Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agent Bayesian Optimization for Unknown Design Space Exploration

This paper proposes a multi-agent Bayesian optimization (MABO) framework as a reference model for rational design teams to study the effects of information exchange on a team’s search performance in finding global optimum of complex objective functions with many local optima. The core idea of the framework has three main steps. First, the design space is divided into regions based on the number of agents involved in the search. In each region, only one agent works on the part of the objective function. Second, a global-local communication strategy is developed to allow agents in local searches to share their sampled design points with a global evaluator. The global evaluator computes the posterior mean and variance based on all sampled points from local agents and evaluates the acquisition function (e.g., the expected improvement) to recommend the next sampling decisions for local agents. Third, when making the decision about where to sample next, each local agent only has access to the expected improvement evaluated in its local region and chooses the design that yields the largest value locally. To evaluate how the information exchange between agents and between local and global impact the search results, our framework is compared with a multi-agent model that does not allow information sharing and global-local interaction. Furthermore, we evaluated the performance of the model based on benchmark functions with varying complexities and also investigated the impact of the number of agents on search performance. We observe that when information sharing is allowed and global-local interaction is enabled in all scenarios, there is a significant improvement in convergence speed as well as the success rate of convergence.
The sweat rate as a digital biomarker in clinical medicine beyond sports science

Sweating is an important physiological reaction and a clinical symptom in a variety of diseases. However, sweating remains underrated in clinical use. Gold standards to measure the sweat rate are neither continuous, nor easily or lab-independently applicable. With the emergence of novel wearable devices, using sweat rate as a novel digital biomarker shows promise for clinical monitoring and diagnostics. In this Commentary, we discuss the potential and importance of the sweat rate as a digital biomarker beyond sport science.
Estimating Rates of Acceleration Based on the 157-Year Record of Sea Level from San Francisco, California, U.S.A.

ABSTRACT Breaker, L.C. and Ruzmaikin, A. , 2013. Estimating rates of acceleration based on the 157-year record of sea level from San Francisco, California, U.S.A. The question of the acceleration of global sea level rise has gained increasing attention because the present rate of sea level rise is relatively small in comparison to the rates that are predicted to occur in the near future. Recent measurements have come under scrutiny on how to correctly analyze and interpret regional estimates of acceleration. In this context, we employ the Ensemble Empirical Mode Decomposition (EEMD), a data-adaptive method developed for the analysis of nonstationary and nonlinear data to estimate acceleration over the 157-year record of sea level from San Francisco, California. We define sea level acceleration (SLA) as the mean of the second differences of the residual from an EEMD. Using the residual provides a means by which to reduce or eliminate the contaminating influence of decadal and longer-period oscillations in sea level that are folded into estimates obtained using the conventional approach. For the entire record, a value of +0.011 ± 0.003 mm/y2 was obtained for the acceleration and its uncertainty, compared with +0.013 mm/y2, using the conventional approach. The effect of record length is examined by estimating the accelerations for truncated versions of the record, one starting in 1900 and a second in 1925. The accelerations differed in each case from the conventional values, as expected, because the methods are based on different definitions of SLA.
Yield and quality of dry direct-seeded rice (Oryza sativa) as influenced by nitrogen and weed management practices in Eastern India

Combined use of FYM and LCC based N management gave the highest grain yield, hulling, milling, head rice recovery, crude protein, protein yield (kg/ha) as well as highest nitrogen, phosphorus and potassium in grain and straw. In case of weed management practices, the sequential application of pendimethalin (1.5 kg/ha) as pre-emergence fb brown manuring with Sesbania aculeata (knocking down Sesbania by bispyribac-Na 25 g/ha at 25 DAS) resulted in better control of diverse weeds. It also proved to be the best treatment in terms of yield, physical and chemical grain quality parameters.
A Remote Sensing Survey of the Role of Landform on the Organization of Orographic Precipitation in Central and Southern Mexico

Abstract Data from NASA’s TRMM satellite and NOAA’s GOES satellites were used to survey the orographic organization of cloud precipitation in central and southern Mexico during the monsoon with two main objectives: 1) to investigate large-scale forcing versus local landform controls, and 2) to compare the results with previous work in the Himalayas. At large scales, the modes of spatial variability of cloudiness were estimated using the empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Terrain modulation of synoptic-scale high-frequency variability (3–5- and 6–9-day cycles normally associated with the propagation of easterly waves) was found to cause higher dispersion in the EOF spectrum, with the first mode explaining less than 30% of the spatial variability in central and southern Mexico as opposed to 50% and higher in the Himalayas. A detailed analysis of the first three EOFs for 1999, an average La Nina year with above average rainfall, and for 2001, a weak La Nina year wit.
Common Ground Buck Type Five-Level Transformerless Inverter With Less Stress

