Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vitro Fermentation of Browsable Native Shrubs in New Zealand

Information on the nutritive value and in vitro fermentation characteristics of native shrubs in New Zealand is scant. This is despite their potential as alternatives to exotic trees and shrubs for supplementary fodder, and their mitigation of greenhouse gases and soil erosion on hill-country sheep and beef farms. The objectives of this study were to measure the in vitro fermentation gas production, predict the parameters of the in vitro fermentation kinetics, and estimate the in vitro fermentation of volatile fatty acids (VFA), microbial biomass (MBM), and greenhouse gases of four native shrubs (Coprosma robusta, Griselinia littoralis, Hoheria populnea, and Pittosporum crassifolium) and an exotic fodder tree species, Salix schwerinii. The total in vitro gas production was higher (p < 0.05) for the natives than for the S. schwerinii. A prediction using the single-pool model resulted in biologically incorrect negative in vitro total gas production from the immediately soluble fraction of the native shrubs. However, the dual pool model better predicted the in vitro total gas production and was in alignment with the measured in vitro fermentation end products. The in vitro VFA and greenhouse gas production from the fermentation of leaf and stem material was higher (p < 0.05), and the MBM lower (p < 0.05), for the native shrubs compared to the S. schwerinii. The lower in vitro total gas production, VFA, and greenhouse gases production and higher MBM of the S. schwerinii may be explained by the presence of condensed tannins (CT), although this was not measured and requires further study. In conclusion, the results from this study suggest that when consumed by ruminant livestock, browsable native shrubs can provide adequate energy and microbial protein, and that greenhouse-gas production from these species is within the ranges reported for typical New Zealand pastures.
IoT-Oriented 6G Wireless Network System for Smart Cities

The smart city is an emerging concept that is based on the integration of various electronic devices and citizens that enhance the flow of information. IoT is an integral part for next generation wireless network infrastructure for acting as an interface of collecting data and controlling delivery of message which are using in smart cities. In this paper, an IoT-oriented relay assisted MIMO for beyond the fifth-generation wireless network system is proposed. The proposed system provides higher capacity and lower BER. The proposed system's BER results are compared with various combinations of transmission and receiving antennas at source, relay, and destination. It is found from BER performance that the developed scheme with relay does provide 1–17 dB gain with respect to direct connection. It is also found from mathematical analysis and simulation results that this scheme provides 3 to 9 b/s/Hz improvement in performance of capacity at 5 to 10 dB by adding a different combination of STBC and VBLAST. Simulation results are also presented to demonstrate the diversity and multiplexing gain that is a key to providing high data rates with reliable communication with many interferences for the IoT system. This system can also be used for massive antennas-based IoT system by raising the number of transmitting and receiving antennas with proposed encoding and decoding techniques explained in this paper.
Antioxidant, Antidiabetic, and Antihypertension Inhibitory Potentials of Phenolic Rich Medicinal Plants

Veronica (Plantaginaceae) and Schoenoplectus have a unique chemotaxonomic and phytochemical importance and are widely utilized in Turkish and Traditional Chinese Herbal Medicine (TCM) for treating tonics, influenza, diuretics, expectorants, restoratives, and respiratory diseases, and both are very useful in treating infectious and metabolic disorders as well. This study evaluates two medicinal plant species, Veronica biloba and Schoenoplectus triqueter (L.) Palla; extraction was performed through Soxhlet and maceration methods as well as determination of free and bound phenolics. Evaluated biological screening of (extracts and phenolics) angiotensin-I converting enzyme (ACE), Type-II diabetes (α-glucosidase and α-amylase), and antioxidants potential was performed using modified assays. The angiotensin-I converting enzyme (ACE) 50% inhibition potential in Veronica biloba was found at IC50 = 210.68 μg/mL and in Schoenoplectus triqueter (L.) Palla at IC50 = 229.40 µg/mL, respectively. Meanwhile Type-II diabetes with α-amylase 50% inhibition shown by bound phenolics of Veronica biloba at IC50 = 219.66 µg/mL and its water extract at IC50 = 110.09 µg/mL possesses higher potential, and α-glucosidase potential by free phenolics was found to be active at IC50 = 469.56 µg/mL, while water and ethyl acetate extracts showed higher potential, IC50 = 78.65 µg/mL and IC50 = 97.03 µg/mL, than the standard acarbose, recorded lower. In case of amylase, α-glucosidase showed IC50 = 88.73 μg/mL. Our results showed that both plants possess a direct relationship with the increase in the concentration of extracts and inhibited very strongly angiotensin-I converting enzyme (ACE) and Type-II diabetes (α-glucosidase and α-amylase). The properties of enzyme hindrance may be associated with phenolic compounds and rich phenolic plant antioxidant potential provides a route to the elucidation of natural antihypertension and antidiabetes.
BAYESIAN ESTIMATION OF COMMON LONG-RANGE DEPENDENT MODELS

