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Advancing sociotechnical systems theory: New principles for human-robot team design and development.
Full text linkThis paper reviews and adapts sociotechnical systems (STS) principles for the design and development of Human-Robot Teams (HRTs). Through a collaborative review process, the authors identify existing STS principles relevant to HRTs, suggest modifications, and introduce new ones to address the unique challenges of designing and developing human-robot teams. A framework of 34 STS principles grouped into seven themes is presented: Systems Design and Adaptation, Human-centered Approach, Integration and Optimization, Collaboration and Participation, Information and Communication, Organizational Alignment and Process Management, and Trust and Reliability. To address the dynamic nature of HRTs incorporating mutual understanding between humans and intelligent robots, eight new principles are introduced: Adaptive Autonomy, Agility and Responsiveness (future thinking), Cognitive Workload Management, Ethical Considerations, Transparency and Explainability, Collaborative Sensemaking, Trustworthiness and Unpredictability Management. This STS framework bridges traditional STS theory and AI-enhanced HRTs, guiding developers in creating effective, trustworthy, and ethical HRTs. The paper benefits researchers, developers, and organizations by addressing sociotechnical complexities and upholding a more balanced, ethical, and human-centered collaboration in HRT development
Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems
Full text linkBringing together the Internet of Things (IoT), LLMs, and Federated Learning (FL) offers exciting possibilities, creating a synergy to build smarter, privacy-preserving distributed systems. This review explores the merging of these technologies, particularly within edge computing environments. We examine current architectures and practical methods enabling this fusion, such as efficient low-rank adaptation (LoRA) for fine-tuning large models and memory-efficient Split Federated Learning (SFL) for collaborative edge training. However, this integration faces significant hurdles: the resource limitations of IoT devices, unreliable network communication, data heterogeneity, diverse security threats, fairness considerations, and regulatory demands. While other surveys cover pairwise combinations, this review distinctively analyzes the three-way synergy, highlighting how IoT, LLMs, and FL working in concert unlock capabilities unattainable otherwise. Our analysis compares various strategies proposed to tackle these issues (e.g., federated vs. centralized, SFL vs. standard FL, DP vs. cryptographic privacy), outlining their practical trade-offs. We showcase real-world progress and potential applications in domains like Industrial IoT and smart cities, considering both opportunities and limitations. Finally, this review identifies critical open questions and promising future research paths, including ultra-lightweight models, robust algorithms for heterogeneity, machine unlearning, standardized benchmarks, novel FL paradigms, and next-generation security. Addressing these areas is essential for responsibly harnessing this powerful technological blend
Time-Frequency Characteristics of Vehicle-Bridge Interaction System for Structural Damage Detection Using Multi-Synchrosqueezing Transform.
Full text linkStructural damage in bridges is typically a local phenomenon. When a vehicle passes over the damage location, it induces a local response, which is highly sensitive to the damage. The interaction between the bridge and moving vehicle is a non-stationary time-varying process. The local damage can be accurately identified by analyzing the time-varying characteristics of the bridge response subjected to a moving vehicle. Synchrosqueezing transform, a reassignment method used to sharpen time-frequency representations, offers an effective tool to decompose the non-stationary signal into distinct components. This paper proposes a novel method based on multi-synchrosqueenzing transform to extract the time-varying characteristics of the vehicle-bridge interaction systems for bridge structural health monitoring. A vehicle-bridge interaction model is built to simulate the bridge under moving vehicles. Different damage scenarios of concrete bridges have been simulated. The effect of bridge damage parameters, the vehicle speed, the road surface roughness on the time-varying characteristics of the vehicle-bridge interaction system is studied. Numerical and experimental results demonstrate that the proposed method efficiently and accurately extracts the time-varying features of the vehicle-bridge interaction system, which could serve as potential indicators of structural damage in bridges
Soil moisture mapping in Indian tropical islands with C-band SAR and artificial neural network models.
