Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional properties of raw and cooked Azolla caroliniana Willd., an aquatic wild edible plant

Abstract Azolla caroliniana Willd. is an understudied wild edible plant native to the Eastern United States. Other species of Azolla have been used across the world for several thousand years as a livestock feed and as “green manure. ” The use of Azolla for human consumption is thought to be limited by its high total polyphenolic content (TPC). However, the TPC and nutritional content of A. caroliniana has not been thoroughly studied. We measured TPC and other nutrients before and after cooking methods designed to lower TPC. We found that TPC was 4.26 g gallic acid equivalent (GAE) kg−1 DW in raw A. caroliniana. All cooking methods significantly lowered TPC. Protein content was 19% DW, and the apparent protein digestibility was 78.45%. Our yield was 173 g FW m−2 day−1 and 5.53 g DW m−2 day−1. Azolla caroliniana is a high‐yielding plant with great potential for cultivation and domestication.
Clinical outcomes of ROS1-positive non-small cell lung cancer with limited access to ROS1-tyrosine kinase inhibitors (TKIs): experience from an Indian tertiary referral centre

Introduction ROS1 as a driver mutation is observed in approximately 1%–2% of all non-small cell lung cancer (NSCLC). Given its rarity, we share our experience regarding ROS1-positive NSCLC including the access to ROS1 tyrosine kinase inhibitors (TKIs) in a low-middle income country like India. Methods It is a retrospective analysis of ROS1-positive NSCLC patients registered between January 2015 to December 2021 for demographics, treatment patterns and outcomes i.e., overall survival (OS) and progression free survival (PFS). Results Baseline characteristics were available for 70 patients of 78 patients positive for ROS1 by fluorescent in situ hybridisation. Median age at presentation was 52 years, 39 (55.7%) were males, most (51, 72.86%) were non-smokers and ten patients (14.3%) had poor Eastern Cooperative Oncology Group (ECOG) performance status (PS) i.e., PS >2 at presentation. A total of 67 patients receiving cancer directed therapy were analysed for survival. The first line (1L) therapies included – ROS1 TKIs in 38, chemotherapy in 20, epidermal growth factor receptor TKI in eight and chemotherapy-bevacizumab in one only. ROS1 TKI was provided to 20 patients as part of an assistance programme. The median OS for patients who received ROS1 TKI was not attained (95% CI 37.85–NA), while it was 8.11 (95% CI 6.31–NA) months for those who did not (HR-0.1673). The median PFS for the 1L ROS1 TKI compared to the no-TKI group was 27.07 (95% CI 24.28–NA) months versus 5.78 (95% CI 3.42–12) months (HR: 0.2047). Poor ECOG PS at presentation was the only independent prognosticator for survival. Conclusion Using ROS1 TKI improves clinical outcomes in all-comers though statistically not significant. To further improve outcomes, future trials should pay special attention to patients with poor PS and find a way to increase the current limited access to TKI.
A Decentralized Approach to Geometric Video Correction for Network-based Video Wall

This paper describes a decentralized approach to geometric video correction for networked client server based video wall with an art layout of heterogeneous displays. The proposed method is implemented on a mobile device, a video control server and multiple thin clients. The geometric mismatches due to such changes can be corrected even while the video wall is in operation. This proposed correction system is robust to change of positions or angles of the displays to some extent.
Bulk Density Measurement in Forest Soils

Many of the physical properties important for assessing soils in agricultural systems are the same for forest soils. However, because of the nature of forest soils and terrain associated with forest ecosystems, the most appropriate methods for agricultural soils are not always suitable for forest soils. Coarse fragments, large roots, and steep slopes limit the suitability of some methods for forest soils (Page-Dumroese et al. 1999). In addition, surface organic horizons cover many forest soils and measuring their physical properties such as bulk density requires different sampling methods than mineral soils.
Vehicle Trajectory Prediction Based on Local Dynamic Graph Spatiotemporal–Long Short-Term Memory Model

Traffic congestion and frequent traffic accidents have become the main problems affecting urban traffic. The effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Point of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal–Long Short-Term Memory (LDGST-LSTM) was proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information was learned by constructing the traffic knowledge graph, and spatial and temporal features were extracted by combining the Graph Attention Network (GAT) and temporal attention mechanism. The effectiveness of LDGST-LSTM was verified on two datasets, including Chengdu taxi trajectory data in August 2014 and October 2018. The accuracy and robustness of the proposed model were significantly improved compared with the benchmark models. The effects of major components in the proposed model were also evaluated through an ablation experiment. Moreover, the weights of POI that influence location prediction were visualized to improve the interpretability of the proposed model.
Validación de un modelo hidrodinámico y calidad del agua para el Río Magdalena, en el tramo adyacente a Barranquilla, Colombia Validation of a hydrodynamic and water quality model for the Magdalena River, at the adjacent stretch of Barranquilla, Colombia