We present a sampling-based Bayesian approach for modeling and forecasting a bivariate process having a long-range dependent component that is common to each series and additive short-range dependent noise terms that are unique to each series. A common long-range dependent component might be observed, for instance, in two climatological series collected in the same geographic region. We use an ARMA model to approximate the common long-range dependent behavior in a Gibbs sampling framework and then use importance sampling to adjust for the approximation. The sampling-based approach is computationally more feasible than methods that depend on exact evaluation of the likelihood function and gives comparable results. Additionally, complete posterior distributions for the estimated parameters, longand short-range dependent component series, and future series values can be obtained. We illustrate the technique using two wind speed series collected at different meteorological stations in Ireland.
Effective communication and patient safety among nurses in perioperative settings: a best practice implementation project

ABSTRACT Objectives: The aim of this implementation project was to promote evidence-based best practice regarding effective communication and patient safety amongst nurses in perioperative settings. Introduction: One of the main causes of surgical errors is inadequate communication. To address this issue, published research has shown that effective communication among healthcare professionals (HCPs) within and between all phases of perioperative care, as well as the proper transfer of all patient information at all transition points, are essential for ensuring patient safety and quality of care. Methods: This best practice implementation project was conducted based on the JBI implementation model and included three phases of activity: a baseline audit, a strategies implementation stage and a follow-up audit. The audit criteria used were based on a JBI evidence summary and referred to: education, interdisciplinary team, conflict resolution, team communication, transfer of patient information and safety intraoperative processes. The project was carried out in the perioperative environment of a university hospital, and the sample included 52 nurses. Results: Eleven audit criteria were used in the baseline audit. For four of these criteria (on education and information transfer) the compliance was zero, for five criteria (on intraoperative processes) the compliance had values between 31 and 66% and for two criteria (on interdisciplinary team/conflict resolution documentation and team communication monitoring), the identified compliance was maximum (100%). Following the identification of four barriers to compliance and the implementation of targeted strategies, the follow-up audit showed complete compliance (100%) for all criteria except three, for which the identified compliance values were 96, 95 and 25%. Conclusion: The implementation of appropriate strategies in this project has led to a significant improvement in nurses’ compliance with all audit criteria except one, regarding the verbal transfer of patient information. However, future audits and strategies are needed not only to support the improvements obtained but also to significantly increase the compliance rate for the audit criterion for which only a slight increase in compliance was recorded.
Thinning Apples with Ethylene-releasing Chemicals1

Successful fruit thinning of apples (Malus domestica Borkh.) with CGA 15281, an ethylene-releasing compound, was demonstrated at 20 and 46 days past bloom. A rapid drop in fruit removal force was followed by fruit abscission on days 4 and 5. In contrast, ethephon often resulted in protracted abscission and complete fruit removal. Differential sensitivity to CGA 15281 existed among cultivars. Explants were less sensitive to ethylene than intact fruit, and could not be used for controlled experiments. No adverse effects of CGA 15281 were noted with the exception of some leaf drop in the interior of the tree.
Genet: automatic curriculum generation for learning adaptation in networking

As deep reinforcement learning (RL) showcases its strengths in networking, its pitfalls are also coming to the public's attention. Training on a wide range of network environments leads to suboptimal performance, whereas training on a narrow distribution of environments results in poor generalization. This work presents Genet, a new training framework for learning better RL-based network adaptation algorithms. Genet is built on curriculum learning, which has proved effective against similar issues in other RL applications. At a high level, curriculum learning gradually feeds more "difficult" environments to the training rather than choosing them uniformly at random. However, applying curriculum learning in networking is nontrivial since the "difficulty" of a network environment is unknown. Our insight is to leverage traditional rule-based (non-RL) baselines: If the current RL model performs significantly worse in a network environment than the rule-based baselines, then further training it in this environment tends to bring substantial improvement. Genet automatically searches for such environments and iteratively promotes them to training. Three case studies---adaptive video streaming, congestion control, and load balancing---demonstrate that Genet produces RL policies that outperform both regularly trained RL policies and traditional baselines.
Full-Waveform Classification and Segmentation-Based Signal Detection of Single-Wavelength Bathymetric LiDAR

