Miracle-dz/UniFAR-FTUs huggingface dataset:
Riverine Sediment Response to Deforestation in the Amazon Basin

The Amazon experiences thousands of square kilometres of deforestation annually with recent rates increasing to levels unseen since the late 2000s. These increased rates of deforestation within the basin have led to changes in sediment concentration within its river systems, with potential impacts on ecological functioning, freshwater availability, and fluvial and coastal geomorphic processes. The relationship between deforestation and fluvial sediment dynamics at large scales has 10 not been extensively studied, in the Amazon or elsewhere, primarily due to lack of data. In this study, we utilize a novel remote sensing-derived sediment concentration dataset to analyze the impact of deforestation from 2001 to 2020 on suspended sediment in large rivers (> 50 m wide) across the Amazon River Basin. These impacts are studied using a lag-based approach to quantify the spatiotemporal relationships between observed suspended sediment and changes in landcover over time. The results show that large scale deforestation of the Amazon during the 2001-2020 period are associated with significant changes 15 in sediment concentration in the eastern portion of the basin. In the heavily deforested eastern regions, the hydrogeomorphic response to deforestation occurs relatively rapidly (within a year), whereas the less disturbed western areas exhibit delays of one to two years before responses are observable. Moreover, we observe that deforestation must be substantial enough to overcome the collective influences of human activities and natural sediment variations to result in a discernible impact on sediment concentration in large rivers. In 69% of Amazonian major tributary basins with an immediate response, more than 20 5% of the basin was deforested during the 2001-2020 period, while in 85% of basins with lagged responses, less than 5% of the land was cleared. These findings suggest severe implications for future sediment dynamics across the Amazon if deforestation is to further expand into the basin.
Van precisielandbouw naar smart farming technology

Tijdens deze inaugurele rede wordt toegelicht hoe precisielandbouw wordt uitgevoerd en in de toekomst uitgevoerd gaat worden en hoe de 2 lectoren met het lectoraat daar een bijdrage aan willen leveren. Zij focussen zich op precisielandbouw in akkerbouwmatige teelten en in melkveehouderij.
Analysing empowerment‐oriented email consultation for parents: development of the Guiding the Empowerment Process model

Online consultation is increasingly offered by parenting practitioners, but it is not clear if it is feasible to provide empowerment-oriented support in a single session email consultation. Based on the empowerment theory, we developed the Guiding the Empowerment Process model (GEP model) to evaluate text-based consultation. By content analysis of the email advice (n = 129; 5997 sentences in total), we investigated the feasibility of the newly developed model (inter-observer agreement, internal consistency and factor structure) and its validity. Concurrent validity was evaluated by comparing coding results using the GEP model and a Social Support model that partially intersects with empowerment. Results showed good inter-observer reliability and internal consistency of the GEP model. The results provided evidence for its concurrent validity by a significant correlation of the coding results from the GEP model with the Social Support model, although it was also distinctive. All described techniques which practitioners may employ to guide the parental process towards empowerment were observed in the sample. Also, guidance was provided in all components of the empowerment process. Feasibility of the GEP model for content analysis of email consultation in parental support from a theoretical empowerment perspective has been demonstrated.
Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

The significant advances in Wireless Sensor Networks (WSNs) facilitate many latest applications, such as intelligent battlefield, home automation, traffic control, and more. WSNs comprise small autonomously organized sensor nodes that are powered by batteries. The processes of collecting information and data storage, processing, and transmission deplete the energy of these small devices. Energy efficiency is still a major issue to address in WSN routing. Clustering is the best method that has been developed to reduce node energy consumption. However, current clustering methods are unable to effectively distribute the energy requirements of the nodes without considering energy characteristics, number of nodes, and flexibility. This study proposed a new cluster-based routing model for WSNs and emphasized the need for an improved clustering process with new optimization techniques. In particular, the improved DeepMaxout model was adopted to predict the energy of the nodes. Cluster Head (CH) selection is performed considering the nodes' energy as a prime factor. After choosing the CH, the CIOO algorithm incorporates new link quality and trust evaluations while determining the routing process. Finally, a comparison of energy utilization factors was performed between the suggested and traditional approaches.
Distinguishing EGFR mutant subtypes in stage IA non-small cell lung cancer using the presence status of ground glass opacity and final histologic classification: a systematic review and meta-analysis

