Miracle-dz/UniFAR-FTUs huggingface dataset:
A pansharpening method combining iterative filtering and NSST-NSML-PAPCNN to optimize spatial detail extraction and injection

ABSTRACT Spatial injection-based pansharpening methods are prone to spatial or spectral distortions in pansharpening images due to insufficient extraction of spatial details and a mismatch between the amount of spatial detail information injected and the required amount. To this end, this paper proposes a pansharpening method that optimizes spatial detail extraction and injection. Firstly, a method to optimize the amount of spatial detail injection is proposed, that is, to extract the high-frequency information of the image through iterative filtering and determine the optimal number of iterations based on the global analysis of the method. Then, to fully extract and combine the spatial detail information of the source image, the detailed high-frequency image extracted corresponding to the optimal iterative filtering times is decomposed by non-subsampled shearlet transform (NSST), and a new multi-scale sum-modified-Laplacian (NSML) as an external stimulus to a parameter-adaptive pulse-coupled neural network model (PAPCNN). A fusion rule based on multi-scale morphological gradients is designed to extract a small amount of detailed information for the low-frequency subband. The fused spatial detail image can be obtained by combining the fused low-frequency and high-frequency subbands and inverse NSST transformation. Finally, pansharpening can be realized by combining spatial detail image, injection coefficient, and MS image. In this paper, many experiments are carried out on the QuickBird, GeoEye-1, and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods. Experimental results show that the method proposed in this paper can achieve better fusion results.
Electron's horizon in 6-D complex space

In a previous work, we found that an object's gravity can be regarded as its buoyancy in space when it displaces gravitons. However, this seems to contradict the pointlike elementary particle in the standard model. In this work, by combining Klein's curled fifth dimension and Newman's complex space, we find a particle have a micro complex horizon, which can be described by the complex Kerr-Newman metrics in a 6-D complex space, the 3-D imaginary subspace of which is curled in the points of its 3-D real subspace (vice versa). A particle can appear as a pointlike particle or a particle with no-zero volume in different slices of the 6-D complex space. The ring singularity of a particle is hidden in one slice of its complex horizon. As two phases of a complex black hole, a article and a black hole can be transformed into each other through a phase transformation.
Theoretical characterization of performance effectiveness of photon-counting technique for digital radiography applications

Photon-counting (PC) technique has been paid attention to digital radiography applications due to its potential in lowdose operation and multi-energy imaging capability. In this study, we theoretically investigate the performance gain in digital radiography when the PC detectors are used instead of the conventional energy-integrating (EI) detectors. We use the Monte Carlo technique for estimating energy-absorption distributions in detector materials such as CdTe for the PC detector and CsI for the EI detector. To estimate the signal and noise transfers through the two different detectoroperation schemes, we use the cascaded linear-systems approach. In the Monte Carlo simulations, the square and rectangle focal spots are considered to mimic the advanced carbon nanotube (CNT) and conventional filament cathodes, respectively. From the simulation results, the modulation-transfer functions of the PC detector are more sensitive to asymmetric focal spot geometry than those of the EI detector. On the other hand, the PC detector shows better image signal-to-noise ratio than the EI detector; hence better dose efficiency with the PC detector. The dose efficiency of the PC detector in comparison with the EI detector is however marginal for the filament x-ray beam whereas the dose efficiency is not negligible for the CNT x-ray beam. The theoretical upper limits of the imaging performance of the advanced digital radiography technology are reported in this study.
Non-Coding RNAs in Oral Cancer: Emerging Roles and Clinical Applications

Simple Summary Oral cancer (OC) is one of the most prevalent cancers in the world. Despite improvements in therapies, OC still has a poor survival rate of about 50%, with metastasis being the worst-case scenario. Thus, there is an urgent need to understand the disease process and to develop diagnostic and therapeutic strategies for OC. Advancement of high throughput genome sequencing shows that more than 90% of the human genome encodes non-coding transcripts that do not code for any protein. In this review, we discuss the role of various types of these non-coding RNAs (ncRNAs) in OC and their promising clinical implications. Dysregulated expressions of ncRNAs are associated with OC initiation and progression, as well as therapy resistance. Differential expressions of these ncRNAs in blood or saliva have indicated their potential diagnostic and prognostic importance. In this review, we have summarized all the promising aspects of ncRNAs in the management of OC. Abstract Oral cancer (OC) is among the most prevalent cancers in the world. Certain geographical areas are disproportionately affected by OC cases due to the regional differences in dietary habits, tobacco and alcohol consumption. However, conventional therapeutic methods do not yield satisfying treatment outcomes. Thus, there is an urgent need to understand the disease process and to develop diagnostic and therapeutic strategies for OC. In this review, we discuss the role of various types of ncRNAs in OC, and their promising clinical implications as prognostic or diagnostic markers and therapeutic targets. MicroRNA (miRNA), long ncRNA (lncRNA), circular RNA (circRNA), PIWI-interacting RNA (piRNA), and small nucleolar RNA (snoRNA) are the major ncRNA types whose involvement in OC are emerging. Dysregulated expression of ncRNAs, particularly miRNAs, lncRNAs, and circRNAs, are linked with the initiation, progression, as well as therapy resistance of OC via modulation in a series of cellular pathways through epigenetic, transcriptional, post-transcriptional, and translational modifications. Differential expressions of miRNAs and lncRNAs in blood, saliva or extracellular vesicles have indicated potential diagnostic and prognostic importance. In this review, we have summarized all the promising aspects of ncRNAs in the management of OC.
The multi-criteria optimization for the formation of the multiple-valued logic model of a robotic agent

