Miracle-dz/UniFAR-FTUs huggingface dataset:
From Plane to Hierarchy: Deformable Transformer for Remote Sensing Image Captioning

With the growth of remote sensing images, understanding image content automatically has attracted many researchers' interests in deep learning for remote sensing image. Inspired from the natural image captioning, the model with convolutional neural network (CNN)-Recurrent neural network (RNN) as the backbone and supplemented by attention has been widely used in remote sensing image captioning. However, it is inefficient for the current attention layer to simultaneously mine hidden foreground from the background of remote sensing image and perform feature interactive learning. Meanwhile, the new mainstream language model has recently surpassed the traditional long short-term memory (LSTM) in sentence generation. For solving the above problems, in this article, we proposed a novel thought to make the flat remote sensing images stereoscopic by separating the foreground and background. Based on hierarchical image information, we designed a novel Deformable Transformer equipped with deformable scaled dot-product attention to learn multiscale feature from foreground and background through the powerful interactive learning ability. Evaluations are conducted on four classic remote sensing image captioning datasets. Compared with the state-of-the-art methods, our Transformer variant achieves higher captioning accuracy.
Personalizing Loneliness Detection Through Behavioral Grouping of Passive Sensing Data From College Students

Loneliness among college students is an increasingly prevalent issue. While technology-based methods for detection using behavioural patterns have been proposed, there remains an opportunity for improvement as insufficient attention has been given to the individual behavioral differences among students. Loneliness is a highly subjective experience and people’s routines differ, making it a challenge for generic models to accurately determine its presence and severity. In response to this challenge, it is particularly helpful to identify subgroups within the population that exhibit similar behavioral characteristics, enabling a more nuanced understanding and detection of loneliness. This paper introduces a novel approach to loneliness detection, leveraging a data set gathered through passive sensing using mobile phones, which provides a rich source of user behavioral data. We utilized unsupervised clustering to find subgroups of students exhibiting similar behavioral patterns over time within the data set. This approach is essential for continuous monitoring, identifying changes in behavioral patterns, and facilitating the early detection of loneliness. Using data from 41 students’ smartphones, we created group-specific classification models to identify loneliness. Group-based prediction models for loneliness detection have shown significant improvement in accuracy over generalized models. These findings can lead to the development of more effective, tailored methods for loneliness detection in diverse populations. This study emphasizes the importance of personalized approaches in mental health interventions and highlights the potential of passive sensing data in creating tailored loneliness detection methods.
Ezrin Inhibition Overcomes Acquired Resistance to Vemurafenib in BRAFV600E-Mutated Colon Cancer and Melanoma Cells In Vitro

Despite the advancements in targeted therapy for BRAFV600E-mutated metastatic colorectal cancer (mCRC), the development of resistance to BRAFV600E inhibition limits the response rate and durability of the treatment. Better understanding of the resistance mechanisms to BRAF inhibitors will facilitate the design of novel pharmacological strategies for BRAF-mutated mCRC. The aim of this study was to identify novel protein candidates involved in acquired resistance to BRAFV600E inhibitor vemurafenib in BRAFV600E-mutated colon cancer cells using an integrated proteomics approach. Bioinformatic analysis of obtained proteomics data indicated actin-cytoskeleton linker protein ezrin as a highly ranked protein significantly associated with vemurafenib resistance whose overexpression in the resistant cells was additionally confirmed at the gene and protein level. Ezrin inhibition by NSC305787 increased anti-proliferative and pro-apoptotic effects of vemurafenib in the resistant cells in an additive manner, which was accompanied by downregulation of CD44 expression and inhibition of AKT/c-Myc activities. We also detected an increased ezrin expression in vemurafenib-resistant melanoma cells harbouring the BRAFV600E mutation. Importantly, ezrin inhibition potentiated anti-proliferative and pro-apoptotic effects of vemurafenib in the resistant melanoma cells in a synergistic manner. Altogether, our study suggests a role of ezrin in acquired resistance to vemurafenib in colon cancer and melanoma cells carrying the BRAFV600E mutation and supports further pre-clinical and clinical studies to explore the benefits of combined BRAF inhibitors and actin-targeting drugs as a potential therapeutic approach for BRAFV600E-mutated cancers.
Asymptotically Efficient Moving Target Localization in Distributed Radar Networks

