Miracle-dz/UniFAR-FTUs huggingface dataset:
Life Course Trajectories and Wealth Accumulation in the United States: Comparing Late Baby Boomers and Early Millennials

Millennials are often assumed to be economically worse off than previous generations because of more precarious employment and unstable family lives. Using sequence analysis and unconditional quantile decomposition, we analyze the work and family trajectories of late baby boomers and early millennials and relate them to wealth holdings at age 35. We find that the poorest millennials have less wealth than their baby boomer counterparts, but the wealthiest millennials have more. Millennials are less likely to enter high-status occupations and are more likely to work in low-skilled service jobs, and family trajectories show a strong decline of traditional early marriage and parenthood; however, changes in life course trajectories cannot account for the increase in wealth inequality. Instead, the distribution of wealth has become more unequal because the economic returns to typical middle-class trajectories have increased, while the returns to typical working-class trajectories have stagnated or declined.
Identification of High-Yielding Genotypes of Barley in the Warm Regions of Iran

One of the most important effects of climatic changes is increasing temperatures and expanding water deficit stress in tropical and subtropical regions. As the fourth most important cereal crop, barley (Hordeum vulgare L.) is crucial for food and feed security, as well as for a sustainable agricultural system. The present study investigates 56 promising barley genotypes, along with four local varieties (Norooz, Oxin, Golchin, and Negin) in four locations to identify high-yielding and adapted genotypes in the warm climate of Iran. Genotypes were tested in an alpha lattice design with six blocks, which were repeated three times. Traits measured were the number of days to heading and maturity, plant height, thousand kernels weight, and grain yield. A combined analysis of variance showed the significant effects of genotypes (G), environments (E), and their interaction (GEI) on all measured traits. Application of the additive main-effect and multiplicative interaction (AMMI) model to the grain yield data showed that GEI was divided into three significant components (IPCAs), and each accounted for 50.93%, 30.60%, and 18.47%, respectively. Two selection indices [Smith–Hazel (SH) and multiple trait selection index (MTSI)] identified G18, G24, G29, and G57 as desirable genotypes at the four test locations. Using several BLUP-based indices, such as the harmonic mean of genotypic values (HMGV), the relative performance of genotypic values (RPGV), and the harmonic mean of the relative performance of genotypic values (HMRPGV), genotypes G6, G11, G22, G24, G29, G38, G52, and G57 were identified as superior genotypes. The application of GGE analysis identified G6, G24, G29, G52, and G57 as the high-yielding and most stable genotypes. Considering all statistical models, genotypes G24, G29, and G57 can be used, as they are well-adapted to the test locations in warm regions of Iran.
The Histomorphologic Profile of Skin Diseases in Kuwait

Background Although dermatological disorders are common in all countries, their spectrum varies greatly, with a wide histological variation. This study aimed to investigate the frequency and spectrum of different histopathological patterns of skin lesions in relation to age and gender in Kuwait. Methodology This was a retrospective descriptive study. Skin biopsy samples collected over a five-year period from 2018 to 2022 at the dermatopathology department of a tertiary dermatology center in Kuwait were included in this study. The distribution of lesions according to age and gender was analyzed. Results Of the 1,796 skin tissues reviewed, the ages ranged from one month to 93 years, with a mean age of 38.9 years. A female predominance was noted, with a female-to-male ratio of 1.8:1. Most patients belonged to the 30-39-year age group. The most frequent diagnostic categories were neoplasms and papulosquamous diseases. The five most common diseases were psoriasis, lichen planus, mycosis fungoides, benign melanocytic nevus, and epidermal inclusion cysts. The most commonly encountered diseases were similarly distributed according to gender (p > 0.05). Conclusions Neoplasms and papulosquamous lesions dominated this investigation. Therefore, understanding the genetic and environmental factors that contribute to psoriasis is crucial for developing effective treatment strategies and comprehensively managing the condition. Additionally, the community should be educated to prevent repeated unprotected ultraviolet light sun exposure and early diagnosis of any suspicious lesions to reduce the prevalence of neoplastic skin diseases. Histopathological research on cutaneous lesions is rare, with none reported from Kuwait. Our histopathology-based retrospective analysis provides a baseline for population-specific skin disease studies.
Exploring the Impact of Potassium on Growth, Photosynthetic Performance, and Nutritional Status of Lemon Trees (cv. Adamopoulou) Grafted onto Sour Orange and Volkamer Lemon Rootstocks

