Neural rendering methods represent a crucial and challenging task in computer vision. Recent advancements in 3D Gaussian Splatting have set new benchmarks in rendering quality and speed by combining the strengths of geometric primitives and volumetric representations. In contrast to the point-based approach of 3D Gaussian Splatting, Scaffold-GS obtains anchors to achieve significant memory reduction. In this paper, we use Scaffold-GS as the baseline to achieve high quality rendering with minimal anchors. We propose a depth guide structured 3D Gaussian method for real-time rendering, called DS-GS. Our method employs depth priors to guide two types of anchor growth, ensuring that anchors develop in the correct positions during training. And we implement a differentiable depth rasterizer to enhance the similarity between rendered depth and estimated depth. Additionally, we use multi-scale training to improve rendering quality. We demonstrate the advantages and potential of our approach across a variety of scenarios, effectively reducing anchor redundancy and improving rendering quality. For intance, in the Mip-NeRF360 dataset, we increased the PSNR by 0.6 and reduced the storage size by 30%. Furthermore, our approach shows robustness in experiments with real-world scenes.

The migration of health workers remains one of the most pressing challenges facing many countries in the global South. This short communication seeks to reignite debate on the effectiveness of the 2010 WHO Global Code of Practice as a tool for managing the migration of health workers from the South. While the WHO Code of Practice was effective in reducing the migration of health workers from countries such as Zimbabwe during its first five years of its implementation, demand for health workers in the UK after Brexit and the COVID-19 pandemic has accelerated the rate of migration of health workers from countries facing critical shortages. Clearly, new solutions are needed that strike a balance between the right of the health workers in the South to migrate and the right of citizens in the region to a stable supply of health workers.

The growing recognition of the importance of microbial communities in shaping plant health and ecosystem function underscores the potential value of metagenomic analysis for plant conservation. We analyzed the bacterial diversity present in the leaves of ebony, a vulnerable species endemic to Sulawesi, Indonesia and one of the most expensive woods worldwide. The bacterial diversity was compared between symptomatic and asymptomatic leaves from two developmental categories, young plants (pole) and mature plants (tree). The microbial communities in asymptomatic and symptomatic leaves of D. celebica were analyzed using the 16S rRNA metagenomics to identify differences associated with the fitness of D. celebica. The results showed that the contributing factors to bacterial diversity in asymptomatic and symptomatic leaves were the plant’s developmental stage, environmental conditions, and the severity of pathogen attacks. Abundant bacteria at the phylum level across samples were Proteobacteria, Actinobacteria, and Firmicutes, whereas at the genus level were Sphingomonas, Jatrophihabitants, Bacillus, Methylobacterium, and Methylocella. At the genus level, a higher abundance of potentially beneficial bacteria was more evident in symptomatic leaves than their asymptomatic counterparts, specifically during the pole phase (young plant) than the tree phase (mature plant). This suggests that plants, particularly the young ones, activate defence mechanisms to ward off pathogen invasion by recruiting antipathogenic bacteria to fortify their less developed defence systems than mature plants. This research offers valuable insights into the potential roles of bacteria in D. celebica fitness.

This research introduces a novel approach, fusing together insights from philosophy, sociology, psychology, and linguistics. It ventures into the realm of how specific theoretical perspectives shaped the public speaking strategies of Joe Biden, Donald Trump, Robert F. Kennedy Jr., and Kamala Harris in the 2024 presidential campaign [Due to Joe Biden withdrawing from the election, this research needs to include a new Democratic candidate for analysis: Kamala Harris, Vice President and Democratic candidate]. The study uncovers pivotal themes, narratives, and theoretical influences in the candidates’ speeches and public statements, unveiling the underlying patterns and strategies of their rhetoric. This analysis employs a meticulous content analysis and readability measures, including the Flesch Formula and the Lexical Diversity Coefficient, to grasp the influence of these theoretical frameworks on political views. Word clouds will vividly depict the prominent themes and terms used by the candidates, party representatives, and U.S. bloggers, including ones from the Former Soviet Union (FSU). The anticipated outcomes of this research provide a comprehensive understanding of how integrated theoretical perspectives shape public speaking strategies and voter engagement. They offer valuable insights into the efficacy of these rhetorical approaches and deepen understanding of political polarization. Upholding ethical standards, including the Goldwater Rule, ensures the integrity and reliability of the findings. The practical implications of this study illuminate the role of theoretical perspectives in shaping public speaking strategies and voter engagement.