In this article, we investigate the joint estimation of the position and velocity of a moving target in distributed networks of moving radars using Time Of Arrival (TOA) and Doppler Shift (DS) measurements. In contrast to most of the existing/recent methods, we avoid the use of Nuisance Variables (NVs) by employing algebraic manipulations. We reformulate a new set of equations that are linear with respect to the target's position and velocity, resulting in a significant performance improvement. Subsequently, we propose a Two-Stage Weighted Least Squares (TSWLS) estimator and recommend two alternative algorithms to reduce computational complexity while preserving the accuracy by selecting either a transmitter or receiver as the reference sensor. We implement the proposed method over fully and partially connected networks. Our theoretical derivations and numerical simulations reveal that the proposed estimators are asymptotically efficient, i.e., they attain the CRLB, at relatively high noise levels. Moreover, the simulation results show that the proposed methods outperform state-of-the-art algorithms.
Contagion and Loss Redistribution in Crypto Asset Markets: Modelling the Intersection of DeFi and CeFi

: This paper addresses the growing concern of shock propagation in crypto asset markets. The integration of conventional (cid:28)nancial institutions (CeFi) and decentralized (cid:28)nancial protocols (DeFi) has introduced a set of complexities that are not adequately accounted for in current models. We build on the well-established framework by Eisenberg and Noe (2001) and propose a generalized extension that can be applied to mixed DeFi/CeFi networks. Our model serves as a tool for comprehending potential contagion channels and loss redistribution resulting from the non-recourse nature of DeFi loans.
Inflammatory Bowel Disease and Colorectal Cancer: Epidemiology, Etiology, Surveillance, and Management

Simple Summary Although the incidence of inflammatory bowel disease (IBD)-related cancer has been decreasing, its prognosis remains worse than that of non-IBD-related cancers, owing to its multiple risk factors. This review explores the risk factors, epidemiology, surveillance strategies, and treatment recommendations for IBD-related cancers, as well as potential future research directions. Abstract Patients with inflammatory bowel diseases (IBDs), such as ulcerative colitis and Crohn’s disease, have an increased risk of developing colorectal cancer (CRC). Although advancements in endoscopic imaging techniques, integrated surveillance programs, and improved medical therapies have contributed to a decreased incidence of CRC in patients with IBD, the rate of CRC remains higher in patients with IBD than in individuals without chronic colitis. Patients with IBD-related CRCs exhibit a poorer prognosis than those with sporadic CRCs, owing to their aggressive histological characteristics and lower curative resection rate. In this review, we present an updated overview of the epidemiology, etiology, risk factors, surveillance strategies, treatment recommendations, and prognosis of IBD-related CRCs.
Cork Oak Woodlands on the Edge

“Cork oak forests have coevolved with human societies for thousands of years; they support the livelihoods of millions of people and are a key component of treasured mediterranean landscapes, but the pressures on these forests have never been greater. This scholarly work offers a wealth of knowledge on the management and restoration of a critical forest system and contains much of significance to those concerned with our relationship to all forests worldwide. ” —Jeff sayer, science advisor, iuCn
Different Size Formulations of Fluopyram: Preparation, Antifungal Activity, and Accumulation in the Fungal Pathogen Botrytis cinerea

Nanotechnology is revolutionizing the efficient production and sustainable development of modern agriculture. Understanding the pesticide activity of both nano- and conventional methods is useful for developing new pesticide formulations. In this study, three solid fluopyram formulations with varying particle sizes were developed, and the mechanisms underlying the difference in the antifungal activity among these formulations were investigated. Wet media milling combined with freeze drying was used to prepare fluopyram nanoparticles (FLU-NS) and a micron-sized solid formulation (FLU-MS), and a jet grinding mill was employed to fabricate fluopyram wettable powder (FLU-WP). The mean particle sizes of FLU-NS, FLU-MS, and FLU-WP were 366.8 nm, 2.99 μm, and 10.16 μm, respectively. Notably, FLU-NS displayed a toxicity index against Botrytis cinerea (gray mold) that was approximately double those of FLU-MS and FLU-WP. Similar trends were noticed in the antifungal tests on Alternaria solani. The uptake of FLU-NS by B. cinerea was approximately twice that of FLU-MS and FLU-WP, indicating that fluopyram nanoparticles are more easily taken up by the pathogen (B. cinerea), and display better bioactivity than the larger fluopyram particles. Therefore, the nanosizing of pesticides appears to be a viable strategy to enhance efficiency without increasing the amount of pesticide used.
An Energy-Efficient Learning Automata and Cluster-Based Routing Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs), which may be used for a broad variety of applications, have recently emerged as a prominent data collection paradigm. The fundamental concerns in wireless sensor networks are the efficient use of energy and the reliable delivery of data, both of which are largely determined by the rate at which packets are dropped. When developing an energy-efficient routing protocol, one of the most important steps is selecting a node to act as a successor node in a routing path. The application of learning automata theory to guide the routing decisions made by the sensors in a WSN has recently been the subject of research in the field of WSNs, where it has been shown to have several advantages. In this paper, a learning automata-based PSO relay selection scheme for energyefficient relay selection and reliable data delivery is proposed. The network is clustered using the LEACH protocol. The random number in the traditional LEACH protocol will be stabilized with the sensor node energy level for CH stability. Every sensor node in the network estimates the best possible routes to the sink node using the PSO algorithm. Instead of retransmissions, here we introduce learning automata for successor node selection during packet loss. The proposed learning automata calculate the next node’s selection probability in a routing path using multi-objective parameters like communication cost, residual energy, distance from BS, buffer size, and previous selection probability. Performance evaluation clearly showed that the proposed approach decreases energy usage, transmission delays, and data transfers while extending network lifetime. According to the experimental results, the proposed scheme can improve energy efficiency by 21.68%, delay by 31%, PDR by 87%, routing overhead by 0.5%, and throughput by 18.76% as compared to existing techniques like O-LEACH (Optimized Low Energy Adaptive Clustering Hierarchy Protocol) and EEPC (enhanced energy proficient clustering).
Upregulated CD8+ MAIT cell differentiation and KLRD1 gene expression after inactivated SARS-CoV-2 vaccination identified by single-cell sequencing

