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Vegetation plays a key role in the global carbon cycle and thus is an important component within Earth system models (ESMs) that project future climate. Many ESMs are adopting methods to resolve plant size and ecosystem disturbance history, using vegetation demographic models. These models make it feasible to conduct more realistic simulation of processes that control vegetation dynamics. Meanwhile, increasing understanding of the processes governing plant water use, and ecosystem responses to drought in particular, has led to the adoption of dynamic plant water transport (i.e., hydrodynamic) schemes within ESMs. However, the extent to which variations in plant hydraulic traits affect both plant water stress and the risk of mortality in trait-diverse tropical forests is understudied. In this study, we report on a sensitivity analysis of an existing hydrodynamic scheme (HYDRO) model that is updated and incorporated into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (FATES–HYDRO V1.0). The size- and canopy-structured representation within FATES is able to simulate how plant size and hydraulic traits affect vegetation dynamics and carbon–water fluxes. To better understand this new model system, and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We assembled 942 observations of plant hydraulic traits on 306 tropical plant species for stomata, leaves, stems, and roots and determined the best-fit statistical distribution for each trait, which was used in model parameter sampling to assess the parametric sensitivity. We showed that, for simulated leaf water potential and loss of hydraulic conductivity across different plant organs, the four most important traits were associated with xylem conduit taper (buffers increasing hydraulic resistance with tree height), stomatal sensitivity to leaf water potential, maximum stem hydraulic conductivity, and the partitioning of total hydraulic resistance above vs. belowground. Our analysis of individual ensemble members revealed that trees at a high risk of hydraulic failure and potential tree mortality generally have a lower conduit taper, lower maximum xylem conductivity, lower stomatal sensitivity to leaf water potential, and lower resistance to xylem embolism for stem and transporting roots. We expect that our results will provide guidance on future modeling studies using plant hydrodynamic models to predict the forest responses to droughts and future field campaigns that aim to better parameterize plant hydrodynamic models.

An investigation was carried out in Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore during 2015- 2016 rabi season for evaluation of Coriander (Coriandrum sativum L.) genotypes for growth, leaf yield and quality. The experiment was laid out in the Randomized Block Design with 30 genotypes and replicated thrice. The study revealed that the relationship of characters implied that plant weight showed a significant positive association with the number of shoots per plant and leaf weight. Maximum significant positive genotypic and the phenotypic correlation coefficient was significantly recorded for leaf weight and stem weight with the number of shoots per plant respectively. Path analysis investigation on plant weight revealed that the leaf weight and the number of shoots per plant recorded very high positive direct effect on plant weight. But for the traits, stem weight, plant height and the number of leaves registered negative direct effect. The residual effect was observed to be high. Therefore, greater emphasis should be given on these characters while selecting for higher leaf yield and related traits.

Networks of coupled parametric resonators (parametrons) hold promise for parallel computing architectures. En route to realizing complex networks, we report an experimental and theoretical analysis of two coupled parametrons. In contrast to previous studies, we explore the case of strong bilinear coupling between the parametrons, as well as the role of detuning. We show that the system can still operate as an Ising machine in this regime, even though careful calibration is necessary to ensure that the correct solution space is available. Apart from the formation of split normal modes, new states of mixed symmetry are generated. Furthermore, we predict that systems with N>2 parametrons will undergo multiple phase transitions before arriving at a regime that can be equivalent to the Ising problem.

Coupling a system to a nonthermal environment can profoundly affect the phase diagram of the closed system, giving rise to a special class of dissipation-induced phase transitions. Such transitions take the system out of its ground state and stabilize a higher-energy stationary state, rendering it the sole attractor of the dissipative dynamics. In this paper, we present a unifying methodology, which we use to characterize this ubiquitous phenomenology and its implications for the open system dynamics. Specifically, we analyze the closed system's phase diagram, including symmetry-broken phases, and explore their corresponding excitations' spectra. Opening the system, the environment can overwhelm the system's symmetry-breaking tendencies, and changes its order parameter. As a result, isolated distinct phases of similar order become connected, and new phase-costability regions appear. Interestingly, the excitations differ in the newly connected regions through a change in their symplectic norm, which is robust to the introduction of dissipation. As a result, by tuning the system from one phase to the other across the dissipation-stabilized region, the open system fluctuations exhibit an exceptional pointlike scenario, where the fluctuations become overdamped, only to reappear with an opposite sign in the dynamical response function of the system. The overdamped region is also associated with squeezing of the fluctuations. We demonstrate the pervasive nature of such dissipation-induced phenomena in two prominent examples, namely, in parametric resonators and in light-matter systems. Our work draws a crucial distinction between quantum phase transitions and their zero-temperature open system counterparts.