(1) Background: This study investigates the effects of potassium (K) and rootstock on the growth, photosynthetic activity, and mineral nutrition of lemon trees; (2) Methods: Lemon trees (cv. Adamopoulou) grafted onto sour orange (Citrus aurantium) (SO) or Volkamer lemon (Citrus volkameriana) (VL) rootstock were cultivated hydroponically under 0.00, 0.75, 1.50, 3.00, and 6.00 mM K. Plant growth and nutrition parameters, as well as leaf photosynthetic rate, stomatal conductance, transpiration rate, intracellular CO2, chlorophyll, and carotenoid concentration were assessed; (3) Results: Under K deficiency (0 mM K), plants exhibited chlorotic and necrotic symptoms, more pronounced in older leaves. Potassium deficiency adversely affected various physiological processes in lemon leaves, including a decrease in photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, water use efficiency, CO2 utilization efficiency, chlorophyll a/b ratio, and carotenoid concentrations (some effects were rootstock-dependent). Low photosynthetic rates under K deficiency were due to both stomatal- and non-stomatal limitations. Elevated K in the nutrient solution consistently reduced the total plant uptake of P, Ca, Mg, B, Mn, and Zn, resulting in nutrient imbalances, as evidenced by the significant decrease in P, Ca, Mg, and Zn concentrations found in scion tissues (especially at the 6 mM K treatment). Rootstock-dependent responses were also observed in scion leaf and stem growth and in mineral nutrient concentrations, uptake, and distribution across plant parts; (4) Conclusions: Our study reveals interesting aspects on how to optimize K fertilization in lemon trees in the context of sustainable agriculture by considering nutrient interactions and rootstock-dependent effects. Understanding these complex interactions and improving K fertilization practices is expected to improve lemon tree performance, yield, and fruit quality.
Spatial and temporal variability in webs of Metepeira gressa (Keyserling, 1892) (Araneae, Araneidea): a year field study

Webs of females and juveniles were analyzed monthly during one annual cycle in a population of Metepeira gressa (Keyserling 1892) in Punta Espinillo (Montevideo, Uruguay) in field conditions, with temperature and humidity registered. Five stages of development were found. Size, distribution and population changes during a year were analyzed. The egg-sacs was also registered. Territorial tolerance in the distance among webs was observed. The annual individual distribution showed the family pattern, being stenochronus species according Schaefer (1987). The buildings of spiders in the same stage did not show seasonal changes on the year. The webs of females versus juveniles showed statistical differences in their sizes. Webs of females showed constant asymmetry, a presence of retreat and a bigger angle of inclination to vertical line, respect the juvenile webs. Also the female webs was put in the vegetation higher than the webs of juveniles in a vertical stratum. A strong association between the webs and the plants
Purchase decision in terms of content marketing and e-WOM on social media

This study examined how Generation Z consumers decide whether or not to buy skincare products based on Marketing Content and Electronic Word-of-Mouth (e-WOM) on Instagram. The study was conducted quantitatively, employing descriptive and verification research techniques. The study employed analytical techniques such as multiple linear regression analysis to discover that, while e-WOM had no influence on purchase decisions, marketing content had a moderate influence. However, a simultaneous examination of marketing content and electronic word-of-mouth revealed a significant influence on purchasing decisions. As a result, when viewing skincare products online, potential customers' perceptions of content marketing and e-WOM would influence their decision-making process, potentially leading to a purchase decision.
Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications

Abstract The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model’s interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.
Silent Data Corruption in Robot Operating System: A Case for End-to-End System-Level Fault Analysis Using Autonomous UAVs

Safety and resiliency are essential components of autonomous vehicles. In this research, we introduce ROSFI, the first robot operating system (ROS) resilience analysis methodology, to assess the effect of silent data corruption (SDC) on mission metrics. We use unmanned aerial vehicles (UAVs) as a case study to demonstrate that system-level parameters, such as flight time and success rate, are necessary for accurately measuring system resilience. We demonstrate that downstream ROS tasks such as planning and control are more susceptible to SDCs than the visual perception stage in the perception–planning–control (PPC) compute pipeline. This observation only becomes apparent when we consider the complete end-to-end system-level pipeline, as opposed to isolated compute kernels, as previous work does. To enhance the safety and robustness of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection techniques are validated in numerous simulated environments. We demonstrate that the autoencoder-based technique can recover up to all failure cases in our studied scenarios with a computational overhead of no more than 0.0062%. Finally, our open-source methodology can be utilized to comprehensively test the robustness of other ROS-based applications. It is available for public download at https://github.com/harvard-edge/MAVBench/tree/mavfi.
STATE OF THE CALIFORNIA CURRENT 2009-2010: REGIONAL VARIATION PERSISTS THROUGH TRANSITION FROM LA NIÑA TO EL NIÑO (AND BACK?)

