Miracle-dz/UniFAR-FTUs huggingface dataset:
A minimal pair of Π10 classes

A pair of sets (A0, A1) forms a minimal pair if A0 and A1 are nonrecursive, and if whenever a set B is recursive both in A0 and in A1 then B is recursive. C. E. M. Yates [8] and independently A. H. Lachlan [4] proved the existence of a minima] pair of recursively enumerable (r.e.) sets thereby establishing a conjecture of G. E. Sacks [6]. We simplify Lachlan's construction, and then generalize this result by constructing two disjoint pairs of r.e. sets (A0, B0) and (A1B1) such that if C0 separates (A0, A1 and C1 separates (B0, B1), then C0 and C1 form a minimal pair. (We say that C separates (A0, A1) if A0 ⊂ C and C ∩ = ∅. ) The question arose in our study of (Turing) degrees of members of certain classes, where we proved the weaker result [2, Theorem 4.1] that the above pairs may be chosen so that C0 and C2 are merely Turing incomparable. (There we used a variation of the weaker result to improve a result of Scott and Tennenbaum that no complete extension of Peano arithmetic has minimal degree.)
The challenges of integrating explainable artificial intelligence into GeoAI

Although explainable artificial intelligence (XAI) promises considerable progress in glassboxing deep learning models, there are challenges in applying XAI to geospatial artificial intelligence (GeoAI), specifically geospatial deep neural networks (DNNs). We summarize these as three major challenges, related generally to XAI computation, to GeoAI and geographic data handling, and to geosocial issues. XAI computation includes the difficulty of selecting reference data/models and the shortcomings of attributing explanatory power to gradients, as well as the difficulty in accommodating geographic scale, geovisualization, and underlying geographic data structures. Geosocial challenges encompass the limitations of knowledge scope—semantics and ontologies—in the explanation of GeoAI as well as the lack of integrating non‐technical aspects in XAI, including processes that are not amenable to XAI. We illustrate these issues with a land use classification case study.
An Automatic Detection of Pneumonia from Chest Ionizing Radiation Images Using Machine Learning Algorithm

As a result of absence of adequate healthcare facilities in developing countries, pneumonia is the most common cause of death now a days. In 2019, pneumonia was responsible for the deaths of as many as 740180 children less than 5 years old. Rapid death occurs in pneumonia patients due to fluid leaks in the lungs. Therefore, early identification and treatment with appropriate medication are crucial for preventing the spread of the disease. Chest x-ray is the mainly used & standard method of diagnosing pneumonia. Different lung illnesses, like volume loss, hemorrhage, fluid overload, lung cancer, or post-radiation or surgical modifications, make it extremely difficult to diagnose pneumonia in chest x-rays. Hospitals often struggle to detect pneumonia due to a lack of qualified radiologists. Therefore, computer-assisted diagnosis techniques are urgently needed to aid doctors in making more informed decisions. This research proposes a YOLO-based model and evaluates it on the CXR-14 subset of the Chest X-ray dataset. Through the use of a dataset of about 29860 images, we were able to train a computer to distinguish between three distinct pneumonia stages. The model’s mAP@0.5 on train data was 97.65%. This study proposed a low weight object detection algorithm for medical image processing.
On the regularity criteria for the generalized Navier-Stokes equations and Lagrangian averaged Euler equations

We obtain some regularity conditions for solutions of the 3D generalized Navier-Stokes equations with fractional powers of the Laplacian, in terms of the velocity, the vorticity, and the pressure in Besov space, Triebel-Lizorkin space, and Lorentz space, respectively. We also present a regularity condition for the 3D Lagrangian averaged Euler equations.
Molecular Oncology Of Colorectal Cancers Prognosis And Prediction, Radiation Oncologist Perspective

Colorectal cancers (CRC) are a common cancer globally. The commonest symptoms were rectal bleeding (57%), pain (44%), and altered bowel habits (26%). 13 percent of the patients of colorectal cancer found signet ring tumor Histopathology which shows poor outcome of this very tumor , The CEA (Carcinoembryonic antigen) level estimation in patient serum commonly perform, surgery is a backbone of treatment unless until contraindicating factors are present after surgery tissue specimen required to send for not only histopathological studies rather also to look for tumor Immunohistochemistry studies in order to plan colorectal cancer very well directed chemotherapy according to its receptor as well as genetic or epigenetic alterations in BRAF, K-RAS, MMR, Most patients had localized or locally advanced disease. Twenty-eight percent of the patients had metastatic disease with liver being the commonest site of metastases (14%) followed by peritoneum and lung. More than half of the patients received treatment with a curative intent. Colorectal cancer in India differs from data than what we have found in data from western countries, In India we had more young patients, higher proportion of signet ring carcinomas, and more patients presenting with an advanced stage.
A High-Precision Continuous Scan and Step Scan System for Compact Spectrometer Applications