The C-valued Allen-Givone algebra is the attractive tool for modeling of a robotic agent, but it requires the consensus method of minimization for the simplification of logic expressions. This procedure substitutes some undefined states of the function for the maximal truth value, thus extending the initially given truth table. This further creates the problem of different formal representations for the same initially given function. The multi-criteria optimization is proposed for the deliberate choice of undefined states and model formation.
On Iterated Exponentiation - The Hyperexponentials

for a geometric sequence. Here, in both cases, n is a positive integer. Notice that each consecutive term in the above arithmetic sequence is obtained by adding x to its proceeding term. This process of adding x to each proceeding term in order to form the next term in the sequence is called repeated or iterated addition. Similarly, in the above geometric sequence each consecutive term is obtained by multiplying x to its proceeding term in a process called iterated multiplication. Now imagine the sequence which would be formed by exponentiating x to its proceeding term in a process which is referred to as iterated exponentiation. Here a hyperexponential sequence whose terms consists of hyperexponents (or hyperpowers) in x would result. It is the terms in this sequence, and some of their associated properties, which this article will focus on.
Exploring the Nexus: Female Employees' Perspectives on Organizational Learning and Organizational Productivity in Nigeria's Small and Medium-Sized Businesses

The purpose of this study is to investigate the building blocks of organizational learning among female workers in the SMEs. A descriptive quantitative research methodology was adopted. A survey was distributed to 702 female employees working for SMEs. Descriptive and multiple regression analyses were used to describe the data and test the hypothesis. This result reveals that SMEs are making efforts at creating and gathering knowledge among female employees, and it also demonstrates that the utilization of organizational learning has a positive impact on the productivity of organizations; however, there is an issue in the layout of the supportive learning environments in SMEs where the positive result is very small in comparison to concrete learning processes and leadership that reinforces learning processes and practices. Conclusion, recommendations, and future research orientation are discussed in the paper.
EFFECT OF STRETCHING EXERCISE ON GASTROCNEMIUS MUSCLES IN LOW BACK PAIN

Background: The gastrocnemius muscle is essential for walking and maintaining a straight posture. The gastrocnemius is a large muscle that covers the bulk of the lower thigh's posterior region. Symptoms of low back pain may be described in several ways, depending on the underlying condition. An official definition of a low back injury is "pain and discomfort below the lumbar spine's costal margin and above the gluteal folds, with or without leg pain. " The study's primary goal is to determine if stretching exercises for low back pain affect the gastrocnemius muscles.
Data-driven approaches demonstrate legacy N accumulation in Upper Mississippi River Basin groundwater

Increases in nitrogen (N) fertilizer application, livestock densities, and human population over the last century have led to substantial increases in nitrate contamination. While increases in riverine N loads are well-documented, the total magnitude of N accumulation in groundwater remains unknown. Here we provide a first data-driven estimate of N mass accumulation in groundwater within the Upper Mississippi River Basin (UMRB), an area of intensive row-crop agriculture and the primary contributor to Gulf of Mexico hypoxia. Using approximately 49 000 groundwater nitrate well concentration values and a suite of geospatial predictors, we developed a Random Forest model to produce gridded predictions of depth-varying nitrate concentrations. Our results suggest that approximately 15 Tg of N (328 ± 167 kg-N ha−1) is currently stored in UMRB groundwater recharged over the last 50 years. For context, we compare these predictions to those from a lumped statistical model, which predicts accumulation of 387 ± 133 kg-N ha−1, as well as to a simple N mass balance model of the UMRB, which puts an upper bound on accumulation of approximately 1000 kg-N ha−1 (1967–2017). These findings highlight the importance of considering legacy N when forecasting future water quality, as N in the subsurface will continue to impair drinking water quality and elevate surface water N concentrations for decades to come.
Optimal Design of Cluster Randomized Trials with Binary Outcomes - eScholarship