The presented brief introduces a new switched capacitor-based transformer-less inverter (SC TLI). This SC TLI configuration is based on a reduced switch count employing only six switches, one of which is a bidirectional switch. It uses only one power source and two SCs to obtain a five-level output voltage. A level shift pulse width modulation (LS-PWM) based simple logic circuit controls SC TLI. It also handles capacitor charging and discharging during operation for each fundamental cycle. The SCs are self-balanced and do not require any specific controller. Further, detailed comparative studies are performed with existing arts of literature to determine the merits of the SC TLI. A prototype SC TLI model is made in the laboratory. The experimental results from the laboratory prototype validate the feasibility of the SC TLI. The voltage, current stress, and junction temperature across the power component are analyzed for the SC TLI design. Finally, this SC TLI performance characteristic is presented for different loading conditions.
Ion Channel Disturbances in Migraine Headache: Exploring the Potential Role of the Kynurenine System in the Context of the Trigeminovascular System

Migraine is a primary headache disorder, which is an enormous burden to the healthcare system. While some aspects of the pathomechanism of migraines remain unknown, the most accepted theory is that activation and sensitization of the trigeminovascular system are essential during migraine attacks. In recent decades, it has been suggested that ion channels may be important participants in the pathogenesis of migraine. Numerous ion channels are expressed in the peripheral and central nervous systems, including the trigeminovascular system, affecting neuron excitability, synaptic energy homeostasis, inflammatory signaling, and pain sensation. Dysfunction of ion channels could result in neuronal excitability and peripheral or central sensitization. This narrative review covers the current understanding of the biological mechanisms leading to activation and sensitization of the trigeminovascular pain pathway, with a focus on recent findings on ion channel activation and modulation. Furthermore, we focus on the kynurenine pathway since this system contains kynurenic acid, which is an endogenous glutamate receptor antagonist substance, and it has a role in migraine pathophysiology.
Analysis of Modeling and 2-D Resolution of Satellite–Missile Borne Bistatic Forward-Looking SAR

Bistatic synthetic aperture radar (SAR) has received increasing attention from researchers due to the advantage of achieving forward-looking imaging, which cannot be achieved by a monostatic SAR system. Most of the existing studies focus on airborne bistatic SAR imaging, while the research on satellite–missile borne bistatic SAR system is relatively scarce. In this article, the characteristics of the satellite–missile borne bistatic forward-looking SAR (SMBFSAR) system is mainly studied. First, the method of modeling an SMBFSAR system, including the establishment of the satellite orbit equation and coordinate system transformation, is described at length. Then, the 2-D resolution of the SMBFSAR system is redefined, and the relationship between the 2-D resolution and the configuration of SMBFSAR is discussed in detail. Finally, the analysis of 2-D resolution of SMBFSAR system is illustrated by the simulation results of a ship target in different configurations.
Numerical Simulation of Equal and Opposing Subcritical Flow Junctions

This study presents measured and computational results of a flow pattern at a junction with equal and opposing flows in the upstream channel that collide and turn 90° into the branch channel. The computational results are obtained using a two-dimensional, depth-averaged model with the k-e turbulent closure scheme. The aim is to predict the recirculation zones that form as the flow turns into the branch channel. The simulated depth and velocity profiles in the upstream main and the downstream branch channels are found to compare well with the measurements made in the physical model for various inlet Froude numbers and width ratios of the main channel to the branch channel. The absolute relative error between the measured and computed contraction coefficient, a measure of the recirculation zone size, is less than 4.7%. The computational model is then used to develop curves for the contraction coefficient for various inlet Froude numbers and ratios of main channel width to the branch channel width for design.
On the Effectiveness of Activation Noise in Both Training and Inference for Generative Classifiers

