Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and Sequential Cause in Hidden Markov Models

Causal reasoning for sequential time-series observations is an important problem in a number of disciplines. Recently, Baier et al. have formalized the notion of cause of a language in Markov chains as prefixes that raise the probability of an effect. In this paper, we extend this notion of p -causes for Hidden Markov Models. A natural question is to investigate the relationship between this notion of p -causes and Pearl’s notion of total causal effect defined using structural causal models and interventions. We explore this relationship for Hidden Markov Models. We find that even when we construct a SCM to have the same probability distribution as the HMM, the notions are incompatible: p -causes do not imply total causal effect and total causal effect does not imply a p -cause. This incompatibility comes from underlying conceptual differences. A p -cause is set of specific observation sequences that leads to a specific effect with high probability. A total causal effect in the constructed SCM implies that the current state at one time will effect the state at a later time.
Academic Learning Strategies for Students with Autism

: Students with ASD have difficulties when it comes to responsive and expressive interaction, leading to failure in understanding social situations. Due to this, students with ASD may face social rejection by peers as a consequence of their social awkwardness, which can negatively impact the child’s emotional stability. The classroom is an incredibly social place, however, this causes anxiety because of the social challenges that these students might face, and even go as far as to obstruct their academic learning. Social skills are incredibly important as they’re needed for many things, from asking for help to cooperating with fellow peers. Several strategies have been researched to assist students with ASD in improving their social interactions, furthermore improving their education. One of these strategies includes using Computer Assisted Instruction, which has shown that they react positively to visual cues as they typically possess adept visual processing. The second is Mind Reading Software, which had led to significant improvement in recognizing emotions. The final exercise was shared reading activities, which served to improve literary skills.
Prevalence and characterisation of Bacillus cereus in vacuum packed potato puree

Summary Refrigerated processed foods of extended durability (REPFED) potato puree was analysed for Bacillus cereus contamination along the production line and during the product shelf-life. Isolated B. cereus strains were tested for their psychrotrophic character and the ability to produce enterotoxins. Bacillus cereus contamination during four subsequent productions was in the range of 2.3–4.0 log cfu g−1. Productions five and six were significantly less contaminated with B. cereus (≤1 log cfu g −1). All B. cereus isolates from the first four productions were able to grow at 7 ° and 10 °C, whereas the majority of the isolates from productions five and six did not. No B. cereus isolates grew at 4 °C. randomly amplified polymorphic DNA (RAPD) fingerprinting showed that the most of B. cereus contamination originated from one source. In total, 30.4% of isolates expressed enterotoxic character. The present study points out the necessity to prevent an ‘in house’ colonisation and contamination during food processing in order to accomplish the safety of REPFED throughout the shelf-life. It also indicates the most critical steps in the production line of ready-to-eat potato puree and impact of failures regarding the food safety. The data provided can be used for risk assessment studies regarding B. cereus in REPFED.
TrajGDM: A New Trajectory Foundation Model for Simulating Human Mobility

Capturing the universal movement pattern and simulating human mobility is one of the most important trajectory data-mining tasks. Most of the current mobility modeling methods are specially designed to solve a specific task, which leads to questions regarding generalizability. Aiming to construct a general trajectory foundation model to overcome this weakness, we proposed a generative Trajectory Generation framework based on Diffusion Model (TrajGDM) to capture the universal mobility pattern and simulate human mobility. It is capable of solving multiple trajectory tasks through learning the generation of the trajectory. The generation process of a trajectory is modeled as a step-by-step uncertainty reducing process. A trajectory generator network is proposed to estimate the uncertainty in each step, and a trajectory diffusion and generation process is defined to train the model to simulate the real dataset. Finally, we compared the proposed method with 6 baselines on 2 public trajectory datasets: T-Drive and Geo-life. By comparing 5 different evaluation metrics, the result showed that the similarity between generated and real trajectories' movement character measured by Jensen-Shannon Divergence (JSD) improved by at least 50.3% in both datasets. It also addresses the problem of generating diverse trajectories, which is ignored by most previous models. Moreover, we applied zero-shot inferences on two basic trajectory tasks: trajectory prediction and trajectory reconstruction. The zero-shot prediction accuracy of our model is up to 23.4% higher than the benchmark, and the reconstruction accuracy improves by a maximum of 25.6%.
The Kleene-Post and Post's Theorem in the Calculus of Inductive Constructions