In this article, a dual-stage positioning system for precisely tracking continuous scan and step scan profiles to meet the high-precision dynamic positioning requirements of space optical instruments is reported. A piezoelectric walking stage provides a long-stroke platform to which a short-stroke voice coil-actuated shuttle is mounted. The shuttle corrects for submicrometer deviations in the walking stage position and is designed with a low resonant frequency to passively reject disturbances produced by the walking stage. Both the walking stage and shuttle are instrumented with high-resolution interferometric encoders and a feedback system regulates the shuttle's position to achieve less than 7 nm RMS position error for constant velocity references of up to 0.5 mm/s. The shuttle is also capable of tracking step scan profiles with 10 ms settling times and 1.1 nm RMS position errors for steps up to 500 nm.
A variational principle for certain dissipative evolution equations

We formulate a variational principle which models several first order parabolic Cauchy problems. Unlike the one proposed by Brezis-Ekeland in [2], this principle does not require the identification of the extremal value, which was a major hurdle for the applicability of their approach. Our method is based on a concept of "self-duality" inherent in many parabolic evolution equations, including those driven by the gradient of a convex energy functional. The new principle is used to provide global existence and uniqueness results of solutions for the heat equation (of course) but also for quasi-linear parabolic equations, porous media and fast diffusion equations.
The symmetry transform and its applications

Recent improvements in methods for acquiring and generating 3D shape data over the last few decades have motivated the need for ever complex tools to analyze and edit such information. As a result, in the last few years, numerous methods have been introduced to improve the ways in which computers understand shape. These methods vary in the type of data they measure, ranging from low-level, local geometry information to high-level semantic information. Symmetry has long been known as an important cue for non-local human recognition of shape, and as such has been a key component in Vision and Graphics applications. A second important feature of symmetry is that it is prevalent in real-world-models. Pipe and gear models contain rotational symmetries, buildings and square objects contain 4-point symmetry, and nearly every natural or man-made object will contain some reflectional symmetry. This prevalence is important because this means that applications relying on symmetry information will work on a large range of models. In this thesis, we introduce the notion of a Symmetry Transform, a measure of “the amount of” or “the degree of” symmetry present in a 3D shape, under some class of transformations. This can be the set of all plane-reflectional symmetries, the set of all point reflections or some arbitrary set of transformations. We concentrate on point and reflection symmetry transforms, providing some theoretical reasoning for the transforms. We show efficient means for computing them, discuss storing them, and analyze some of their properties, including noise resistance, continuous variation under deformation, and stability with missing data. While computing the transform of an entire model can be useful for a general understanding of shape, strong symmetries (such as the center of an ellipse or the main reflection planes of a rectangle), are easily identified by humans and represent an important subset of the transform. In this thesis we provide a method for efficiently extracting these “principal symmetries”, and discuss some of their properties. Finally, the symmetry transform provides mid-level information about the nature of a 3D object that can be utilized in a wide range of applications. We explore how symmetry may be used to improve alignment, matching, viewpoint selection, remeshing, and segmentation.
DUSP6 Deficiency Attenuates Neurodegeneration after Global Cerebral Ischemia

Transient global cerebral ischemia (tGCI) resulting from cardiac arrest causes selective neurodegeneration in hippocampal CA1 neurons. Although the effect is clear, the underlying mechanisms directing this process remain unclear. Previous studies have shown that phosphorylation of Erk1/2 promotes cell survival in response to tGCI. DUSP6 (also named MKP3) serves as a cytosolic phosphatase that dephosphorylates Erk1/2, but the role of DUSP6 in tGCI has not been characterized. We found that DUSP6 was specifically induced in the cytoplasm of hippocampal CA1 neurons 4 to 24 h after tGCI. DUSP6-deficient mice showed normal spatial memory acquisition and retention in the Barnes maze. Impairment of spatial memory acquisition and retention after tGCI was attenuated in DUSP6-deficient mice. Neurodegeneration after tGCI, revealed by Fluoro-Jade C and H&E staining, was reduced in the hippocampus of DUSP6-deficient mice and DUSP6 deficiency enhanced the phosphorylation and nuclear translocation of Erk1/2 in the hippocampal CA1 region. These data support the role of DUSP6 as a negative regulator of Erk1/2 signaling and indicate the potential of DUSP6 inhibition as a novel therapeutic strategy to treat neurodegeneration after tGCI.
Cellular Automata Based on Cryptosystem