Abstract Background: The incidence of small intestine neuroendocrine neoplasms (SI-NEN) has increased significantly, posing challenges in early diagnosis and effective management due to non-specific symptoms and complex tumor biology, especially in predicting distant metastasis (DM). Methods: This retrospective study analyzed 3,157 patients diagnosed with SI-NEN from 2005 to 2015 using the Surveillance, Epidemiology, and End Results (SEER) database. We employed multifactorial logistic regression to identify independent risk factors for DM and developed several machine learning models to predict its occurrence. These included a suite of nine key models: XGBoost, Logistic Regression, LightGBM, Random Forest, Complement Naive Bayes, Multi-Layer Perceptron Classifier, Decision Tree, Gradient Boosting Decision Tree, and Support Vector Machine, all validated through k-fold cross-validation and hyperparameter optimization. Furthermore, we extended our analysis to survival studies to identify prognostic factors that may significantly influence patient outcomes. Results: Logistic regression demonstrated the highest efficacy, achieving an AUC of 0.774 in the training set and 0.747 in the validation set, values which are considered high, indicating superior performance in detecting early DM. Additionally, a machine learning-enhanced clinical nomogram was constructed, incorporating individual patient characteristics for personalized treatment planning. Survival analysis identified key prognostic indicators, and the resulting prognostic nomogram displayed high predictive accuracy, validated through calibration curves and decision curve analysis. Conclusion: The study underscores the utility of advanced predictive models in enhancing the diagnostic and prognostic assessment of SI-NEN, suggesting a framework for future clinical application and continuous improvement of these models. The developed predictive and prognostic nomograms provide crucial tools for clinical decision-making, potentially improving overall survival and quality of life by facilitating personalized treatment strategies based on detailed risk profiles. Keywords: SI-NEN, distant metastasis, machine learning, logistic regression, prognostic nomogram, survival analysis, SEER database.

Background/Objective: Compared to most of the South Asian nations Sri Lanka enjoys commendable health outcomes. The Sri Lankan health system is a combination of both private and public sectors. The number of private health institutions have grown rapidly in recent years. The aim of this study was to examine pricing/fees variations across private healthcare sector in Sri Lanka. Method: This study explored whether there are pricing/fees variations in hospitals and clinician’s , across ten of the most commonly performed surgical/medical procedures within eight leading private hospitals distributed across three provinces in Sri Lanka. Results: Findings revealed that the pricing/fees variations were statistically significant (P <.001) across all eight hospitals and between three provinces, for ten procedures tested for both hospital fees and total fees. Pricing variations of the clinician’s fees were statistically significant (P <.001) across all ten procedures, except for one. Conclusion: Unilateral price/fees fixing by private healthcare providers and the subsequent information asymmetry could be the main reason for the significant pricing/fees variations that exist between hospitals within and outside provinces/regions in hospital and clinician’s fees. Policy makers can consider bench marking the mean values of the prices/fees charged by the clinicians against the national per capita income of the population to refine policy parameters on pricing.