Cluster randomized trials (CRTs) are increasingly used in many fields including public health and medicine. We consider two-arm CRTs with binary outcomes with possibly unequal intraclass correlations coefficients (ICCs) in the two arms. The efficacy of the intervention may be measured in terms of the risk difference (RD), relative risk (RR) or odds ratio (OR). We define cost efficiency (CE) as the ratio of the precision of the efficacy measure to the study cost and develop optimal allocations to the two arms for maximizing CE. The optimal design, which is based on the optimal allocation, could be different for different measures. We define relative cost efficiency (RCE) of a design as the ratio of its CE to CE of the optimal design and use RCE to compare different designs. Because the optimal allocation can be highly sensitive to the unknown ICCs and anticipated success rates, we propose a Bayesian method and a maximin method to construct an efficient and robust design. We show that the RCE of the designs based on the Bayesian method or the maximin method is generally larger than the balanced design. Based on the optimal allocation, we derive optimal sample size formulas which satisfy the power requirement and minimize the total study cost. All the results above are based on the assumption of constant cluster size. When there is extreme variation in cluster size, the usually used sample size formula assuming a constant cluster size may result in a design with low power. Assuming a balanced design, we develop a sample size formula for a two-arm CRT which obtains the desired power even though the cluster sizes are very different. This formula can be modified to incorporate optimal allocation consideration, hence it minimizes the study cost while satisfying the power requirement for a CRT with varying cluster sizes. Simulation is used to verify that our formulas can obtain the desired power.
New delay-dependent stability conditions for linear systems with delay

In this work, delay-dependent stability conditions for systems described by delayed differential equations are pre- sented. The employment of a special transformation to another state space representation named Benrejeb characteristic arrow matrix permit to determine a practical asymptotic stability condition. An Illustrative example is presented permitting to understand the application of the proposed methods.
The Indonesian EFL Learners’ Motivation and Autonomy

A successful autonomous learner is usually inﬂuenced by numerous factors. Motivation is one of the main factors that have a connection and the greatest inﬂuence associated with it. This study focuses to scrutinize the correlation between motivation and learner autonomy of Indonesian EFL learners. It also aims to identify the signiﬁcant difference between gender and degree level of study in motivation and autonomy. The investigation was conducted using a sample of 40 university EFL learners from some regions in Indonesia. The data were collected through a questionnaire and analysed using Pearson’s Product-Moment Correlation and independent-samples T-test. The result of the study showed that there is a positive correlation between learning motivation and learner autonomy. Results of the independent sample T-test showed there was no signiﬁcant difference between male and female learners in motivation and autonomy, and also there was no signiﬁcant difference between undergraduate and postgraduate learners in motivation and autonomy. The result of the study recommends the vital role of motivation and autonomy in teaching and learning English especially in the EFL context. They should receive great attention, as they can lead to more effective teaching and learning.
Brands are calling your AVATAR in Metaverse–A study to explore XR‐based gamification marketing activities & consumer‐based brand equity in virtual world

Brands are moving towards the Metaverse (3D immersive virtual spaces), where the growth of intangible products and nonfungible tokens (NFTs) are evolving into a new type of hybrid experience for the users. This paper aims to establish the role of a “ gamification of marketing activities ” and its influence on consumer-based brand equity for intangible products (NFTs) in the Metaverse and examine the mediating role of consumers' brand engagement and brand love. To evaluate the conceptual model based on the cross-cultural data from two emerging countries in Asia and Africa, the study followed a two-stage, hybrid mechanism using PLS-SEM and neural network modeling. This study provides insights into the Metaverse – a new taxonomy of technology, in the context of embodiment, presence of AVATAR, and interactivity in the virtual world, supported by the social exchange theory. This study also suggests practitioners focus on brand authenticity while projecting their brand in the Metaverse.
Hierarchical MARG Fusion Method With Human Constraints for Upper Limb Motion Estimation

Motion capture (MoCap) provides technical support for human motion tracking and orientation estimation, which has enormously benefited the research of human–computer interaction in the aerospace field. Compared with the optical MoCap system, inertial measurement units (IMUs) play an irreplaceable role in accurately calculating the orientation and position of the human body without being affected by light. This article presents a novel approach that incorporates the coupling of position and orientation into the state equation, enabling accurate estimation of human motion. Also, we incorporate constraints into the optimization process using a least squares-based projection method to reduce drift and improve long-term stability. First, on the basis of the established upper limb kinematics model, the quaternion-based IMU fusion extended Kalman filter (EKF) method is leveraged to compensate roll and pitch angles. Then, the magnetic, angular rate, and gravity (MARG)-EKF method performs a complete compensation of 3-D orientation and position through the data fusion of MARG. In order to further reduce the drift, human constraints are introduced. The novel constrained MARG-EKF algorithm has been tested by various movements, which shows the position tracking accuracy in <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-, <inline-formula> <tex-math notation="LaTeX">${Y}$ </tex-math></inline-formula>-, and <inline-formula> <tex-math notation="LaTeX">${Z}$ </tex-math></inline-formula>-axes of 10 cm. Also, the imposed constraints can reduce the error by 2.4%–15.0% for the forearm and 3.2%–11.4% for the upper arm. The experimental results represent that our proposed method can track upper limb motion more accurately with higher feasibility and stability.
MAOA uVNTR Polymorphism Influence on Older Adults Diagnosed with Diabetes Mellitus/Systemic Arterial Hypertension

