Environmental, developmental, and genetic factors controlling root system architecture

A better understanding of the development and architecture of roots is essential to develop strategies to increase crop yield and optimize agricultural land use. Roots control nutrient and water uptake, provide anchoring and mechanical support and can serve as important storage organs. Root growth and development is under tight genetic control and modulated by developmental cues including plant hormones and the environment. This review focuses on root architecture and its diversity and the role of environment, nutrient, and water as well as plant hormones and their interactions in shaping root architecture

Effect of massive graviton on dark energy star structure

The presence of massive gravitons in the field of massive gravity is considered as an important factor in investigating the structure of compact objects. Hence, we are encouraged to study the dark energy star structure in the Vegh's massive gravity. We consider that the equation of state governing the inner spacetime of the star is the extended Chaplygin gas, and then using this equation of state, we numerically solve the Tolman-Oppenheimer-Volkoff (TOV) equation in massive gravity. In the following, assuming different values of free parameters defined in massive gravity, we calculate the properties of dark energy star such as radial pressure, transverse pressure, anisotropy parameter, and other characteristics. Then, after obtaining the maximum mass and its corresponding radius, we compute redshift and compactness. The obtained results show that for this model of dark energy star, the maximum mass and its corresponding radius depend on the massive gravity's free parameters and anisotropy parameter. These results are consistent with the observational data, and cover the lower mass gap. We also demonstrate that all energy conditions are satisfied for this model, and in the presence of anisotropy, the dark energy star is potentially unstable.Comment: 17 pages, 10 figures, 4 table

Influence of phonons on exciton-photon interaction and photon statistics of a quantum dot

In this paper, we investigate, phonon effects on the optical properties of a spherical quantum dot. For this purpose, we consider the interaction of a spherical quantum dot with classical and quantum fields while the exciton of quantum dot interacts with a solid state reservoir. We show that phonons strongly affect the Rabi oscillations and optical coherence on first picoseconds of dynamics. We consider the quantum statistics of emitted photons by quantum dot and we show that these photons are anti-bunched and obey the sub-Poissonian statistics. In addition, we examine the effects of detuning and interaction of quantum dot with the cavity mode on optical coherence of energy levels. The effects of detuning and interaction of quantum dot with cavity mode on optical coherence of energy levels are compared to the effects of its interaction with classical pulse

Effect of rainbow function on the structural properties of dark energy star

Confirming the existence of compact objects with a mass greater than $2.5M_{\odot}$ by observational results such as GW190814 makes that is possible to provide theories to justify these observational results using modified gravity. This motivates us to use gravity's rainbow, which is the appropriate case for dense objects, to investigate the dark energy star structure as a suggested alternative case to the mass gap between neutron stars and black holes in the perspective of quantum gravity. Hence, in the present work, we derive the modified hydrostatic equilibrium equation for an anisotropic fluid, represented by the extended Chaplygin equation of state in gravity's rainbow. Then, for two isotropic and anisotropic cases, using the numerical solution, we obtain energy-dependent maximum mass and its corresponding radius, and the other properties of the dark energy star including the pressure, energy density, stability, etc. In the following, using the observational data, we compare the obtained results in two frameworks of general relativity and gravity's rainbow.Comment: 12 pages, 8 figures, 4 table

Neutron spectroscopic study of crystal field excitations in Tb2Ti2O7 and Tb2Sn2O7

We present time-of-flight inelastic neutron scattering measurements at low temperature on powder samples of the magnetic pyrochlore oxides Tb2Ti2O7 and Tb2Sn2O7. These two materials possess related, but different ground states, with Tb2Sn2O7 displaying "soft" spin ice order below Tn~0.87 K, while Tb2Ti2O7 enters a hybrid, glassy spin ice state below Tg~0.2 K. Our neutron measurements, performed at T=1.5 K and 30 K, probe the crystal field states associated with the J=6 states of Tb3+ within the appropriate Fd\bar{3}m pyrochlore environment. These crystal field states determine the size and anisotropy of the Tb3+ magnetic moment in each material's ground state, information that is an essential starting point for any description of the low-temperature phase behavior and spin dynamics in Tb2Ti2O7 and Tb2Sn2O7. While these two materials have much in common, the cubic stanate lattice is expanded compared to the cubic titanate lattice. As our measurements show, this translates into a factor of ~2 increase in the crystal field bandwidth of the 2J+1=13 states in Tb2Ti2O7 compared with Tb2Sn2O7. Our results are consistent with previous measurements on crystal field states in Tb2Sn2O7, wherein the ground state doublet corresponds primarily to m_J=|\pm 5> and the first excited state doublet to mJ=|\pm 4>. In contrast, our results on Tb2Ti2O7 differ markedly from earlier studies, showing that the ground state doublet corresponds to a significant mixture of mJ=|\pm 5>, |\mp 4>, and |\pm 2>, while the first excited state doublet corresponds to a mixture of mJ=|\pm 4>, |\mp 5>, and |\pm 1>. We discuss these results in the context of proposed mechanisms for the failure of Tb2Ti2O7 to develop conventional long-range order down to 50 mK.Comment: 12 pages, 6 figures. Version is the same as the published one, except for figure placement on page