Previous cellular automata bases cryptosystem are either insecure or ineffective. This paper proveds several theorems about cellular automata based on cryptosystem and presents two classes of secure cryptosystem.The first class cryptosystem is that increase nonlinear transform during cellular automata transform such as index S-box. In order to adopt property of cellular automata that low cost, high-speed encryption. We present second class cryptosystem that combine simple nonlinear transform and one time pad to reach both secure and high-speed encryption. Cryptosystems constructed from this scheme can be implemented in simply constructed massively parallel hardware. It can be counted on to deliver high encryption/decryption rates at low cost on data grid. At last, we implement these two schemes in VLSI and evaluate these two schemes' performance.
Patient History Summarization on Outpatient Conversation

Among various medical practices, outpatient conversation is a process that most patients experience when seeking medical assistance. Due to patient privacy concerns, the collection of outpatient conversations and patient medical records is subject to many limitations. Furthermore, researchers studying outpatient conversations are often unable to make their datasets public. Therefore, most of the previous work used consultation conversations in online medical communities as research materials, but these consultation conversations are still quite different from outpatient conversations. We collaborated with the hospital to obtain outpatient conversations and patient medical records for the study. We use Transformer-based models for summarization of outpatient conversations. During the training process, we introduce external medical datasets to help the model learn medical knowledge. Since our proposed method performs summarization through segmented conversations, the model can handle relatively long outpatient conversations. Additionally, we use our outpatient dataset to train a writing style conversion model to mimic medical notes made by physicians. Experimental results show that the outpatient dialogue summaries generated by our method have a certain reference value.
A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices

Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, IoT devices can be interconnected through sensors and other technical means to ultimately achieve the goal of automatic intelligent computing. Therefore, this paper proposes a low-cost distributed thunder-localization system based on IoT smart devices, namely ThunderLoc. The main idea of ThunderLoc is to collect dual-microphone data from IoT smart devices, such as smartphones or smart speakers, through crowdsourcing, turning the localization problem into a search problem in Hamming space. We studied the dual microphones integrated with smartphones and used the sign of Time Difference Of Arrival (TDOA) as measurement information. Through a simple generalized cross-correlation method, the TDOA of thunderclaps on the same smartphone can be estimated. After quantifying the TDOA measurement from the smartphone node, thunder localization was performed by minimizing the Hamming distance between the binary sequence and the binary vector measured in a database. The ThunderLoc system was evaluated through extensive simulations and experiments (a testbed with 30 smartphone nodes). The extensive experimental results demonstrate that ThunderLoc outperforms the main existing schemes in terms of effectively locating position and good robustness.
Convolution Neural Network with Coordinate Attention for Real-Time Wound Segmentation and Automatic Wound Assessment

Background: Wound treatment in emergency care requires the rapid assessment of wound size by medical staff. Limited medical resources and the empirical assessment of wounds can delay the treatment of patients, and manual contact measurement methods are often inaccurate and susceptible to wound infection. This study aimed to prepare an Automatic Wound Segmentation Assessment (AWSA) framework for real-time wound segmentation and automatic wound region estimation. Methods: This method comprised a short-term dense concatenate classification network (STDC-Net) as the backbone, realizing a segmentation accuracy–prediction speed trade-off. A coordinated attention mechanism was introduced to further improve the network segmentation performance. A functional relationship model between prior graphics pixels and shooting heights was constructed to achieve wound area measurement. Finally, extensive experiments on two types of wound datasets were conducted. Results: The experimental results showed that real-time AWSA outperformed state-of-the-art methods such as mAP, mIoU, recall, and dice score. The AUC value, which reflected the comprehensive segmentation ability, also reached the highest level of about 99.5%. The FPS values of our proposed segmentation method in the two datasets were 100.08 and 102.11, respectively, which were about 42% higher than those of the second-ranked method, reflecting better real-time performance. Moreover, real-time AWSA could automatically estimate the wound area in square centimeters with a relative error of only about 3.1%. Conclusion: The real-time AWSA method used the STDC-Net classification network as its backbone and improved the network processing speed while accurately segmenting the wound, realizing a segmentation accuracy–prediction speed trade-off.
Robust Task Offloading and Resource Allocation in Mobile Edge Computing With Uncertain Distribution of Computation Burden