Background The progression of early stage non-small cell lung cancer (NSCLC) is closely related to epidermal growth factor receptor (EGFR) mutation status. The purpose of this study was to systematically investigate the relationship between EGFR mutation status and demographic, imaging, and ultimately pathologic features in patients with NSCLC. Methods A complete literature search was conducted using the PubMed, Web of Science, EMBASE, and Cochrane Library databases to discover articles published by May 15, 2023 that were eligible. The relationship between EGFR mutation status and specific demographic, imaging, and ultimately pathologic features in patients with NSCLC was evaluated using pooled odds ratios (ORs) and their 95% confidence intervals (CIs). The standardized mean difference (SMD) with 95% CIs was the appropriate statistic to summarize standard deviations (SDs) means for continuous variables. Results A total of 9 studies with 1789 patients were included in this analysis. The final findings suggested that patients with a greater age, female gender, and non-smoking status would have a relatively higher incidence of EGFR mutations. Additionally, the risk of EGFR mutations increased with larger tumor diameter, tumor imaging presentation of mixed ground glass opacity (mGGO), and tumor pathological findings of minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). Significantly, malignancies presenting as MIA are more likely to contain L858R point mutations (OR = 1.80; 95% CI: 1.04–3.13; p = 0.04) rather than exon 19 deletions (OR = 1.81; 95% CI: 0.95–3.44; p = 0.07). Conclusion This meta-analysis showed that imaging parameters and histological classifications of pulmonary nodules may be able to predict stage IA NSCLC genetic changes.
The Influence of Savings and Deposits on The Credit Position of Village Kredit Institutions (LPD) in Gianyar District During The COVID-19 Pandemic

The background of this research is to analyze the influence of savings and deposits on the credit position of LPDs in Gianyar District during the COVID-19 pandemic. The data used in this research is quantitative data, which includes the value of savings, deposits, and credit. Research on the Effect of Savings and Deposits on the Credit Position of Village Credit Institutions (LPD) in Gianyar District During the Covid-19 Pandemic. The object of this research is community savings funds (Savings and Deposits) and (Credit) in LPDs in Gianyar District. The data analysis technique used is Panel Data Regression Analysis, where the independent variables are Savings and Deposits while the dependent variable is Credit. The test results explain Savings and deposits, together with all the independent variables used in the research, can influence the credit position of Village Credit Institutions (LPD) in Gianyar District during the COVID-19 pandemic. Savings have had a significant effect on the credit position of Village Credit Institutions (LPD) in Gianyar District during the COVID-19 pandemic. Deposits have had a significant effect on the credit position of Village Credit Institutions (LPD) in Gianyar District during the COVID-19 pandemic.
A Reconstructed Wind Stress Dataset for Climate Research over the Tropical Pacific during a 153-Year Period

Abstract There are close relationships between the sea surface temperature (SST) and the surface wind over the tropical Pacific. To study the past climate variability over the tropical Pacific, the long-term monthly wind stress anomalies over the tropical Pacific for the period of 1856-2008 are reconstructed with an SVD (singular value decomposition)-based statistical atmospheric model, where the wind stress anomalies are slave and directly correspond to the SST anomalies. The verification results show that the reconstructed wind stress data have high correlations and a small root mean square (RMS) error with the three reanalysis/simulated surface wind datasets from the last 50 years. In addition, the simulated SST anomalies from an intermediate oceanic model (IOM), which is forced by the reconstructed wind stress, can simulate the realistic interannual and decadal variability of the ENSO (El Niño-Southern Oscillation); this indicates that this new long-term wind stress dataset is useful for various climate studies, especially for the large-scale interannual and decadal variability.
RSS flows, world structure & community detection

We investigate the relevance of community detection methods from a thematic and a geographic perspective when applied to a network of co-citations in newspapers international RSS flows. We compare the results of different state-of-the-art algorithms. We find that building a consensus from the possible decompositions found is important in order to capture a macro global organization as well as local phenomena.
Modeling Training in Nb3Sn Superconducting Magnets

Stress managed magnet designs allow to limit the strain and stresses applied to the conductor during assembly and operation. In canted cos($\theta$) (CCT) designs, the conductor is wound around a mandrel: the impregnation process creates a bonding between the two, that can fail during magnet powering. The energy releases due to debonding are considered a potential cause of training quenches. In this study, we investigate these events modeling the mandrel-conductor interfaces by means of cohesive zone material models. The material properties were calibrated by means of measurements performed on representative interfaces, and the models were validated comparing the results with strain gauge measurements. A thermal model was used to compute the local temperature increase in the strands as a function of the energy released by debonding and frictional sliding across the newly formed interfaces. The result was then used to define a quench condition for the model, allowing to simulate the full training process of the CCT magnet. The obtained training curve is in reasonable agreement with the experimental results.
Comparison on the Determination of Soil Total Phosphorus Concentration by Continuous Flow Injection Analyzer and Traditional Analysis Methods

