Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Optimization of Interdigitated Microwave Sensor for Multidimensional Sensitive Characterization of Solid Materials

This paper presents an interdigital capacitor (IDC)-based high sensitivity microwave sensor for the multidimensional (complex permittivity and thickness) characterization of solid materials. The proposed sensor was implemented by etching a slot resonator with an IDC on the ground layer of 0.76 mm-thick RF-35 substrate with relative permittivity of 3.5 and loss tangent of 0.0018. The IDC fingers were configured as a spur-line structure to create an intense electric field zone that interacted significantly with the material under test (MUT), resulting in high sensitivity. A sensor prototype with optimized dimensions generated dual resonant frequencies of 2.35 GHz and 5.79 GHz and was used to measure the MUT’s complex permittivity and thickness, respectively. Experimental results revealed that the resonant frequencies of the developed sensor exhibited high sensitivities (276 MHz/ $\varepsilon _{r}$ for permittivity and 143 MHz/mm for thickness) and resolutions (0.27678 for permittivity and 0.1439 for thickness) of solid materials. Moreover, the sensing accuracies were 99.9% for real permittivity and 97.7% for imaginary permittivity. The demonstrated high performance of the developed sensor validates its applications in the multidimensional characterization of various solid materials.
Economic analysis for centralized battery energy storage system with reused battery from EV in Australia

With battery energy storage technology development, the centralized battery energy storage system (CBESS) has a broad prospect in developing electricity. In the meantime, the retired lithium-ion batteries from electric vehicles (EV) offer a new option for battery energy storage systems (BESS). This paper studies the centralized reused battery energy storage system (CRBESS) in South Australia by replacing the new lithium-ion batteries with lithium-ion second-life batteries (SLB) and evaluating the economic benefits with economic indicators as net present value (NPV), discounted payback period (DPBP), Internal rate of return (IRR) to depict a comprehensive understanding of the development potential of the CRBESS with the lithium-ion SLB as the energy storage system. This paper proposes a calculation method of frequency control ancillary services (FCAS) revenue referring to market share rate (MSR) when building the economic model. Moreover, the residual value of lithium-ion batteries is considered. This paper uses the economic model to calculate the profitability and development potential of CRBESS. From an economic perspective, the superiority and feasibility of CRBESS compared with CBESS were analyzed.
The Decorated Learning Environment: Simply Noise or an Opportunity for Incidental Learning?

Author(s): Godwin, Karrie E. ; Kaur, Freya | Abstract: Maintaining attention during instruction is challenging as children face various sources of distraction (peers, announcements, noise) as well as competition from the visual environment itself. Prior studies found decorated environments promote off-task behavior and reduce learning. Additionally, many classroom displays are not relevant to ongoing instruction. This raises the possibility that increasing alignment between displays and instructional content may afford opportunities for incidental learning, reducing the detrimental effects of environmental off-task behavior. To investigate this possibility, participants completed a lesson in which alignment between the lesson content and displays was manipulated (relevant, educational but irrelevant, or no displays). Attention to the lesson and learning gains for content presented in the lesson and/or displays were measured. Results suggest younger children’s learning can benefit from displays that reinforce the lesson content. However, there was no evidence of incidental learning from displays without additional lesson support. Implications for classroom design are discussed.
Dissolved oxygen-quenched fluorescence/materials movement-induced beam deflection: A novel method for measuring heavy metal stress in aquatic plants

Heavy metal ions greatly impact the physiological activities of aquatic plants. Effects of the ions Cu 2+ , Ni 2+ , and Co 2+ on the physiological activities of aquatic plant were studied using the dissolved oxygen (DO)-quenched ﬂuorescence/materials movement-induced probe beam deﬂection method. An Ru(II) complex was used as a ﬂuorescence probe, with Egeria densa as the model aquatic plant for this study. DO-quenched ﬂuorescence of the Ru(II) complex as well as the materials movement-induced beam deﬂection at vicinities of the aquatic plant were simultaneously monitored for real-time observa-tions. The simulataneous monitoring were conducted on various plant parts with and without the presence of 1- μ M heavy metal ions, such as Cu 2+ , Ni 2+ , and Co 2+ . Results showed that the presence of the 1- μ M heavy metal ions greatly altered changing trends of both DO and beam deﬂection over time in both the photosynthetic and respiration processes for the plant. This altering suggests that the existence of the 1- μ M heavy metal ions has greatly changed the physiological processes in the aquatic plant. Based on study results, this method has the potential to be used as a new sensitive tool for monitoring heavy metal stress effects on aquatic plants.
SNP Identification from RAD-Seq Data in Faba Bean (Vicia faba L.)