In mobile edge computing (MEC) supporting multiple mobile users (MUs), it is essential to optimize the offloading policy and communication and computation resource allocation. A main challenge is that the computation burden of a computation task may be random and even with uncertain probabilistic distribution. To address this challenge, we investigate a multiple-MU MEC system with random computation burden. For the random computation burden of an MU, only the mean and variance are known, but its distribution is unknown. Robustness is provided such that computation outage probabilities (due to uncertain distribution of computation burden) are bounded by a predefined threshold. We minimize the weighted sum of the MUs’ energy consumption. The formulated optimization problem is non-deterministic and non-convex, and thus, is hard to solve. To deal with the challenge, we transform the formulated problem into a deterministic and convex problem by applying the Chebyshev-Cantelli inequality and some mathematical manipulations. We further decompose the convex problem to a lower-level and an upper-level problem. Low-complexity algorithms are developed for the lower-level and upper-level problems. The overall complexity of our proposed method is linear with the number of MUs.
Mechanism of Immune Evasion in Mosquito-Borne Diseases

In recent decades, mosquito-borne illnesses have emerged as a major health burden in many tropical regions. These diseases, such as malaria, dengue fever, chikungunya, yellow fever, Zika virus infection, Rift Valley fever, Japanese encephalitis, and West Nile virus infection, are transmitted through the bite of infected mosquitoes. These pathogens have been shown to interfere with the host’s immune system through adaptive and innate immune mechanisms, as well as the human circulatory system. Crucial immune checkpoints such as antigen presentation, T cell activation, differentiation, and proinflammatory response play a vital role in the host cell’s response to pathogenic infection. Furthermore, these immune evasions have the potential to stimulate the human immune system, resulting in other associated non-communicable diseases. This review aims to advance our understanding of mosquito-borne diseases and the immune evasion mechanisms by associated pathogens. Moreover, it highlights the adverse outcomes of mosquito-borne disease.
GmTCP and GmNLP Underlying Nodulation Character in Soybean Depending on Nitrogen

Soybean is a cereal crop with high protein and oil content which serves as the main source of plant-based protein and oil for human consumption. The symbiotic relationship between legumes and rhizobia contributes significantly to soybean yield and quality, but the underlying molecular mechanisms remain poorly understood, hindering efforts to improve soybean productivity. In this study, we conducted a transcriptome analysis and identified 22 differentially expressed genes (DEGs) from nodule-related quantitative trait loci (QTL) located in chromosomes 12 and 19. Subsequently, we performed functional characterisation and haplotype analysis to identify key candidate genes among the 22 DEGs that are responsive to nitrate. Our findings identified GmTCP (TEOSINTE-BRANCHED1/CYCLOIDEA/PCF) and GmNLP (NIN-LIKE PROTEIN) as the key candidate genes that regulate the soybean nodule phenotype in response to nitrogen concentration. We conducted homologous gene mutant analysis in Arabidopsis thaliana, which revealed that the homologous genes of GmTCP and GmNLP play a vital role in regulating root development in response to nitrogen concentration. We further performed overexpression and gene knockout of GmTCP and GmNLP through hairy root transformation in soybeans and analysed the effects of GmTCP and GmNLP on nodulation under different nitrogen concentrations using transgenic lines. Overexpressing GmTCP and GmNLP resulted in significant differences in soybean hairy root nodulation phenotypes, such as nodule number (NN) and nodule dry weight (NDW), under varying nitrate conditions. Our results demonstrate that GmTCP and GmNLP are involved in regulating soybean nodulation in response to nitrogen concentration, providing new insights into the mechanism of soybean symbiosis establishment underlying different nitrogen concentrations.
Collaborating Effect of Employees’ Interpersonal Influence Skills and Perceived Workplace Politics on Turnover Intention

In the literature, there are contradicting findings regarding the impact of perceived workplace politics on employees’ outcomes. In this context, this study was carried out to measure the direct and interactive (with interpersonal influence skills) effect of perceived workplace politics on turnover intention. Moreover, this study was dedicated to measuring the remedial role of interpersonal influence skills under the different situations of perceived workplace politics. Applying a survey strategy, 725 responses were collected from the Grade A Nepalese bank employees. Perceptual cross-sectional data were analyzed in SPSS by adopting positivist research philosophy and deductive reasoning approach. This study revealed that employees’ perceived workplace politics positively impact their turnover intention. Likewise, interpersonal influence skills moderated the relationship between the perception of workplace politics and turnover intention. Employees’ interpersonal influence skills worked as an antidote for the detrimental effect of perceived workplace politics on turnover intention. The antidotal power was measured differently under different conditions (high, medium, and low) of workplace politics. For example, the strength of the mitigation power of interpersonal influence skill is strong when perceived workplace politics is high, but the mitigating power is weak when the perception of workplace politics is low.