Background The primary strategy for reducing the incidence of COVID-19 is SARS-CoV-2 vaccination. Few studies have explored T cell subset differentiation and gene expressions induced by SARS-CoV-2 vaccines. Our study aimed to analyze T cell dynamics and transcriptome gene expression after inoculation with an inactivated SARS-CoV-2 vaccine by using single-cell sequencing. Methods Single-cell sequencing was performed after peripheral blood mononuclear cells were extracted from three participants at four time points during the inactivated SARS-CoV-2 vaccination process. After library preparation, raw read data analysis, quality control, dimension reduction and clustering, single-cell T cell receptor (TCR) sequencing, TCR V(D)J sequencing, cell differentiation trajectory inference, differentially expressed genes, and pathway enrichment were analyzed to explore the characteristics and mechanisms of postvaccination immunodynamics. Results Inactivated SARS-CoV-2 vaccination promoted T cell proliferation, TCR clone amplification, and TCR diversity. The proliferation and differentiation of CD8+ mucosal-associated invariant T (MAIT) cells were significantly upregulated, as were KLRD1 gene expression and the two pathways of nuclear-transcribed mRNA catabolic process, nonsense-mediated decay, and translational initiation. Conclusion Upregulation of CD8+ MAIT cell differentiation and KLRD1 expression after inactivated SARS-CoV-2 vaccination was demonstrated by single-cell sequencing. We conclude that the inactivated SARS-CoV-2 vaccine elicits adaptive T cell immunity to enhance early immunity and rapid response to the targeted virus.
Equivalence between shot-profile and source-receiver migration

Shot-profile migration and source-receiver migration seem different, but the image and Common Image Gather they obtain is the same. In this paper, we prove that shot-profile migration and source-receiver migration are equivalent, assuming that the imaging condition is cross-correlation and the method for propagating the source and receiver wavefields is a one-way wave equation. This is achieved after generalizing source-receiver migration to arbitrary sources.
Exploring the landscape of CA-125 testing: A comprehensive analysis of Ministry of Health data

OBJECTIVE The aim of this study was to investigate the utilization patterns and clinical implications of cancer antigen 125 (CA-125) testing in the diagnosis of ovarian and endometrial cancers using a large-scale dataset obtained from the Ministry of Health. METHODS A retrospective analysis was conducted on anonymized data collected between 2017 and 2021, comprising 3.917. 240 individuals who underwent CA-125 testing. The data included demographic information, test results, diagnoses, and clinical characteristics. Descriptive statistics and comparative analyses were performed to assess the utilization trends and clinical outcomes associated with CA-125 testing. RESULTS Among the study population, CA-125 testing was primarily requested for female individuals, with the highest number of tests performed in the age group of 18–64 years. The overall positive rate for CA-125 was 13.31%, with slightly lower rates observed in females (13.18%) than males (14.07%). The study identified a significant association between elevated CA-125 levels and cancer diagnoses, with 19.88% of positive CA-125 results indicating cancer, whereas 10.51% had no cancer diagnosis. Furthermore, the study revealed a higher likelihood of cancer detection among individuals aged 65 years and above, with a positive rate of 17.79%. CONCLUSION Our findings provide valuable insights into the utilization patterns and clinical implications of CA-125 testing in ovarian and endometrial cancer diagnosis. While CA-125 remains a prominent tumor marker, its interpretation should consider age, gender, and clinical context. The study emphasizes the potential benefits of integrating additional markers and imaging modalities to enhance diagnostic accuracy. These findings contribute to optimizing the use of CA-125 testing for early detection and management of gynecological malignancies, thereby improving patient outcomes.
The elderly in the post-anesthesia care unit

