Miracle-dz/UniFAR-FTUs huggingface dataset:
Genotypic Variations in Ferulic Acid, Antioxidant Capacity and Yield Components of Thai Landrace Rice Genotypes

Ferulic acid is a potent antioxidant in rice. The objective of this study was to evaluate the variations in ferulic acid and antioxidant capacity among landrace rice genotypes. The experiment is conducted under paddy field conditions in two locations. It uses a randomized complete block design with three replications, and the treatments consist of 24 landrace rice genotypes. Data are collected for yield and yield components, ferulic acid, and antioxidant capacity. Rice genotypes are significantly different for plant height, number of panicles per plant, number of seeds per panicle, 1,000 - seed weight and grain yield. Grain yields of 24 rice genotypes ranged from 1,476.9 to 4,348.1 kg/ha, and G24 is a good source for high grain yield. Variations in ferulic acid content and antioxidant capacity are found among genotypes. Ferulic acid contents range from 11.56 to 45.68 mg/100 g seed, and antioxidant capacity determined by the DPPH method ranged from 15.46 to 86.26%. G6 has the highest ferulic acid content and antioxidant capacity. These two genotypes are promising for parents in breeding programs targeting improved ferulic acid content, antioxidant capacity, and yield.
Greenhouse gas emissions from New Zealand sheep and beef farms

This project aimed to develop a dataset containing animal policies, production efficiencies, and greenhouse gas (GHG) emissions of a large number of sheep and beef farms, and to examine the relationships between farm management and farm physical constraints, and GHG emissions. We used the farm-scale model Farmax to estimate feed inventories, livestock policies and GHG emissions of 170 New Zealand sheep and beef farms. Emissions were calculated from Farmax outputs using Agricultural Inventory methodology. We used a quantitative approach to cluster farms based on physical constraints and management attributes. Mean annual biological GHG emissions from the modelled farms were 3,662 kg CO2 equivalents (CO2-e) per effective hectare, and ranged from 157 to 7,096 kg CO2-e/effective ha. As stocking rate and animal product (wool + net carcass weight) per effective hectare increased, GHG emissions increased. However, there was considerable variability in the data; farms with GHG emissions of approximately 4,000 kg CO2-e/effective ha had an almost three-fold difference in animal product (range 129 to 360 kg/effective ha). Our work provides a holistic assessment of the farm-scale drivers of GHG emissions and a comprehensive current state of affairs or baseline from which future trends in farm-scale GHG emissions can be established.
Optimization assisted deep learning based intrusion detection system in wireless sensor network with two‐tier trust evaluation

Nowadays, owing to the openness of transmission medium, wireless sensor networks (WSNs) suffer from a variety of attacks, together with DoS attacks, tampering attacks, sinkhole attacks, and so on. Therefore, an effectual system is necessary for recognizing the intrusions in WSN. This paper aims to set up a novel intrusion detection system (IDS) via a deep learning model. Initially, optimal cluster head (CH) is selected among the sensor nodes, from which the sensor nodes that have high energy will be prioritized to act as CH. In this proposed work, the CH selection is evaluated optimally by not only considering the energy parameter, further under the constraints like delay and distance. For optimal selection, a novel approach named as self‐improved sea lion optimization (SI‐SLnO) model is introduced in this work. As per the proposed strategy, the trust of CH and nodes is evaluated based on a multidimensional two‐tier hierarchical trust model by considering content trust, honesty trust, and interactive trust. Finally, the deep learning‐based intrusion detection takes place via optimized neural network (NN), where the training is done by the proposed SI‐SLnO algorithm via the optimal weight tuning process. At last, the supremacy of the developed approach is examined via evaluation over numerous extant techniques.
Considerations for the Use of Consumer-Grade Wearables and Smartphones in Population Surveillance of Physical Activity

As smartphone and wearable device ownership increase, interest in their utility to monitor physical activity has risen concurrently. Numerous examples of the application of wearables in clinical and epidemiological research settings already exist. However, whether these devices are all suitable for physical activity surveillance is open for debate. In this article, we discuss four key issues specifically relevant to surveillance that we believe need to be tackled before consumer wearables can be considered for this measurement purpose: representative sampling, representative wear time, validity and reliability, and compatibility between devices. A recurring theme is how to deal with systematic biases by demographic groups. We suggest some potential solutions to the issues of concern such as providing individuals with standardized devices, considering summary metrics of physical activity less prone to wear time biases, and the development of a framework to harmonize estimates between device types and their inbuilt algorithms. We encourage collaborative efforts from researchers and consumer wearable manufacturers in this area. In the meantime, we caution against the use of consumer wearable device data for inference of population-level activity without the consideration of these issues.
Compact Carbon-Based Membrane Reactors for the Intensified Anaerobic Decolorization of Dye Effluents