Faba bean is an important edible legume crop in China. Due to its huge genome size and no available reference genome, SNP marker is very limited in faba bean. To identify genome-wide SNP markers, we obtained 35.47 Gb data from eight landraces by RAD-sequencing, with an average of 4.77 Gb data for each accession. A total of 245443516 reads were generated, the single accession has an average of 30680439.5 reads, and the average length of the reads reaches to 144 bp. The Q20 and Q30 values were over 97.89% and 93.83%, respectively. The GC content between the reads varied from 38.05% to 40.09%. Using a special Bayesian method under the situation of no reference genome, we identified 3722 SNPs among the eight landraces. Regarding the single accession, the detected SNPs varied from 3278 to 3578, and the homozygous SNPs number was larger than that of heterozygous SNPs for most of accessions. For the SNP types, T:A->C:G type has the largest proportion (38.8%), followed by C:G->T:A (28.0%) and the smallest is T:A->A:T (7.5%). 31 SNPs were selected to convert into KASP markers, and they showed a success rate of 66.7% through amplifying on 46 accessions. The SNPs in this study provide a strong genetic tool for germplasm identification, gene mapping and molecular breeding in faba bean.
Corn Tiller Yield Contributions and Ear Development in Low Plant Densities

Research in modern corn (Zea mays L.) hybrids investigating tiller contributions and ear development at low plant densities is scarce, particularly in water-limited environments. To fill this research gap, a second season of replicated experiments was conducted in 2020 at 7 sites across Kansas (Keats, Buhler, Greensburg, Garden City, Goodland, and two sites in Colby) evaluating two common, tiller-prone corn hybrids (P0805AM and P0657AM) at three target plant density levels (10000, 17000, and 24000 plants/a). Five of the listed sites also considered a tillering factor (tiller removal at development stage V10 [tenthleaf] or tiller maintenance). Seasonal phenology, partitioned grain yield, harvested ear type characterizations, and environmental conditions were recorded and analyzed to quantify tiller contributions in each site. Results showed that intact tillers had either no effect or were able to boost yields. In the best environments, tillers were able to successfully compensate for losses of 60% in plant density. Five of the seven tested sites produced approximately 50% of total harvested ears as desirable tiller lateral ears in the 10000 plants/a target plant density. The highest percentage of undesirable tiller tassel ear development in the 10000 plants/a density was 13%. Future research will seek to find explanations of the ear type relationships on a deeper level and predict tiller yield contributions considering various environments and ear development outcomes.
Assessing the Role of Machine Learning in Robotics

Machine learning is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Neural framework offers wide support for machine learning algorithms. It is an interface, library or tool which allows developers to build machine learning models easily, without getting into the depth of the underlying algorithms. The neural framework is an exceptionally intricate piece of a person that co-ordinate its activities Moreover, tactile data by transmitting signs to and from various pieces of the body. Neural frameworks are applied to perform object gathering and a grasp orchestrating task. Machine Learning techniques have been applied to many sub problems in robot perception – pattern recognition and self-organisation. Modern robot framework which demands a complete detail of each movement of the robot, which breaks the pick-and-spot issue into about free, computationally conceivable sub-issues as a phase toward a comprehensive endeavour level framework
Overexpression of cytoplasmic Solanum tuberosum Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene improves PSII efficiency and alleviates salinity stress in Arabidopsis.

