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Ultra-reliable low-latency communication (URLLC) is a fundamental technology that plays a crucial role in enabling fifth-generation new radio (5G-NR) communication. URLLC aims to provide a highly reliable connection with strict block error probability requirements and extremely low latency for mission-critical and remote operations. Meanwhile, the advent of sixth-generation (6G) communication, marked by its novel, immersive, and high-stakes control applications, imposes notably more stringent demands on reliability and latency, alongside the added requirements of high data rates, scalability, precision, security, and real-time operation. This scenario introduces unparalleled challenges for both system architecture and the solutions it entails. Several previously proposed solutions, such as retransmission schemes, error correction techniques, and grant-free access, have been insufficient for emerging requirements, as most of these solutions primarily facilitate either low latency or high reliability, but not both. Latency and reliability are conflicting objectives of URLLC. Therefore, an in-depth understanding of the associated issues and careful mitigation of these challenges are essential. This article provides an extensive review of 5G URLLC, emphasizing its technical evolution from 3GPP Release 15 through 19, while also detailing its inherent shortcomings and the potential solutions required for 6G and beyond. We investigate the prerequisites and enabling technologies necessary for URLLC services, exploring related issues across various network components, including frame structure, propagation, processing, retransmission, scheduling, fading, and interference. An important discussion is provided on the fundamental trade-off between latency and reliability, particularly due to retransmission mechanisms. Furthermore, we examine the practical limitations of 5G URLLC when coexisting with other 5G application use cases, such as enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC). Finally, we discuss the future trajectory of URLLC in 6G, identifying key research challenges and opportunities to meet the escalating demands of future mission-critical applications.

Robust information on the spatial distribution of global carbon fluxes is required to project the future trajectory of carbon-climate feedback effects and atmospheric CO2 concentrations. Estimates of the latitudinal partitioning of carbon fluxes from top-down atmospheric CO2 inverse models currently diverge widely, because of methodological limitations or systematic biases in models or observations. We use airborne CO2 observations from the NASA Atmospheric Tomography Mission to evaluate and refine inverse model estimates from the Orbiting Carbon Observatory version 10 Model Intercomparison Project of total CO2 exchange for the two-year period of June 2016-May 2018. Applying emergent concentration-flux relationships as constraints reduces zonal total flux uncertainties by 46 to 56% relative to the full v10 MIP ensemble and by 17 to 28% relative to the subset excluding satellite observations over ocean. Subtracting independent estimates of fossil-fuel emissions and air-sea gas exchange results in residual land fluxes with a large northern extratropical sink, a small southern extratropical sink, and a small tropical source. The airborne-derived tropical land source disagrees with a large tropical land sink from process-based terrestrial models combined with estimates of land use emissions and river fluxes, representing an important challenge for our understanding of the global carbon cycle. The large implied northern extratropical sink can be explained either by underestimated land uptake by process models or a combination of process model bias and overestimated fossil fuel emissions.

The INSPIRE project, funded by the European Marie Skłodowska-Curie Action (MSCA) program, was a doctoral network designed to train early-stage researchers (ESRs) and stimulate exploratory research in cardiovascular safety pharmacology (SP). From 2020 until 2024, INSPIRE trained 15 doctoral candidates hosted by a consortium of academic and industrial partners. The current publication summarizes the outcomes of INSPIRE and reflects on the impact to the field. As part of INSPIRE, ESRs were engaged in individual research projects covering a range of topics, yet grounded in 4 thematic clusters to facilitate collaboration. The first cluster focused on human induced pluripotent stem cells-derived cardiomyocyte (hiPSC-CM) assays and explored additional readouts such as morphological profiling, miRNAs panels and artificial intelligence assisted analyses. The second cluster aimed to develop a new telemetry platform with integrated positioning system, but technical drawbacks required initiation of alternative plans still aligned with the mission to refine in vivo SP studies. A third cluster explored new concepts for hemodynamic assessment, such as arterial stiffness and in silico modelling. Finally, the fourth cluster involved mechanistic research in cardio-oncology. Some ESR projects, especially those hosted by industry partners, delivered tangible prototype products or services with potential for commercialization. Alternative outcomes included optimized experimental workflows, datasets with reference compounds and mechanistic insights. Overall, INSPIRE delivered on its mission to initiate explorative research in SP, although many results will require further development, standardization and establishment of their context of use. This highlights the difficulty of achieving regulatory relevance within PhD timelines. For the latter, sustained engagement from industry beyond the duration of the doctoral program seems essential. Educationally, 8 ESRs have successfully defended their PhD thesis and a few ESRs have transitioned into permanent roles in drug R&D. Mobility of ESRs was a crucial, mandatory element of the training resulting in 23 secondments. The close interaction with industrial partners significantly enhanced trainee awareness of regulatory thinking, experimental robustness, and translational constraints. A yearly summer school was organized to facilitate mutual learning and build group cohesion. Meanwhile, the INSPIRE summer school has become a legacy with close to 100 alumni and offers a mix of theoretical lectures and broader sessions on career development. Looking ahead, ongoing revisions of international SP guidance (ICH S7A) and increasing interest in new approach methodologies (NAMs) create opportunities for a next-generation training network. In this context, we propose a roadmap for initiating INSPIRE v2.0 and have launched a call for input and participation to help shaping the future of SP.

