Miracle-dz/UniFAR-FTUs huggingface dataset:
Combating Dual Challenges in Maize Under High Planting Density: Stem Lodging and Kernel Abortion

High plant density is considered a proficient approach to increase maize production in countries with limited agricultural land; however, this creates a high risk of stem lodging and kernel abortion by reducing the ratio of biomass to the development of the stem and ear. Stem lodging and kernel abortion are major constraints in maize yield production for high plant density cropping; therefore, it is very important to overcome stem lodging and kernel abortion in maize. In this review, we discuss various morphophysiological and genetic characteristics of maize that may reduce the risk of stem lodging and kernel abortion, with a focus on carbohydrate metabolism and partitioning in maize. These characteristics illustrate a strong relationship between stem lodging resistance and kernel abortion. Previous studies have focused on targeting lignin and cellulose accumulation to improve lodging resistance. Nonetheless, a critical analysis of the literature showed that considering sugar metabolism and examining its effects on lodging resistance and kernel abortion in maize may provide considerable results to improve maize productivity. A constructive summary of management approaches that could be used to efficiently control the effects of stem lodging and kernel abortion is also included. The preferred management choice is based on the genotype of maize; nevertheless, various genetic and physiological approaches can control stem lodging and kernel abortion. However, plant growth regulators and nutrient application can also help reduce the risk for stem lodging and kernel abortion in maize.
Effect of Aquatic Exercise Training on Aortic Hemodynamics in Middle-Aged and Elderly Adults

Aquatic exercise is an attractive form of exercise that utilizes the various properties of water to improve physical health, including arterial stiffness. However, it is unclear whether regular head-out aquatic exercise affects aortic hemodynamics, the emerging risk factors for future cardiovascular disease. The purpose of this study was to investigate whether head-out aquatic exercise training improves aortic hemodynamics in middle-aged and elderly people. In addition, to shed light on the underlying mechanisms, we determined the contribution of change in arterial stiffness to the hypothesized changes in aortic hemodynamics. Twenty-three middle-aged and elderly subjects (62 ± 9 years) underwent a weekly aquatic exercise course for 15 weeks. Aortic hemodynamics were evaluated by pulse wave analysis via the general transfer function method. Using a polar coordinate description, companion metrics of aortic pulse pressure (PPC = √{(systolic blood pressure)2 + (diastolic blood pressure)2}) and augmentation index (AIxC = √{(augmentation pressure)2 + (pulse pressure)2}) were calculated as measures of arterial load. Brachial-ankle (baPWV, reflecting stiffness of the abdominal aorta and leg artery) and heart-ankle (haPWV, reflecting stiffness of the whole aortic and leg artery) pulse wave velocities were also measured. The rate of participation in the aquatic training program was 83.5 ± 13.0%. Aortic systolic blood pressure, pulse pressure, PPC, AIxC, baPWV, and haPWV decreased after the training (P < 0.05 for all), whereas augmentation index remained unchanged. Changes in aortic SBP were correlated with changes in haPWV (r = 0.613, P = 0.002) but not baPWV (r = 0.296, P = 0.170). These findings suggest that head-out aquatic exercise training may improve aortic hemodynamics in middle-aged and elderly people, with the particular benefits for reducing aortic SBP which is associated with proximal aortic stiffness.
Insights Into Manganese Solubilizing Bacillus spp. for Improving Plant Growth and Manganese Uptake in Maize