This report summarizes observations of the California Current System (CCS) from Baja California, Mexico to Oregon for the period from spring 2009 through spring 2010. During this period, changes in the state of the CCS reflected a transition from cool La Nina conditions into and through a short-lived, relatively weak El Nino event. Weaker than normal upwelling and several extended relaxation events contributed to warming over much of the CCS during summer 2009, especially in the north. Moderation of La Nina conditions in the CCS coincided with the development of El Nino conditions in the equatorial Pacific, yet manifested well in advance of any evidence for direct effects of El Nino on the CCS. Responses to El Nino in fall 2009 and winter 2009–2010 appear to have varied substantially with latitude: conditions off southern California returned to near climatological values with the decline of La Nina,
KNOWLEDGE EXTENT OF FARMERS ABOUT CHICKPEA PRODUCTION TECHNOLOGY

This study was undertaken on 100 respondents (Chickpea growers) selected from 4 villages of Asoha block of Unnao district. The maximum percentages of the respondents were found having medium level of knowledge, which the respondents were distributed accordingly to the knowledge categories viz. low, medium and high. Out of 12 agricultural practices of chickpea production, knowledge about field preparation was ranked at first (92%) followed by seed rate (90.00%) and harvest and post harvest (83.71%) ranked at second and third respectively. The poor extent of knowledge was reported for the practices viz. insects and pests control (28.22%), seed treatment (24.83%) and disease control (12.88%). The overall extent of knowledge was found to be 52.86 per cent. The variables like caste, education, housing pattern, annual income, house hold materials, transportation materials, communication media possession, overall materials possession, extension contact with information sources, economic motivation, risk orientation, adoption, occupation, social participation and scientific orientation had significant and positive correlation ship with extent of knowledge; means that the value of these variables increases, the knowledge extent is also increased.
THE FLUX AND PARTICLE SIZE DISTRIBUTION OF SEDIMENT COLLECTED IN HILLSLOPE TRAPS AFTER A COLORADO WILDFIRE

Deborah A. Martin, Hydrologist, U.S. Geological Survey, Boulder, Colorado; John A. Moody , Hydrologist, U.S. Geological Survey, Lakewood, Colorado 1 3215 Marine Street, Suite E-127, Boulder, Colorado, 80303-1066, (303) 541-3024, fax (303) 447-2505, damartin@usgs.gov, 2 Box 25046, Denver Federal Center, Mail Stop 413, Lakewood, Colorado, 80225-0046, (303) 236-0606, fax (303) 236-5034, jamoody@usgs.gov
MetaGraspNetV2: All-in-One Dataset Enabling Fast and Reliable Robotic Bin Picking via Object Relationship Reasoning and Dexterous Grasping

—Grasping unknown objects in unstructured environments is one of the most challenging and demanding tasks for robotic bin picking systems. Developing a holistic approach is crucial to building such dexterous bin picking systems to meet practical requirements on speed, cost and reliability. Proposed datasets so far focus only on challenging sub-problems and are therefore limited in their ability to leverage the complementary relationship between individual tasks. In this paper, we tackle this holistic data challenge and design MetaGraspNetV2, an all-in-one bin picking dataset consisting of (i) a photo-realistic dataset with over 296k images, which has been created through physics-based metaverse synthesis; and (ii) a real-world test dataset with 3.2k images featuring task-specific difficulty levels. Both datasets provide full annotations for amodal panoptic segmentation, object relationship detection, occlusion reasoning, 6-DoF pose estimation, and grasp detection for a parallel-jaw as well as a vacuum gripper. Extensive experiments demonstrate that our dataset outperforms state-of-the-art datasets in object detection, instance segmentation, amodal detection, parallel-jaw grasping, and vacuum grasping. Furthermore, leveraging the potential of our data for building holistic perception systems, we propose a single-shot-multi-pick (SSMP) grasping policy for scene understanding accelerated fast picking in high clutter. SSMP reasons about suitable manipulation orders for blindly picking multiple items given a single image acquisition. Physical robot experiments demonstrate that SSMP effectively speeds up cycle times through reducing image acquisitions by more than 47% while providing better grasp performance compared to state-of-the-art bin picking methods.
The Comparison of the Selected Parameters of Brain Injury and Interleukins in the CSF in Patients Diagnosed De Novo with RRMS Compared to the Control Group