Carbon-based membranes integrated with anaerobic biodegradation are presented as a unique wastewater treatment approach to deal with dye effluents. This study explores the scope of ceramic-supported carbon membrane bioreactors (B-CSCM) and ceramic-supported graphene oxide membrane bioreactors (B-CSGOM) to decolorize azo dye mixtures (ADM) and other dyes. The mixture was prepared using an equimolar composition of monoazo Acid Orange 7, diazo Reactive Black 5, and triazo Direct Blue 71 dye aqueous solution. Afterwards, as in the ADM experiment, both compact units were investigated for their ability in the biodecolorization of Methylene Blue (MB) and Rhodamine B (RhB) dye solutions, which do not belong to the azo family. The obtained outcomes revealed that the conductive surface of the graphene oxide (GO) membrane resulted in a more efficient and higher color removal of all dye solutions than B-CSCM under a wide feed concentration and permeate flux ranges. The maximum color removal at low feed concentration (50 mg·L−1) and permeate flux (0.05 L·m−2·h−1) was 96% for ADM, 98% for MB and 94% for RhB, whereas it was 89%, 94% and 66%, respectively, for B-CSCM. This suggests that the robust, cost-effective, efficient nanostructures of B-CSGOM can successfully remove diverse azo dye solutions from wastewater better than the B-CSCM does.
Teacher Assessment Literacy: Implications for Diagnostic Assessment Systems

ABSTRACT Assessments scored with diagnostic models are increasingly popular because they provide fine-grained information about student achievement. Because of differences in how diagnostic assessments are scored and how results are used, the information teachers must know to interpret and use results may differ from concepts traditionally included in assessment literacy trainings for assessments that produce a raw or scale score. In this study, we connect assessment literacy and score reporting literature to understand teachers’ assessment literacy in a diagnostic assessment context as demonstrated by responses to focus groups and surveys. Results summarize teachers’ descriptions of fundamental diagnostic assessment concepts, understanding of the diagnostic assessment and results produced, and how diagnostic assessment results influence their instructional decision-making. Teachers understood how to use results and were comfortable using the term mastery when interpreting score report contents and planning next instruction. However, teachers were unsure how mastery was calculated and some misinterpreted mastery as representing a percent correct rather than a probability value. We share implications for others implementing large-scale diagnostic assessments or designing score reports for these systems.
A 10-Bit 100-MHz 70-mW 2-Channel Time-Interleaved Pipelined ADC

A 10-bit 100 MHz 2-channel time-interleaved pipelined A/D converter for IF receiver in 3G wireless communication was designed and implemented in 0.18-μm HJTC CMOS process.The ADC consumes less than 70 mW of power from 3.3 V supply.Op-amp sharing and telescopic structure were used to reduce power consumption of the ADC.Linearity of interleaved ADC is limited by offset mismatch,gain mismatch and time skew mismatch of channels.In this circuit,op-amp sharing,increasing accuracy of every channel and global passive sampling were adopted to reduce effects of these mismatches.Also,linearity deterioration caused by charge injection of the output switch and crosstalk of the off-switch capacitor was investigated,and non-idealities of the switches were removed by modifying clock signal arrangement,thus significantly improving linearity of the time-interleaved ADC.Results from post-layout simulation showed that the 2-channel time-interleaved ADC had an input signal bandwidth of 47.6 MHz at a sampling rate of 100 MSPS,and at the worst process corner(ss,120 ℃),it had an SFDR over 70 dB and an SNDR above 60 dB.
Combining both spectral and textural indices for alleviating saturation problem in forest LAI estimation using Sentinel-2 data

Abstract Leaf area index (LAI) is an important structural parameter that controls energy, water, and gas exchanges of plant ecosystems. At present, remote sensing techniques, especially passive optical remote sensing, are the most widely used approaches to estimate LAI. However, the prevalent usage of spectral information alone often saturates, which is a major issue for retrieving LAI in particular for forests. In this study, our goal was to improve LAI estimation using Sentinel-2 data by combining both spectral and texture information to minimize the saturation effect in a typical cool-temperate deciduous forest in Japan. Two LAI field datasets obtained using digital hemispherical photographs, representing a range of LAI conditions, were used to investigate the performance of spectral and texture features or a combination of both. Texture information was derived using the grey-level co-occurrence matrix (GLCM) method. Commonly used vegetation indices and newly developed textural indices were computed for comparison. Spatial autocorrelation was determined prior to exploratory regression analyses to explore methods for estimating LAI using a combination of spectral- and texture-based features. Regression methods included generalized least squares (GLS), stepwise, principal components (PCR), and partial least squares (PLS). The results demonstrate that the texture information derived from Sentinel-2 imagery helps estimate LAI by minimizing the saturation problem, especially in the period of maximum LAI values. The combination of both features using the PLS model provided a higher accuracy estimation scheme for LAI (R 2: 0.66–0.75, RMSE: 0.23–0.37) and we foresee the wide application of the approach especially for a closed-canopy forest facing serious saturation problem when deriving LAI from reflected spectral information.
Sensor Fault Diagnosis Based on Wavelet Packet and Support Vector Machines

