Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Plants Sense Humans? Using Plants as Biosensors to Detect the Presence of Eurythmic Gestures

This paper describes the preliminary results of measuring the impact of human body movements on plants. The scope of this project is to investigate if a plant perceives human activity in its vicinity. In particular, we analyze the influence of eurythmic gestures of human actors on lettuce and beans. In an eight-week experiment, we exposed rows of lettuce and beans to weekly eurythmic movements (similar to Qi Gong) of a eurythmist, while at the same time measuring changes in voltage between the roots and leaves of lettuce and beans using the plant spikerbox. We compared this experimental group of vegetables to a control group of vegetables whose voltage differential was also measured while not being exposed to eurythmy. We placed a plant spikerbox connected to lettuce or beans in the vegetable plot while the eurythmist was performing their gestures about 2 m away; a second spikerbox was connected to a control plant 20 m away. Using t-tests, we found a clear difference between the experimental and the control group, which was also verified with a machine learning model. In other words, the vegetables showed a noticeably different pattern in electric potentials in response to eurythmic gestures.
Data Driven to Improve Root Cause Analysis: A Bibliometric Analysis

Data Root Cause Analysis (RCA) allows organizations to improve their product and services, increasing their competitiveness. This process aims to identify the root cause of the failures or non-conformities, in this aspect, RCA plays a key role in continuous improvement process by correcting the failures from the root and minimizing its recurrence [3]. The exponential growth in computer’s processing capacity has allowed applying and improving data driven techniques such as Machine Learning, Data Science, Data Mining, Big Data, among others related; in the process of identifying root causes.
Effects of dietary L-Citrulline supplementation on growth performance, meat quality, and fecal microbial composition in finishing pigs

Gut microbiota play an important role in the gut ecology and development of pigs, which is always regulated by nutrients. This study investigated the effect of L-Citrulline on growth performance, carcass characteristics, and its potential regulatory mechanism. The results showed that 1% dietary L-Citrulline supplementation for 52 days significantly increased final weight, liveweight gain, carcass weight, and average backfat and markedly decreased drip loss (p < 0.05) of finishing pigs compared with the control group. Microbial analysis of fecal samples revealed a marked increase in α-diversity and significantly altered composition of gut microbiota in finishing pigs in response to L-Citrulline. In particular, these altered gut microbiota at the phylum and genus level may be mainly involved in the metabolic process of carbohydrate, energy, and amino acid, and exhibited a significant association with final weight, carcass weight, and backfat thickness. Taken together, our data revealed the potential role of L-Citrulline in the modulation of growth performance, carcass characteristics, and the meat quality of finishing pigs, which is most likely associated with gut microbiota.
High Gain Observer-Based Saturated Controller for Feedback Linearizable System

The presence of pulse width modulation driven components, limiters, and electronic switches constrain the magnitude of control input in electrical and electro-mechanical systems. The presence of nonlinearities in these system models and unavailability of full state measurements, further complicate the controller design process. This brief introduces a novel saturated output feedback design to solve the tracking problem for a class of feedback linearizable circuits. The feedback scheme comprises of a modified approximate dynamic inversion controller in conjunction with a standard high gain observer. Unlike the Lyapunov-based approaches, the proposed contraction theory-based methodology details a quantitative framework for the convergence analysis of closed loop trajectories. This, in turn, serve the dual purpose of proving ultimate boundedness and quantifying the convergence bounds in terms of known parameters.
The integrated stress response in cancer progression: a force for plasticity and resistance

During their quest for growth, adaptation, and survival, cancer cells create a favorable environment through the manipulation of normal cellular mechanisms. They increase anabolic processes, including protein synthesis, to facilitate uncontrolled proliferation and deplete the tumor microenvironment of resources. As a dynamic adaptation to the self-imposed oncogenic stress, cancer cells promptly hijack translational control to alter gene expression. Rewiring the cellular proteome shifts the phenotypic balance between growth and adaptation to promote therapeutic resistance and cancer cell survival. The integrated stress response (ISR) is a key translational program activated by oncogenic stress that is utilized to fine-tune protein synthesis and adjust to environmental barriers. Here, we focus on the role of ISR signaling for driving cancer progression. We highlight mechanisms of regulation for distinct mRNA translation downstream of the ISR, expand on oncogenic signaling utilizing the ISR in response to environmental stresses, and pinpoint the impact this has for cancer cell plasticity during resistance to therapy. There is an ongoing need for innovative drug targets in cancer treatment, and modulating ISR activity may provide a unique avenue for clinical benefit.
Advancements in radiochemistry and nuclear methods of analysis for safer and sustainable applications