In this study, transgenic Arabidopsis lines expressing a potato gene (D43), encoding Glyceraldehyde 3phosphate dehydrogenase, were studied. The D43 plants exhibited improved morphological parameters and accumulation of photosynthetic pigments compared to wild-type (WT) plants under salinity stress conditions. In addition, the D43 transgenic plants showed significantly reduced electrolyte leakage, higher stomatal conductance, lower malondialdehyde (MDA) content, and higher proline content than the WT plants under salinity stress. The gene expression analysis showed that the D43 plants accumulated 1.7-fold, 2.2-fold, and 1.3-fold higher mRNA transcripts of genes encoding the antioxidant enzymes ascorbate peroxidase (APX), superoxide dismutase (SOD), and catalase (CAT), respectively under salt-stress conditions. Furthermore, they significantly altered the expression of seven major stress-responsive genes, which indicated that overexpression of the potato D43 gene gave salinity stress resistance to Arabidopsis. Chlorophyll-a fluorescence kinetics confirmed the efficient photon absorption, electron transport, and overall PSII efficiency that led to improved photosynthesis in the D43 plants subjected to NaCl-induced salinity stress. Overall, our findings have suggested that potato D43 is a potential candidate gene for developing salinity stress resistance in higher plants. ARTICLE HISTORY Received 28 March 2021 Accepted 24 July 2021
Hyperferritinemia in pediatric scrub typhus: A prospective observational study

Background: Hyperferritinemia is increasingly associated with mortality in sepsis. Studies estimating the prevalence of hyperferritinemia in pediatric scrub typhus is limited.Methods: We conducted a prospective observational study from a tertiary care teaching hospital in North India where 72 children with confirmed scrub typhus;17(24%)-PCR positive, 68(94%)-IgM ELISA positive and 13(18%)-both PCR and ELISA positive. Serum ferritin was measured in 62 children to identify the prevalence of hyperferritinemia and determine its association with mortality. Results: Hyperferritinemia (>500µg/L) was seen in 72.6% [n=45] children; 26(42%) were mild (500-2000µg/L), 13(21%) were moderate (2000-10000µg/L) and 6(9.7%) were severe (>10000µg/L). A biphasic pattern of hyperferritinemia was seen with two peaks when plotted against duration since symptoms onset; one between 2 and 3 days (early onset hyperferritinemia) and second peak between 12 and14 days (late onset hyperferritinemia). Early onset hyperferritinemia had more survivors than late onset hyperferritinemia. Non survivors had significantly higher PRISM III, PELOD-2, hyperlactatemia, hypoalbuminemia, organ dysfunction, need for mechanical ventilation and need of RRT. Ferritin had poor sensitivity and specificity in predicting survival with AUC of 0.56. Organ dysfunction and risk scores as PRISM III, PELOD 2 and VIS at admission were better predictors with AUC(95%CI) of 0.72(0.56,0.89), 0.77(0.63,0.92) and 0.90(0.78,1.0) respectively.Conclusions: Hyperferritinemia is common in scrub typhus. A distinct biphasic response was observed with worse outcome in late onset hyperferritinemia. Organ dysfunction and risk scores were better predictors of mortality than ferritin.
Upper bound solution of collapse pressure and permanent displacement of 3D tunnel faces using the pseudo-dynamic method and the kinematic approach

This work presents seismic stability analysis of tunnel faces under three-dimensional (3D) conditions. To consider the temporal and spatial features of seismic force, the pseudo-dynamic approach was employed and incorporated into the 'horn-like' mechanism. According to the limit analysis method and the Hoek-Brown strength criterion, analytical solution of collapse pressure on tunnel faces was derived. The permanent displacement of tunnel face was then calculated by virtue of the Newmark method. The effects of the parameters of seismic force and Hoek-Brown strength criterion on collapse pressure and failure range of tunnel faces were analyzed. The relationship between the Hoek-Brown strength criterion parameters, the supporting force, and the yield acceleration was discussed. Moreover, the permanent displacements at the top, center, and bottom of tunnel faces under seismic effect were examined. This paper proposes a new idea for seismic stability analysis of tunnel faces.
DeepBiome: a phylogenetic tree informed deep neural network for microbiome data analysis