No full textThis study aims at analyzing the patterns of soil moisture in the South Andaman district using an integrated approach that incorporates Sentinel-1A C-band synthetic aperture radar (SAR) data and other auxiliary data from Sentinel-2A and Landsat 8. A total of 60 surface soil samples (0-10 cm) were collected from four predominant land uses for 2020-2022 years to represent real-time soil moisture status. Soil moisture index (SMI) is assessed based on thermal remote sensing data besides, normalized difference vegetation index (NDVI) from red and infrared bands, and dielectric constants (ε) from soil textural analysis. Artificial neural network (ANN) models were developed along with multiple linear regression (MLR) to retrieve the soil moisture accurately using input parameters such as backscatter coefficients (σ°: VV and VH), NDVI, SMI, and ε. The performance of modelled soil moisture is evaluated using different statistical index-based criteria concerning field-based volumetric soil moisture measurements (SMCv). It is found that positive correlation among (σ°: VV + VH) and (SMCv: %) for all land uses and high R2 values for barren and vegetable fields. The vegetation interferes the backscatter signal and misinterprets the soil moisture estimation solely with only SAR data. However, consideration of NDVI and SMI improves the soil moisture estimation in case of vegetation abundance land uses. The comparative results showed that ANN models surpass MLR models in soil moisture estimation with high R2 (0.67-0.99) and η (62.6-99.9) and low RMSE (0.05-2.19%) and MAE (0.03-1.74%) values. By providing essential baseline data for hydrological modeling, this study supports the design of efficient irrigation systems
Work-related musculoskeletal injuries among Australian osteopaths: A preliminary investigation
No full textBackground: Work-related musculoskeletal injury (WRMI) is a significant risk factor for registered manual therapists, including physiotherapists, occupational therapists and chiropractors. The physically demanding nature of manual therapy has been identified as the common factor in WRMIs among these professions. There is currently no available literature on the prevalence of WRMIs among osteopaths. Objective: This research sought to collect preliminary data to establish the prevalence and characteristics of WRMIs among Australian osteopaths; including body area injured, risk factors and strategies used to manage injury. Method: Registered osteopaths, who were members of the professional association Osteopathy Australia, were invited to participate via an online survey. Results: A total of 160 surveys were completed. The incidence of WRMI was high, with 58% of respondents having sustained one or more injuries. Results indicated that the wrist and the fingers are the most frequently injured areas (41%), while the least injured body part was the knee (1.1%). Performing repetitive tasks accounted for 52% of injuries, followed by performing manipulative techniques (23%). Working too many hours per week (43%) and fatigue (38%) were the main factors contributing to injury. Conclusions: The findings highlight the risk to osteopaths of sustaining musculoskeletal injuries while working in clinical practice
Colony morphotype variation in Burkholderia: implications for success of applications and therapeutics.
Full text linkThe Burkholderia genus includes both environmental and pathogenic isolates known for their phenotypic plasticity and adaptability. Burkholderia spp. are intrinsically resistant to many antibiotics, often requiring prolonged therapies during infection. A key feature of Burkholderia spp. is colony morphotype variation (CMV), which allows for rapid adaptation to environmental changes and influences virulence, antibiotic resistance, and pathogenicity by impacting the expression of key virulence factors such as lipopolysaccharides, extracellular DNA, efflux pumps, and flagella. While alternative treatments, such as vaccines and phage therapies, hold promise, CMV has the potential to undermine their efficacy by modifying essential therapeutic targets. Despite its importance, the prevalence and underlying mechanisms of CMV remain poorly understood, leaving critical gaps in our knowledge that may hinder the development of sustainable solutions for managing Burkholderia infections. Addressing these gaps is crucial not only for improving infection management but also for enabling the safe reuse of Burkholderia in biotechnology, where their plant growth-promoting and bioremediation properties are highly valuable. Our goal is to raise awareness within the scientific community about the significance of CMV in Burkholderia, highlighting the urgent need to uncover the mechanisms driving CMV. A deeper understanding of CMV's role in virulence and resistance is essential to developing robust, long-term therapeutic strategies
The green construction framework: a strategic pathway to emission reduction through technological innovations in the built environment
No full textAdvancements in thermal management of lithium-ion batteries: the role of nanofluids and phase change materials
Full text linkImagery rescripting for social anxiety disorder via internet videoconferencing: An open trial.
Full text linkImagery rescripting (ImR) has demonstrated efficacy in reducing symptoms of social anxiety disorder (SAD). However, there are many logistical and psychological barriers that prevent individuals with SAD from accessing treatment. The efficacy of remote treatment methodologies, such as internet videoconferencing, has recently been demonstrated across a range of mental disorders. However, the efficacy of videoconferencing-delivered ImR (vImR) has not yet been examined. The present study aims to examine the efficacy and acceptability of vImR for SAD in a multiple baseline trial utilising the waitlist control group from a larger randomised controlled trial (RCT). 35 participants (Mage = 37.86; SD = 12.90) received no intervention for 8-weeks, then received an 8-session manualised vImR treatment protocol. Within-group analyses indicated negligible effect sizes from baseline to pre-treatment (SIAS-6: d = 0.22; 95 % CI: 0.25 - 0.69; SPS-6: d = -0.03; 95 % CI: 0.49 - 0.44). Large effect sizes were found from pre-treatment to post-treatment (SIAS-6: d = 0.81; 95 % CI: 0.32-1.29; SPS-6: d = 0.80; 95 % CI: 0.30-1.27) and pre-treatment to 3-month follow-up (SIAS-6: d = 0.85; 95 % CI: 0.36-1.33; SPS-6: d = 0.90; 95 % CI: 0.40-1.38). At post-treatment, 66 % of participants no longer met criteria for SAD (74 % at 3-month follow-up). Benchmarking analyses indicated similar treatment effect sizes to in-person ImR for SAD. Participants rated the program as highly acceptable. The results indicate that the mechanisms of ImR appear to be transferable to vImR and therefore this may be a viable remote treatment option for individuals with SAD who do not respond to first-line treatments
Evaluating the Relationship Between Vegetation Status and Soil Moisture in Semi-Arid Woodlands, Central Australia, Using Daily Thermal, Vegetation Index, and Reflectance Data
Full text linkWet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands
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