This research investigates radiochemistry and nuclear analysis technologies to make applications safer and more sustainable. A mathematical optimization approach optimizes radiopharmaceutical synthesis parameters for positron emission tomography (PET) imaging to maximize yield and minimize radioactive waste. Optimizing critical parameters improves the efficiency, safety, and sustainability of radiochemistry and nuclear technologies in medical imaging, nuclear energy, and environmental monitoring. The numerical example shows that optimization achieves the study goal. Optimization strategies improve medical imaging by increasing radiopharmaceutical yield and decreasing radioactive waste volume. Real-world implementation requires cost-effectiveness, safety restrictions, and numerous synthesis factors. Researchers, policymakers, and industry professionals must collaborate to enhance human and environmental welfare, according to the report. In conclusion, this research advances radiochemistry and nuclear procedures for safer and more sustainable applications, mitigating hazards and environmental effect and ensuring a safer and more sustainable future for nuclear technology.
The Insertion of an Evolutionary Lost Four-Amino-Acid Cytoplasmic Tail Peptide into a Syncytin-1 Vaccine Increases T- and B-Cell Responses in Mice

Human endogenous retrovirus type W (HERV-W) is expressed in various cancers. We previously developed an adenovirus-vectored cancer vaccine targeting HERV-W by encoding an assembled HERV-W group-specific antigen sequence and the HERV-W envelope sequence Syncytin-1. Syncytin-1 is constitutively fusogenic and forms large multinucleated cell fusions when overexpressed. Consequently, immunising humans with a vaccine encoding Syncytin-1 can lead to the formation of extensive syncytia, which is undesirable and poses a potential safety issue. Here, we show experiments in cell lines that restoring an evolutionary lost cleavage site of the fusion inhibitory R-peptide of Syncytin-1 inhibit cell fusion. Interestingly, this modification of the HERV-W vaccine’s fusogenicity increased the expression of the vaccine antigens in vitro. It also enhanced Syncytin-1-specific antibody responses and CD8+-mediated T-cell responses compared to the wildtype vaccine in vaccinated mice, with a notable enhancement in responses to subdominant T-cell epitopes but equal responses to dominant epitopes and similar rates of survival following a tumour challenge. The impairment of cell–cell fusion and the enhanced immunogenicity profile of this HERV-W vaccine strengthens the prospects of obtaining a meaningful immune response against HERV-W in patients with HERV-W-overexpressing cancers.
The effects of PPI and CPI in prediction of interest rate value

Interest, which is as old as the history of humanity, is at the center of life today in parallel with the developments in technology and communication. Predicting the future values of interest, which is very important in every field from state administration to individual investments and expenditures, is very important for individuals/companies/ states to continue their activities in a stable manner. In pioneering studies, it has been emphasized that the main components of interest are real interest and future inflation expectations. Over time, the effects of goods, money, and international markets on the formation of interest have been analyzed theoretically and empirically. The effects of Central Banks on monetary policies and their long-term reflections have been the aim of many studies in recent history. The fact that the expected inflation in interest studies does not reflect the truth for many times has prevented the right results from being found. For this reason, it is seen that the realized CPI is generally used in estimating the interest rate. In addition, when countries are examined, it is seen that there are significant differences between CPI and PPI. The aim of this study is to empirically investigate which of the CPI and PPI values realized in estimating the interest rate should be used and to what extent.
Reconfigurable Compact Quad-port MIMO Antennas for sub-6 GHz Applications

This paper presents compact quad-port frequency reconfigurable multi-input multi-output (MIMO) antennas for 5 G applications that operate at sub-6 GHz. The proposed design provides more isolation (>13 dB) and pattern diversity using four orthogonal radiating elements. C-shaped metal is used to extend the antenna's radiating elements by inserting one positive intrinsic negative diode (PIN) in the metal. The C-shaped metal and matching stub achieve frequency reconfigurability with a consistent radiation pattern. The PIN diode's switching characteristics allow the frequency to be shifted between two communication bands. One mode (diode ON) covers 2.5 and 5 GHz, while the second mode (diode OFF) covers another dual band of 3.5 and 5.7 GHz. Substrate dimensions are only 50 x 50 x1.6 mm3, making the proposed design compact. Antenna peaks at a gain of 4.18 dB and radiation efficiencies of 80 and 94% in the four frequency bands. The antenna design is appropriate for multi-functional wireless systems and cognitive radio applications since it spans frequency bands below 6 GHz and can be reconfigurable between wide and narrow bands.
Educating Youth to Civic Engagement for Social Justice: Evaluation of a Secondary School Project