Manganese (Mn) is an essential micronutrient for plant growth that is involved in the structure of photosynthetic proteins and enzymes. Mn deficiency is widespread mainly in dry, calcareous, and sandy soil, which leads to a significant decrease in crop yield. Mn-reducing bacteria promote the solubilization of Mn minerals, thus increasing Mn availability in soil. The present study aimed to assess the Mn solubilizing ability and plant growth-promoting potential of Bacillus spp. strains for maize plants with insoluble Mn compounds. Several Mn-solubilizing bacterial (MSB) strains were isolated from the maize rhizosphere using nutrient agar media amended with 50 mM MnO2. These strains were screened based on qualitative and quantitative solubilization of Mn, phosphorus, potassium, and zinc and production of ammonia. The majority of MSB strains were positive for catalase, protease, amylase, and oxidase activity, while more than 60% of tested strains were positive for lipase activity, and the production of indole-3-acetic acid and siderophores. Forty-five percent of the tested strains also showed solubilization of potassium. All the MSB strains were evaluated for their ability to promote plant growth and Mn uptake in the presence of MnO2 under axenic sand culture conditions. The results revealed that inoculation with MSB strains under sand culture significantly improved the growth of maize seedlings except for strains ASH7, ASH10, and ASH12. Comparatively, strains ASH6, ASH11, ASH19, ASH20, and ASH22 demonstrated a better increase in plant growth, fresh and dry biomass, and Mn uptake in roots and shoots than the other strains tested. All of these strains were identified as Bacillus spp. through 16S rRNA partial gene sequencing. Maize inoculation with these selected identified MSB strains also resulted in an increase in maize growth and nutrient uptake in maize roots and shoots under soil culture conditions in the presence of native soil Mn. The current study highlights the importance of MSB strain inoculation which could be a potential bioinoculants to promote plant growth under Mn deficiency.
Cumulative Roles for Epstein-Barr Virus, Human Endogenous Retroviruses, and Human Herpes Virus-6 in Driving an Inflammatory Cascade Underlying MS Pathogenesis

Roles for viral infections and aberrant immune responses in driving localized neuroinflammation and neurodegeneration in multiple sclerosis (MS) are the focus of intense research. Epstein-Barr virus (EBV), as a persistent and frequently reactivating virus with major immunogenic influences and a near 100% epidemiological association with MS, is considered to play a leading role in MS pathogenesis, triggering localized inflammation near or within the central nervous system (CNS). This triggering may occur directly via viral products (RNA and protein) and/or indirectly via antigenic mimicry involving B-cells, T-cells and cytokine-activated astrocytes and microglia cells damaging the myelin sheath of neurons. The genetic MS-risk factor HLA-DR2b (DRB1*1501β, DRA1*0101α) may contribute to aberrant EBV antigen-presentation and anti-EBV reactivity but also to mimicry-induced autoimmune responses characteristic of MS. A central role is proposed for inflammatory EBER1, EBV-miRNA and LMP1 containing exosomes secreted by viable reactivating EBV+ B-cells and repetitive release of EBNA1-DNA complexes from apoptotic EBV+ B-cells, forming reactive immune complexes with EBNA1-IgG and complement. This may be accompanied by cytokine- or EBV-induced expression of human endogenous retrovirus-W/-K (HERV-W/-K) elements and possibly by activation of human herpesvirus-6A (HHV-6A) in early-stage CNS lesions, each contributing to an inflammatory cascade causing the relapsing-remitting neuro-inflammatory and/or progressive features characteristic of MS. Elimination of EBV-carrying B-cells by antibody- and EBV-specific T-cell therapy may hold the promise of reducing EBV activity in the CNS, thereby limiting CNS inflammation, MS symptoms and possibly reversing disease. Other approaches targeting HHV-6 and HERV-W and limiting inflammatory kinase-signaling to treat MS are also being tested with promising results. This article presents an overview of the evidence that EBV, HHV-6, and HERV-W may have a pathogenic role in initiating and promoting MS and possible approaches to mitigate development of the disease.
Trust and Performance in Human-AI Systems for Multi-Domain Command and Control

—Command and Control is one of the core tenants of joint military operations, however, the nature of modern security threats, the democratization of technology globally, and the speed and scope of information ﬂows are stressing traditional operational paradigms, necessitating a fundamental shift to better concurrently integrate and operate across multiple physical and virtual domains. In this paper, we aim to address these challenges through the proposition of three concepts that will guide the creation of integrated human-AI Command and Control systems, inspired by recent advances and successes within the commercial sector and academia. The ﬁrst concept is a framework for integration of AI capabilities into the enterprise that optimizes trust and performance within the workforce. The second is an approach for facilitating multi-domain operations though real-time creation of multi-organization multi-domain task teams by dynamic management of information abstraction, teaming, and risk control. The third is a new paradigm for multi-level data security and multi-organization data sharing that will be a key enabler of joint and coalition multi-domain operation in the future. Lastly, we propose a set of recommendations towards the research, development, and instantiation of these transformative advances in Command and Control capability.
Total Factor Productivity in Indian Agriculture