It is increasingly conceivable that elderly patients will be treated in perioperative settings as the world's population shifts toward an older age distribution. They are more prone to a variety of unfavorable outcomes as a consequence of the physiological changes that accompany aging and the coexistence of multiple medical conditions. Postoperative complications in elderly patients are linked to a large increase in morbidity and mortality and the burden placed on the healthcare system. Our goal is to determine how elderly patients' recovery after anesthesia differs from that of younger patients. In addition, we will discuss the main postoperative complications experienced by elderly patients and the measures that are utilized to limit the risk of these complications developing.
Green Human Resource Management: Past, Present and Future Research Agenda

This research study presents a narrative literature review on green human resource management, its related organizational dynamics, and strategic orientation. Using the approach of preferred review of items for systematic review and meta-analysis (PRISMA) framework, 141 research studies published on the topic of green HRM by established publisher databases and Google scholar having relevance of topic and abstract were analyzed in this study. The latest conceptualization of GHRM and its conceptual boundaries have been discussed as well. The study found that GHRM is the extension of traditional HRM with a strategic and environmental orientation. Multiple organizational dynamics are associated with green human resource management which has been discussed in the light of available literature. Finally, a useful discussion has been made and future research directions have been identified.
Anuário do Instituto de Geociências

Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique employs a Multi-Temporal InSAR (MT-InSAR) approach to accurately measure subsidence. This technique, a type of Differential Interferometry (DInSAR), mitigates errors that traditional DInSAR techniques cannot, including temporal and geometric decorrelation, and phase unwrapping errors. In order to verify the subsidence process in the Pinheiro neighborhood of Maceio Brazil following a 2018 earthquake, we tested free processing packages such as SNAP-StaMPS integration. Our investigation was conducted in two stages: first, using a stack of Sentinel-1A SLCSAR (Single Look Complex-SAR) images acquired before and after the earthquake, and second, using more recent images to determine if the subsidence process is ongoing. Results from the first stage identified the area affected by subsidence and the second stage confirmed the continued presence of subsidence events. From 2017 to 2018, the subsidence process exhibited the highest displacement amplitude of -32.3 mm/year, whereas, between 2021 and 2022, the amplitude decreased to -24.09 mm/year, indicating a deceleration in the subsidence process.
THE ENVIRONMENTAL IMPACT OF TOURISM AND AIR TRANSPORT ON THE SUSTAINABLE DEVELOPMENT OF SMALL ISLAND DEVELOPING STATES

The United Nations General Assembly recognized that many small island developing countries were confronted with compelling factors such as their smallness in size, susceptibility and vulnerability to natural disasters, remoteness of access and geographical dispersion. All of these factors worked to their detriment. The fragility of their ecosystems and constraints on transportation and communications created almost insurmountable distances between market centers and a highly limited internal market for these developing countries. This paper discusses these problems and the impact of United Nations initiatives.
Skip DETR: end-to-end Skip connection model for small object detection in forestry pest dataset

Object detection has a wide range of applications in forestry pest control. However, forest pest detection faces the challenges of a lack of datasets and low accuracy of small target detection. DETR is an end-to-end object detection model based on the transformer, which has the advantages of simple structure and easy migration. However, the object query initialization of DETR is random, and random initialization will cause the model convergence to be slow and unstable. At the same time, the correlation between different network layers is not strong, resulting in DETR is not very ideal in small object training, optimization, and performance. In order to alleviate these problems, we propose Skip DETR, which improves the feature fusion between different network layers through skip connection and the introduction of spatial pyramid pooling layers so as to improve the detection results of small objects. We performed experiments on Forestry Pest Datasets, and the experimental results showed significant AP improvements in our method. When the value of IoU is 0.5, our method is 7.7% higher than the baseline and 6.1% higher than the detection result of small objects. Experimental results show that the application of skip connection and spatial pyramid pooling layer in the detection framework can effectively improve the effect of small-sample obiect detection.
Photo electromotive force induced by running fringes for determination of ion mobility in perovskite semiconductors

We have demonstrated theoretically and experimentally that the current induced by an interference pattern moving at constant velocity can be used to determine the ion mobility and activation energy in perovskite semiconductors. The frequency dependence of the signal has features predicted by theory: the high-frequency peak corresponds to the electron/hole photoconductivity relaxation process, and the low-frequency peak occurs when the velocity of the interference pattern synchronizes with the ion motion; by determining the peak’s position, it becomes possible to estimate the ion mobility. The values of ion mobility and activation energy agree with the data reported in the literature.