The positive effects of youth civic engagement can be felt both at the individual level (e.g., better emotional regulation, a greater sense of empowerment) and at the community level (e.g., a greater likelihood of participation in civic and political activities). They may also be a protective factor for at-risk youth in the short and long term and a valuable element for positive identity development in general. The purpose of this longitudinal study was to assess the impact of an educational intervention implemented in secondary schools to promote youth civic engagement (N = 508 at Time 1, N = 116 at Time 2). The study is divided into two parts: first, it examines the changes stimulated by the project, and second, it uses a path analysis model to explain the intention to participate. Results show that after participation, hostile and benevolent sexism, classic and modern ethnic prejudice, and social dominance orientation decreased, while trust in institutions increased. In addition, the path analysis showed that policy control, social trust, and civic engagement increased the intention of civic engagement at time T1. Despite some limitations, this study may provide useful guidance for those designing and implementing civic education interventions for young people.
Circular Economy Investments: A Portfolio Selection Framework

This research explores the practical implementation of the circular economy into operations since it’s been communicated as one of the main strategic goals of private and public organizations. Despite the commitments in society, there is still a debate on what a circular economy is. Such structural changes in the operations require investments, which will be prioritized based on existing business constraints. Therefore, a framework to assess projects is being proposed for organizations to implement their strategic sustainability targets, such as greenhouse gas neutralization. Through Design Science Research, the following work presents a literature review of the knowledge areas required to design and demonstrate a generic artifact. As a result, developing a specific portfolio to assess circular economy capital investments is recommended, increasing the strategic alignment between projects and sustainability targets. After collecting data at the project level, it starts with Multi-Attribute Utility Theory as the multicriteria decision tool to balance economic criteria and sustainability metrics measured by Life Cycle Assessment. Then it incorporates specific risks to the ranking and utilizes mathematical programming to adjust the portfolio to financial constraints. The artifact demonstration proves its potential to be applied in circular economy capital investment decision-making and as a foundation for further development under specific scenarios of different organizations.
Pancreatic steatosis and iron overload increases cardiovascular risk in non-alcoholic fatty liver disease

Objective To assess the prevalence of pancreatic steatosis and iron overload in non-alcoholic fatty liver disease (NAFLD) and their correlation with liver histology severity and the risk of cardiometabolic diseases. Method A prospective, multicenter study including NAFLD patients with biopsy and paired Magnetic Resonance Imaging (MRI) was performed. Liver biopsies were evaluated according to NASH Clinical Research Network, hepatic iron storages were scored, and digital pathology quantified the tissue proportionate areas of fat and iron. MRI-biomarkers of fat fraction (PDFF) and iron accumulation (R2*) were obtained from the liver and pancreas. Different metabolic traits were evaluated, cardiovascular disease (CVD) risk was estimated with the atherosclerotic CVD score, and the severity of iron metabolism alteration was determined by grading metabolic hiperferritinemia (MHF). Associations between CVD, histology and MRI were investigated. Results In total, 324 patients were included. MRI-determined pancreatic iron overload and moderate-to severe steatosis were present in 45% and 25%, respectively. Liver and pancreatic MRI-biomarkers showed a weak correlation (r=0.32 for PDFF, r=0.17 for R2*). Pancreatic PDFF increased with hepatic histologic steatosis grades and NASH diagnosis (p<0.001). Prevalence of pancreatic steatosis and iron overload increased with the number of metabolic traits (p<0.001). Liver R2* significantly correlated with MHF (AUC=0.77 [0.72-0.82]). MRI-determined pancreatic steatosis (OR=3.15 [1.63-6.09]), and iron overload (OR=2.39 [1.32-4.37]) were independently associated with high-risk CVD. Histologic diagnosis of NASH and advanced fibrosis were also associated with high-risk CVD. Conclusion Pancreatic steatosis and iron overload could be of utility in clinical decision-making and prognostication of NAFLD.
An Improved Gradient Boosted Algorithms Based Solutions Predictive Model (Trade)

In this paper, we describe a general process on how to integrate different types of predictive models within an organization to fully leverage the benefits of predictive modeling. The three major predictive modeling applications discussed in this paper are marketing, pricing, and GBAAlgorithm models. These applications have been well applied and published over the past several years for the Property and Casualty Manufacturing Industry, but this paper and discussions focused on their individual application. We believe that significant value can be realized if they are fully integrated, offering manufacturing companies the opportunity to take an enterprise wide view of managing their business through analytics. Therefore, the paper will discuss a general process on how they can be integrated and how the integrated result can assist insurance companies with managing the complex insurance business, such as minimizing the GBA(Gross Building Area) Algorithm cycle and achieving profitable growth and reacting to external market forces faster than their competition.
Gabor-Modulated Grouped Separable Convolutional Network for Hyperspectral Image Classification