Quality improvement (QI) is widely used in global health to improve patient outcomes, reduce costs, and strengthen service delivery. The Texas Children's Global Health Network (TCGHN) includes nine independent non-governmental organizations supporting healthcare in low- and middle-income countries (LMICs), with pediatric HIV clinical centers of excellence in six countries in sub-Saharan Africa (SSA), supported technically by Baylor College of Medicine. We describe the development of a virtual QI Community of Practice (QICoP) to connect geographically dispersed teams and strengthen local QI capacity. In 2022, QI and global health experts convened to design the QICoP and assess site readiness. Participants were recruited from the sites based on their interest. Meetings were held via Zoom, with attendance, evaluations, and organizer notes tracked. QI tools were used to identify site strengths, challenges, and strategies to improve engagement. From January 2023 to September 2024, the QICoP held 15 sessions, including 3 abstract-writing workshops, averaging 35 participants per session. QI abstract submissions to the annual Network meeting doubled from 2023 to 2024. Across 15 sessions, 83% of participants reported positive experiences. Based on participant feedback and QI sessions from the 2022-2024 Network meetings, we developed a blended QI basics curriculum, recruited site champions to improve communication, and launched a WhatsApp platform to enhance engagement. A virtual QICoP may be a feasible model to support professional development, increase knowledge and idea sharing, and connect individuals across geographies over a shared mission to improve healthcare quality in LMICs.

This study investigates long-term air quality variability in Dhaka, Bangladesh, across pre-lockdown, lockdown, and post-lockdown phases of the COVID-19 pandemic (2017-2023). Unlike prior pollutant-specific or satellite-based analysis, this work integrates ground-based pollutant records with meteorological datasets to extricate anthropogenic influences from climatic variability. A methodological framework combining meteorological adjustment with multiple linear regression (MLR) modeling was applied to evaluate the relative effects of traffic activity and atmospheric conditions on major urban pollutants. The lockdown period provided a unique natural experiment, revealing the sensitivity of urban air quality to reduced human activity, while post-lockdown rebounds underscored the persistence of structural emission sources. The lockdown period was associated with substantial reductions in primary pollutants, whereas ozone exhibited an increasing trend linked to altered atmospheric chemistry. Seasonal analysis identified winter as the most polluted period due to unfavorable dispersion conditions and elevated combustion-related emissions. Regression results demonstrated that meteorological variables, particularly temperature, exerted strong control over particulate matter variability, while traffic activity remained strongly associated with nitrogen dioxide (NO 2 ) concentrations. Overall, the study advances regression-based urban air quality assessment by explicitly incorporating meteorological adjustment and provides a scientific basis for data-driven and meteorologically informed air quality management in rapidly urbanizing megacities.

Significant physiatric workforce disparities exist across the United States (US), linked to regional presence of academic Physical Medicine and Rehabilitation (PM&R) departments. This analysis provides an updated catalogue of PM&R departments and divisions at US medical schools to appraise growth trends, geographic distribution, and naming variability. Findings reveal regional differences. The Northeast has the highest concentration of PM&R academic entities, with 74.1% of medical schools having a PM&R affiliation, compared to only 32.1% in the South, 38.5% in the West, and 54.2% in the Central region. Likewise, since 2018, PM&R's academic growth has failed to match the pace of new medical school creation in all regions but the Northeast, despite creation of new PM&R departments or divisions in all but one region. Additionally, PM&R departmental and divisional names were noted to be variable. While most entities were named "Physical Medicine and Rehabilitation," others employed names such as "Rehabilitation Medicine," "Rehabilitation and Regenerative Medicine," and "Rehabilitation and Human Performance." Overall, persistent geographic disparities in academic physiatry are seen with PM&R's growth failing to match the pace of medical school growth, likely propagating workforce disparities. It is unclear if the name variability undermines the mission, public perception, or academic value of PM&R.