Background: Evidence linking microbiome and human health is rapidly growing. Microbiome profile can be a novel predictive biomarker for many diseases. However, bacteria count tables are typically sparse and bacteria are classified at a hierarchy of taxonomic levels, ranging from species to phylum. Existing analysis tools focus on identifying microbiome associations either at the community level or at a specific pre-defined taxonomic level. They fail to incorporate the evolutionary relationship between bacteria and cannot learn from the data to aggregate microbiome contribution, thus leading to less accurate and less interpretable results in prediction, classification or selection. Results: We present DeepBiome, a phylogney-informed neural network architecture for predicting phenotypes from microbiome counts and uncovering the microbiome-phenotype association network. It takes microbiome abundance as the input and let the phylogenetic taxonomy guide the neural network architecture. Commonly used neural network architectures are targeted towards image and text analysis and typically require huge amount of training data, which is scarce in biomedical applications. By leveraging the phylogenetic information, DeepBiome relieves the heavy burden of tuning for the optimal deep learning architecture, avoids overfitting, and more importantly enables visualizing the path from microbiome counts to disease. It is applicable to both regression and classification problems. The simulation study and real-life data analysis demonstrate that DeepBiome is highly accurate and efficient and and provides a deep understanding of complex microbiome-phenotype associations even using small to moderate training sample sizes. Conclusions: In practice, it is unknown at which taxonomic level that microbiome clusters tag the association. Therefore, the central advantage of the presented method over other analytical methods is that it offers an ecological and evolutionary understanding of host-microbe interactions which is important for microbiome-based medicine. DeepBiome is implemented using Python packages Keras and the Tensorflow. It is an open-source tool available at (https://github.com/Young-won/DeepBiome).
Performance Evaluation and Optimization of MIMO Radars Using Biomimetic Antenna Arrays

The improvement of the angular resolution of radar sensors is one of the crucial goals of current radar research. A promising approach to achieve this goal is inspired by the ears of a fly called Ormia ochracea. The working principle was adapted for antennas, and the so-called biomimetic antenna arrays (BMAAs) aroused the interest of several research groups. In this work, BMAAs are incorporated into multiple-input multiple-output (MIMO) arrays, a very common approach of improving the angular resolution, to gain more degrees of freedom in array design. The MIMO BMAAs are modeled utilizing the effective biomimetic antenna distance, a fundamentally new measure introduced in this article to translate the special biomimetic phase progression into a spatial quantity. We present straightforward antenna configurations but also describe how a genetic algorithm can be utilized to optimize both antenna positions and BMAA parameters. The proposed arrays show various beneficial effects such as a wider angular range for unambiguous angle estimation or a narrower beamwidth. The impact of MIMO BMAAs on the angular resolution is thoroughly analyzed both theoretically and by radar measurements in the range of 77 GHz. The measurements confirm the modeling method very well and show a significant increase in the angular separability.
Integrating the Entrepreneurial Mindset Into the Engineering Classroom

Contribution: This article highlights one approach to fostering the entrepreneurial mindset in the engineering classroom. Background: Entrepreneurship and innovation are currently trending topics in engineering education and will continue developing for the foreseeable future. Intended Outcomes: The guiding research question is: how can an entrepreneurial mindset focused learning experience improve student self-regulation, seeing value, and lifelong learning through metacognitive reflections? Application Design: The study is implemented within a five-week module focused on developing the entrepreneurial mindset as part of a required course on supply chain management technology. The supporting pedagogical interventions include authentic learning, information literacy, a mix of low stakes and high stakes assessment, and parallel scaffold and collaborative learning. The module is assessed through a metacognitive reflection centered around self-regulated learning, seeing value, and lifelong learning. Findings: First, the metacognitive reflections showed ~50% of participants would approach their work differently (e.g., make changes to management and scheduling, using credible sources, obtaining writing assistance through the university writing lab). Second, participants were able to articulate soft skill development and/or subject matter expertise as benefits as it relates to entering the workforce. Third, participants were able to express opportunities for extending their knowledge in the future.
Developing strategies to support social-ecological resilience in flammable landscapes