Throughout the last two decades, complex systems methodologies have gained an increasing importance in both neuroscience and the cognitive sciences. Researchers in these fields are increasingly interested in characterizing patterns of interactions between neurophysiological and bodily processes occurring simultaneously at different spatiotemporal scales, and how these interactions constitute psychological and behavioral phenomena in humans. In the contemporary philosophy of mind, there exist two general approaches to answering this fundamental question: First, mechanistic frameworks, which conceptualize cognitive systems as mechanisms, composed of functionally-individuated components whose functions are narrowly defined by their ranges of possible inputs and outputs in relation to other component states. Second, dynamicist frameworks, which conceptualize cognitive systems as assemblies of smaller-scale processes continuously shaping each others’ dynamics through interactions extended in time – a process which is, in turn, constrained by the dynamics of the larger-scale system they constitute. In this paper, I argue that dynamicist frameworks provide a superior philosophical toolkit for neuroscientists interested in conceptual questions about causal explanations in neural systems. I do so by showcasing an example in which the causal contribution of a neural system (a neuronal population) to a behavior is better explained within a dynamicist, rather than a mechanist framework of causal explanation. I then argue that general characteristics of such dynamicist causal explanations are applicable to other neural and cognitive systems as well, pointing out various shortcomings of mechanistic approaches in this area.

The impact of nitrogen (N) and phosphorus (P) on the physiological and biochemical processes crucial for tree seedling growth is substantial. Although the study of plant hydraulic traits in response to N and P is growing, comprehensive research on their combined effects remains limited. Malus sieversii, a key ancestral species of modern apples and a dominant species in Xinjiang’s Tianshan wild fruit forest, is witnessing a decline due to climate change, pests and diseases, compounded by challenges in seedling regeneration. Addressing this, a four-year study was conducted to determine the optimal fertilization method for it. The experiment explored varying levels of N (N10, N20, N40) and P (P2, P4, P8), and their combined effects (N20Px: N20P2, N20P4, N20P8; NxP4: N10P4, N20P4, N40P4), assessing their impact on gas exchange, hydraulic traits, and the interplay among functional traits in Tianshan Mountains’ M. sieversii seedlings. Our study revealed that all nitrogen treatments enhanced gas exchange, while phosphorus addition negatively impacted it. N10 significantly increasing leaf hydraulic conductivity. All phosphorus-inclusive fertilizers adversely affected hydraulic conductivity. P8, N20P4 and N20P8 notably increased seedlings’ vulnerability to embolism. Seedlings can adaptively adjust multiple functional traits in response to nutrient changes. The research suggests N10 and N20 as the most effective fertilization treatments for M. sieversii seedlings in this region, while fertilization involving phosphorus is less suitable. This study contributes valuable insights into the specific nutrient needs of it, vital for conservation and cultivation efforts in the Tianshan region.

Microcurrent therapy (MCT) is an emerging field within medicine. Our goal was to explore MCT effect on autism spectrum disorder (ASD) patients using a pilot study. 21 pediatric patients and 1 adult with ASD received an average of 32 MCT sessions using a standardized protocol. The Autism Treatment Evaluation Checklist (ATEC) pre- and post-MCT assessed symptom severity. We compared pre- and post-MCT ATEC scores; compared MCT ATEC scored with aged-matched historical controls, and determined dose response. There were no serious side effects, and the therapy was well tolerated. 22 patients completed 32 sessions on average. In paired t-testing, MCT produced a statistically significant average decrease of 28.6 (42.8%) on ATEC (p = 0.007; 95% CI = 8.3-48.9). In unpaired t-testing comparing MCT with age-matched historical controls, the treatment group average improvement was 26.4 (42.6%), compared to aged-matched controls of 7.9 (13.2%) (p=0.0001; 95% CI 19.9 - 47.5). Linear regression showed a strong direct relationship between the number of microcurrent sessions and magnitude of improvement R = 0.693; F = 18.5; P = 0.0003; CI: 95%. Initial evidence showed MCT can reduce ASD symptoms.

A document by Nebyu Yonas Shanka. Click on the document to view its contents.

We describe a workshop which prompts chemistry students in the final two years of secondary school to apply their understanding of modern analytical chemistry techniques to a ‘real world’ example. The scenario used is that of a forensic science laboratory that has been asked to determine the structure of an illicit compound, Revisomed (methamphetamine) being sold as a revision aid, and seized by police. Over the course of an hour, the students use a combination of infrared (IR) spectroscopy, liquid chromatography (LC), high resolution mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy data to determine the structure of Revisomed from first principles. The bulk of the workshop is spent reviewing MS data and using m/z, the isotope pattern, elemental composition and product ion data to reach three plausible isomeric structures for Revisomed which are then distinguished by NMR spectroscopy. More broadly, the workshop focusses on the use of the scientific method and the concept that ‘no ideas are bad’ when exploring hypotheses. We describe the structure of the workshop, and our experience delivering it to a local academy over the last 9 years.