Background Monoamine oxidase (MAO) is involved in several biological processes associated with well-being and mental health, and alterations in its function might directly impact various mental disorders. Some mental disorders concomitantly occur in individuals with clinical characteristics, such as substance abuse and diabetes. Objective To analyze the functional MAOA uVNTR polymorphism genotype frequency in an older adult population with diabetes mellitus/arterial hypertension and associate this frequency with clinical characteristics impacting daily life. Methodology. Older adults diagnosed with diabetes mellitus, systemic arterial hypertension, or both (DM/SAH) were selected and had their MAOA gene genotyped for uVNTR polymorphism. The revised Beck Depression Inventory (BDI) and a questionnaire were also applied to determine their mental health and clinical characteristics. Results The allelic variants detected among the participants were the 2R, 3R, 4R, and 3R/4R heterozygous genotypes. Genotypes solely containing the 3R allele had patients who marked yes for smoking and alcoholism, and only those with the 3R genotypes (female 3R/3R homozygote or male 3R∗ hemizygote) were significant. Although not statistically significant, only 3R and 3R/4R genotypes presented cases of severe depression per the revised BDI interpretations. Conclusion The MAOA uVNTR polymorphism's low-activity 3R allele presence in an older adult population diagnosed with DM/SAH may represent a risk for developing substance use (alcohol and smoking) dependence.
Multi-Granularity Interaction for Multi-Person 3D Motion Prediction

Multi-person 3D motion prediction is an emerging task that involves predicting the future 3D motion of multiple individuals based on current observations. In contrast to motion prediction for a single person, this task requires a strong emphasis on learning the interacting dynamics among multiple individuals. Broadly speaking, current methods can be categorized into two groups: The first group involves the straightforward adaptation of models originally developed for single-person scenarios to multi-person scenarios, which is evidently suboptimal. The second group focuses on utilizing off-the-shelf tools like graph convolutional networks to model interactions. While this approach has shown improved results, the interactions primarily consider entire human identities rather than finer details. This motivates the introduction of our novel solution to address this limitation and enhance the task’s performance. In this work, we strive to craft a novel framework that can effectively address two key issues ignored in previous works, namely the multi-granularity interaction and time-varying inter-person dynamics. In implementation in accord with above aims, the proposed model has mainly comprised two modules: a person-level interaction module and a part-level interaction module. The former is designed to learn the holistic and dynamic interaction among multiple persons in a coarse-grained sense. Critically, we would emphasize that a unique trait of the former module is learning temporal dynamics. For example, it recognizes that two individuals exhibit a strong correlation during handshaking but less correlation after parting ways. The latter part-level interaction module learns the interaction between the body joints of different persons. This module operates at a more fine-grained level, distinguishing it from existing approaches. By aggregating information from both granularities, our model enables accurate motion prediction. To validate the effectiveness of the proposed model, we conducted comprehensive experiments on three benchmark datasets: 3DPW, CMU-Mocap, and MuPoTS-3D. The results of these evaluations unequivocally demonstrate the empirical superiority of our model compared to previous state-of-the-art methods.
Victim Service Agency Perspectives on the Impact of the COVID-19 Pandemic

Abstract Victim service agencies felt widespread impacts from the COVID-19 pandemic on their staff, organizational practices, and client populations. This study offers a qualitative exploration of the impact of the COVID-19 pandemic across a broad spectrum of victim service settings, including court-based, mental health, medical, child welfare, interpersonal violence, and advocacy agencies. An electronic survey was completed by Ohio agency workers (N = 170) serving child and young adult victims of trauma. Codebook thematic analysis was used to identify agency and staff experiences, client needs, and changes to service delivery during the pandemic. Results revealed victim impacts in six categories: increases in mental/behavioral health needs, challenges resulting from school closures, safety concerns for victims of interpersonal violence, higher levels of crisis needs, isolation/stress, and service access issues. Agency-level impacts focused on the transition to virtual services, challenges with staffing and resources, worker burnout/stress, and resilience and flexibility. Implications are discussed for organizational staffing and trauma-informed training needs, burnout prevention and mitigation, and client service gaps. Future research should continue exploration of system- and organizational-level adaptation in response to the pandemic, as well as the long-term implementation of trauma-informed organizational practices as a strategy to enhance victim service worker satisfaction and retention.