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Thermogravimetric analysis of Ti-6Al-4V powders (virgin Grade 5 and a once used Grade 23) was conducted up to 800 ∘C in an industrial grade Ar environment. A standard starting particle size distribution (PSD) for electron beam powder-bed fusion (PBF-EB) additive manufacturing (AM) was divided into two smaller ranges based on the D50 to observe the effects of oxidation (e.g. mass gain) resulting from the differences in available, reactive surface area during testing. Significant mass gain was still recorded throughout the testing owing to titanium’s extreme reactivity at elevated temperatures even with the Ar cover gas. The mass gain can be attributed to impurities in the purge gas and absorbed water in the powder. The smaller particle size distribution powders accumulated more % mass gain than the full range and larger PSD, likely due to the increased surface area per unit volume of the smaller PSD powder. The virgin Grade 5 powder generally accumulated more oxygen/mass gain than the once used Grade 23 powder, likely resulting from a previously existing thicker TiO2 shell on the once used Grade 23 powder. The results suggest that within a single AM PBF powder-bed, there may be varying levels of oxidation (both interstitial and oxide) depending on the particle size distribution and resulting surface area exposed to high temperatures for a given volume, and level of reuse (thickness of the oxide layer) on a per-particle basis if the batch had been blended.11 This work is an official contribution of the National Institute of Standards an Technology and is not subject to copyright in the United States.

Growing concerns regarding the safety, flammability and hazards posed by Li-ion systems have led to research on alternative rechargeable metal-ion electrochemical storage technologies. Among the most notable of these are Na-ion supercapacitors and batteries, motivated, in part, by the similar electrochemistry of Li and Na ions. However, sodium ion batteries (SIBs) come with their own set of issues, especially the large size of the Na+ ion, its relatively sluggish kinetics and low energy densities. This makes the development of novel materials and appropriate electrode architecture of absolute significance. Transition metal dichalcogenides (TMDs) have attracted a lot of attention in this regard due to their relative ease of exfoliation, diverse morphologies and architectures with superior electronic properties. Here, we study the electrochemical performance of Mo-based two-dimensional (2D) layered TMDs (e.g. MoS2, MoSe2 and MoTe2), exfoliated in a superacid, for battery and supercapacitor applications. The exfoliated TMD flakes were interfaced with reduced graphene oxide (rGO) to be used as composite electrodes. Electron microscopy, elemental mapping and Raman spectra were used to analyse the exfoliated material and confirm the formation of 2D TMD/rGO layer morphology. For supercapacitor applications in aqueous electrolyte, the sulfide-based TMD (MoS2) exhibited the best performance, providing an areal capacitance of 60.25 mF cm−2. For SIB applications, TMD electrodes exhibited significantly higher charge capacities than the neat rGO electrode. The initial desodiation capacities for the composite electrodes are 468.84 mAh g−1 (1687.82 C g−1), 399.10 mAh g−1 (1436.76 C g−1) and 387.36 mAh g−1 (1394.49 C g−1) for MoS2, MoSe2 and MoTe2, respectively. Also, the MoS2 and MoSe2 composite electrodes provided a coulombic efficiency of near 100 % after a few initial cycles.

Sand tigers are frequently observed at shipwrecks and other artificial reef habitats off North Carolina (USA), but data about occupancy, movement ecology, and site fidelity are lacking. Spot A Shark USA researchers used Wildbook© photoidentification software to spot map sand tigers in images provided by recreational SCUBA divers, or collected from remotely operated vehicles, and an offshore live-streaming camera. We uniquely identified 1837 sand tigers, 101 of which were resighted on more than one date between 2005–2021. Sand tigers of both sexes and various ages were found year-round along the northern, central and southern coast. We identified shipwrecks or artificial reef sites with consistently high numbers of shark encounters reported, sometimes with seasonal occupancy patterns. Resighted sharks were often encountered at the same or nearby locations, confirming high levels of residency and site fidelity to specific locations. Together, the mating scars seen on 121 female sand tigers and 202 females documented with rotund abdomens consistent with pregnancy highlight the importance of NC waters for reproduction. We also quantified other characteristics of the sand tigers visible in the photographs including wounds, parasitic copepods, and attached fishing gear. Our results reflect the importance of habitats off the NC coast to the movement and reproductive ecology of sand tigers at multiple life history stages. Sand tiger populations have declined in the Northwest Atlantic, so information about residence at specific locations, seasonal patterns of occupancy, and sex-dependent behaviors associated with migration and reproduction are important to future management and conservation of the species.

