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BackgroundAlthough national organizations recognize the importance of regionalized acute ischemic stroke (AIS) care, data informing expansion are sparse. We assessed real-world regional variation in emergent AIS treatment, including growth in revascularization therapies and stroke center certification. We hypothesized that we would observe overall growth in revascularization therapy utilization, but observed differences would vary greatly regionally.MethodsA retrospective cross-sectional analysis was carried out of de-identified national inpatient Medicare Fee-for-Service datasets from 2016 to 2019. We identified AIS admissions and treatment with thrombolysis and endovascular thrombectomy (ET) with International Classification of Diseases, 10th Revision, Clinical Modification codes. We grouped hospitals in Dartmouth Atlas of Healthcare Hospital Referral Regions (HRR) and calculated hospital, demographic, and acute stroke treatment characteristics for each HRR. We calculated the percent of hospitals with stroke certification and AIS cases treated with thrombolysis or ET per HRR.ResultsThere were 957 958 AIS admissions. Relative mean (SD) growth in percent of AIS admissions receiving revascularization therapy per HRR from 2016 to 2019 was 13.4 (31.7)% (IQR −6.1–31.7%) for thrombolysis and 28.0 (72.0)% (IQR 0–56.0%) for ET. The proportion of HRRs with decreased or no difference in ET utilization was 38.9% and the proportion of HRRs with decreased or no difference in thrombolysis utilization was 32.7%. Mean (SD) stroke center certification proportion across HRRs was 45.3 (31.5)% and this varied widely (IQR 18.3–73.4%).ConclusionsOverall growth in AIS treatment has been modest and, within HRRs, growth in AIS treatment and the proportion of centers with stroke certification varies dramatically.

Over the last decade, increasing numbers of emergency department attendances and an even greater increase in emergency admissions have placed severe strain on the bed capacity of the National Health Service (NHS) of the United Kingdom. The result has been overcrowded emergency departments with patients experiencing long wait times for admission to an appropriate hospital bed. Nevertheless, scheduling issues can still result in significant underutilization of bed capacity. Bed occupancy rates may not correlate well with bed availability. More accurate and reliable long-term prediction of bed requirements will help anticipate the future needs of a hospital's catchment population, thus resulting in greater efficiencies and better patient care. This study aimed to evaluate widely used automated time-series forecasting techniques to predict short-term daily nonelective bed occupancy at all trusts in the NHS. These techniques were used to develop a simple yet accurate national health system-level forecasting framework that can be utilized at a low cost and by health care administrators who do not have statistical modeling expertise. Bed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily nonelective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: exponential smoothing; Seasonal Autoregressive Integrated Moving Average; and Trigonometric, Box-Cox transform, autoregressive moving average errors, and Trend and Seasonal components. The NHS Modernisation Agency's recommended forecasting method prior to 2020 was also replicated. The accuracy of the models varied on the basis of the season during which occupancy was forecasted. For the summer season, percent root-mean-square error values for each model remained relatively stable across the 6 forecasted weeks. However, only the trend and seasonal components model (median error=2.45% for 6 weeks) outperformed the NHS Modernisation Agency's recommended method (median error=2.63% for 6 weeks). In contrast, during the winter season, the percent root-mean-square error values increased as we forecasted further into the future. Exponential smoothing generated the most accurate forecasts (median error=4.91% over 4 weeks), but all models outperformed the NHS Modernisation Agency's recommended method prior to 2020 (median error=8.5% over 4 weeks). It is possible to create automated models, similar to those recently published by the NHS, which can be used at a hospital level for a large national health care system to predict nonelective bed admissions and thus schedule elective procedures.

One of the few synthetic resins from petroleum-based sources that are biodegradable and can reduce the environmental pollution is polyvinyl alcohol (PVA). PVA film is non-toxic, transparent, and biocompatible in line with green ecologically friendly requirements. It is completely biodegradable and possesses good mechanical properties, chemical resistance, and gas barrier. PVA’s hydroxyl groups, however, decrease its mechanical and thermal properties and make it water-soluble, which limits its use. However, it is possible to control the water solubility or absorption by partially cross-linking the polymer chains. This work aims to enhance the mechanical properties while making an insoluble film through cross-linking with oxalic acid (OA). In the present investigation, PVA film was cross-linked with OA (with variable weight percentage (wt%) of OA) to limit its ability to absorb moisture. Tensile testing was utilized to evaluate the mechanical properties of the films, revealing their ultimate tensile strength, % elongation, and Young’s modulus. With increase in wt% of OA, % elongation of PVA-based films decreases, whereas maximum tensile strength was observed at 10 wt% of OA configuration. In comparison to neat PVA, the differential thermo-gravimetric analysis peak migrated towards the higher temperature side with an increase in the OA concentration. The peak value of tan δ curve also increased and shifted towards higher temperature side as the OA concentration increased. This concludes that prepared films have high damping coefficient and can absorb impact load. Finally, the films’ creep-recovery behaviour was also examined in detail. To represent the potential application of these films as packaging materials, anti-microbial tests were also conducted.