To solve the fault diagnosis problem of self-validating pressure sensor,a sensor fault diagnosis approach based on wavelet packet transform and support vector machines is proposed.After a three-level decomposition of wavelet packet,the coefficients of each node are achieved.With some cutting algorithm,the reconstructed signals with fault character are strengthened.The energy of each node is calculated with reconstructed signals,and the average cutting ratios of all nodes are regarded as the feature vector.The support vector machines for multi-classification used as fault classifiers are established to identify the condition and fault pattern of the sensor.The results of fault diagnosis on self-validating pressure sensors,temperature and flow sensor show that the proposed approach can be applied to the sensor fault diagnosis effectively.
Self‐discipline predictive control of autonomous vehicles against denial of service attacks

This paper presents a novel (SPC) scheme for the path following of autonomous vehicles (AVs) subject to denial of service (DoS) attacks. First, by considering the energy constraints of DoS attacks, bounded but arbitrary packet dropouts is used to characterize the effects of DoS attacks. Then, a switched control system model is established to describe the path following control of AVs under DoS attacks. In what follows, the SPC scheme, which depends on the detection of packet dropout from actuator side, is developed to compensate the packet dropout while there are no feedback measurements available. The main advantage of the proposed SPC scheme is that the predictive control design only depends on the latest available measurement and not affected by external disturbance. This is important to the control of networked systems under DoS attacks. At last, the corresponding experiments on path following control of the AVs are carried out in order to verify the validity of theory results.
Constructing a prognostic immune-related lncRNA model for colon cancer

Colon cancer is a common digestive tract tumor. Although many gene prognostic indicators have been used to predict the prognosis of colon cancer patients, the accuracy of these prognostic indicators is still uncertain. Thus, it is necessary to construct a model for the prognostic analysis of colon cancer. We downloaded the original transcriptome data of colon cancer and performed a differential coexpression analysis of immune-related genes to obtain different immune-related long noncoding RNAs, which were paired as differentially expressed immune-related lncRNA pairs (DEirlncRNAPs). Then, the 1-year overall survival rate receiver operating characteristic curve was calculated, and the Akaike information criterion value was evaluated to determine the maximum inflection point, which was used as the cutoff point to identify groups of colon cancer patients at high and low risk for death. Subsequently, the optimal prediction model was established. Finally, we used the patients’ survival times, clinicopathological features, tumor infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers to verify the DEirlncRNAP model. Seventy-one DEirlncRNAPs were obtained to build the risk assessment model. The patients were divided into a high-risk group and a low-risk group according to the cutoff point. Then, the DEirlncRNAP model was verified using patient survival times, clinicopathological features, tumor-infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers. A new DEirlncRNAP model for predicting the prognosis of colon cancer patients was established, which could reveal new insights into the relationships of colon cancer with tumor-infiltrating immune cells and antitumor immunotherapy.
The evolution and impact of the massive open online course

x Chapter 1: The Massive Open Online Course Phenomenon . 1 Purpose of Research . 5 Research Objectives . 5 Conceptual Focus . 6 Research Design . 8 Significance of the Study . 9 Summary . 12 Chapter 2: A Review of MOOC-related Literature . 13 Foundation & Definition . 13 The Dominant Ideology Perspective . 16 MOOCs – A Subversive Ideology . 38 Summary . 62 Chapter 3: Methodology . 64 Statement of Research Questions . 64 Research Design & Methodology . 64 Reliability and Validity of Methodology . 67 Sampling Procedure . 68 Expert Participants in the Delphi Study . 72 Instrumentation & Data Collection . 77 Participation Rate of Expert Panel . 82 Defining Consensus . 84 Summary . 85 Chapter 4: Results . 86 Restatement of Research Questions . 86 Restatement of Research Prompts . 86 Results . 91 Summary . 130
Dockless bike-sharing’s impact on mode substitution and influential factors: Evidence from Beijing, China