Salinity, which induces changes in the soil microbial function and metabolic pathways, has important implications for soil carbon cycling. We carried out 13CO2 labelling experiments and metagenomic sequencing analysis for soil samples from Zhalong, Momoge, Niuxintaobao, and Xianghai sites in Songnen Plain. We found that the CO2 fixation rate of soil microbe in Songnen Plain wetlands ranged from 39.78 to 147.22 mg C m-2 day-1 in the natural salinity condition. Soil salinity increasing 1% and 2% decreased the microbial CO2 fixation amount. Elevated soil salinity increased soil microbial diversity (Chao 1 index) and altered its composition. Soil carbon fixing microbial abundances (Sphingomonadaceae and Lysobacter) decreased with salinity increase and were positively correlated with soil microbial CO2 fixing amount. The abundances of salt resistant microbes (Rhodohalobacter, Nitriliruptor, and Halomonas) increased with soil salinity increase, but were negatively correlated with soil microbial CO2 fixing amount. The rTCA cycle, 3-HP cycle, DC/4-HB and Calvin cycles were the predominant soil carbon fixing pathways under the natural salinity condition. Soil microbial abundances involved in rTCA cycle in Xianghai, Niuxintaobao wetlands and 3-HP, DC/4-HB cycles in Zhalong, Momoge wetlands decreased with salinity increase. 3-HP cycle was the predominant soil carbon fixing pathway in the 1% salinity addition treatment. rTCA cycle and Calvin cycle were the predominant soil carbon fixing pathways in the 2% salinity addition treatment. These findings contribute to a holistic view of soil microbial communities and carbon fixation functions in response to soil salinity, and provide new insights into carbon sequestration and carbon management in wetlands.

Quantifying trade-offs within populations is important in life-history theory. However, most studies focusing on life-history trade-offs focus on two traits and assume trade-offs to be static. Our work provides a framework for understanding covariation among multiple traits and how population density influences the traits. Using detailed individual-based data for Soay sheep, we find density strongly shapes life-history trade-offs and distribution of lifetime reproductive success (LRS). At low density, a trade-off between juvenile survival and growth structures life-history variation whereas at equilibrium density (K), trade-off between reproduction and juvenile survival is the major structuring axes. Contrary to Lomnicki’s prediction, we find the distribution of LRS is highly constrained at K, with mothers of adult sizes contributing the most to reproduction. Our results offer insights into how high density limits diversity of individual life-histories, advance an understanding of dynamic nature of trade-offs and have implications for evolution via density-dependent selection.

Objective To investigate the association between Body Mass Index (BMI) and the odds of reaching the active phase of induced labour. Design Cohort study. Setting Delivery sites in the Central Denmark Region from 2013 to 2022. Population Pregnant women with a singleton foetus in cephalic presentation induced at 37 weeks of gestation or beyond. We excluded women with spontaneous pre-labour rupture of membranes, uterine scar or intrauterine foetal demise. Methods Information was retrieved from the electronic patient record. We used logistic regression analyses adjusting for potential confounders and modelled BMI as categorical and continuous variable using restricted cubic spline analysis. Exposure was defined according to the World Health Organization BMI groups. Main Outcome Measure Reaching active phase of labour. Results Of the 22,114 women in the cohort, the proportion reaching the active phase of labour was 98%, 97%, 96%, 95% and 95%, for each BMI group. Compared to normal weight, the adjusted odds ratios (aOR) and 95% confidence intervals (CI) were aOR 0.6 (95% CI 0.5, 0.8) for overweight, aOR 0.5 (95% CI 0.3, 0.7) for obesity class I, aOR 0.4 (95% CI 0.3, 0.5) for obesity class II and aOR 0.4 (95% CI 0.2, 0.7) for obesity class III. A similar pattern was found when using restricted cubic splines. Conclusion In singleton pregnant women induced at term, higher BMI was associated with lower odds of reaching the active phase of labour. Regardless of BMI, 95% to 98% of all women in the cohort reached the active phase of labour, and 80% to 90% gave birth vaginally.

