall, and 4.4% in precision. The proposed method also demonstrates a much more effective utilization of linear probe images through a 5.1% performance improvement in recall when such images are added to the training dataset, while all other methods show a decrease in recall when trained on the combined linear–convex dataset. We further verify the validity of the model by assessing what the network considers to be the critical regions of an image with our contribution clinician.
Role of Locus of Control in the Relationship between Behavioral Biases and Investment Decision

The current study focuses on three heuristics commonly used in decision making; anchoring bias and representative bias with investor’s numeracy capability. This research explains the relationship between biases of investors in investment decision making of Pakistan Stock Exchange. The research also identifies the moderating role of locus of control between behavioral biases and decision making. Data was taken from 400 investors of Pakistan Stock Exchange. The results of the study revealed that representative bias and anchoring bias has a positive impact on investment decisions of the investors. However, the results also revealed that individual’s numeric capabilities do not affect his/her investment decision. Locus of control is used as a moderator in the study. Results show that presence of internal locus of control enhances the impact of biases on their investment decisions. Nonetheless, locus of control does not moderate the relationship between individual’s numeric capabilities and investment decisions. This study is helpful for both individual investors and the regulator in understanding the role of biases in the investment decision making.
An Unsupervised Character Recognition Method for Tibetan Historical Document Images Based on Deep Learning

As there is a lack of public mark samples of Tibetan historical document image characters at present, this paper proposes an unsupervised Tibetan historical document character recognition method based on deep learning (UD-CNN). Firstly, using the Tibetan historical document character component, the Tibetan historical document character sample data set is constructed for model-aided training. Then, the character baseline information is introduced, and a fine-grained feature learning strategy is proposed. For the samples above and below the baseline, the Up-CNN recognition model and Down-CNN recognition model are established. The convolution neural network model is trained and optimized for the samples above and below the baseline, respectively, to improve the recognition accuracy. The experimental results show that the proposed method obviously affects the unmarked character classification and recognition of real Tibetan historical document images. The recognition rate of Top5 can reach 92.94%, and the recognition rate of Top1 can be increased from 82.25% to 87.27% using the CNN model only.
Candida dubliniensis in Japanese Oral Microbiota: A Cross-Sectional Study of Six Geographic Regions in Japan

Introduction: Candida dubliniensis was reclassified from the C. albicans genotype D, and reports show its frequent detection in HIV-positive individuals and easy acquisition of antifungal drug resistance. However, the oral carriage rate in healthy people and contribution to candidiasis in Japan is unclear. Methods: We conducted a cross-sectional survey of the C. dubliniensis carriage rate, performed genotyping and tested antifungal drug susceptibility and protease productivity. Specimens from 2432 Japanese subjects in six regions (1902 healthy individuals, 423 with candidiasis individuals, 107 HIV-positive individuals) were cultured using CHROMagarTMCandida, and the species was confirmed via 25S rDNA amplification and ITS sequences analyzed for genotyping. Results: The C. dubliniensis carriage rate in healthy Japanese was low in the central mainland (0–15%) but high in the most northerly and southerly areas (30–40%). The distribution of these frequencies did not differ depending on age or disease (HIV-infection, candidiasis). Genotype I, previously identified in other countries, was most frequent in Japan, but novel genotypes were also observed. Six antifungal drugs showed higher susceptibility against C. albicans, but protease productivity was low. Conclusions: Oral C. dubliniensis has low pathogenicity with distribution properties attributed to geography and not dependent on age or disease status.
Description and Pathogenicity of Colletotrichum kapreanum sp. nov, a Cherelle Wilt Pathogen Belonging to the Gigasporum Species Complex

Similar to cacao pod rot, cherelle wilt decreases production from cacao fields. Among all known fungal pathogens of the cacao, Colletotrichum spp. are common infectious agents that affect the cherelles and pods of cacao; thus, cacao diseases are often classified by stage. Therefore, knowing whether these pathogens are common in both fruit stages is necessary for implementing disease control measures. Symptoms of cherelle wilt were found in cacao plants in Pangasinan, Philippines, in 2022. The fungal strain obtained from the lesion was found to be pathogenic towards cherelles, but not towards pods. The strain was classified as an unknown species belonging to the gigasporum species complex, based on the morphological and molecular phylogenetic analyses of ITS, GAPDH, CHS1, ACT, and TUB2. We propose Colletotrichum kapreanum sp. nov. as a causal agent of cacao cherelle wilt, but not pod rot.
Simulation modeling and safety analysis of the mixed traffic flow with connected automated trucks platoon and human-driven vehicles

The rapid development of intelligent connected vehicles makes it possible to achieve breakthroughs in the application of technology in the connected automated trucks platoon(CATP) in the scenarios of ports, logistics parks, etc., but the safety of the mixed traffic flow formed by it and traditional human-driven vehicles has not been fully recognized. This paper builds a simulation scene based on SUMO(Simulation of Urban Mobility) simulation software, builds traditional human-driven vehicles and CATP car-following model, and studies the safety analysis of the mixed traffic flow of CATP and human-driven vehicles. According to the Traci interface of SUMO and PYTHON programming language, the functions of connected automated environment configuration and real-time data acquisition are realized, and the safety of mixed traffic flow is evaluated using multiple safety surrogate measures(SSM) such as time exposed time-to-collision(TET) and time-integrated time-to-collision(TIT). The simulation results show that when the CATP penetration rate is less than 0.5, the security risk of mixed traffic flow increases with the increase of penetration rate; When the CATP penetration rate exceeds 0.5, the safety risk of mixed traffic flow will be significantly reduced. The research conclusion of this paper has reference significance for the safety and practical implementation of CATP in ports and logistics parks.
ASSESSING THE SPATIAL ACCURACY AND PRECISION OF LIDAR FOR REMOTE SENSING IN AGRICULTURE