As a newly emerged bike-sharing system, dockless bike-sharing has the potential to positively influence urban mobility by encouraging active cycling and drawing users from car, public transit and walking. However, scant empirical research explores the extent to which dockless bike-sharing replaces other travel modes for different travel purposes. There is a lack of knowledge about how dockless bike-sharing users’ personal characteristics and neighborhood environment features influence their mode substitution behaviors. Using survey data collected from residents in Beijing and geodata of land use and public transit, we conduct four multinomial logistic models to explore potential mode-substitution behaviors influenced by dockless bike-sharing for four travel purposes: work or education commuting, sports and leisure, grocery shopping, and recreational activities such as shopping, eating and drinking. The results indicate that, for the majority of respondents, dockless bike-sharing systems potentially substitute for walking or public transit. In addition, our analysis of travel attitudes points out that dockless bike-sharing not only attracts bicycle lovers but also users with a preference or positive attitude toward other travel modes. The positive association between the length of bicycle paths and the likelihood of potentially replacing public transit or motorized vehicles by dockless bike-sharing also reveals that the cycling infrastructure of residential neighborhood could be an important facilitator for users of public transit and motorized vehicles to switch to dockless bike-sharing systems.
The Use of Dictogloss to Teach Writing in Junior High School

Kondisi yang terjadi di dalam kelas adalah siswa sulit untuk menulis paragraf sederhana dalam bahasa Inggris melalui teknik menghafal. Siswa mudah lupa dan sulit menyusun poin-poin penting dalam proses menulis dengan teknik menghafal. Penelitian ini bertujuan untuk mengkaji penggunaan dictogloss dalam meningkatkan kemampuan siswa dalam menulis dan menyusun ide, khususnya dalam menulis teks deskriptif. Dalam penelitian ini digunakan penelitian tindakan kelas. Hal ini dilakukan untuk melihat peningkatan kemampuan menulis teks deskriptif siswa menggunakan dictogloss dalam proses pembelajaran. Pengumpulan data dilakukan melalui observasi dan instrumen tes. Hasil penelitian menunjukkan bahwa teknik dictogloss memotivasi siswa untuk memiliki ide yang lebih luas untuk menulis teks deskriptif mereka. Dengan meminta siswa mengerjakan tugas dalam kelompok, mereka senang menyelesaikan tugas secara bersama-sama. Mereka juga lebih baik dalam tugas individu ketika mereka mencoba mengembangkan paragraf mereka. Meskipun komponen menulis mereka masih belum cukup baik, hasil menulis mereka meningkat dari tes pra-penilaian ke tes akhir siklus. Berdasarkan permasalahan tersebut, dictogloss dapat menjadi panduan yang berguna untuk membantu siswa lebih lancar dalam menulis ide-ide mereka. Ini adalah teknik guru membacakan teks pendek kepada siswa dan siswa hanya mendengarkan. Kemudian, guru membacakan kembali teks tersebut untuk kedua kalinya dan siswa menuliskan kata kunci teks tersebut di kertas mereka, kemudian siswa membuat paragraf lengkap berdasarkan kata kuncinya.
Automation and Standardization—A Coupled Approach towards Reproducible Sample Preparation Protocols for Nanomaterial Analysis

Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by automation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been developed, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.
Stress-Mediated Attenuation of Translation Undermines T Cell Tumor Control

Protein synthesis enables cell growth and survival, but the molecular mechanisms through which T cells suppress or maintain protein translation in the stress of solid tumors are unknown. Using mouse models and human tumors we demonstrate that protein translation in T cells is repressed by the solid tumor microenvironment (TME) due to activation of the unfolded protein response (UPR) via phosphorylation of the α subunit of eukaryotic translation initiation factor 2 (p-eIF2α). Given that acute glucose deprivation in T cells exacerbated p-eIF2α, we show that metabolic reprogramming toward glycolytic independence allays the UPR and p-eIF2α, enabling sustained protein translation in T cells in TME stress. UPR mitigation was associated with enhanced degradation of proteins in antitumor T cells, as proteasome inhibition resulted in eIF2α phosphorylation, attenuation of translation, and loss of antitumor efficacy. In contrast, proteasome stimulation relieved translation inhibition, inducing robust T cell tumor control, offering a new therapeutic avenue to fuel the efficacy of tumor immunotherapy.
Event-triggered passivity and synchronization of coupled reaction–diffusion neural networks with and without time-varying delay

This paper is devoted to deal with the event-triggered passivity and synchronization problems for two types of coupled reaction–diffusion neural networks (CRDNNs). In the first type, several passivity and synchronization criteria for CRDNNs are acquired through designing suitable event-triggered controllers, combining some inequality techniques and Lyapunov functional approach. In the second type, the problems of event-triggered synchronization and passivity for coupled delayed reaction–diffusion neural networks (CDRDNNs) are investigated. Finally, two numerical examples are provided to illustrate the effectiveness of the derived passivity and synchronization conditions.