Invasive pests threaten food security and devastate ecosystems. A universal problem in the management of such species is that small populations can easily evade detection. This makes identifying new incursions challenging and also complicates efforts to eradicate or contain established populations. If invasive populations exhibited strong Allee effects, such that small populations tend towards extinction, then many of the issues associated with detecting small populations could be avoided; these populations would go extinct without intervention. Of course, invasive species rarely exhibit a strong Allee effect, but new genetic technologies mean that it is conceivable to impose one. Here we consider whether genetic Allee effects could be used to control invasive species. We examine the simple situation of founder establishment to test this idea. We use numerical and individual-based modelling to examine the fate of founder populations sampled from a larger core population containing a genetic load. Analysis of the effect of various load and population parameters reveal that a genetic load can dramatically reduce the establishment probability of small populations, across a wide range of parameter space. A sterile effect is substantially more effective than a lethal effect, but X-linkage has minimal benefit over an autosomal load. Founder population extinction is readily seen with less than one hundred loci when other load and population parameters are constrained within realistic ranges. Our findings suggest that driving a genetic load into a population may be a means of making that population less invasive and easier to contain and eradicate.

We are grateful to the authors for sharing the results of this very precise and detailed analysis of the diagnostic performance of apical myocardial perfusion by combining high-frequency linear probe and contrast enhanced ultrasound (CEUS) for the detection of left anterior descending artery (LAD) stenosis. there are many imaging modalities to assess coronary artery stenosis. For example, invasive coronary angiography, coronary computed tomography angiography (CCTA), myocardial nuclear perfusion imaging, cardiac magnetic resonance (CMR)[1], it’s crucial to choose the most appropriate imaging modality for diagnosis, treatment and procedural planning. In previous studies, the quantitative analysis of myocardial perfusion by CEUS were based on 17-segment model to assess the stenosis of the relevant vessels[2], it is relatively cumbersome to perform. Since most of the apical LV is supplied by LAD, the authors quantitative analysis of myocardial blood flow in the apical LV to evaluate the stenosis of the LAD vessels by combining high-frequency linear probe and CEUS, overcoming insufficient near-field resolution and artifacts by the conventional phased-array probe. The authors found that it is feasible and convenient to to assess apical perfusion to reflect LAD stenosis by combining high-frequency linear probe and CEUS with high Area under the curve of β, T, A, and MBF (0.880, 0.881, 0.761, and 0.880 respectively). And the best cut-off of β, T, A, and MBF were 10.32, 3.28, 9.39, and 4.99 respectively. What is more, compared with phased-array probe, the quantitative analysis of high-frequency linear probe is of high reproducibility and could get good curve fitting

Objective: To assess in vitro nebulized drug delivery during invasive and non-invasive ventilation models, comparing jet and vibrating mesh nebulizers. Aimed to compare differences in absolute inhaled dose, delivery rate and residual volume in various pediatric ventilation models with either face mask, mechanically ventilated, high flow nasal therapy or blow-by methods utilizing approved nebulizer locations. Methods: Compared drug delivery performance of a continuous output jet nebulizer (JN) with a vibrating mesh. The non-invasive model simulated a spontaneously breathing 9-month-old child using an anatomically correct model of upper airways and breathing simulator. The intubated model consisting of a mechanical ventilator with a heated humidifier in a pediatric breathing circuit and endotracheal tube. A JN (Aquineb), driven with 6 L·min -1 or a VMN (Aerogen Solo) driven with 2 L·min -1 supplemental oxygen were assessed by dose, delivery rate and residual volume. Drug dose was quantified using spectrophotometric analysis. Results: During normal spontaneous breathing, VMN dose was almost double that of JN ( P < 0.001), while delivery rate by VMN was also quicker, ( P < 0.001). Residual volume was significantly higher using JN ( P < 0.0001). During mechanical ventilation, VMN had a greater than 3-fold dose ( P < 0.0001), while the rate of delivery by VMN was also quicker ( P < 0.0001). Residual volume was also significantly greater using JN ( P < 0.001) during ventilation. During HFNT, aerosol delivery via nasal cannula was shown to be affected by gas flow rate for both VMN and JN, with again VMN delivering a greater dose over JN . Salbutamol delivery was also significantly greater using VMN for blow-by delivery. Conclusion: This study demonstrates significantly increased dose and rate of delivery, and significantly decreased residual volumes post-nebulization for airway deposition using a VMN compared to JN. Use of VMNs could improve drug delivery in pediatric populations, potentially altering the clinical course.