The Kleene-Post theorem and Post’s theorem are two central and historically important results in the development of oracle computability theory, clarifying the structure of Turing reducibility degrees. They state, respectively, that there are incomparable Turing degrees and that the arithmetical hierarchy is connected to the relativised form of the halting problem defined via Turing jumps. We study these two results in the calculus of inductive constructions (CIC), the constructive type theory underlying the Coq proof assistant. CIC constitutes an ideal foundation for the formalisation of computability theory for two reasons: First, like in other constructive foundations, computable functions can be treated via axioms as a purely synthetic notion rather than being defined in terms of a concrete analytic model of computation such as Turing machines. Furthermore and uniquely, CIC allows consistently assuming classical logic via the law of excluded middle or weaker variants on top of axioms for synthetic computability, enabling both fully classical developments and taking the perspective of constructive reverse mathematics on computability theory. In the present paper, we give a fully constructive construction of two Turing-incomparable degrees à la Kleene-Post and observe that the classical content of Post’s theorem seems to be related to the arithmetical hierarchy of the law of excluded middle due to Akama et. al. Technically, we base our investigation on a previously studied notion of synthetic oracle computability and contribute the first consistency proof of a suitable enumeration axiom. All results discussed in the paper are mechanised and contributed to the Coq library of synthetic computability.
Use of gammaspectrometric airborne data as tool in regional geologic mapping in the Parana State – Brazil

This paper presents results of the processing of gammaspectrometric data (TC, K, eU, and eTh) from a 300 km2 area to the North of Curitiba, PR. Samples are separated 67 m from each other along 1,000 m spaced, 120 m high flight lines.
Real-time Rendering of Glossy Reflections using Ray Tracing and Two-level Radiance Caching

Estimation of glossy reflections remains a challenging topic for real-time renderers. Ray tracing is a robust solution for evaluating the specular lobe of a given BRDF; however, it is computationally expensive and introduces noise that requires filtering. Other solutions, such as light probe systems, offer to approximate the signal with little to no noise and better performance but tend to introduce additional bias in the form of overly blurred visuals. This paper introduces a novel approach to rendering reflections in real time that combines the radiance probes of an existing diffuse global illumination framework with denoised ray-traced reflections calculated at a low sampling rate. We will show how combining these two sources allows producing an efficient and high-quality estimation of glossy reflections that is suitable for real-time applications such as games.
Effect of Psychological First Aid Intervention Program on Post-traumatic Growth among Recently Diagnosed Cancer Patients

: Cancer diagnosis and related treatment represent one of the most painful and distressing events that human beings can meet in their life. This causes a serious crisis in the lives of patients, their families, and those around them. This study aims to evaluate the effect of psychological first aid intervention program on post-traumatic growth among recently diagnosed cancer patients. Study design: A quasi-experimental design was utilized. Setting: The study conducted in the following hospitals at Ismailia city: Suez Canal University Hospitals and Ismailia Oncology Hospital. Subject: A sample of 52 recently diagnosed cancer patients. Tools of data collection: Two tools were used: Tool (I) demographic characteristics and medical background. Tool (II) Post-traumatic Growth Inventory (PTGI). Results: Findings revealed that, recently diagnosed cancer patients had low level of post-traumatic growth in pre-program phase while, post and follow up phases, the results revealed that patients had high level of post-traumatic growth. Conclusion: Psychological first aid intervention program had significant effect on improvement of post-traumatic growth of recently diagnosed cancer patients in post, and follow up study phases compared with pre-program phase. Recommendation: Psychological first aid based interventions can be combined with care programs of hospitals and daily clinical works as a complementary intervention.
A lattice BGK model for the diffusion of pore fluid pressure, including anisotropy, heterogeneity, and gravity effects

Fluid flow in the Earth's crust can be accurately described by a diffusion equation, with highly anisotropic, heterogeneous and time dependent diffusivities. The so-called Bhatnagar-Gross-Krook (BGK) models provide a simple and efficient way to solve the scalar diffusion equation with complex boundary conditions. We show that it can be adapted to include an anisotropic, space and time dependent diffusivity, and a gravity body force, hence providing a suitable numerical model for realistic simulations of incompressible pore fluid diffusion in heterogeneous, anisotropic rocks in the presence of a gravity field. A stability condition limits in some cases the magnitude of anisotropy that can be simulated. Numerical results are in very good agreement with a known analytical solution.
On the Selection of Intermediate Length Representative Periods for Capacity Expansion

As the decarbonization of power systems accelerates, there has been increasing interest in capacity expansion models for their role in guiding this transition. Representative period selection is an important component of capacity expansion modeling, enabling computational tractability of optimization while ensuring fidelity between the representative periods and the full year. However, little attention has been devoted to selecting representative periods longer than a single day. This prevents the capacity expansion model from directly simulating interday energy sharing, which is of key importance as energy generation becomes more variable and storage more important. To this end, we propose a novel method for selecting representative periods of any length. The method is validated using a capacity expansion model and production cost model based on California's decarbonization goals. We demonstrate that the representative period length has a substantial impact in the results of the capacity expansion investment plan.
convergence constraint precoder design for iterative frequency domain multiuser SISO detector