This paper provides a conceptual foundation for stochastic duels and contains a further study of the game models based on the theory of stochastic duels. Some other combat assessment techniques are looked upon briefly; a modern outlook on the applications of the theory through video games is provided; and the possibility of usage of data generated by popular shooter-type video games is discussed. Impactful works to date are carefully chosen; a timeline of the developments in the theory of stochastic duels is provided; and a brief literature review for the same is conducted, enabling readers to have a broad outlook at the theory of stochastic duels. A new evaluation model is introduced in order to match realistic scenarios. Improvements are suggested and, additionally, a trust mechanism is introduced to identify the intent of a player in order to make the model a better fit for realistic modern problems. The concept of teaming of players is also considered in the proposed mode. A deep-learning model is developed and trained on data generated by video games to support the results of the proposed model. The proposed model is compared to previously published models in a brief comparison study. Contrary to the conventional stochastic duel game combat model, this new proposed model deals with pair-wise duels throughout the game duration. This model is explained in detail, and practical applications of it in the context of the real world are also discussed. The approach toward solving modern-day problems through the use of game theory is presented in this paper, and hence, this paper acts as a foundation for researchers looking forward to an innovation with game theory.

Tar in producer gas causes detrimental effects on downstream pipelines in gasifier-based power generation systems. Char-supported Ni-based catalysts have been proven to be effective and low-cost solution for tar reduction. Experiments have been conducted with chars derived from various types raw materials available onsite or offsite. This paper investigates the influence of char derived from pigeon pea stalk on the tar reduction of producer gas generated from pigeon pea stalk pellets. The effects of char bed temperature, Ni loading, particle size of char, and gas residence time have also been studied. A laboratory scale downdraft gasifier was used for gasification and an ex situ reactor was used for tar reduction experiments. Taguchi method was used for the optimization analysis of the variables affecting tar reduction efficiency. Experiments showed significant impact of temperature and Ni loading and lesser impact of particle size and gas residence time on reduction of tar in producer gas. Optimum values of operating parameters were 700°C bed temperature, 0.4 mole Ni loading, 1–5 mm char particle size, and 8-s gas residence time. The gas yield due to catalyst activity was enhanced by 3.8%, which further caused an improvement in the gas calorific value by 15.2% and cold gas efficiency by 10.8%. The catalyst was found to be active after 8 h of operation giving tar reduction of 81% at 700°C temperature. Graphical abstract

The study investigated the fractional distillation of bio-oil obtained from the pyrolysis of pine needles under reduced pressure (vacuum) conditions. The fractions were characterized in terms of energy content, viscosity, pH, elemental composition, and chemical composition to assess the efficacy of the distillation process in segregating components and enhancing product properties. The findings revealed substantial yields (by weight) of heavy fractions (boiling point 200–250 °C at 1 bar and 0.5 bar). The heating values, especially for light and middle distillates, increased notably under both vacuum pressures. Chemical analysis revealed the distribution of aromatic and oxygenated compounds across the light and middle fractions, with only a few phenolic compounds concentrated in these fractions, thereby enhancing the higher heating value (HHV) and viscosity of the fractions. Various rheological models were applied to analyze the rheological properties of different bio-oil fractions. It was observed that bio-oil viscosity decreased significantly as temperature rose from 20 to 80 °C, emphasizing temperature’s impact on viscosity. The Vogel–Fulcher–Tammann (VFT) model provided the most accurate description of the temperature-viscosity relationship. Additionally, the viscosity of all four fractions closely matched that of high-speed diesel (HSD). The distillation process efficiently segregated the components, resulting in a product with enhanced fuel properties and composition, rendering it suitable for further utilization as fuel in compression ignition (CI) engines.

Large language models (LLMs) are increasingly being used to evolve solutions to problems in many domains, in a process inspired by biological evolution. However, unlike biological evolution, most LLM-evolution frameworks are formulated as static optimization problems, overlooking the open-ended adversarial dynamics that characterize real-world evolutionary processes. Here, we study Digital Red Queen (DRQ), a simple self-play algorithm that embraces these so-called \"Red Queen\" dynamics via continual adaptation to a changing objective. DRQ uses an LLM to evolve assembly-like programs, called warriors, which compete against each other for control of a virtual machine in the game of Core War, a Turing-complete environment studied in artificial life and connected to cybersecurity. In each round of DRQ, the model evolves a new warrior to defeat all previous ones, producing a sequence of adapted warriors. Over many rounds, we observe that warriors become increasingly general (relative to a set of held-out human warriors). Interestingly, warriors also become less behaviorally diverse across independent runs, indicating a convergence pressure toward a general-purpose behavioral strategy, much like convergent evolution in nature. This result highlights a potential value of shifting from static objectives to dynamic Red Queen objectives. Our work positions Core War as a rich, controllable sandbox for studying adversarial adaptation in artificial systems and for evaluating LLM-based evolution methods. More broadly, the simplicity and effectiveness of DRQ suggest that similarly minimal self-play approaches could prove useful in other more practical multi-agent adversarial domains, like real-world cybersecurity or combating drug resistance.

In this early draft, we provide an overview on similarities and differences in the implementation of a paper card-based vaccine credential system and an app-based vaccine credential system. A vaccine credential's primary goal is to regulate entry and ensure safety of individuals within densely packed public locations and workspaces. This is critical for containing the rapid spread of Covid-19 in densely packed public locations since a single individual can infect a large majority of people in a crowd. A vaccine credential can also provide information such as an individual's Covid-19 vaccination history and adverse symptom reaction history to judge their potential impact on the overall health of individuals within densely packed public locations and workspaces. After completing the comparisons, we believe a card-based implementation will benefit regions with less socioeconomic mobility, limited resources, and stagnant administrations. An app-based implementation on the other hand will benefit regions with equitable internet access and lower technological divide. We also believe an interoperable system of both credential systems will work best for regions with enormous working-class populations and dense housing clusters.