Single-wavelength bathymetric light detection and ranging (LiDAR) (532 nm) can provide seamless meter- and submeter-scale digital elevation model (DEMs) of both the terrestrial surface and seafloor. However, mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and nonterrestrial waveforms from the original waveforms without auxiliary information and flexible detection of peaks based on a new FW theoretical model. A novel FW signal detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FWs are divided into five categories for subsequent signal detection using a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition, and a flexible signal detection framework. To search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than Levenberg–Marquardt algorithm optimized generalized Gaussian model (LM-GGM) and RLD algorithm.
RBF-SCMA: Faster Machine Learning Based SCMA

Machine learning based approaches for Sparse Code Multiple Access (SCMA) have been able to achieve a better performance than the message passing algorithm (MPA) based methods. But these methods have a higher decoding complexity owing to their use of a large number of trainable parameters. This paper proposes a radial basis function (RBF) based SCMA method (RBF-SCMA), which uses an RBF module to efficiently exploit the distance based structure of the decoding problem. This allows it to arrive at better estimates for user symbol probabilities, even with a single hidden layer neural network. Simulations show that RBF-SCMA performs better than the previous methods in an additive white Gaussian noise (AWGN) channel while maintaining a lower computational complexity.
Sedimentological and Geochemical Perspectives on a Marginal Lake Environment Recorded in the Hartmann's Valley and Karasburg Members of the Murray Formation, Gale Crater, Mars

This study utilizes instruments from the Curiosity rover payload to develop an integrated paleoenvironmental and compositional reconstruction for the 65‐m thick interval of stratigraphy comprising the Hartmann's Valley and Karasburg members of the Murray formation, Gale crater, Mars. The stratigraphy consists of cross‐stratified sandstone (Facies 1), planar‐laminated sandstone (Facies 2), and planar‐laminated mudstone (Facies 3). Facies 1 is composed of sandstone showing truncated sets of concave‐curvilinear laminae stacked into cosets. Sets are estimated to be meter‐to sub‐meter‐scale, consistent with low‐height dunes. Thin stratigraphic intervals of Facies 1 and stacking patterns with Facies 2 and 3 support a wet aeolian dune interpretation. Meter‐thick packages of planar‐laminated sandstone (Facies 2) are interpreted to represent interfingering dune‐interdune strata. Facies 3 consists of meter‐thick packages of planar‐laminated mudstone interpreted to represent lacustrine deposition with persistent standing water. Integration of geochemistry with each facies reveals some compositional control based on the depositional process. Models for source rock composition from Alpha Particle X‐Ray Spectrometer measurements show that facies derived from a basaltic source. Alteration indices and geochemical trends provide evidence that moderate chemical weathering occurred before compositional changes due to diagenesis. Differences in wt% FeO(T) and TiO2 between facies are minimal, though trends point to sediment sorting in transport. Comparisons to terrestrial basaltic sedimentary systems indicate that the Hartmann's Valley and Karasburg facies reflect deposition in an environment where diverse subaqueous and subaerial facies persisted adjacent to a long‐lived body of water.
Binary and Fractional MODIS Snow Cover Mapping Boosted by Machine Learning and Big Landsat Data

It is promising to improve Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover mapping by training effective machine learning models. However, considering the strong spatiotemporal heterogeneity of snow cover, the representativeness of training samples becomes a critical issue to ensure the model effectiveness over large areas and long terms. To deal with this issue, we propose the <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-day dynamic training strategy. The core of this strategy is to divide a long-term snow cover mapping task into multiple short-term tasks with consecutive <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> days (<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is greater or equal to the number of days that higher resolution remote sensing images used as equivalent ground references cover the study area once). It can ensure the spatial representativeness of training samples by utilizing all available equivalent ground references data, and reduce the issue caused by snow cover changing over time. We apply this strategy to random forest (RF) for both binary snow cover mapping (BSC) and fractional snow cover mapping (FSC) using Landsat-8 data as equivalent ground reference and validate its effect on the Tibetan Plateau. Results show that this strategy achieves higher accuracies than other training strategies, with <inline-formula> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> of 0.9596 for BSC mapping, <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> and mean square error (MSE) of 0.9037 and 0.0260 for FSC mapping, respectively. In addition, spatiotemporal analysis of results further demonstrates that this strategy holds advantages for snow cover mapping in areas of complex terrain and in periods when snow cover changes rapidly. In conclusion, this strategy can effectively ensure the representativeness of training samples generated from Landsat-8 data and thus improve MODIS snow cover mapping through machine learning.
Measuring the Consistency Between Data and Control Plane in SDN

