Science Talks最新文献

Background

In the United Kingdom, pre-registration nursing students include those with a disability or impairment who can require extra learning support their achievement of clinical proficiencies. Earlier research suggests a Community of Practice (CoP) related to facilitating these reasonable adjustments (reasonable accommodations) in clinical placement. This group of practitioners can optimise the clinical learning environment but are potentially hindered by limited evidence-based knowledge and a lack of national guidance.

Research aim

To confirm the existence of this CoP and investigate the development of these practitioners' reasonable adjustments capability. Additionally, to seek the views from these individuals regarding the efficacy of related criteria and procedures.

Methodology

Using a broad interpretivist approach, semi-structured interviews elicited the opinions from 13 study participants. All interview data were transcribed, coded and then thematically analysed in conjunction with the chosen theoretical lenses.

Findings

Three main themes were identified- ‘lack of consistency’, ‘an experiential approach’, and ‘sharing good practice’.

Conclusions

Findings confirm the emergence of this CoP. A lack of knowledge and national guidance can hinder the capability development of CoP members. Further research is recommended to fully understand this CoP and promote its existence to the wider nursing body.

The need of achieving sustainable resource recovery, particularly in relation to critical metals extracted from brine, is a key focus in the development of a circular economy. This study underlines the significance of such recovery processes, with a special focus on lithium (Li) - a vital element in modern industry. An assessment of Li concentrations across distinct brine sources establishes the background for its recovery. The conventional method of extracting resources through evaporation ponds is thoroughly evaluated, emphasizing its limitations including prolonged operational durations, dependence on climate conditions, reliance on chemicals, challenges related to water constraint, and significant carbon emissions. In contrast, the Membrane Crystallization (MCr) process is described as a possible alternative. The research dives into the concepts of MCr, its configurations, and contrasts its operational range versus other membrane processes such as reverse osmosis (RO), forward osmosis (FO), and capacitive deionization (MCDI) processes. Advantages and market potential of MCr are introduced against its limitations. Progress in MCr process development (e.g., LiCl recovery, Li₂CO₃ recovery, etc.) and membrane developments (e.g., multi-layer nanofiber and 3D printed membranes) are explored. The paper finishes by outlining future research directions for MCr, emphasizing its contribution to the advancement of sustainable resource recovery strategies.

The music industry is interested in the future success of a song and its presence in popular rankings such as the Billboard charts. However, a song's popularity might be impacted by variables such as music trends and social influences, which are indifferent to audio signals. In this paper, we present HSP-TL, a deep learning model, to identify likely hit songs. Our work combines temporal information and features derived from audio and lyrics to estimate the success of a recording. We adopt the concept of the triplet loss function to minimize the distance between objects with similar popularity. Also, we use convolutional neural networks on 2-D low-level audio features, contrary to the current approach. We use pre-trained language models for text-based feature extraction. Our method is evaluated on the Hit Song Prediction Dataset, which we enrich with the lyrics of each song. Our results show that the inclusion of lyrics improves song uniqueness and reflects musical trends. The proposed model outperforms the current approach by up to 8%.

The present study highlights the green synthesis of SnS₂ quantum dots over a chitosan surface (SnS₂/Ch) using Azadirachta Indica leaf extract via precipitation technique. The photocatalytic performance of the prepared photocatalyst was investigated for the degradation of crystal violet dye through a photocatalytic-assisted advanced oxidation process (AOP). The average crystallite and particle sizes of the SnS₂/Ch photocatalyst were 8.8 nm and 3.3 nm, respectively. The effect of various reaction parameters was optimized to achieve maximum degradation efficiency. Maximum degradation of 98.60 ± 1.34% could be achieved under optimum conditions within 70 min of visible light irradiation.

This work reiterates the potential of buoyancy work energy storage (BWES) systems which has been presented in previously published experimental-based literature. The concept of buoyancy work can be grasped when studying the operation of mobility devices such as hot air balloons or boats, where buoyancy plays a key role in keeping them afloat. Conventional experimental BWES systems that were studied in previous literature consist of a water tank, a submergible buoy, a motor/generator carrying out the charging/discharging processes, respectively, and a data acquisition interface. It is interesting to see the progression of this technology at lab scale, where initial designs included the use of a barrel as the water tank, and Styrofoam cut-outs as buoys. One design utilized a ring with multiple Styrofoam buoys attached to it, and it was later enhanced by adding helical engravings to the buoys' surface to affect its interaction with water positively during descension and ascension. The latest study added to this timeline of ongoing development investigates the effect of different buoy materials, surface coatings, buoy infill gasses, and load.

Ensuring the stability of the Permanent Magnet Synchronous Motor (PMSM) is crucial for the smooth operation of drive systems. The behavior of PMSM can exhibit chaotic characteristics, particularly influenced by its initial operating conditions. Therefore, this paper addresses the stabilisation of chaotic PMSM system by using the concept of finite-time stability. For this purpose, three simple but effective controllers are proposed to suppress chaotic behavior in the machine. Performance evaluation of developed controllers has been presented by considering their settling time and peak overshoot. Developed controller models have been extensively simulated in the MATLAB environment. Further, practical validity has been accessed through real-time test results by using the Typhoon Hardware-in-the-loop (HIL) emulator. A closed agreement between simulation and real-time test results signify the efficacy and practical validity of the proposed controllers.