—Convergence constraint power allocation (CCPA) in single carrier multiuser (MU) single-input single-output (SISO) systems with iterative frequency-domain (FD) soft cancelation (SC) minimum mean-squared error (MMSE) equalization is considered in this paper. In order to exploit full beneﬁt of iterative receiver, convergence properties need to be considered. The proposed scheme can guarantee that the desired mutual information point/value after sufﬁcient amount of iterations is achieved. In this paper, successive convex approximation algorithm is proposed as a solving the non-convex convergence constraint power minimization problem. Furthermore, the results of EXIT-chart analysis demonstrate that the CCPA design can achieve the objectives described above.
Robust 6 DOF Motion Estimation for Non-Overlapping , Multi-Camera Systems

This paper introduces a novel, robust approach for 6DOF motion estimation of a multi-camera system with non-overlapping views. The proposed approach is the first that is able to solve the pose estimation, including scale, for a two camera system with non-overlapping views. In contrast to previous approaches, it degrades gracefully if the motion is close to degenerate. For degenerate motions the technique estimates the remaining 5DOF. The proposed technique is evaluated on real and synthetic sequences.
EFFICIENT NUMERICAL SIMULATION OF OFFSHORE STRUCTURES AND WIND TURBINES ON GENERAL PURPOSE GRAPHICS PROCESSING UNITS

Oﬀshore wind turbines are complex systems that are usually simulated in the time-domain to take account of non-linear eﬀects. The response of the structure is calculated by the ﬁnite-element method, or with the more general multi-body methods, and aerodynamic loads are calculated by specialized software which needs to interact with the structural code at each time step. This leads to problematic performance issues, and a ratio of simulated versus simulation time of 1:1 is about the current state-of-the-art. To tackle this issue that stands in the way of both industrial and research eﬀorts requires a computational approach that fully integrates structural analysis with hydrodynamic and aerodynamic calculations, and recent developments in high-performance computing can be favourably employed here. Highly optimized graphics processor units were originally developed to speed up computer game experiences, but have now been adapted for computing tasks. This has been taken advantage of in many scientiﬁc and engineering contexts, and here we review the possibilities for marine engineering, with special regard for ﬂoating structures and oﬀshore wind turbines. Structural response calculations rely on the solution of large sparse linear systems, and this can lead to drastic increases in performance when implemented in parallel on such general purpose graphics processing units. The main algorithms and remaining challenges are discussed, and we demonstrate the feasibility of such an approach.
Physiological and Metabolic Controls in the Brain

: Astrocytes create lactate and activate glycolysis and glycogen metabolism, which are necessary for neuronal metabolic processes in the brain. Glycogen's role in keeping neurons firing is undeniable, although to what extent this occurs is still hotly discussed. how much of the glycogen-derived energy is utilized by the astrocytes, and where it is for use by neurons; lactate is the form, it takes. The central nervous system of an insect consists of a brain and a ventral nerve cord that runs the length of the thorax and several abdominal segments. One of the effects of hypoglycemia is a slower rate of glucose metabolism in the brain. Alzheimer's disease is characterized by several anomalies in the brain, although early-stage dementia is not characterized by a significant decrease in the brain's oxygen metabolism. In this article, we'll go through the key brain areas.
for cyber-physical

—Technology advances allow us to design smart home system for the purpose to achieve high demands on occupants’ comfort. In this research, we focus on the thermal comfort control (TCC) system to build an energy efﬁcient thermal comfort control (EETCC) algorithm, which is based on the cyber-physical systems (CPS) approach. By optimizing the actuators; air-conditioner, window and curtain, our proposed algorithm can acquire the desired comfort level with high energy efﬁciency. Through the raw data from experiments, we evaluate and verify our proposed algorithm in the cyber-physical home system environment by using MATLAB/Simulink software.
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks, which often exhibit low-level features that can be easily confounded with background textures, foreign objects, or irregularities in construction. Furthermore, the presence of issues like non-uniform lighting and construction irregularities poses significant hurdles for autonomous crack detection during building inspection and monitoring. Convolutional neural networks (CNNs) have emerged as a promising framework for crack detection, offering high levels of accuracy and precision. Additionally, the ability to adapt pre-trained networks through transfer learning provides a valuable tool for users, eliminating the need for an in-depth understanding of algorithm intricacies. Nevertheless, it is imperative to acknowledge the limitations and considerations when deploying CNNs, particularly in contexts where the outcomes carry immense significance, such as crack detection in buildings. In this paper, our approach to surface crack detection involves the utilization of various deep-learning models. Specifically, we employ fine-tuning techniques on pre-trained deep learning architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models are chosen for their established performance and versatility in image analysis tasks. We compare deep learning models using precision, recall, and F1 scores.
Endoscopy without Sedation less Time in the Recovery Unit Post-Procedure