Unmanned aerial vehicles (UAVs) are widely deployed in public and civic areas, especially in environments where human presence is restricted due to safety concerns. UAVs play a vital role in maximizing resource utilization and minimizing operational redundancy in applications such as disaster recovery, infrastructure inspection, and surveillance. However, the conventional task-allocation frameworks often suffer from suboptimal resource utilization and limited adaptability in dynamic operational environments. To resolve these problems, this paper presents a hybrid opposition based Hippopotamus Optimization (HOA-OBL) algorithm framework for efficient UAV task allocation optimization. Particularly, Hippopotamus Optimization Algorithm (HOA) is one of the widely known population-based algorithms, motivated by the inherent behavior of hippopotamuses, but it has a limitation of local optima in certain iterations. To enhance the exploration, the Opposition-Based Learning (OBL) is integrated with HOA to explore the search space to prevent it from converging too early to local optima. We performed simulation experiments in dynamic environments with 20 UAVs and 100 tasks. The experimental results imply that the proposed framework achieves reductions of about 17% to 33% in energy consumption and performance enhancements of about 33% to 50% in load balancing when compared with existing models such as HOA, GA, PSO, and GWO. The results demonstrate that the proposed framework provides a more balanced and scalable approach to task assignment to UAVs, thereby improving efficiency and consistency in real-world UAV missions.

Experimental validation delivers five quantified outcomes. First, optical pheromone detection achieves 88.7% ± 0.6% accuracy (n = 150, 95% CI), and the dual-modality combined channel achieves 86.1% ± 0.9% (n = 200), with robustness confirmed under 50/60 Hz flicker interference, rapid 200-1200 lux light transitions (485 ms settling), and reflective glare spots. Second, the MQ-135 chemical channel calibration holds R2 ≥ 0.999 across temperatures of 15-35 °C and humidity of 30-90%, with maximum voltage drift of 0.093 V at the highest temperature. Third, 3.2× CNN inference speedup through 8-bit quantisation runs at 15 FPS within 1.8 W. Fourth, peripheral subsystems draw a measured mean of 1.19 W ± 0.02 W (n = 60, 95% CI); the complete per-robot system, including the Jetson Orin Nano compute rail, draws 6.15 W ± 0.09 W, enabling six-hour missions from the 55.08 Wh battery. Fifth, localisation across ten trials yields the mean position error 0.074 m and RMSE 0.081 m with 97.5% map coverage; physical multi-robot tests with 5-8 robots confirm map convergence times of 120-210 steps with collision rates below 0.042 per robot per step. To the best of our knowledge, no prior physical swarm platform has simultaneously demonstrated this combination of capabilities under comparable constraints.

The increasing use of cloud computing in hospitals, telemedicine, the Internet of Medical Things (IoMT) and real-time patient monitoring has made for an increasing trend of artificial intelligence-driven cloud security in hospitals. The growing reliance on distributed healthcare clouds layers the cyber-attack surface, however, with critical clinical operations now at risk from ransomware attacks, insider threats, API exploitation, and advanced persistent attacks. This research study introduces a novel AI-integrated cloud security framework tailored for safeguarding mission critical applications in the healthcare sector featuring an intelligent threat detection component, a probabilistic risk evaluation system, and an adaptive response orchestration system. The proposed architecture is built-in by using telemetry normalization, probabilistic behaviour modelling, deep autoencoders for anomaly detection, Bayesian approach for threat probability estimation, multi-objective risk scoring and reinforcement learning for adaptive mitigation. An experimental validation was performed with the CICIDS2017 dataset including around two million samples of network traffic data across various attack categories. The experimental results show that excellent performances have been achieved with an accuracy of 0.96, precision of 0.95, recall of 0.94, F1score of 0.95 and AUC of 0.98 with low latency of around 26 ms and reduced false positive rate of 0.03. A comparative analysis against the current cloud security methods also confirms the effectiveness of the proposed framework in delivering better operational security, response time and the availability of clinical services, providing the continuous clinical service that healthcare organizations require. The research showcases how incorporating AI with responsive cloud security mechanisms can offer a scalable and resilient defense against today's healthcare cloud infrastructures.