Continuous flow injection analyzer and traditional molybdate-blue method,molybdate-yellow method were em-ployed to analyze the total phosphorus concentration of 33 soils collected from thirty regions in China for the aim to pro-vide an evidence for the possibility of using continuous flow injection analyzer.The results show that the average of vari-ance coefficient from the three methods is flow injection method(3.119%) molybdate-blue method(3.985%) mo-lybdate-yellow method(4.203%). The relationship of the analysis results from flow injection method with that from mo-lybdate-blue and molybdate-yellow methods is 0.997 1 and 0.987 0,respectively,both of which reach extremely signifi-cant positive correlation.The recovery rate of the three methods ranges from 95.0% to 105.0%,which is sufficient to meet the demand of the sample analysis.These suggest that continuous flow injection analysis method is an available and reliable method in analyzing soil phosphorus concentration on a large scale.
PERFORMANCE ASSESSMENT OF OBJECT DETECTION FROM MULTI SATELLITES AND AERIAL IMAGES

Abstract. Object detection in remote sensing imagery plays an important role in many applications, such as tracking and change detection. With the development of deep learning algorithms and advancement in hardware systems, improved accuracies have been achieved in the detection of various objects from remote sensing images. However, object detection across heterogeneous remote sensing imagery remains an important issue, particularly for satellite and aerial imagery. The colour variation for the same ground objects, variable resolutions, different platform heights, the parallax effect, and image distortion brought on by diverse shooting angles are the biggest hurdles in satellite-aerial detection applications. The research aims to obtain successful model for detecting aircrafts from satellite and aerial images and reduce cost and the gap of revisit time between sensors. The networks were tested using aerial, GF-2, Jilin-1 (JL-1) and Pleiades satellites test sets after being trained individually using the RGB high-resolution aerial set and panchromatic low-resolution GF-2 satellite set to validate the efficiency of the trained models. Also, the aerial-trained model and GF-2 satellite-trained model as dedicated models were compared with each other, and model trained by all dataset for Object Detection in Aerial Images (DOTA). It is observed that the anchor sizes and augmentation methods can enhance the performance of detection models. k-means algorithm and data augmentation were applied to produce better anchor box selection and avoid overfitting, atmospheric conditions problems, respectively. The accuracy assessment results demonstrate that the aerial-trained model outperforms the GF-2 satellite-trained model. In addition, the results of two dedicated detection models show improved accuracy compared to the DOTA-trained model.
Persistent Positive Real-Time Reverse Transcription Polymerase Chain Reaction (RT-PCR) Results in Recovered COVID-19 Hospital Patients: Implications for Interpretation

Background Real-time reverse transcription polymerase chain reaction (RT-PCR) test results often remain positive in patients with COVID-19, even after their symptoms have improved. We compared the characteristics of patients with persistently positive RT-PCR test results despite improved COVID-19 symptoms to those whose RT-PCR test results turned negative following symptom improvement. Materials and methods A total of 143 adult patients with COVID-19 who were hospitalized at a tertiary university hospital were enrolled. Demographic, clinical, treatment, and epidemiological data were extracted from their electronic medical records. These data were compared between patients with persistently positive RT-PCR test results and those with negative RT-PCR test results after symptom improvement. Results The prevalence of cough and respiratory symptoms was less in COVID-19 patients with persistently positive RT-PCR test results after symptom improvement than in other patients with COVID-19 (p<0.05). Conclusion Persistently positive patients had a lower prevalence of cough than those who became negative. None of the other examined co-variates (hypertension, chronic kidney disease, chronic lung disease, dyslipidemia, etc.) was associated with the persistent positivity.
The Dynwnics of Thrbulent Spots

In this article we present a critical review of the present state of knowledge of turbulent spots. He discuss the properties of both new­ born as weIl as fully-developed spots. Both ensemble-averaged results and also the underlying structure of the spot are presented. It is shown that the ensemble-averaged results can be misleading, and that the spot has many features similar to those of a fUlly-turbulent boundary layer. The mechanisms by which a turbulent spot spreads into the surrounding fluid, which were first suggested by Corrsin and Kistler (1955), will be elaborated. Finally, similarities between spots and other flows will be discussed. 1. I ntroduction In 1951 Emmons, while studying the flow of water over a nearly horizon­ tal water table, observed that transition to turbulence occurred through the appearance of small, individual patches of turbulence in an otherwise laminar boundary layer. These patches, wh ich he termed spots, started at random instants in space and time as very small regions of turbulence, and grew in an approximately linear manner as they were swept downstream. The number and size of the spots increased in the downstream direction until the spots amalgamated into a fully­ turbulent boundary layer. Based upon these results, Emmons proposed that transition from laminar to turbulent flow occurs through the generation, growth, and amalgamation of turbulent spots. The viewpoint that turbulent spots play a central role in boundary layer transition has become generally accepted. Furthermore it has been suggested that spots are dynamically similar to turbulent boundary layers but simpler, so that their study might be profitable in shedding light on the complex dynamics of turbulent boundary layers. It has been also suggested that turbulent boundary layers might be a composite of turbulent spots, so that the spots are the basic building block of the boundary layer. Thus the study of turbulent spots has be­ come an essential part of the study of both boundary layer transition and turbulence.
Investigating Citizens’ Acceptance of Contact Tracing Apps: Quantitative Study of the Role of Trust and Privacy