Water quality models are tools of control, that contribute to the planning, prevention and decision making focused on water resource management. Therefore, different countries, among which is Colombia, have established the relevance of its implementation to determine the discharges assimilation of water bodies. This is why this research aims to simulate the transport of pollutants from efűuents of the industrial corridor of Barranquilla, to study the dispersion and contami nants fate to represent the pollution level in this ecosystem. To develop this work, a two-dimensional numerical model was adapted and implemented, which consists of two modules and is solved in two stages. Initially, the hydrodynamic module is applied to solve the Saint Venant equations derived from Navier-Stokes-Reynolds, using an Eulerian-Lagran gian method. After that, the contaminant transport module that solves the two-dimensional advection-diffusion-reaction equation is applied. With the simulation of river’s discharges, the evolution and behavior of pollutants discharged into it was described, which demonstrate the model capability to represent the water quality in receiving bodies with complex hydraulic and hydrodynamic conditions.
Capper No Cap: Cognitive Load Approach in the Context of Deception Stakes, Age, and Forensic Settings

Deception detection is a crucial yet formidable process within the criminal justice system. The cognitive load approach is a novel methodology for deception detection, which draws upon the cognitive theory emphasising that lying imposes a greater mental tax than truth-telling. This approach has garnered significant attention and is currently undergoing rigorous laboratory testing. Nevertheless, most researchers who advocate for the effectiveness of the cognitive load approach tend to overlook the potential impact of specific contexts that might undermine the accuracy of employing this approach. This review paper addresses three types of contexts that may impact the effectiveness of the cognitive load approach: a) the stakes of deceptions, b) the age of candidates, and c) the authenticity of interviews. In general, the cognitive load approach exhibits more reliability and a higher likelihood of enhancing the deception detection rate in high-stakes contexts than in low-stakes contexts. In comparison with adolescents or adults, this approach appears to be less predictable or consistent when used among young children. Moreover, the cognitive load approach in legal systems seems less viable in real-life applications than in controlled laboratory settings.
Improved Generative Adversarial Network for Bearing Fault Diagnosis with Imbalanced Data

In the actual industrial process, rolling bearings usually operate under normal conditions, making it challenging to collect fault signals from sensors. However, the emergence of a fault can lead to serious accidents and thus fault diagnosis for rolling bearings is of much significance. Moreover, the data imbalance issue arises due to the significantly higher amount of normal data compared to faulty data, resulting in reduced accuracy of fault diagnosis models. To solve this problem, an auxiliary classification generative adversarial network based on spectrum normalization and gradient penalty (ACGAN-SG) is proposed. In this method, spectral normalization ensures that the generator stably generates samples of different label types, effectively avoiding mode collapse. Additionally, the gradient penalty encourages the model to generate fake samples that closely resemble real ones, thereby further enhancing the quality of the generated samples. These generated samples are then incorporated into the original dataset for data augmentation. Moreover, this paper converts the one-dimensional original signal into a two-dimensional gray sample, enabling the model to extract more fault features and thereby further improving the quality of the generated samples and the overall efficiency of fault diagnosis. Furthermore, compared to the original fault classifier and other excellent data generation models, ACGAN- SG demonstrates the highest fault diagnostic accuracy by experiments on the bearing dataset, verifying the feasibility and superiority of the proposed model.
Wireless OTFS-Based Integrated Sensing and Communication for Moving Vehicle Detection

This article presents an approach for joint radar sensing and communications based on the orthogonal time–frequency space (OTFS) modulation, applied to a moving target detection during regular digital wireless transmission (TX). Most communication systems estimate and correct channel influence using specially designed reference signals or pilots. Pilots are usually known to the receiver before the TX, and the receiver estimates the channel impulse response (CIR) by analyzing the input (pilots) and output of the channel. Because of the Doppler effect, the movement of reflection sources (or targets in radar terminology) in multipath channels causes oscillations of CIR coefficients. Based on the frequency analysis of time-varying CIR, one can extract the distances and velocities of moving targets when the data is transmitted through the wireless channel. This article describes the application of OTFS with random pilots using both simulations and real-life field tests. It is shown that applied time-varying CIR estimation based on random pilot signals overcomes the OTFS with zero padding during vehicle velocity estimation in very noisy conditions. The proposed OTFS-based sensing method with the CIR-based channel estimation and vehicle detection is experimentally compared with the cross-ambiguity function (CAF) approach commonly used in radars. A thorough analysis shows that the proposed CIR-based approach outperforms the classical CAF-based radar processing in terms of target detection. Finally, the proposed integrated sensing and communication (ISAC) concept, including bit TX, is experimentally verified.
Essential Error Analysis of Self-Localization From Map Structure in Urban Environment