Long-duration human spaceflight will require medical systems capable of managing illness and injury without rapid evacuation or real-time assistance from Earth. Microgravity physiology, engineering limits, and communication delays reduce the feasibility of conventional surgery and favor imaging-based, minimally invasive approaches. Expeditionary interventional radiology can be defined as a practice model emphasizing image-guided, minimally invasive procedures delivered with compact equipment by small, cross-trained teams in resource-constrained environments. Research shows that astronauts and other non-specialists can obtain diagnostic-quality ultrasound images in microgravity, and analog studies demonstrate that individuals with little experience can learn key ultrasound-guided tasks after focused instruction. These findings support the feasibility of image-guided drainage, decompression, and vascular access as candidate strategies for managing acute conditions encountered during exploration missions. Remaining challenges include procedural ergonomics, equipment design, sterility, fluid containment, and development of autonomous guidance tools. This narrative review outlines a streamlined approach for adapting interventional radiology to spaceflight and highlights research needs for achieving procedural autonomy beyond Earth.

Large-Scale Combat Operations (LSCOs) often involve severe sleep loss. To date, there has been little work on the extent to which anxiety is impacted by these conditions, and the rate at which recovery sleep reverses these effects. The aim of this study was to assess variations in anxiety across two consecutive nights of total sleep deprivation (TSD) and following one night of recovery sleep. Twenty-one healthy adults ranging from 18-33&#x2009;years of age participated. Self-reported state anxiety was assessed using the Spielberger State Trait Anxiety Inventory (STAI-S). The STAI-S was administered at 1000, 1300, and 1600 over four days: 1) Baseline: day following one night of 8&#x2009;hours time-in-bed (TIB), 2) TSD1: day following one night of TSD, 3) TSD2: day following two nights of TSD, 4) Recovery: day following 12&#x2009;hours TIB after 62&#x2009;hours of TSD. There was a significant main effect of Day, f(3,240)&#x2009;=&#x2009;17.64, p&#x2009;<&#x2009;.0001. Post-hoc tests revealed that anxiety was significantly increased after one night of TSD compared to baseline and this further increased after two nights of TSD (p&#x2009;<&#x2009;.01). Anxiety level was restored to baseline following a single night of recovery sleep. These results demonstrate the adverse impact sleep loss has on state anxiety. Additionally, they also suggest that a single 12-hour TIB period is sufficient to reverse the adverse effect of acute, severe sleep deprivation on state anxiety - a finding that has implications for determining appropriate "recycle rates" (duration between missions) for Soldiers and others involved in continuous operations.

Contract management and quoting processes are mission-critical operations in modern enterprises, yet they remain prone to inefficiencies, compliance risks, and human error-particularly in IT and SaaS sectors where contract complexity is high. This paper introduces the Artificial Intelligence Contract Risk and Intelligence Model (AICRIM), a simulation-validated, prototype-level framework that integrates generative AI (GPT-4) and BERT-based semantic matching into Oracle Configure-Price-Quote (CPQ) systems to perform real-time compliance and risk assessment during SaaS and cloud deal negotiations. AICRIM autonomously identifies anomalies in data privacy clauses, service-level agreements (SLAs), and pricing terms by interpreting unstructured contract text and benchmarking it against GDPR, HIPAA, and ISO 27,001 regulatory standards. The framework is evaluated through structured simulation experiments over a corpus of 500 synthetic SaaS contract documents, using manual legal review and rule-based NLP as baselines. Simulation experiments demonstrate a 27-32% reduction in contract errors, a 38.2% reduction in deal cycle time, and a compliance detection accuracy of 92.1%, with statistically significant improvements over both baselines (p&#x2009;<&#x2009;0.01). These results are simulation-derived and serve as indicative upper-bound estimates pending real-world validation. AICRIM also embeds AI governance controls-AES-256 encryption, bias monitoring, and structured audit trails-to ensure ethical and auditable deployment. By extending Oracle CPQ via REST API event hooks, this model provides enterprises with a scalable, reproducible approach to contract risk automation.