Aim: To study the effects of combinations of fenugreek (Trigonella foenum-graecum L.), garlic (Allium sativum), and black pepper (Piper nigrum) powder supplementation on production traits of broiler chickens. Materials and Methods: A total of 288 commercial broiler chicks were randomly assigned to 1-9 groups with 4 replicates each. An experiment was conducted in broilers with different feed formulations; control feed, with no added fenugreek, garlic, and black pepper powder; and 8 treatment groups receiving feed supplemented with different combinations of fenugreek, garlic, and black pepper powder. The individual broilers’ body weight and feed consumption were recorded and calculate the body weight gain and feed conversion ratio (FCR). Results: Broiler’s weight gain and FCR were significantly higher in groups receiving feed supplemented with garlic and black pepper powder combinations (p

We study the heating dynamics of a generic one-dimensional critical system when driven quasiperiodically. Specifically, we consider a Fibonacci drive sequence comprising the Hamiltonian of uniform conformal field theory (CFT) describing such critical systems and its sine-square deformed counterpart. The asymptotic dynamics is dictated by the Lyapunov exponent which has a fractal structure embedding Cantor lines where the exponent is exactly zero. Away from these Cantor lines, the system typically heats up fast to infinite energy in a nonergodic manner where the quasiparticle excitations congregate at a small number of select spatial locations resulting in a buildup of energy at these points. Periodic dynamics with no heating for physically relevant timescales is seen in the high-frequency regime. As we traverse the fractal region and approach the Cantor lines, the heating slows enormously and the quasiparticles completely delocalize at stroboscopic times. Our setup allows us to tune between fast and ultraslow heating regimes in integrable systems.

Cyclic AMP (cAMP) inducible transcription factor cAMP responsive element binding protein 3 like 1 (Creb3l1) is strongly activated in the hypothalamus in response to hyperosmotic cues such as dehydration (DH). We have recently shown that Creb3l1 expression is upregulated by cAMP pathways in vitro, however the exact mechanisms are not known. Here we show that increasing Creb3l1 transcription by raising cAMP levels in mouse pituitary AtT20 cells automatically initiates cleavage of Creb3l1, leading to a greater abundance of the transcriptionally active N-terminal portion. Inhibiting protein synthesis indicated that de novo protein synthesis of an intermediary transcription factor was required for Creb3l1 induction. Strategic mining of our microarray data from dehydrated rodent hypothalamus revealed four candidates, reduced to two by analysis of acute hyperosmotic-induced transcriptional activation profiles in the hypothalamus, and one, orphan nuclear receptor Nr4a1, by direct shRNA mediated silencing in AtT20 cells. We show that activation of Creb3l1 transcription by Nr4a1 involves interaction with a single NBRE site in the promoter region. The ability to activate Creb3l1 transcription by this pathway in vitro is dictated by the level of methylation of a CpG island within the proximal promoter/5′UTR of this gene. We thus identify a novel cAMP-Nr4a1-Creb3l1 transcriptional pathway in AtT20 cells and also, our evidence would suggest, in the hypothalamus.

The Healthcare industry generally clinical diagnosis is done mostly by doctor’s expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

The aim of this study was to demonstrate the relationship between the deep fibular nerve and the dorsalis pedis artery to provide useful anatomical knowledge for safe surgical approaches in plastic surgery. The dissection of 30 cadaver lower limbs was undertaken to describe the relationship of the deep fibular nerve to the dorsalis pedis artery in the anterior tarsal tunnel and on the dorsum of the foot. The anterior tarsal tunnel is a flattened space between the inferior extensor retinaculum and the fascia overlying the talus and navicular. The deep fibular nerve and its branches pass longitudinally through this fibro-osseous tunnel, deep to the tendons of the extensor hallucis longus and extensor digitorum longus. Four distinct relationships of the deep fibular nerve to the dorsalis pedis artery were determined. The dorsalis pedis neurovascular island flap contains both the dorsalis pedis artery and the deep fibular nerve. Because the design of a neurovascular free flap requires detailed knowledge of the nerve and vascular supply, the data presented here are intended to help surgeons during surgical approaches to the foot and ankle.

Variations in the position of the bifurcation of the common carotid artery and the origin or branching pattern of the external carotid artery are well known and documented. Here, we report the trifurcation of the right common carotid artery in a male cadaver aged about 55 years. The right common carotid artery was found to divide into the external and internal carotids and the occipital artery. High division of bilateral common carotid arteries and a lateral position of the right external carotid artery at its origin were also observed in the same cadaver. There were two ascending pharyngeal arteries on the right side - one from the occipital artery and another from the internal carotid artery. The intraarterial approach is one of the most important routes for the administration of anticancer drugs for head and neck cancers. A profound knowledge of the anatomical characteristics and variations of the carotid artery such as its branching pattern and its position is essential to avoid complications with catheter insertion.