Physical and Aerodynamic Characterization of Particle Clusters at Sakurajima Volcano (Japan)

The process of particle aggregation significantly affects ash settling dynamics associated with volcanic explosive eruptions. Several experiments have been carried out to investigate the physics of ash aggregation and dedicated numerical schemes have been developed to produce more accurate forecasting of ash dispersal and sedimentation. However, numerical description of particle aggregation is complicated by the lack of complete datasets on natural samples required for model validation and calibration. Here we present a first comprehensive dataset for the internal structure, aerodynamical properties (e.g., size, density, terminal velocity) and grain size of constituting particles of a variety of aggregate types collected in the natural laboratory of Sakurajima Volcano (Japan). Even though the described particle clusters represent the most common types of aggregates associated with ash-rich fallouts, they are of difficult characterization due to the very low potential of preservation in tephra-fallout deposits. Properties were, therefore, derived based on a combination of high-resolution-high-speed videos of tephra fallout, scanning electron microscope analysis of aggregates collected on adhesive paper and analysis of tephra samples collected in dedicated trays. Three main types of particle clusters were recognized and quantitively characterized: cored clusters (PC3), coated particles (PC2), and ash clusters (PC1) (in order of abundance). A wide range of terminal velocities (0.5–4 m/s) has been observed for these aggregates, with most values varying between 1 and 2 m/s, while aggregate size varies between 200 and 1,200 µm. PC1, PC2, and PC3 have densities between 250 and 500, 1,500 and 2,000, and 500 and 1,500 kg/m3, respectively. The size of the aggregate core, where present, varies between 200 and 750 µm and increases with aggregate size. Grain size of tephra samples was deconvoluted into a fine and a coarse Gaussian subpopulation, well correlated with the grain size of shells and of the internal cores of aggregates, respectively. This aspect, together with the revealed abundance of PC3 aggregates, reconciles the presence of a large amount of fine ash (aggregate shells) with coarse ash (aggregate cores) and better explains the grain size distribution bimodality, the high settling velocity with respect to typical PC1 velocities and the low settling velocities of large aggregates with respect to typical PC2 velocity. Furthermore, ash forming the aggregates was shown to be always finer than 45 µm, confirming the key role played by aggregation processes in fine ash deposition at Sakurajima

Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning

Atherosclerosis, a chronic inflammatory disease affecting the large arteries, presents a global health risk. Accurate analysis of diagnostic images, like computed tomographic angiograms (CTAs), is essential for staging and monitoring the progression of atherosclerosis-related conditions, including peripheral arterial disease (PAD). However, manual analysis of CTA images is time-consuming and tedious. To address this limitation, we employed a deep learning model to segment the vascular system in CTA images of PAD patients undergoing femoral endarterectomy surgery and to measure vascular calcification from the left renal artery to the patella. Utilizing proprietary CTA images of 27 patients undergoing femoral endarterectomy surgery provided by Prisma Health Midlands, we developed a Deep Neural Network (DNN) model to first segment the arterial system, starting from the descending aorta to the patella, and second, to provide a metric of arterial calcification. Our designed DNN achieved 83.4% average Dice accuracy in segmenting arteries from aorta to patella, advancing the state-of-the-art by 0.8%. Furthermore, our work is the first to present a robust statistical analysis of automated calcification measurement in the lower extremities using deep learning, attaining a Mean Absolute Percentage Error (MAPE) of 9.5% and a correlation coefficient of 0.978 between automated and manual calcification scores. These findings underscore the potential of deep learning techniques as a rapid and accurate tool for medical professionals to assess calcification in the abdominal aorta and its branches above the patella. The developed DNN model and related documentation in this project are available at GitHub page at https://github.com/pip-alireza/DeepCalcScoring.Comment: Published in MDPI Diagnostic journal, the code can be accessed via the GitHub link in the pape

An investigation into Unmanned Aerial System (UAS) forensics: Data extraction & analysis

Recent developments of drone technologies have shown a surge of commercial sales of drone devices, which have found use in many industries. However, the technology has been misused to commit crimes such as drug trafficking, robberies, and terror attacks. The digital forensics industry must match the speed of development with forensic tools and techniques. However, it has been identified that there is a lack of an agreed framework for the extraction and analysis of drone devices and a lack of support in commercial digital forensics tools available. In this research, an investigation into the extraction tools available for drone devices and analysis techniques has been performed to identify best practices for handling drone devices in a forensically sound manner. A new framework to perform a full forensic analysis of small to medium sized commercial drone devices and their controllers has been proposed to give investigators a plan of action to perform forensic analysis on these devices. The proposed framework overcomes some limitations of other drone forensics investigation frameworks presented in the literature