Lane-level self-localization is a critical task in the field of autonomous driving. Map-based self-localization is commonly employed to achieve lane-level accuracy in urban settings. However, it is known that in certain locations, such as narrow roads and intersections, map-based self-localization poses challenges, leading to failures of autonomous driving tasks. Potential errors in specific locations can be estimated before driving by leveraging the geometrical structures of the environment and can then be utilized to enhance the safety of autonomous driving operations. In contrast to traditional self-localization error estimation methods, which often rely on statistical analyses or regression specific to a particular map format, this research focuses on identifying fundamental errors in self-localization within various map formats. The proposed method employs a general formulation of environmental representation, specifically normal distributions, and a closed-form uncertainty approximation of optimal solutions, enabling the identification of essential self-localization errors in an environment. The results obtained through this method are not only valuable for autonomous driving tasks but also contribute to discussions on the quality of specific map formats. Experiments highlight locations within an urban environment that are susceptible to significant errors in self-localization based on the proposed metric. Furthermore, a comparison of self-localization errors associated with specific map formats using the proposed metric reveals that essential errors in algorithms can be estimated. The discussion presented in this paper reveals the patterns of geometrical features in the urban environment that are likely to result in self-localization errors.
Tongbian decoction restores intestinal microbiota and activates 5-hydroxytryptamine signaling: implication in slow transit constipation

Introduction Slow transit constipation (STC) is a type of functional constipation. The detailed mechanism of STC, for which there is currently no effective treatment, is unknown as of yet. Tongbian decoction (TBD), a traditional Chinese medicinal formula, is commonly used to treat STC in clinical settings. However, the potential impact of TBD on the management of STC via modulation of the gut microbiota remains unclear. Methods Pseudo-germ-free rats were constructed after 6 days of treatment with bacitracin, neomycin, and streptomycin (abbreviated as ABX forthwith). Based on the successful construction of pseudo-germ-free rats, the STC model (ABX + STC) was induced using loperamide hydrochloride. After successful modeling, based on the different sources of donor rat microbiota, the ABX + STC rats were randomly divided into three groups: Control → ABX + STC, STC → ABX + STC, and STC + TBD → ABX + STC for fecal microbiota transplant (FMT). Body weight, fecal water content, and charcoal power propelling rate of the rats were recorded. Intestinal microbiota was detected by 16S rRNA sequencing, and the 5-hydroxytryptamine (5-HT) signaling pathway was examined by western blots, immunofluorescence, and immunohistochemical analysis. Results After treatment with fecal bacterial solutions derived from rats treated with Tongbian decoction (TBD), there was an increase in body weight, fecal water content, and the rate of charcoal propulsion in the rats. Additionally, activation of the 5-hydroxytryptamine (5-HT) signaling pathway was observed. The 16S rRNA sequencing results showed that the fecal bacterial solution from TBD-treated rats affected the intestinal microbiota of STC rats by increasing the proliferation of beneficial bacteria and suppressing the expansion of harmful bacteria. Conclusion Our study showed that TBD alleviated constipation in STC rats by modulating the structure of the intestinal microbiota.
Dual-Polarized Ultra-Wideband Phased Array Antenna With 9:1 Bandwidth and ±75° Scanning

In this paper, an ultra-wideband (UWB) dual- polarized tightly coupled dipole array (TCDA) is proposed. A resistive frequency selective surface (FSS) is utilized to improve the bandwidth and scanning range. The average radiation efficiency of the designed antenna is above 84% for all scan cases. Furthermore, simulations show that the proposed antenna can achieve the active voltage standing wave ratio (VSWR) below 3 when scanning up to ± 75° and ± 60° in the E and H planes from 2 to 18 GHz, respectively.
Effect of Silica Xerogel Functionalization on Intensification of Rindera graeca Transgenic Roots Proliferation and Boosting Naphthoquinone Production

Secondary metabolites derived from plants are recognized as valuable products with several successful applications in the pharmaceutical, cosmetic, and food industries. The major limitation to the broader implementation of these compounds is their low manufacturing efficiency. Current efforts to overcome unprofitability depend mainly on biotechnological methods, especially through the application of plant in vitro cultures. This concept allows unprecedented bioengineering opportunities for culture system modifications with in situ product removal. The silica-based xerogels can be used as a novel, porous biomaterial characterized by a large surface area and high affinity to lipophilic secondary metabolites produced by plant tissue. This study aimed to investigate the influence of xerogel-based biomaterials functionalized with methyl, hydroxyl, carboxylic, and amine groups on Rindera graeca transgenic root growth and the production of naphthoquinone derivatives. The application of xerogel-based scaffolds functionalized with the methyl group resulted in more than 1.5 times higher biomass proliferation than for reference untreated culture. The naphthoquinone derivatives’ production was noted exclusively in culture systems supplemented with xerogel functionalized with methyl and hydroxyl groups. Applying chemically functionalized xerogels as in situ adsorbents allowed for the enhanced growth and productivity of in vitro cultured R. graeca transgenic roots, facilitating product isolation due to their selective and efficient accumulation.
Selection of variables based on nonconcave penalized likelihood using lasso, elastic net, and SCAD method