Neurons of the brain never produce the same output twice even when the same stimuli are presented; this is because of their internal noisy biological processes. Likewise, in this work, we study activation noise jointly during training, and inference, in a generative energy-based modeling setting, and observe that the joint activation noise yields noteworthy outcomes: while it is known that applying activation noise during training serves the purpose of regularization and data augmentation, activation noise is not quite well-understood during inference, and most importantly, the relationship between the former and the latter, when we adopt both at the same time, is yet unknown. In this work, therefore, we analyze the role of activation noise at inference time and demonstrate it to be utilizing sampling. Then, we prove that the best performance is achieved when the activation noise follows the same distribution both during training and inference. Leveraging the proof of the optimal joint activation noise during training and inference, we achieve significant improvement in performance (classification accuracy). To help understand this phenomenon, we provide theoretical results that illuminate the roles of activation noise during training, inference, and their mutual influence on performance. To further confirm our theoretical results (which is the necessity for the noise distributions during training and inference to match), we conduct extensive experiments on autoencoder architecture for five datasets and seven different distributions of activation noise. The implications of this finding span from the neuroscience of the brain to the design of applied deep learning systems.
Pixel Self-Attention Guided Real-Time Instance Segmentation for Group Raised Pigs

Simple Summary Research on real-time instance segmentation technology in the field of pigs is of great significance, as it can provide accurate pig behavior monitoring and management for the animal husbandry industry, and improve breeding efficiency and animal welfare. In this study, we developed a novel pixel self-attention (PSA) module by combining channel attention and spatial attention, which can integrate into the feature pyramid module of the YOLACT model. In addition, we visually analyzed prototype vectors within the YOLACT model, the attention maps obtained using the PSA attention module, and the model’s robustness across different shooting angles and times. The experimental results indicated that the proposed attention module can improve model performance and transferability. Our model provides a new research perspective for the pig instance segmentation task, can initially provide information on individual parts of pigs, and can provide a useful reference for subsequent pig tracking management. Abstract Instance segmentation is crucial to modern agriculture and the management of pig farms. In practical farming environments, challenges arise due to the mutual adhesion, occlusion, and dynamic changes in body posture among pigs, making accurate segmentation of multiple target pigs complex. To address these challenges, we conducted experiments using video data captured from varying angles and non-fixed lenses. We selected 45 pigs aged between 20 and 105 days from eight pens as research subjects. Among these, 1917 images were meticulously labeled, with 959 images designated for the training set, 192 for validation, and 766 for testing. To enhance feature utilization and address limitations in the fusion process between bottom-up and top-down feature maps within the feature pyramid network (FPN) module of the YOLACT model, we propose a pixel self-attention (PSA) module, incorporating joint channel and spatial attention. The PSA module seamlessly integrates into multiple stages of the FPN feature extraction within the YOLACT model. We utilized ResNet50 and ResNet101 as backbone networks and compared performance metrics, including AP0.5, AP0.75, AP0.5-0.95, and AR0.5-0.95, between the YOLACT model with the PSA module and YOLACT models equipped with BAM, CBAM, and SCSE attention modules. Experimental results indicated that the PSA attention module outperforms BAM, CBAM, and SCSE, regardless of the selected backbone network. In particular, when employing ResNet101 as the backbone network, integrating the PSA module yields a 2.7% improvement over no attention, 2.3% over BAM, 2.4% over CBAM, and 2.1% over SCSE across the AP0.5-0.95 metric. We visualized prototype masks within YOLACT to elucidate the model’s mechanism. Furthermore, we visualized the PSA attention to confirm its ability to capture valuable pig-related information. Additionally, we validated the transfer performance of our model on a top-down view dataset, affirming the robustness of the YOLACT model with the PSA module.
Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI

Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.
Knowledge Graphs Alignment Based on Learning to Rank Methods

: Knowledge graphs (KGs) define facts expressed as triples in representing knowledge. Usually, several knowledge graphs are published in a given domain. It is relevant to create alignments both for classes that model concepts and between instances of those classes defined in different knowledge graphs. In this article, we study techniques for aligning entities expressed in KGs. Our solution explores the supervised ranking aggregation method in the alignment based on similarity values. Our experiments rely on the dataset from the Ontology Alignment Evaluation Initiative to evaluate the proposed method in experimental analyzes. Obtained results indicate the effectiveness in our alignment technique in the investigated datasets.
marine drugs