Background In this study, we compared the length of time in the recovery room following endoscopic procedures with and without sedation and looked for any differences in pain scores that were statistically significant. Methods Retrospective study, which was taken from the patient’s records in the recovery unit, with a sample of 200 patients. Result Regarding the sociodemographic data the mean age was 60.5 years old generally, with 108 males (54%), and 92 females (46%. Regarding the type of sedation, 50 patients had OGD with no sedation (25%), and 50 had it with sedation (25%). 50 patients had colonoscopy with only fentanyl (25%), and 50 had it with both fentanyl and midazolam (25%). Regarding the dose range of fentanyl,100 patients had it, 97 between 25-50 mcg and 3 patients had more than 50 mcg, for the dose of midazolam, 100 patients had it, 95 patients had between 1.5-3.0 mg, 5 had between 4-5 mg. The LOS (min) 99 patients had more than 120 minutes (49.5%), 58 between 61-120 (29%), and 43 between 9-60 minutes (21.5%) in the recovery unit. Conclusion This study has concluded that most of the patients who have had endoscopy with sedation had a prolonged time in recovery compared to patients who didn’t have sedation and had a lower time in recovery. For colonoscopy, most of the patients had a longer time when using both midazolam and fentanyl compared to those who had only fentanyl, which indicates that using midazolam increases the time in recovery. The higher the dose of sedation the more time in the recovery post-endoscopy. There was no significant difference in pain score in colonoscopy with sedation or without.
Energy partitioning over a semi‐arid shrubland in northern China

During the last decade, the widely distributed shrublands in northern China have shown significant signs of recovery from desertification, the result of widespread conservation practices. However, to support the current efforts in conservation, more knowledge is needed on surface energy partitioning and its biophysical controls. Using eddy‐covariance measurements made over a semi‐arid shrubland in northwest China in 2012, we examined how surface energy‐balance components vary on diurnal and seasonal scales, and how biophysical factors control bulk surface parameters and energy exchange. Sensible heat flux (H) exceeded latent heat flux (λE) during most of the year, resulting in an annual Bowen ratio (β, i.e. H/λE) of 2.0. λE exceeded H only in mid‐summer when frequent rainfall co‐occurred with the seasonal peak in leaf area index (LAI). Evapotranspiration reached a daily maximum of 3.3 mm day−1, and summed to 283 mm yr−1. The evaporative fraction (EF, i.e. λE/Rn), Priestley–Taylor coefficient (α), surface conductance (gs) and decoupling coefficient (Ω) were all positively correlated with soil water content (SWC) and LAI. The direct enhancement of λE by high vapour pressure deficit (VPD) was buffered by a concurrent suppression of gs. The gs played a direct role in controlling EF and α by mediating the effects of LAI, SWC and VPD. Our results highlight the importance of adaptive plant responses to water scarcity in regulating ecosystem energy partitioning, and suggest an important role for revegetation in the reversal of desertification in semi‐arid areas. Copyright © 2015 John Wiley & Sons, Ltd.
Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques.
Women in surgery: do surgical specialties keep up with the feminization of medicine in Brazil?

ABSTRACT Introduction: historically, surgical medical specialties are mostly male, a scenario which, in recent years, has undergone changes. In this sense, despite the relevance of the growth of female participation in the medical career, little is discussed about the distribution between genders of the main surgical medical specialties in the country. Objective: discuss the process of feminization in surgical specialties in Brazil over the last few years, tracing a distribution profile of these specialties. Methods: this is a retrospective and cross-sectional study with secondary data from the Censuses of Medical Demography in Brazil in the years 2011, 2013, 2015, 2018, 2020 and 2023, including the surgical specialties: Urology, Orthopedics and Traumatology, Thoracic Surgery, Neurosurgery, Digestive System Surgery, Cardiovascular Surgery, Hand Surgery, General Surgery, Head and Neck Surgery, Vascular Surgery, Plastic Surgery, Ophthalmology, Coloproctology, Otorhinolaryngology, Pediatric Surgery, and Gynecology and Obstetrics. Results: males prevails in numbers, among the surgical specialties, however, with a lower growth rate compared to females. Specialties such as urology, orthopedics and traumatology and neurosurgery are mostly male, while gynecology and obstetrics are female. Conclusion: it is evident that female participation in the surgical medical field has increased significantly over the last few years.