Nowadays, convolutional neural network (CNN)-based deep learning (DL) models have been popularized in hyperspectral image classification (HSIC) and achieved significant accuracy gains, which is due to their hierarchical and nonlinear feature learning patterns. However, too deeper network structures may induce a huge amount of parameters and excessive computing overhead, leading to the need for plenty of labeled samples for training. Besides, highly abstract semantic features may not be the most suitable for hyperspectral land-cover classification tasks. To address these issues, we propose a fairly lightweight network model for HSIC, which is built on a type of exquisitely designed convolution module, namely, grouped separable convolution (GSC). Compared with the standard convolution, the designed GSC module combines grouped convolution with pointwise convolution, which not only greatly reduces the number of parameters of convolution kernels but also caters to the inherent 3-D cube style of hyperspectral image (HSI) data. Moreover, Gabor filters are introduced to modulate the GSC kernels, so as to further use relatively few convolution kernels with additional prior orientation and scale information for feature extraction. The experiments are carried out on four real hyperspectral datasets, and the experimental results reveal that the proposed model has low training cost and memory overhead. Compared with some existing deep network models that have been applied to HSIC, our proposed model can achieve competitive classification accuracy with fewer training samples.
Protein and lipid content estimation in soybeans using Raman hyperspectral imaging

Unlike standard chemical analysis methods involving time-consuming, labor-intensive, and invasive pretreatment procedures, Raman hyperspectral imaging (HSI) can rapidly and non-destructively detect components without professional supervision. Generally, the Kjeldahl methods and Soxhlet extraction are used to chemically determine the protein and lipid content of soybeans. This study is aimed at developing a high-performance model for estimating soybean protein and lipid content using a non-destructive Raman HSI. Partial least squares regression (PLSR) techniques were used to develop the model using a calibration model based on 70% spectral data, and the remaining 30% of the data were used for validation. The results indicate that the Raman HSI, combined with PLSR, resulted in a protein and lipid model Rp 2 of 0.90 and 0.82 with Root Mean Squared Error Prediction (RMSEP) 1.27 and 0.79, respectively. Additionally, this study successfully used the Raman HSI approach to create a prediction image showing the distribution of the targeted components, and could predict protein and lipid based on a single seeds.
Spatially coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: the NASA ACTIVATE dataset

Abstract. The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions, with applications extending from process-based studies to multi-scale model intercomparison and improvement as well as to remote-sensing algorithm assessments and advancements. ACTIVATE used two NASA Langley Research Center aircraft, a HU-25 Falcon and King Air, to conduct systematic and spatially coordinated flights over the northwest Atlantic Ocean, resulting in 162 joint flights and 17 other single-aircraft flights between 2020 and 2022 across all seasons. Data cover 574 and 592 cumulative flights hours for the HU-25 Falcon and King Air, respectively. The HU-25 Falcon conducted profiling at different level legs below, in, and just above boundary layer clouds (< 3 km) and obtained in situ measurements of trace gases, aerosol particles, clouds, and atmospheric state parameters. Under cloud-free conditions, the HU-25 Falcon similarly conducted profiling at different level legs within and immediately above the boundary layer. The King Air (the high-flying aircraft) flew at approximately ∼ 9 km and conducted remote sensing with a lidar and polarimeter while also launching dropsondes (785 in total). Collectively, simultaneous data from both aircraft help to characterize the same vertical column of the atmosphere. In addition to individual instrument files, data from the HU-25 Falcon aircraft are combined into “merge files” on the publicly available data archive that are created at different time resolutions of interest (e.g., 1, 5, 10, 15, 30, 60 s, or matching an individual data product's start and stop times). This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes. The data are publicly accessible through https://doi.org/10.5067/SUBORBITAL/ACTIVATE/DATA001 (ACTIVATE Science Team, 2020).
On the use of ERS SAR interferometry for the retrieval of geo- and bio-physical information

At the present time, most of the research activity in SAR interferometry is directed towards development and improvement of SAR interferometric techniques, mapping of scene topography (DEM), and displacement mapping with differential interferometry. Recently, it has been however shown that SAR interferometry has also a large potential for the retrieval algorithms for bio- and geophysical parameters. Using only the coherence information derived from the complex correlation of two co-registered ERS SAR images, it is possible to retrieve additional information complementary to information contained in the amplitude value of the backscattering coefficient. Phase-unwrapping, one of the most critical steps of the interferometric processing chain, is not required for this type of analysis. Applications of SAR interferometry for forest mapping, forest type discrimination, freezing, land-use classification, soil moisture monitoring, crop classification, crop density, crop growth and field development monitoring, monitoring of open water surfaces, and erosion are discussed. Examples from four different test sites (Bern, Flevoland, Middle Zeeland, and Death Valley) are shown using ERS-1 data collected during 3-day and 35-day repeat orbits as well as ERS-1/2 tandem data.