Agricultural growth and development since economic reforms pose interesting propositions regarding the structural and behavioural characteristics of total factor productivity in the Indian context. Productivity and growth rate of agricultural output are largely influenced by the conventional inputs such as land, labour and capital and also by the contribution of non-conventional factors including technology. The combined effects of these non-conventional factors influence the dynamic change in total factor productivity. Utilising growth accounting framework with production function approach for aggregation of inputs, this study undertook a modest attempt to estimate and analyse total factor productivity (TFP) with and without constant returns to scale restrictions and their determinants respectively in Indian agriculture as a whole since 1991-92 up to 2016-17. Interesting inferences were derived regarding the nature and trend of estimated productivities, their determinants and important policy implications while noticing a falling trend in total factor productivity with constant returns to scale restriction and observing no conclusive linear trend in total factor productivity without constant returns to scale (CRS)restriction.
Edaphic characterization of coastal Western Mediterranean

Spatio-temporal gradients of soil salinity and water determine zonation and distribution of plant taxa of coastal vegetation, and habitats of salt marsh and endoreic basins (Chapman 1974, Álvarez-Rogel et al. 2000, St. Omer 2004). In addition, topography determines distribution of levels salt concentration and, accordingly, plant zoning in these areas (Chapman 1976, van Wijnen et al. 1997, Bockelmann et al. 2002), particularly in Mediterranean ecosystems (ÁlvarezRogel et al. 2001, Costa et al. 2003, Acosta et al. 2007). In forests and other landscapes, the successive stages of colonization (like soil evolution) allow the incorporation of new species that replace the original vegetation. Succession of species defines a vegetation series that leads to a final stable community – climax vegetation – with higher levels of resource optimization (Clements 1916, Braun-Blanquet 1964, Odum 1983). This proposed scheme of dynamic vegetation is consistent when limiting factors of plant development are linked to climatic conditions (climatophilous vegetation). However, over long periods of time, in salt marshes and saline habitats in general, the vegetation succession, as in other types of edaphic vegetation (as sand dunes, aquatic and marsh vegetation), does not always follow the climatic pattern, since the succession is preferably linked to the physical-chemical characteristics of soil (Redfield 1972, Beeftink 1977, Adam 1990). The definition of halophile plants as taxa that can tolerate up to 200 mM NaCl (Flowers & Colmer 2008) is applied to most species colonizing salt marshes where NaCl from sea water is the principal salt component. Moreover, intertidal periods induce variations in water levels, which can even reach dryness. The life cycle of halophytes is generally linked to a progressive limitation of plant development derived from physiological limitations. Among other effects, salinity causes a reduction in growth parameters (Naqvi et al. 1997), a decrease in potassium absorption (Girija et al. 2002), stomatal conductance or CO2 assimilation (Fernán-
Design and CFD Analysis of a Horizontal Axis Diffuser Augmented Tidal Turbine An honors thesis for the Department of Mechanical Engineering

1 Section 1: Introduction 1.1 Summary of Senior Design Project Work 2 1.2 Detailed Description of Prototype Tidal Turbine Design 3 1.3 Literature Review 7 1.4 Summary of existing tidal energy technologies 8 1.5 Tidal Turbine Theory 9 1.6 Scope of Analysis 10 1.7 Simulation Assumptions and Method 11 Section 2: CFD Analysis of 1/3 Scale Prototype Turbine 2.1 Bare Turbine 12 2.2 Diffuser Section 14 2.3 Diffuser Augmented Turbine 15 2.4 Bare Turbine with Extended Blades 17 2.5 Summary of Results for 1/3 Scale Prototype Turbine 19 Section 3: Design of Blades for Full Scale Tidal Turbine 20 Section 4: CFD Analysis of Full Scale Tidal Turbine 4.1 Bare Turbine 23 4.2 Diffuser Section 24 4.3 Diffuser Augmented Turbine 26 4.4 Bare Turbine with Extended Blades 27 4.5 Summary of Results for Full Scale Tidal Turbine 29 Section 5: Conclusion 30
Computational aspects of Commutative Algebra and Algebraic Geometry