Miller, Carol; Higuera, Philip E. ; McWethy, David B. ; Metcalf, Alexander L. ; Metcalf, Elizabeth C. ; Black, Anne E. ; Clarke, Lily; Hodge, Haley. 2021. Developing strategies to support social-ecological resilience in flammable landscapes: A structured approach for natural resource managers and other stakeholders. Res. Note RMRS-RN-92. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 41 p. https://doi.org/10.2737/RMRS-RN-92.
Analysis of Key Performance Indicators for Local Electricity Markets’ Design

Local electricity markets (LEMs) are investigated as a solution to provide consumers and prosumers the opportunity to have control over their electricity-related choices and make savings on their energy bills. This work analyzes market design factors, such as the number of update intervals per trading slot, the production-to-consumption (PtC) ratio, and pricing scenarios that influence the performance of an LEM. The decentralized autonomous area agent (D3A) has been used for running LEM simulations under the German regulatory framework. The results of the simulations compared using self-sufficiency, the share of market savings, and the average buying rate revealed that the performance of an LEM is highly dependent on the market design factors. Also, bidding strategy affects the performance of an LEM compared to the share of the local generation. The results imply that LEM can provide better incentives for both prosumers and consumers by providing them with the opportunity to trade their excess generation at prices higher than the feed-in tariff and lower their regular electricity tariff, respectively. With only a 20% reduction in average buying rate, it is also evident that LEMs provide a great opportunity for keeping smaller PV systems active after their 20 years of fixed remuneration under a state-sponsored incentive scheme in Germany.
Two-tone distortion products in hardware-efficient cochlea model based on asynchronous cellular automaton oscillator

In this paper, we present a cochlea model whose nonlinear dy-namicsaredescribedbyanasynchronouscellularautomaton. Ourproposed model is demonstrated to enable the reproduction of two-tone distortion products. The proposed model is implemented on a ﬁeld-programmable gatearray(FPGA). Itissubsequentlydemonstratedthatourproposedmodelcanbeimplementedusingfewerhardwareresourcesthanaconventional cochleamodel,whichisaHopf-typecochleamodel,implementedonadigitalsignalprocessorperforminganumericalintegration.
Polymorphism Exploration of Growth Family (GH, GHRH and PIT-1) Genes Polymorphisms of Local Swamp Buffalo for Productivity Improvement in North Tapanuli Regency, North Sumatra

Genetic improvement of livestock productivity can be done through molecular selection on the genes controlling growth traits. Genetic polymorphism of the growth family (GH, GHRH, and PIT1) genes were studied in local swamp buffalo (106 hds.) from a government buffalo breeding station (46 heads) and smallholders (60 heads) in North Tapanuli District, North Sumatra Province. Genotype variants of the three genes were identified by PCR-RFLP method using restriction enzymes of MspI (GH gene), HaeIII (GHRH gene) and HinfI (PIT -1 gene). Genotyping on individual GH_g. 1547T>C, GHRH_g. 4666G>C, and PIT -1_g. 1256G >A loci resulted only one type genotype, respectively TT, CC, an d AA, with one type of allele, respectively T, C, and A. Heterozygosity observation (Ho) and expectation (He) values values and the PIC value for each locus was 0.00. It could be suggested to increase genotype frequenciest of the three growth genes that are positively associated with the growth traits and economic traits of the buffalo.
Analysis of Vision and Mission Statements Characteristics and their Association with Organizational Performance: A Guide to Writing Effective Vision and Mission Statements

This paper empirically examines vision and mission statements of Fortune 500 firms for the purpose of identifying and examining specific characteristics and associating these attributes with organizational performance. Additionally, this paper provides a theoretical foundation for the inclusion of various characteristics of vision and mission documents, and thus provides guidance for organizations to develop and revise these important strategic planning documents. JEL CODE: M21, O21