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.

PurposeWe present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs.MethodsWe developed an exam consisting of 100 radiation oncology physics questions based on our expertise. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. The performance of ChatGPT (GPT-4) was further explored by being asked to explain first, then answer. The deductive reasoning capability of ChatGPT (GPT-4) was evaluated using a novel approach (substituting the correct answer with “None of the above choices is the correct answer.”). A majority vote analysis was used to approximate how well each group could score when working together.ResultsChatGPT GPT-4 outperformed all other LLMs and medical physicists, on average, with improved accuracy when prompted to explain before answering. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups or Bard (LaMDA). In evaluating deductive reasoning ability, ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote.ConclusionThis study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants.

The purpose of this work is to investigate collimating individual proton beamlets from a dosimetric perspective and to introduce a new device concept, the spot scanning aperture (SSA). The SSA consists of a thin aperture with a small cylindrical opening attached to a robotics system, which allows the aperture to follow and align with individual beamlets during spot delivery. Additionally, a range shifter is incorporated (source-side) for treating shallow depths. Since the SSA trims beamlets spot by spot, the patient-facing portion of the device only needs to be large enough to trim a single proton beamlet. The SSA has been modelled in an open-source Monte-Carlo-based dose engine (MCsquare) to characterize its dosimetric properties in water at depths between 0 and 10 cm while varying the following parameters: the aperture material, thickness, distance to the water phantom, distance between the aperture and attached range shifter, and the aperture opening radius. Overall, the SSA greatly reduced spot sizes for all the aperture opening radii that were tested (1 – 4 mm), especially in comparison with the extended range shifter (ranger shifter placed at 30 cm from patient); greater than 50% when placed less than 10 cm away from the patient at depths in water less than 50 mm. The peak to entrance dose ratio and linear energy transfer was found to depend on the thickness of the aperture and therefore the aperture material. Neutron production rates were also investigated and discussed.

The world’s largest extant fish, the whale shark Rhincodon typus, is one of the most-studied species of sharks globally. The discovery of predictable aggregation sites where these animals gather seasonally or are sighted year-round – most of which are coastal and juvenile-dominated – has allowed for a rapid expansion of research on this species. The most common method for studying whale sharks at these sites is photographic identification (photo-ID). This technique allows for long-term individual-based data to be collected which can, in turn, be used to evaluate population structure, build population models, identify long-distance movements, and assess philopatry and other population dynamics. Lagged identification rate (LIR) models have fewer underlying assumptions than more traditional capture mark recapture approaches, making them more broadly applicable to marine taxa, especially far-ranging megafauna species like whale sharks. However, the increased flexibility comes at a cost. Parameter estimations based on LIR can be difficult to interpret and may not be comparable between areas with different sampling regimes. Using a unique data-set from the Philippines with ~8 years of nearly continuous survey effort, we were able to derive a metric for converting LIR residency estimates into more intuitive days-per-year units. We applied this metric to 25 different sites allowing for the first quantitatively-meaningful comparison of sightings-derived residence among the world’s whale shark aggregations. We validated these results against the only three published acoustic residence metrics (falling within the ranges established by these earlier works in all cases). The results were then used to understand residency behaviours exhibited by the sharks at each site. The adjusted residency metric is an improvement to LIR-based population modelling, already one of the most widely used tools for describing whale shark aggregations. The standardised methods presented here can serve as a valuable tool for assessing residency patterns of whale sharks, which is crucial for tailored conservation action, and can cautiously be tested in other taxa.