Fix a ﬁnite commutative ring R . Let u and v be power series over R , with v (0) = 0. I presented an algorithm that computes the ﬁrst n terms of the composition u ( v ), given the ﬁrst n terms of u and v , in n 1+ o (1) ring operations. The algorithm is very fast in practice when char R is a product of small primes.
Compact Wideband Dual-Band SIW Bandpass Filters

─ A novel method to design compact wideband dual-band substrate integrated waveguide (SIW) bandpass filters (BPF) is proposed in this paper. By loading a novel beeline compact microstrip resonant cells (BCMRCs) with band-gap characteristics on top layer of SIW, two wide passbands separated by a stopband are generated. In order to enable the filter to have lower reflection coefficients in the two passbands, we use a tapered gradient line embedded with rectangular slots and loaded open stubs as the transition structure from microstrip line to SIW. The wideband dual-band BPF (DBBPF) is fabricated. The lower-band and upper-band fractional 3-dB bandwidths are 58.2% and 22.6%, while the measured minimum insertion losses (ILs) are 0.7 and 0.92 dB, respectively. The stepped-impedance openloop ring resonator (SIOLRR) is introduced in order to improve the selectivity of the filter. The wideband DBBPF with SIOLRR is studied, simulated and measured. Two transmission zeros are generated in the stop band between the two passbands. Good agreement between simulated and measured results can be obtained. Index Terms ─ Complementary split ring resonators, defected ground structure, dual-band bandpass filter, stepped-impedance open-loop ring resonator, substrate integrated waveguide.
A Hierarchical Tree-Based Syslog Clustering Scheme for Network Diagnosis

With the continuous development of Information Technology, modern networks have been widely utilised. Since the complex network structure causes growing difficulties in maintenance, log analysis has been widely studied in recent years for network diagnosis. System log clustering is mainly focused for root cause analysis. In this paper, a hierarchical tree-based clustering scheme is proposed that could accurately group system logs according to both time and network constraints without any training and parameter settings. Furthermore, it largely accelerates the matching process by reducing matching times and significantly boosts the performance of hit rate (100%) and match efficiency (16%) comparing to other clustering strategies, which greatly helps with precise network diagnosis.
Parsimonious statistical learning models for low flow estimation

Abstract. Statistical learning methods offer a promising approach for low flow regionalization. We examine seven statistical learning models (lasso, linear and non-linear model based boosting, sparse partial least squares, principal component regression, random forest, and support vector machine regression) for the prediction of winter and summer low flow based on a hydrological diverse dataset of 260 catchments in Austria. In order to produce sparse models we adapt the recursive feature elimination for variable preselection and propose to use three different variable ranking methods (conditional forest, lasso and linear model based boosting) for each of the prediction models. Results are evaluated for the low flow characteristic Q95 (Pr(Q>Q95) = 0.95) standardized by catchment area using a repeated nested cross validation scheme. We found a generally high prediction accuracy for winter (R2CV of 0.66 to 0.7) and summer (R2CV of 0.83 to 0.86). The models perform similar or slightly better than a Top-kriging model that constitutes the current benchmark for the study area. The best performing models are support vector machine regression (winter) and non-linear model based boosting (summer), but linear models exhibit similar prediction accuracy. The use of variable preselection can significantly reduce the complexity of all models with only a small loss of performance. The so obtained learning models are more parsimonious, thus easier to interpret and more robust when predicting at ungauged sites. A direct comparison of linear and non-linear models reveals that non-linear relationships can be sufficiently captured by linear learning models, so there is no need to use more complex models or to add non-liner effects. When performing low flow regionalization in a seasonal climate, the temporal stratification into summer and winter low flows was shown to increase the predictive performance of all learning models, offering an alternative to catchment grouping that is recommended otherwise.
ESN Reinforcement Learning for Spectrum and Flight Control in THz-Enabled Drone Networks