Aim: To know the the relationship between body weight and linear body measurements such as body length, height at withers and heart girth in Malabari adult female goats under field conditions. Materials and Methods: A total of 152 adult female Malabari goats' body weight and body measurements (body length, height at withers and heart girth) were used to develop the step wise multiple regression equations. Results: The average body weight, body length, height at withers and heart girth were 24.23 0.73 kg, 59.42 0.98 cm, 61.02 1.03 cm and 66.54 1.09 cm, respectively. Prediction of body weight based on heart girth alone or combined with other body measurements had higher coefficient of determination (R2) and lower residual mean square (MSE). Conclusion: It could be concluded that the prediction model for body weight of Malabari female adult goats with independent variable heart girth can be better fit. [Vet. World 2012; 5(7.000): 409-411]

Parasitic infections are known to affect donkeys causing considerable mortality and morbidity. Fresh faecal samples were directly collected from the rectum of donkeys belonging to four age groups viz. < 0.5 years, 0.5 � 3.0 years, 3.1 �8.0 years and > 8 years. Donkeys of different age groups in the present study had slightly different helminth profiles. Young donkeys (

A deep understanding of retinal images is used to identify vascular diseases, such as Diabetic Retinopathy (DR) in individuals who experience high blood sugar levels and high blood pressure. DR is a progressive disease that starts from minute red saccular out pouches on blood vessels known as Micro-Aneurysm (MA). DR can be cured by eradicating MA on the retina. Detecting microaneurysms (MAs) in retinal digital images is a challenging task due to various factors. These factors include the diverse sizes, shapes, levels of noise, and contrasts exhibited by the images found in the publicly available datasets for Diabetic Retinopathy (DR). Moreover, the limited number of labelled examples in these datasets and the inherent difficulty faced by deep learning algorithms in accurately identifying small objects in retinal digital images further contribute to the complexity involved in MA detection. Here proposing a Deep Learning based MA detection using modified ResNet-50 with a Support Vector Machine. The suggested approach was training, tuning, and evaluation, both qualitatively and quantitatively, using publicly available datasets like E-ophthaMA and DIARETDB1. The suggested approach demonstrates improved outcomes in terms of time efficiency and resource utilisation. [ABSTRACT FROM AUTHOR]

Amidst the rise of the new digital economy, combating financial fraud has become a critical challenge, requiring numerous collaborative efforts to detect fraudulent transactions in real-time. However, any collaborative solution needs to take privacy considerations into account while sharing sensitive data, which is a crucial hindrance to obtain optimal performance. This paper presents an in-depth analysis of the benefits of utilizing a 3-tiered Federated Learning architecture with DPFedAvg aggregation strategy as a collaborative alternative means to tackle financial fraud, allowing institutions to collaborate while maintaining privacy and further improving security. Additionally, we assess the effectiveness of this approach in detecting fraudulent transactions, comparing its performance to traditional methodologies. Our findings reveal a notable performance improvement using the Federated Learning model, surpassing conventional models. [ABSTRACT FROM AUTHOR]

Comfort is a universally understood concept of physical and mental well-being. Maximizing comfort is of paramount importance to everyone be it at home or in a workspace. Smart Buildings offers the technology to facilitate a greater enhancement of comfort in every occupied space. This research delves into the realm of intelligent climate control within smart buildings by leveraging new machine-learning techniques. This research proposes a holistic approach to climate control by incorporating federated learning, to learn the user’s preferences across many buildings. The federated learning framework ensures that data privacy is maintained at all times. A global neural network regression model has been created that has trained on a wide variety of input data. The study contributes to the evolving field of smart building technologies, offering a sustainable and efficient solution for enhanced living and climate control. [ABSTRACT FROM AUTHOR]

BACKGROUND A retrosternal goiter is an enlarged thyroid which extends below clavicle and sternum. Retrostemal goitre can be defined as thyroid which extends below manubrium but some include only goitres that extends more than 50% or 3 cm below thoracic inlet. Because they are expansions or extensions of multinodular goitres based on the superior or inferior thyroid vasculature, the majority of intrathoracic or substernal goitres are referred to be secondary." They spread outward into the mediastinum anterior. AIM To find out the differences for the need for cervical approach or if it mandates a median sternotomy based on various factors. MATERIAL AND METHODS The present retrospective comparative study was conducted among 31 patients of Retrosternal goitre admitted in PSGIMSR and Hospital over the past 10 years from period 2013-2023. Patient details, history, relevant investigations, operating procedure, postoperative complications and duration of hospital stay were collected. The following factors were examined in these patients' medical records: clinical symptoms, prior thyroidectomy history, surgical technique (only cervical or cervical with median sternotomy), thyroid histology results, thyroid weight, and complications following surgery. RESULTS Of the thirty-one patients, twenty-six (83.87%) had thyroidectomies by cervical incisions, while five (16.13%) required median sternotomies. Mean operative time (min) and hospital stay (days) among subjects who underwent surgery through cervical and thoracic approach was 214.81, 7.92 and 321.92, 15.83 respectively. Hence mean operative time (min) and hospital stay (days) was significantly more in thoracic as compared to cervical approach as p<0.05. Most common postoperative complications among the study subjects was transient hypocalcemia followed by postoperative bleeding. CONCLUSION Due to the low surgical morbidity, high incidence of compression-related symptoms, lack of effective medicinal treatment, and possibility of cancer, the existence of an RSG is an indication for surgery. Although surgically removing a retrosternal goitre is a difficult procedure, it can usually be done safely using a cervical approach. The rate of complications associated with this procedure is slightly higher than that of a cervical goitre thyroidectomy, particularly in regards to hypoparathyroidism and bleeding after surgery. [ABSTRACT FROM AUTHOR]

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work. [ABSTRACT FROM AUTHOR]