This work introduces compressed air energy storage (CAES) systems and their role in mitigating the lag between energy supply and demand. The concept of energy storage relies on storing energy during periods of low demand to supply it during periods of high demand, where CAES stands as a scalable solution with the ability to house ancillary systems in order to harvest useful effects such as expansion cooling. CAES on its own compares well with electrochemical batteries, while bearing a simpler system design and less stringent operational requirements; however, with the integration of thermal management the role of CAES can be extended to produce chilled water that can be used for cooling applications. Not only does expansion cooling broaden the utilization of CAES, it has also enhanced the energetic efficiency of the system by 4%. Future work within the scope of CAES includes the integration of heat pipes and latent heat energy storage, and experimenting with different working gases.

Humpback whales (Megaptera novaeangliae) have a highly migratory existence, encountering a range of environmental factors throughout their lifetimes. The spatial and temporal attributes of these factors profoundly shape the suitability of habitats for essential activities like breeding, feeding, and resting. We have synthesised insights from peer-reviewed literature spanning the past four decades. This comprehensive review identifies key environmental drivers and evaluates the methodologies employed to delineate their relationships with humpback whales. Our analysis incorporates findings from 148 studies derived through an online literature search. These studies, cumulatively spanning 105,000 humpback whale observations across 1216 study years investigating the connections between humpback whales and environmental drivers across both Northern and Southern Hemispheres. Research focusing on humpback whales in feeding areas underscores preferences for upwelling, elevated chlorophyll-a concentrations, and frontal areas marked by variations in temperature, depth, and currents—all indicative of potentially high prey concentrations. In calving grounds, shallow, warm environments with slow water movement are favored to facilitate the survival of calves. Available data migration suggests a preference for shallow waters near shorelines characterised by moderate temperature. The extraction of such information on environmental drivers has important implications for conservation efforts, particularly in a fast-changing climate.

Previous work employing Volume-of-Fluid simulations of high-speed liquid injection has shown that the most unstable modes obtained from instability theory are not the ones responsible for fragmenting the liquid core. Their associated wavelength is much smaller than the liquid jet diameter; hence, their action is limited to stripping the jet surface leaving the liquid core intact. A much larger sinuous mode develops many diameters downstream of the nozzle orifice, and it is ultimately responsible for fragmenting the liquid jet. The genesis of this sinuous mode is studied in the present work by focusing our attention first on its 2D manifestation. By employing a spatial instability analysis based on a two-phase Orr-Sommerfeld system, it is discovered that as the gas boundary layer grows, the peak growth rate obtained from the dispersion curve shifts to a new maximum located at a much larger wavelength. And this much larger wavelength coincides directly with the onset of atomizing sinuous mode. It is shown that the smaller varicose or sinuous modes do not contribute in a significant way. A subsequent analysis is presented where it is shown that the pressure fluctuations in the gas are the key agents promoting the growth of the sinuous mode.

Breast cancer, the most commonly diagnosed cancer in women worldwide, presents a significant challenge with approximately one in every three newly diagnosed cancers being breast cancer. The United States alone witnesses around 250,000 new cases annually, and the global count of diagnosed women reached 2.3 million in 2020. Recently, artificial intelligence (AI) and deep learning have emerged as promising tools in the field of Digital and Computational Pathology. They offer transformative capabilities, assisting pathologists in their clinical routine by enhancing diagnostic and prognostic abilities.

Traditionally, Pathology involves the analysis of tumor tissue samples obtained through biopsies, which are then scanned to create digital slides or Whole-Slide Images (WSIs). Once these slides are digitized, AI algorithms can perform tasks such as cell counting, pattern detection, and prediction of risk factors, survival rates, and treatment options.

This thesis focuses on two key aspects: diagnosis and prognosis in breast cancer. At the cellular level, we explore the counting of mitosis. This corresponds to a process where pathologists must manually count dividing nuclei from the hematoxilyn and eosin (H&E) WSIs. This is because proliferation is a very strong biomarker in breast cancer, linked to metastasis and survival. Therefore, the first part of the thesis focuses on automatic mitoses counting and an objective tool for assessing proliferation in WSIs using convolutional neural networks (CNNs) under a weakly-supervised paradigm.

Secondly, this thesis delves into the significance of molecular subtypes of breast cancer. These subtypes display varying levels of aggressiveness, prognosis, and treatment responses. Pathologists are unable to derive the molecular subtype from an H&E-stained WSI, and they recur to expensive gene-expression profiling or immunohistochemistry to determine them. For this reason, we employ context-aware approaches and leverage graph-convolutional networks (GCNs) to classify these molecular subtypes only using H&E-stained WSIs, facilitating personalized treatment strategies for pathologists.

Finally, attention is directed towards prognosis, particularly the prediction of survival and distant metastases. Leveraging the power of deep learning, we propose combining the previously mentioned, automatic mitotic score and the image features extracted from the molecular subtypes to develop models capable of accurately forecasting patient outcomes, including the likelihood of metastatic spread. Such predictions hold immense potential for guiding clinical decisions, enabling early interventions, and improving patient care.

In summary, this thesis explores the integration of deep learning and AI in Digital and Computational Pathology, addressing both the diagnostic aspects of automatic proliferation scoring and molecular subtype prediction, as well as the prognostic aspect