Photographic-identification (photo-ID) of bottlenose dolphins using individually distinctive features on the dorsal fin is a well-established and useful tool for tracking individuals; however, this method can be labor-intensive, especially when dealing with large catalogs and/or infrequently surveyed populations. Computer vision algorithms have been developed that can find a fin in an image, characterize the features of the fin, and compare the fin to a catalog of known individuals to generate a ranking of potential matches based on dorsal fin similarity. We examined if and how researchers use computer vision systems in their photo-ID process and developed an experiment to evaluate the performance of the most commonly used, recently developed, systems to date using a long-term photo-ID database of known individuals curated by the Chicago Zoological Society’s Sarasota Dolphin Research Program. Survey results obtained for the “Rise of the machines – Application of automated systems for matching dolphin dorsal fins: current status and future directions” workshop held at the 2019 World Marine Mammal Conference indicated that most researchers still rely on manual methods for comparing unknown dorsal fin images to reference catalogs of known individuals. Experimental evaluation of the finFindR R application, as well as the CurvRank, CurvRank v2, and finFindR implementations in Flukebook suggest that high match rates can be achieved with these systems, with the highest match rates found when only good to excellent quality images of fins with average to high distinctiveness are included in the matching process: for the finFindR R application and the CurvRank and CurvRank v2 algorithms within Flukebook more than 98.92% of correct matches were in the top 50-ranked positions, and more than 91.94% of correct matches were returned in the first ranked position. Our results offer the first comprehensive examination into the performance and accuracy of computer vision algorithms designed to assist with the photo-ID process of bottlenose dolphins and can be used to build trust by researchers hesitant to use these systems. Based on our findings and discussions from the “Rise of the Machines” workshop we provide recommendations for best practices for using computer vision systems for dorsal fin photo-ID.

Isotopic labeling techniques were employed to study alkene addition to hydrogen- and deuterium-terminated silicon nanoparticles. Deuterium-terminated silicon nanoparticle synthesis is described, as is the characterization of fresh deuterium-terminated particles by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and in situ Fourier transform infrared spectroscopy (FTIR). Particles were refluxed in pure 1-dodecene and subsequently characterized by FTIR and nuclear magnetic resonance (NMR) spectroscopy. 1H NMR results showed features consistent with dodecyl-terminated nanoparticles. Infrared absorption spectra of refluxed particles showed strong evidence of new C−D bond formation, which is consistent with a radical chain mechanism for alkene addition by hydrosilylation. [ABSTRACT FROM AUTHOR]

The predictable occurrence of whale sharks, Rhincodon typus, has been well documented in several areas. However, information relating to their migratory patterns, residency times and connectivity across broad spatial scales is limited. In the present study photo-identification data is used to describe whale shark population structure and connectivity among known aggregation sites within the Western Central Atlantic Ocean (WCA). From 1999 to 2015, 1,361 individuals were identified from four distinct areas: the Yucatan Peninsula, Mexico (n = 1,115); Honduras (n = 146); northern Gulf of Mexico, United States (n = 112), and Belize (n = 49). Seasonal patterns in whale shark occurrence were evident with encounters occurring in the western Caribbean Sea earlier in the year than in the GOM. There was also a significant sex bias with 2.6 times more males present than females. Seventy sharks were observed in more than one area and the highest degree of connectivity occurred among three aggregation sites along the Mesoamerican Reef. Despite this, the majority of resightings occurred in the area where the respective sharks were first identified. This was true for the WCA as a whole, with the exception of Belize. Site fidelity was highest in Mexico. Maximum likelihood modelling resulted in a population estimate of 2,167 (95% c.i. 1585.21-2909.86) sharks throughout the entire region. This study is the first attempt to provide a broad, regional population estimate using photo-identification data from multiple whale shark aggregations. Our aim is to provide population metrics, along with the description of region-scale connectivity, that will help guide conservation action in the WCA. At a global level, rapidly growing photographic databases are allowing for researchers to look beyond the description of single aggregation sites and into the ocean-scale ecology of this pelagic species.