Background The COVID-19 pandemic accelerated the need to understand citizen acceptance of health surveillance technologies such as contact tracing (CT) apps. Indeed, the success of these apps required widespread public acceptance and the alleviation of concerns about privacy, surveillance, and trust. Objective This study aims to examine the factors that foster a sense of trust and a perception of privacy in CT apps. Our study also investigates how trust and perceived privacy influence citizens’ willingness to adopt, disclose personal data, and continue to use these apps. Methods Drawing on privacy calculus and procedural fairness theories, we developed a model of the antecedents and behavioral intentions related to trust and privacy perceptions. We used structural equation modeling to test our hypotheses on a data set collected at 2 time points (before and after the launch of a national CT app). The sample consisted of 405 Irish residents. Results Trust in CT apps was positively influenced by propensity to trust technology (β=.074; P=.006), perceived need for surveillance (β=.119; P<.001), and perceptions of government motives (β=.671; P<.001) and negatively influenced by perceived invasion (β=−.224; P<.001). Perceived privacy was positively influenced by trust (β=.466; P<.001) and perceived control (β=.451; P<.001) and negatively influenced by perceived invasion (β=−.165; P<.001). Prelaunch intentions toward adoption were influenced by trust (β=.590; P<.001) and perceived privacy (β=.247; P<.001). Prelaunch intentions to disclose personal data to the app were also influenced by trust (β=.215; P<.001) and perceived privacy (β=.208; P<.001) as well as adoption intentions before the launch (β=.550; P<.001). However, postlaunch intentions to use the app were directly influenced by prelaunch intentions (β=.530; P<.001), but trust and perceived privacy only had an indirect influence. Finally, with regard to intentions to disclose after the launch, use intentions after the launch (β=.665; P<.001) and trust (β=.215; P<.001) had a direct influence, but perceived privacy only had an indirect influence. The proposed model explained 74.4% of variance in trust, 91% of variance in perceived privacy, 66.6% of variance in prelaunch adoption intentions, 45.9% of variance in postlaunch use intentions, and 83.9% and 79.4% of variance in willingness to disclose before the launch and after the launch, respectively. Conclusions Positive perceptions of trust and privacy can be fostered through clear communication regarding the need and motives for CT apps, the level of control citizens maintain, and measures to limit invasive data practice. By engendering these positive beliefs before launch and reinforcing them after launch, citizens may be more likely to accept and use CT apps. These insights are important for the launch of future apps and technologies that require mass acceptance and information disclosure.
Knockouts of TNFRSF1A and TNFRSF1B Genes in K562 Cell Line Lead to Diverse Long-Lasting Responses to TNF-α

This research delves into the intricate landscape of tumor necrosis factor-alpha (TNF-α) signaling, a multi-functional cytokine known for its diverse cellular effects. Specifically, we investigate the roles of two TNF receptors, TNFR1 and TNFR2, in mediating TNF-α-induced transcriptional responses. Using human K562 cell lines with TNFR1 and TNFR2 knockouts, we explore changes in gene expression patterns following TNF-α stimulation. Our findings reveal distinct transcriptional profiles in TNFR1 and TNFR2 knockout cells, shedding light on the unique contributions of these receptors to TNF-α signaling. Notably, several key pathways associated with inflammation, apoptosis, and cell proliferation exhibit altered regulation in the absence of TNFR1 or TNFR2. This study provides valuable insights into the intricate mechanisms governing TNF-α signaling and its diverse cellular effects, with potential implications for targeted therapeutic strategies.
Computer Vision and Image Understanding

With millions of users and billions of photos, web-scale face recognition is a challenging task that demands speed, accuracy, and scalability. Most current approaches do not address and do not scale well to Internet-sized scenarios such as tagging friends or finding celebrities. Focusing on web-scale face identification, we gather an 800,000 face dataset from the Facebook social network that models real-world situations where specific faces must be recognized and unknown identities rejected. We propose a novel Linearly Approximated Sparse Representation-based Classification (LASRC) algorithm that uses linear regression to perform sample selection for ‘-minimization, thus harnessing the speed of least-squares and the robustness of sparse solutions such as SRC. Our efficient LASRC algorithm achieves comparable performance to SRC with a 100–250 times speedup and exhibits similar recall to SVMs with much faster training. Extensive tests demonstrate our proposed approach is competitive on pair-matching verification tasks and outperforms current state-of-the-art algorithms on open-universe identification in uncontrolled, web-scale scenarios. 2013 Elsevier Inc. All rights reserved.