: Microbial life present in the marine environment has to be able to adapt to rapidly changing and often extreme conditions. This makes these organisms a putative source of commercially interesting compounds since adaptation provides different biochemical routes from those found in their terrestrial counterparts. In this work, the goal was the identiﬁcation of a marine bacterium isolated from a sample taken at a shallow water hydrothermal vent and of its red product. Genomic, lipidomic, and biochemical approaches were used simultaneously, and the bacterium was identiﬁed as Serratia rubidaea . A high-throughput screening strategy was used to assess the best physico-chemical conditions permitting both cell growth and production of the red product. The fatty acid composition of the microbial cells was studied to assess adaptation at the lipid level under stressful conditions, whilst several state-of-the-art techniques, such as DSC, FTIR, NMR, and Ultra-High Resolution Qq-Time-of-Flight mass spectrometry, were used to characterize the structure of the pigment. We hypothesize that the pigment, which could be produced by the cells up to 62 ◦ C, is prodigiosin linked to an aliphatic compound that acts as an anchor to keep it close to the cells in the marine environment.
Active mobility and mental health: A scoping review towards a healthier world

Abstract Abstract Research has proven that engaging in active mobility (AM), namely walking and cycling for transportation, significantly enhances physical activity levels, leading to better physical health. It is still unclear whether AM could also offer any mental health benefits. This scoping review aims to provide a comprehensive understanding of the current knowledge on the relationship between AM and mental health, given its crucial role in public health. The authors searched online databases to isolate primary studies written in English involving an adult sample (16 or over). AM was the exposure factor. Many mental health elements were included as outcomes (depression, anxiety, self-esteem, self-efficacy, stress, psychological and subjective well-being, resilience, loneliness and social support, quality of life, mood, life satisfaction and sleep). The results were organised in a narrative summary per each outcome selected, graphical syntheses and an overview of gaps to be further examined. The authors identified a total of 55 papers as relevant. The results show inconsistency in study designs, definition and operationalisation of the variables, approach and methodologies used. A cross-sectional design was the dominant choice, primarily examining data from national public health surveys. Nonetheless, there has been improvement in outcomes of interests, initially mainly the quality of life and affect. Lately, authors have focused on a broader range of mental health-related factors (such as travel satisfaction). The experimental studies showed promising mental health improvements in those who used active modes more than those who used motorised vehicles. It creates a rationale for further research towards implementing a unified theoretical and methodological framework to study the link between AM and mental health. The ultimate goal is to generate solid conclusions that could support building societies and cities through public health promotion and sustainable strategies, like walking and cycling as a means of transport.
INFLUENCE OF COMPOST PARTICLE SIZE ON PATHOGEN SURVIVAL UNDER GREENHOUSE CONDITION

Animal waste directly applied to agricultural field is one of the possible contamination sources for fresh produce. Although manure-borne pathogens can be eliminated by proper composting process, pathogens are able to survive, recolonize and regrow on compost heap surfaces. Due to the outdoor nature of composting process, bioaerosols with different particle sizes can be generated on compost surfaces which can carry pathogens, travel via air and contaminate fresh produce field nearby. This study was to investigate the survival of Escherichia coli O157: H7 and Salmonella enterica Typhimurium in dairy compost with different particle sizes as affected by initial moisture content and seasonality under greenhouse conditions. The mixture of avirulent E. coli O157: H7 and S. Typhimurium strains were inoculated into the finished composts with initial moisture content of 20, 30 and 40%. Then, the finished compost samples were sieved into three portions with particle sizes of >1000, 500-1000 and <500 μm, and stored in greenhouse for 30 days. At selected intervals, compost samples were enumerated for the target pathogens. The moisture contents in compost samples dropped rapidly to under 10% within 5 days of storage followed by gradual decline till 30 days in all treatments. For compost with moisture contents of 20 and 30%, the average Salmonella reductions in compost with particle sizes of >1000, 500-1000 and <500 μm were 2.15, 2.27 and 2.47 log CFU g-
Generating pit-free canopy height models from airborne lidar

Canopy height models (CHMs) derived from lidar data have been applied to extract forest inventory parameters. However, variations in modeled height cause data pits, which form a challenging problem as they disrupt CHM smoothness, negatively affecting tree detection and subsequent biophysical measurements. These pits appear where laser beams penetrate deeply into a tree crown, hitting a lower branch or the ground before producing the first return. In this study, we develop a new algorithm that generates a pit-free CHM raster, by using subsets of the lidar points to close pits. The algorithm operates robustly on high-density lidar data as well as on a thinned lidar dataset. The evaluation involves detecting individual trees using the pit-free CHM and comparing the findings to those achieved by us ing a Gaussian smoothed CHM. The results show that our pit-free CHMs derived from high- and low-density lidar data significantly improve the accuracy of tree detection.