Background: Multiple sclerosis (MS) is a chronic autoimmune disorder affecting the central nervous system (CNS). Due to the different phenotypes of the disease and non-specific symptoms of MS, there is a great need for a validated panel of biomarkers to facilitate the diagnosis, predict disease progression, and evaluate treatment outcomes. Methods: We determined the levels of the parameters of brain injury (NF-H, GPAF, S100B, and UCHL1) and the selected cytokines in the cerebrospinal fluid (CSF) in 101 patients diagnosed de novo with RRMS and 75 healthy controls. All determinations were made using the Bio-Plex method. Results: We found higher levels of NF-H and GFAP in the relapsing-remitting multiple sclerosis (RRMS) group compared to the controls. The concentrations of both molecules were significantly increased in patients with Gd+ lesions on brain MRI. The level of S100B did not differ significantly between the groups. UCHL1 concentrations were higher in the control group. We found some correlations between the selected cytokines, the levels of the parameters of brain injury, and the time from the first symptoms to the diagnosis of MS. Conclusions: The role of the above molecules in MS is promising. However, further research is warranted to define their precise functions.
Online Learning from Evolving Feature Spaces with Deep Variational Models

—In this paper, we explore a novel online learning setting, where the online learners are presented with “doubly-streaming” data. Namely, the data instances constantly streaming in are described by feature spaces that over-time evolve, with new features emerging and old features fading away. The main challenge of this problem lies in the fact that the newly emerging features are described by very few samples, resulting in weak learners that tend to make error predictions. A seemingly plausible idea to overcome the challenge is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional feature spaces that entail very complex feature interplay. Specifically. a tradeoff between onlineness , which biases shallow learners, and expressiveness , which requires deep models, is inevitable. Motivated by this, we propose a novel paradigm, named Online Learning Deep models from Data of Double Streams (OLD 3 S), where a shared latent subspace is discovered to summarize information from the old and new feature spaces, building an intermediate feature mapping relationship. A key trait of OLD 3 S is to treat the model capacity as a learnable semantics, aiming to yield optimal model depth and parameters jointly in accordance with the complexity and non-linearity of the input data streams in an online fashion. To ablate its efficacy and applicability, two variants of OLD 3 S are proposed namely, OLD-Linear that learns the relationship by a linear function; and OLD-FD learns that two consecutive feature spaces pre-and-post evolution with fixed deep depth. Besides, instead of re-starting the entire learning process from scratch, OLD 3 S learns multiple newly emerging feature spaces in a lifelong manner, retaining the knowledge from the learned and vanished feature space to enjoy a jump-start of the new features’ learning process. Both theoretical analysis and empirical studies substantiate the viability and effectiveness of our proposed approach. The code is available online at github.com/X1aoLian/OLD3S-L
RADON CONCENTRATION IN GROUNDWATER OF AREAS OF HIGH BACKGROUND RADIATION LEVEL IN SOUTHWESTERN NIGERIA

For over two decades, groundwater has been the dominant source of drinking water in Nigeria. Despite the reported radiological risk associated with the exposure to radon and its progenies, the level of awareness of the presence of this carcinogenic radioisotope in drinking water in Nigeria is low. Water from underground sources is consumed and used for other various household activities without prior treatment, thereby may serve as a radiation source of concern if the concentration of radon in such water is high. A total of 112 samples of groundwater in areas of elevated background radiation level in some cities and towns in southwestern Nigeria were collected and assayed for the radon ( 222 Rn) concentration. The measurement was carried out using RAD7 (DURRIDGE Company Inc., USA). The result revealed a steady trend of variation in the concentration of 222 Rn in water samples from different sources. Highest concentration was found in water from borehole sources. The concentration of radon in all the water samples were found to be above the maximum contaminant level (MCL) presented by the Standards Organisation of Nigeria for radionuclide concentration in drinking water in Nigeria. The 222 Rn concentration from all the water from borehole sources were found to be higher than 11.1 Bq.L -1 stated by the United States Environmental Protection Agency as the MCL in states in the USA without radon monitoring policy and enhanced indoor air program. Irrespective of the sources, 62.8% of the samples collected had radon concentration above the 11.1 Bq.L -1 MCL, while 2.8% of the samples had a concentration above 100Bq.L -1 , a MCL recommended by European Union for measurement that warrants consideration of possible remedial actions. None of the measurements was up to 1000Bq.L -1 recommended by European Union as upper bound value above which remedial action is definitely required.