OF THESIS ASSESSING THE SPATIAL ACCURACY AND PRECISION OF LIDAR FOR REMOTE SENSING IN AGRICULTURE The objective of this whole study was to evaluate a LiDAR sensor for high-resolution remote sensing in agriculture. A linear motion system was developed to precisely control the dynamics of LiDAR sensor in effort to remove uncertainty in the LiDAR position/velocity while under motion. A user control interface was developed to operate the system under different velocity profiles and log LiDAR data synchronous to the motion of the system. The LiDAR was then validated using multiple test targets with five different velocity profiles to determine the effect of sensor velocity and height above a target on measurement error. The results indicated that the velocity of the LiDAR was a significant factor affecting the error and standard deviation of the LiDAR measurements, although only by a small margin. Then the concept of modeling the alfalfa using the linear motion system was introduced. Two plots of alfalfa were scanned and processed to extract height and volume and was compared with photogrammetric and field measurements. Insufficient alfalfa plots were scanned which prevented any statistical analysis from being used to compare the different methods. However, the comparison between LiDAR and photogrammetric data showed some promising results which may be further replicated in the future.
Comparative analysis of the stability and natural vibrations of shallow panels under the action of thermomechanical loads

The work is a continuation of research devoted to substantiating the reliability of solutions obtained by the finite element method for the analysis of nonlinear deformation, buckling and vibrations of thin elastic shells under the action of thermomechanical loads. The method is based on geometrically nonlinear relations of the three-dimensional theory of thermoelasticity and the principles of the moment finite element scheme. A thin elastic shell of an inhomogeneous structure is modeled by a universal spatial isoparametric finite element. The modal analysis of the shell is implemented at each step of the static thermomechanical load. The subspace iteration method is used to determine the spectrum of the lowest frequencies of natural vibrations of shells. A shallow spherical panel with a square plan is considered. The effect of preheating on the loss of stability and vibrations of an elastic isotropic shell under uniform pressure loading is investigated. The behavior of the shell weakened by two pairs of cross-channels is analyzed. The weakening of the panel by narrow and wide channels, which can be eccentrically located relative to the middle surface of the shell, is considered. The effectiveness and adequacy of the method is confirmed by a comparative analysis of solutions with results obtained using modern multifunctional software systems LIRA-SAPR and SCAD. The features of using the systems for solving the problems under consideration are given. Analysis of the results made it possible to evaluate the possibilities of using these software systems to substantiate the reliability of solutions to certain classes of problems of geometrically nonlinear deformation, buckling and vibrations of elastic shells.
Programme evaluation and spillover effects

This paper is a practical guide for researchers and practitioners who want to understand spillover effects in programme evaluation. It defines spillover effects and discusses why it is important to measure them. It explains how to design a field experiment to measure the average effects of the treatment on subjects, both eligible and ineligible, for the programme in the presence of spillover effects. In addition, it discusses the use of nonexperimental methods for estimating spillover effects when the experimental design is not a viable option. Evaluations that account for spillover effects should be designed such that they explain both the cause of these effects and whom they affect. Such an evaluation design is necessary to avoid inappropriate policy recommendations and neglecting important mechanisms through which the programme operates.
Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods.
Gut microbiota and metabolites in patients with COVID-19 are altered by the type of SARS-CoV-2 variant

Introduction Patients with COVID-19 have dysbiosis of the intestinal microbiota with altered metabolites in the stool. However, it remains unclear whether the differences among SARS-CoV-2 variants lead to differences in intestinal microbiota and metabolites. Thus, we compared the microbiome and metabolome changes for each SARS-CoV-2 variant in patients with COVID-19. Materials and methods We conducted a multicenter observational study of patients with COVID-19 and performed fecal microbiome, metabolome, and calprotectin analyses and compared the results among the different SARS-CoV-2 variants. Results Twenty-one patients with COVID-19 were enrolled and stratified according to the SARS-CoV-2 strain: six with the Alpha, 10 with the Delta, and five with the Omicron variant. Fecal microbiome analysis showed that α-diversity was reduced in the order of the Omicron, Delta, and Alpha variants (p = 0.07). Linear discriminant analysis revealed differences in the abundance of short-chain fatty acid-producing gut microbiota for each SARS-CoV-2 variant. Fecal metabolome analysis showed that the Omicron and Delta variants had markedly reduced propionic and lactic acid levels compared to the Alpha strain (p < 0.05). Conclusion The intestinal microbiota of patients with COVID-19 varies depending on the SARS-CoV-2 variant. Dysbiosis of the intestinal microbiota due to differences in SARS-CoV-2 variants causes a decrease in intestinal short-chain fatty acids.