In the background of adversarial engagement between fixed-wing unmanned aerial vehicles of both enemies and friends, reliable prediction of the future posture of the enemy UAV is of great significance. However, the overconfidence problem of most existing point estimation predictive models makes their predictive results lack credibility. To solve this problem, this paper proposes an R-Bayesian neural network (R-BNN) predictive model based on gradient balance parameters, which finely adjusts the gradient balance between the error cost loss function and complexity cost loss function in the BNN loss function by adjusting the numerical size of the parameter, so as to effectively balance the trade-off between model complexity and prediction accuracy, enabling the network to achieve better performance. This model places probability distributions on network weights to capture the uncertainty of predictive results, and further improves the reliability of predictions by integrating the various predictive results of the model through Monte Carlo sampling during the network prediction phase. In the verification stage, the paper compares long short-term memory neural network (LSTM), MultiLayer Perceptron (MLP) and R-BNN algorithms under the same conditions. The experimental results show that the R-BNN predictive model performs better in diving and climbing maneuvers with larger motions and has significantly higher prediction accuracy with limited data than the comparative algorithms, and can provide reliable support for military operations.

In alpine ecosystems, greater overlap in flowering phenology among species at higher elevations could be due to evolutionary convergence among lineages or environmental filtering for taxa preadapted to colder conditions. We hypothesize that the flowering phenology of high alpine communities, subjected to colder and shorter reproductive seasons, is the result of convergence due to strong selective pressure imposed by the environment rather than environmental filtering for conservated traits. To test this hypothesis, we analyzed phylogenetic signal for first flowering date, peak flowering date, flowering duration and thermal sums to first and peak flowering and community phylogenetic structure considering Mean Nearest Taxon Distance (MNTD) and Mean Pairwise Distance (MPD) on four sites encompassing a total of 86 species derived from the subalpine and high alpine in the central Chilean Andes. After discarding possible richness effects on phylogenetic signal, the high alpine sites continued to show significant phylogenetic signal for a smaller number of floral traits than the subalpine sites. This was particularly evident for thermal sums. The two high elevation communities show significant values of SES(MNTD) but not for SES(MPD), indicating clustering related to the tips of the phylogeny. Overall, results suggest that environmental filtering for preadapted lineages is not the main driver of the phylogenetic structure and composition in high alpine communities. Rather, species at higher elevation have been subjected to greater environmental pressures leading to trait convergence. We conclude that phylogenetic conservatism in floral phenology has been overridden by the harsh environmental conditions in the high Andes. The high alpine environment can be seen as an evolutionary promoter rather than a gatekeeper of lineages preadapted to cold conditions.

The thermal tolerance of a species may be exceeded by the predicted temperature increases and thus contribute to population extinctions. However, the impact of temperature increases is thought to vary between climate regions, with latitude and by local climate, life history traits and inter- and intraspecies interactions. Here, we aim to establish the vulnerability of ectothermic insects to a warming climate by estimating the thermal buffer, the difference between critical thermal maximum (CTmax) and the maximum temperature of the warmest month in Ischnura heterosticta damselflies across a 2700 km cline. We measured CTmax along a latitudinal gradient of seventeen degrees from twenty-one populations along the eastern coast of Australia. Our results showed that damselflies inhabiting in tropical regions had higher CTmax than temperate damselflies and CTmax increased with increasing temperatures but not with decreasing latitudes as predicted. We further found that individuals with high parasite numbers had higher CTmax, while body size, body condition and sex had no impact on CTmax. Our projections showed that damselfly thermal buffer will be narrower in the tropics compared to temperate regions under a predicted 2.6°C degree annual mean temperature increase. Therefore, damselflies in the tropics are likely to be more vulnerable to climate change driven extinction even though they have a relatively higher CTmax.