Variable selection is essential in linear regression analysis to improve predictability and select significant variables. Estimating the regression coefficient on high-dimensional data cannot be done using the least squares method, so it requires specific analytical techniques. Approaches that can take on high-dimensional data include SCAD, LASSO, and Elastic Net. This research will analyze the most crucial method between SCAD, LASSO, and Elastic Net on Low Birth Weight (LBW) data in East Nusa Tenggara (NTT). Two methods are used in this study, first, comparing the SCAD, LASSO, and Elastic Net methods using simulation data, and second, applying the logistic regression method to actual data. The data used in this study is the LBW data by fertile women in NTT from the 2017 IDHS (Indonesian Demographic and Health Survey) data. The analysis shows that the results obtained through simulation and data reveal, based on the value of the AIC model goodness test, the SCAD is better than the other methods with the smallest AIC value of 17. 58878, smaller than the AIC LASSO value of 17.90169 and Elnet of 17.88728.
CORPORATE SOCIAL RESPONSIBILITY AND THE ALLOCATION OF PROCUREMENT CONTRACTS : EVIDENCE FROM A NATURAL EXPERIMENT

This study examines whether corporate social responsibility (CSR) influences the allocation of procurement contracts. To obtain exogenous variation in companies’ social engagement, I exploit a quasi-natural experiment provided by the enactment of state-level constituency statutes, which allow directors to consider stakeholders’ interests when making business decisions. Using constituency statutes as instrumental variable (IV) for CSR, I find that companies with higher CSR receive more procurement contracts. The effect is stronger for more complex contracts and in the early years of the government-company relationship, suggesting that CSR helps mitigate information asymmetries by signaling non-opportunistic behavior and trustworthiness. In addition, I find that the effect is stronger in competitive industries, indicating that CSR can serve as a differentiation strategy to compete against other bidders.
Apoptotic-induced Effects of Castanea sativa Bark Extract in Human SH-SY5Y Neuroblastoma Cells

Castanea sativa Mill. wood extract (ENC) has health-promoting qualities such as antibacterial, antiviral, antispasmodic, cardio- and neuroprotective-activities. Its potential towards cancer cells of the central nervous system, however, is still unexplored. This study investigates the apoptotic-enhancing effect of ENC in human neuroblastoma SH-SY5Y cell line. This extract irreversibly decreased cell viability with IC50 of 142.8±20.2, 109.3±16.6 and 82.5±8.6 μg/mL for 24h, 48 and 72h of treatment, respectively. This was accompanied by cell shrinkage and tendency to round-up. A concentration-dependent increase in sub G0/G1 cells as well as a rise in cells with nuclear fragmentation and chromatin condensation, both typical of apoptosis, was also observed. In conclusion, the present findings provide initial data on the potential of ENC for neuroblastoma treatment and offer the rationale for further studies aimed at elucidating the full mechanism(s) underlying its effects.
A Neural Network-Based Beampattern Optimization Method for Arbitrary Sensor Arrays

A beamforming method based on neural network is proposed in this paper. The input of the neural network is some parameters of beamformers, such as signal frequency, main lobe direction, sidelobe level, and robustness constraint level. The output of the neural network is the weighting vectors. Supervised learning is used to train the neural network, and the output weighting vectors in the training process are calculated using the Second-Order Cone programming (SOCP) algorithm. The proposed neural network-based method is actually an approximate version of the SOCP-based method, but it requires small amounts of computation and storage memory when applied in practice. The trained neural network can quickly output corresponding weighting vectors for beampatterns with different main lobe directions, different frequencies, different robustness constraints, and different beam widths. The good performance of the neural network-based beamforming method is demonstrated in simulations.
A Study of Double Condylar Facet in North Indian Crania

Studies of nonmetric cranial variants have been a field of considerable interest to research workers, especially, because of their racial and regional importance. A total of 28 Indian skulls of Northern India (Uttar Pradesh) were studied for the double condylar facet, a cranial variant in the present study. Findings are gathered, discussed, and compared with other studies from other parts of the world and are found to be of considerable significance.