Planetary-entry and sample-return missions demand thermal protection materials that simultaneously minimize mass, suppress recession, and withstand prolonged exposure to ultrahigh-temperature oxidative environments. Here, we report a metal-phenolic-network (MPN) engineered low-density-ablator that resolves this longstanding trade-off through molecularly programmable multimetal ceramization. The material is constructed by controlled ligand exchange between a quasi-linear Ti/Zr/Hf multimetal polymer and phenolic ligands, followed by polymerization into a nanoporous aerogel-like-matrix with low density, low thermal conductivity, and scalable processability. The molecular-level dispersion of multimetal species governs the in situ evolution of hierarchical ceramic architectures during extreme heating: the surface transforms into a dense interpenetrating oxide protection layer, in which (Hf, Zr)O form a rigid skeleton while (Ti, Si)O fill the intergranular space to suppress oxygen penetration and outward mass transport; meanwhile, the interior develops a mass-fractal carbon-ceramic network that disrupts heat-flux propagation. The composite exhibits near-zero recession at ultrahigh temperatures, with linear ablation rates of 0.0017 mm s-1 at 2800 K and 0.0031 mm s-1 at 2900 K, while sustaining 2500 K for 1500 s with a back-temperature-rise of only 369 K. This work establishes an MPN-based materials platform for lightweight thermal protection systems that integrate ultrahigh-temperature stability, oxidation resistance, and effective thermal insulation.

Wilderness search and rescue (SAR) missions are time-critical and terrain-dependent, so planners must quickly allocate resources across complex landscapes. In practice, they rely on expert judgment and experience-based assumptions to coordinate individuals and teams, yet few of these assumptions have been formally validated with field data and modeling. We address this gap by analyzing GPS tracks from 64 SAR incidents, selecting 61 tracks from 13 cases. The tracks are categorized by search tactic: hasty, sweep, or team sweep, with the last divided into six teams of varying size. To quantify how tactic and terrain shape movement, we bootstrapped exponential speed-slope fits, ran Kolmogorov-Smirnov tests, and used a nested ANOVA with random effects. Median uphill and downhill speeds are statistically indistinguishable (0.48 m/s vs. 0.52 m/s; KS p&#x2009;=&#x2009;0.093), suggesting that slope penalties on pedestrian speed can be modeled symmetrically. Hasty searches are faster than sweeps (0.53 m/s vs. 0.39 m/s; KS p&#x2009;<&#x2009;10-3), with no interaction between slope and tactic. Team-level analyses using Spearman correlation, time-lagged cross-correlation, and transfer entropy revealed tightly coupled movement and identifiable leaders, with follower reaction lags of only a few seconds. These empirically derived parameters-search-type specific baseline speeds, a single slope coefficient, and realistic coordination bounds-offer practical inputs for SAR coverage calculations and agent-based models. Incorporating values drawn from real field data could refine human mobility assumptions while remaining compatible with the existing SAR operational framework.

Fable Hospital 3.0 aims to demonstrate the economic return on investment (ROI) of high-performing design strategies towards regenerative healthcare. Building on the legacy of Fable Hospitals 1.0 (2004) and 2.0 (2011), this third iteration seeks to refute the persistent myth that high performance is unaffordable while addressing current healthcare challenges, including financial pressures, workforce burnout, climate resilience and technological integration. Fable Hospital 3.0 is a cost-benefit analysis of a hypothetical 300-bed, 750 000-square-foot community hospital located in 'Anywhere, USA'. The design incorporates more than 20 high-performance and healing strategies, ranging from biophilic design and energy-efficient systems to flexible infrastructure and community integration. Each strategy is evaluated for its construction cost and potential operational benefits. Excluding the cost of the land, the analysis uses conservative cost estimates based on national averages and industry benchmarks, quantifying ROI reductions in medical errors, energy and water consumption, staff turnover, renovation costs and inpatient length of stay, along with improvements in operational resiliency, material first costs and speed to market. The study demonstrates that an estimated additional investment of $25-30 million (approximately 3% of total construction costs) can be recovered within the first 2 years of operation. Key annual savings include: a major contribution of ~$7.25 million from reduced inpatient length of stay, ~$1.0 million from fewer medical errors, ~$1.2 million from improved staff retention, ~$500 000 from maintaining operations during emergencies, ~$250 000 from energy savings, ~$1.5 million from lowered renovation costs, ~$5.4 million from lower material first costs and ~$100 000 from water use reduction. Fable Hospital 3.0 proposes a model for analysing the impact of adopting high-performance design strategies in healthcare and demonstrates that this approach is environmentally and socially responsible and financially wise, aligning with healthcare's mission to promote health and resilience within and beyond hospital walls.