MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings

Head detection in the indoor video is an essential component of building occupancy detection. While deep models have achieved remarkable progress in general object detection, they are not satisfying enough in complex indoor scenes. The indoor surveillance video often includes cluttered background objects, among which heads have small scales and diverse poses. In this paper, we propose Motion-aware Pseudo Siamese Network (MPSN), an end-to-end approach that leverages head motion information to guide the deep model to extract effective head features in indoor scenarios. By taking the pixel-wise difference of adjacent frames as the auxiliary input, MPSN effectively enhances human head motion information and removes the irrelevant objects in the background. Compared with prior methods, it achieves superior performance on the two indoor video datasets. Our experiments show that MPSN successfully suppresses static background objects and highlights the moving instances, especially human heads in indoor videos. We also compare different methods to capture head motion, which demonstrates the simplicity and flexibility of MPSN. Finally, to validate the robustness of MPSN, we conduct adversarial experiments with a mathematical solution of small perturbations for robust model selection. Code is available at https://github.com/pl-share/MPSN

Large Language Models in Mental Health Care: a Scoping Review

Objective: The growing use of large language models (LLMs) stimulates a need for a comprehensive review of their applications and outcomes in mental health care contexts. This scoping review aims to critically analyze the existing development and applications of LLMs in mental health care, highlighting their successes and identifying their challenges and limitations in these specialized fields. Materials and Methods: A broad literature search was conducted in November 2023 using six databases (PubMed, Web of Science, Google Scholar, arXiv, medRxiv, and PsyArXiv) following the 2020 version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 313 publications were initially identified, and after applying the study inclusion criteria, 34 publications were selected for the final review. Results: We identified diverse applications of LLMs in mental health care, including diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability and reliability, nuanced handling of mental states, and effective evaluation methods. Despite successes in accuracy and accessibility improvement, gaps in clinical applicability and ethical considerations were evident, pointing to the need for robust data, standardized evaluations, and interdisciplinary collaboration. Conclusion: LLMs show promising potential in advancing mental health care, with applications in diagnostics, and patient support. Continued advancements depend on collaborative, multidisciplinary efforts focused on framework enhancement, rigorous dataset development, technological refinement, and ethical integration to ensure the effective and safe application of LLMs in mental health care

Pregnancy length and health in giant pandas: what can metabolic and urinary endocrine markers unveil?

Mature female giant pandas usually ovulate once a year. This is followed by an obligatory luteal phase, consisting of a long-lasting corpus luteum dormancy phase (CLD; primary increase in progestogens) and a much shorter active luteal phase (AL; secondary increase in progestogens). Varying duration of both the dormant (embryonic diapause) and AL (post-embryo reactivation) phases has hampered unambiguous pregnancy length determination in giant pandas until today. Additionally, progestogen profiles have been considered not to differ between pregnant and pseudopregnant cycles. Only ceruloplasmin, 13,14-dihydro-15-keto-PGF2α (PGFM) and – more recently – estrogens have been assigned diagnostic power so far. Our study investigated the competence of metabolic (fecal output) and Urinary Specific Gravity (USpG)-normalized urinary endocrine (progestogens, PGFM, glucocorticoids (GCM) and ceruloplasmin) markers for pregnancy monitoring including defining the duration of the AL phase length. Research on 24 (6 pregnant, 8 pseudopregnant and 10 non-birth) cycles of 6 giant pandas revealed a fixed AL phase length of 42 days in giant pandas, e.g. representing 6 weeks of post- diapause development in case of pregnancy. Progestogen concentrations were significantly higher in pregnant cycles throughout the majority of the AL phase, with significant higher values during the AL phase in healthy twin compared to singleton pregnancies. GCM concentrations were also markedly higher in giant pandas expecting offspring, with a clear increase towards birth in the final 2 weeks of pregnancy. This increase in GCM was running in parallel with elevating estrogen and PGFM concentrations, and decreasing progestogens. In addition, during the AL phase, a more pronounced decrease in fecal output was obvious for pregnant females. The combined profiles of non-invasive metabolic and endocrine markers, the latter normalized based on USpG, showed a true pregnancy signature during the AL phase. The findings of this study are applicable to retrospective evaluations of non-birth cycles facilitating categorizing those into pseudopregnant or lost pregnancies, with USpG-normalization of the urinary endocrine markers as a prerequisite

Assessing the role of shared mobility services in reducing travel-related greenhouse gases (GHGs) emissions : focusing on America’s young adults