Software Defined Networking (SDN) simplifies network control and management by decoupling the control plane from the data plane. However, the actual packet behaviors, conforming to the rules in the data plane flow tables, may violate the original policies in the controller due to the inconsistency between the data plane and control plane. To address this problem, we propose 2MVeri, a framework for measuring the consistency between the Data and Control plane, defined as the consistency between the control plane policies and data plane rules. 2MVeri uses a modules, a Bloom filter and a two-dimensional vector as a tag which is inserted in the packet header and is updated in each switch that the packet traverses. By exploiting path information compressed in the tag, 2MVeri can verify the consistency between the data and control plane. Moreover, when verification fails, 2MVeri is able to localize the faulty switch. Experimental results show that in the k = 4 fat tree topology, the verification accuracy of 2MVeri is as high as 100%. In addition, when the actual path is inconsistent with the expected path, 2MVeri can locate the wrong switch with an accuracy of 99.8%.
JointNIDS: Efficient Joint Traffic Management for On-Device Network Intrusion Detection

Data plane programmability enables the embedding of a network intrusion detection system (NIDS) on programmable switches to dynamically control the efficiency of attack type detection and the overhead in the computation and network side. However, it is a challenging task to implement a feasible embedded detection model with advanced machine learning techniques such as deep learning. It is due to the limited support provided by programming languages on the data plane and the computing resource constraints at the edge. We propose a joint traffic classification architecture called JointNIDS that splits a classification model into two sequential sub-models. In this model, the primary switch is dedicated to major attack classification. The secondary switch is used mainly for a further in-depth inspection of the rest of the minor traffic types. The presence of some partially overlapping hidden units in the two sequential switches can help to reduce the computational overhead at the edge, while increasing the packet inspection throughput. Experimental results on the P4 framework demonstrate that JointNIDS has reduced attack detection time, while achieving a similar accuracy performance, as other counterpart algorithms. To further develop the proposed architecture, JointNIDS implements an optimization step. It maximizes the amount of data to be inspected by a system, taking into account the constraints of computing resources and network bandwidth for a given performance requirement. We validate the effectiveness of collaborative joint optimization in various scenarios.
A scalable incremental algorithm for computing the evolution of structural virality in social networks

The analysis of social networks is a concern nowadays. The importance they acquired over the last few years forces one to analyze suspicious behaviors and how false information spreads. Nevertheless, there are many parameters to consider when analyzing these networks. When the content of the messages is taken into account, semantic analysis can be considered the most important element. However, the relationships established between users and their interactions can reveal the different tendencies of the social network. To analyze several aspects of the structure of the conversations, it is required to know how the cascades spread. This can be represented by the use of virality. This paper reveals a scalable algorithm that drastically reduces the complexity of the calculation of the virality evolution. With the use of graph properties, the algorithm is a key component, able to relate time and size, to characterize the shape of the conversations in social networks in a simple way.
Moving Toward Gender-Equitable Transportation in Post-COVID-19 Urban South Asia

This research examines the impacts of COVID-19 on the mobility of resource-poor women and its linkage with livelihoods in urban South Asia, and how gender-responsive transport measures could be adopted. The study, conducted in Delhi between October 2020 and May 2021, used a mixed methods, multi-stakeholder, and reflexive approach. A literature review was conducted on the gender and mobility context in Delhi, India. Quantitative data were collected through surveys with resource-poor women, while qualitative research methods consisted of in-depth interviews with them. Different stakeholders were engaged through round tables and key informant interviews before and after data collection to share the findings and recommendations. The sample survey (n = 800) revealed that only 1.8% of working resource-poor women have access to a personal vehicle, making them dependent on public transport. While 81% of their trips are by bus, 57% of their peak hour trips are by paratransit, despite free travel on buses. Only 10% of the sample have access to a smart phone, which restricts their access to digital initiatives based on smart phone applications. The women expressed concerns such as poor bus frequencies and buses not stopping for them under the free ride scheme. These were consistent with issues faced before the COVID-19 pandemic. These findings highlight the need for targeted strategies for resource-poor women to achieve equity in gender-responsive transport. These include a multimodal subsidy, short messaging service to obtain real-time information, increased awareness on filing complaints, and an effective grievance redressal system.