Terahertz (THz)-band communications have been envisioned as a key technology to support ultra-high-data-rate applications in 5G-beyond (or 6G) wireless networks. Compared to the microwave and mmWave bands, the main challenges with the THz band are in its i) large path loss hence limited network coverage and ii) visible-light-like propagation characteristics hence poor support of mobility in blockage-rich environments. This paper studies quantitatively the applicability of THz-band communications in blockage-rich mobile environments, focusing on a new network scenario called <italic>FlyTera</italic>. In <italic>FlyTera</italic>, a set of hotspots mounted on flying drones collaboratively provide data streaming services to ground users, in the microwave, mmWave and THz bands. We first provide a mathematical formulation of the <italic>FlyTera</italic> control problem, where the objective is to maximize the network spectral efficiency by jointly controlling the flight of the drone hotspots, their association to the ground users, and the spectrum bands used by the users. To solve the resulting problem, which is shown to be a mixed integer nonlinear nonconvex programming (MINLP) problem, we design distributed solution algorithms based on a combination of echo state learning and reinforcement learning. An extensive simulation campaign is then conducted with SimBAG, a newly developed <underline>Sim</underline>ulator of <underline>B</underline>roadband <underline>A</underline>erial-<underline>G</underline>round wireless networks. It is shown that no single spectrum band can meet the requirements of high data rate and wide coverage simultaneously. Moreover, from the network-level point of view, THz-band communications can significantly benefit from the mobility of the flying drones, and on average <inline-formula> <tex-math notation="LaTeX">$4 - 6$ </tex-math></inline-formula> times <italic>higher (rather than lower)</italic> throughput can be achieved in mobile than in static environments.
Investigation of Induced Resistance in Wheat to Sitobion avenae (Hemiptera: Aphididae) under Greenhouse Conditions

English grain aphid, Sitobion avenae (Fabricius), is an important worldwide phloemfeeding pest of wheat due to direct sucking damage and transmission of viruses. Here, we tested the effects of six treatments on the fitness traits of S. avenae including: (1) Wheat with a 6-days aphid infestation, (2) Wheat sprayed with Biomin zinc, (3) Wheat seed treated with Bacillus subtilis, and (4 and 5) Combined treatments of Biomin zinc+preinfestation and B. subtilis+pre-infestation as well as (6) A control treatment. Results revealed that there were significant differences among treatments concerning some allelochemical contents and aphid fitness traits. Treated with B. subtilis+pre-infestation and B. subtilis increased the total contents of phenol in the wheat leaves versus Biomin zinc+pre-infestation (183.63 mg g FW). The net Reproductive rate (R0) of S. avenae was significantly reduced by B. subtilis+pre-infestation (1.533 offspring per adult) compared to the control (6.887 offspring per adult). Treated with B. subtilis+pre-infestation (0.022 d ) significantly reduced the intrinsic rate of increase (r) of S. avenae compared with the control (0.105 d). The lowest (0.024) and highest (0.058) Nymph Growth Index (NGI) of aphid were found on B. subtilis+ pre-infestation and the control, respectively. Hence, it was concluded that B. subtilis+pre-infestation in wheat plants can induce systemic resistance to S. avenae, which can be used in the IPM of this aphid.
Consent and the illusion of autonomy in EU data protection: the necessary utopia

In the progressive development of European data protection laws, the consent of the data subject was consistently designated as a valid legitimating ground for the processing of personal data. However, due to a variety of reasons, research has frequently called into question the meaningfulness of consent given in digital environments, which is particularly concerning in light of the fact that the right to data protection is recognised as fundamental in the EU. Drawing from legal and academic documents, the present review analyses the development of the notion of consent over time, referring to existing literature to pinpoint the main models, on the basis of which persistent problems are then identified, clustered and discussed. What emerges from this inquiry is that, although the EU framework theoretically sees consent as an act of autonomy and as the functional expression of freedom and dignitaries values, this view is not reflected in common practice: empirical data show in fact that users are often compelled to make choices upon which they are not able or willing to deliberate, and for this reason typically end up giving their consent without even reading notices. The appreciation that certain problems have maintained constant relevance over time casts doubts on the strategic choices underlying the EU approach to consent; possible implications and future directions are discussed.