This study analyzes the relation between shared mobility services and greenhouse gases (GHGs) emissions by using a nationally representative sample of US young adults. We conduct a comprehensive analysis based on the data collected in the 2017 National Household Travel Survey (NHTS). These trip-level emissions are calculated following MOtor Vehicle Emission Simulators (MOVES) and Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET) workflows. We find that the weekday sample has a significantly higher value in daily GHGs emissions than the weekend sample. Controlling for other factors, shared micromobility services usage is found to have a significant impact on daily GHGs emissions for both weekday and weekend travel. Our analyses further indicate that carsharing complements public transit, and its users are more likely to reside in areas with better public transit supply. We find that the use of transportation network companies (TNCs) has a positive relationship with young adults' GHGs emissions on weekdays only. The study results and implications may be useful for planners and professionals interested in tracking the impacts of new mobility services on transportation and the relevant environmental outcomes

Genetic Structure and Differentiation of Endangered <i>Cycas</i> Species Indicate a Southward Migration Associated with Historical Cooling Events

Understanding the genetic structure and differentiation in endangered species is of significance in detecting their phylogenetic relationships and prioritizing conservation. Here we sampled five endangered Cycas species endemic to southwest China and genotyped genetic structure and differentiation among them using the genotyping-by-sequencing (GBS) method. C. hongheensis showed high genetic diversity, but the other four species showed low genetic diversity. The genetic diversity between wild and cultivated populations was similar for C. debaoensis and C. guizhouensis, respectively. Low genetic differentiation and high gene flow were found among C. debaoensis, C. guizhouensis, and C. fairylakea, and C. hongheensis differentiated from them at ~1.74 Mya. TreeMix results showed historic migration events from C. guizhouensis to C. hongheensis, showing southward migration pathways. C. hongheensis showed increased effective population size with time, while the other four species underwent bottleneck events at ~1–5 Mya when continuous cooling events occurred. Our results indicate that the migration, differentiation, and speciation of Cycas species are associated with historical cooling events

Exploring year-to-year changes in station-based bike sharing commuter behaviors with smart card data

Station-based bike sharing (SBBS) not only provides commuters with direct "door-to-door" trips, but also plays a vital role in addressing the "first/last mile" challenges for public transportation system. However, there is a lack of research into portraying year-to-year changes in SBBS commuter behaviors. With five-year (from 2016 to 2020) SBBS smart card data collected in Nanjing, China, a longitudinal analysis is performed in this study to trace yearly dynamics of commuter behaviors at an individual level. We identify two sorts of SBBS commuters (i.e., SBBS-alone and SBBS-metro commuters) based on users' spatial-temporal travel regularities. The paper finds that (i) the number of SBBS users presented a considerable fluctuation trend over a five-year span, while the pro-portion of SBBS commuters stabilized at an equilibrium level; (ii) the COVID-19 outbreak accelerated the decline in the proportion of female and young SBBS commuters; (iii) most SBBS commuters were recorded for only one year out of five, while the share of commuters who used SBBS for four years or more is tiny, <5%; (iv) the trip duration of SBBS-alone commuters was significantly longer than that of SBBS-metro commuters, and both showed some increase during the COVID-19 pandemic; (v) the number of non-loop trip chains was dramatically higher than that of loop trip chains, which is more prominent among SBBS-metro commuters. Our findings could provide valuable insights into the behavioral dynamics of SBBS commuters and offer recommendations on how policy makers and transportation planners could respond to these precipitate changes

The Seepage and Stability Performance Assessment of a New Drainage System to Increase the Height of a Tailings Dam

Effective methods for extending the storage capacity of tailings for a mining company include expanding and increasing the height of the tailings dam. However, this change could lead to an uplift in the phreatic line and a decrease in the slope stability. In this paper, a new drainage system combining a horizontal drainage pipe with an upward bending slotted pipe was proposed and applied to the design of a seepage-proof system for the Xigou tailings dam with an increased height. To accurately simulate the performance of the seepage control system, a three-dimensional finite element model was established on the basis of a geological investigation of the site conditions. In this work, a substructure technique was used to model the drainage pipe with a small radius and dense spacing to reduce the difficulty in mesh generation, and a back-analysis method called MPSO-BP (modified particle swarm optimization algorithm and a back propagation neural network) was used to correct the measured permeability coefficients. The results show that the new drainage system can effectively dissipate the seepage pressure, decrease the phreatic surface, and improve the safety factors of the slope stability. The proposed drainage system can also meet the seepage stability requirements of the higher tailings dam. Additionally, this system can be widely deployed in similar projects