Abstract: The current study was carried out in areas of the Darrang District of Assam, India’s North-Eastern state, to collect data on traditional plants used. The study region was chosen because of the varied groups that have sufficient knowledge about medicinal plants used by the indigenous people in the area. Residents and practitioners in the area were questioned about their knowledge of medicinal plants. According to the findings, 35 percent of the tribes in the Darrang district used medicinal herbs in their homes in a variety of ways, both for nutritional purposes and for traditional healing processes. Total 60 plant species belonging to 42 different families used as herbal remedies for the treatment of various diseases such as skin diseases, stomach problems, dysentery and diarrhea, wounds and cuts, insect or snake bites, boils, jaundice, fever and cough, and throat problem, etc. from the investigator result states that the major parts of the plant used were 46% Leaves, 1.4% flowers, 5.7% fruits, 11.5% seeds, 4.3% barks, 1.4% rhizomes, 1.4% stem, 1.4% roots, 2.8% whole plants, 5.7% aerial part, 2.8% flora body, 4.3% tender shoot, 1.4% leaves base, 4.3% oils, 1.4% nuts, 1.4% straw, 1.4% cloves and 1.4% latex. The most used plant parts for curing disease are leaves followed by seeds, fruits, stems, barks, rhizomes, roots, and flowers. Leaves are made into a paste, warmed and applied or made into juice mixed with water and administered it. Keyword: Darrang District, Medicinal Plants, North Eastern State, Traditionally used

A document by Benniamin A. Click on the document to view its contents.

Neuromorphic computing has attracted great attention for its massive parallelism and high energy efficiency. As the fundamental components of neuromorphic computing systems, artificial neurons play a key role in information processing. However, the development of artificial neurons that can simultaneously incorporate low hardware overhead, high reliability, high speed, and low energy consumption remains a challenge. To address this challenge, we propose and demonstrate a piezoelectric neuron with a simple circuit structure, consisting of a piezoelectric cantilever, a parallel capacitor, and a series resistor. It operates through the synergy between the converse piezoelectric effect and the capacitive charging/discharging. Thanks to this efficient and robust mechanism, the piezoelectric neuron not only implements critical leaky integrate-and-fire functions (including leaky integration, threshold-driven spiking, all-or-nothing response, refractory period, strength-modulated firing frequency, and spatiotemporal integration), but also demonstrates small cycle-to-cycle and device-to-device variations (~1.9% and ~10.0%, respectively), high endurance (1010), high speed (integration/firing: ~9.6/~0.4 μs), and low energy consumption (~13.4 nJ/spike). Furthermore, spiking neural networks based on piezoelectric neurons are constructed, showing capabilities to implement both supervised and unsupervised learning. This study therefore opens up a new way to develop high-performance artificial neurons by using piezoelectrics, which may facilitate the realization of advanced neuromorphic computing systems.

This paper studies the problem of obstacle avoidance and trajectory tracking for a class of uncertain nonlinear systems with unmeasured states and actuator faults. The main difficulty is that actuator faults may cause significant transient tracking errors, which might lead to collisions. To overcome this difficulty, an adaptive observer is developed to estimate system states and compensate for actuator faults. Additionally, the integral-multiplicative Barrier Lyapunov function (BLF) is integrated into the backstepping procedure to overcome the dynamics mismatching problem of the existing SUM-type BLF. The proposed adaptive scheme can avoid collisions in a multi-obstacle environment even if the actuator faults occur, and all the signals are uniformly ultimately bounded. Simulation results demonstrate the effectiveness of this approach.