Explore the vast range of titles available.

In the long history of European music, Beethoven’s music in the Classical period and even the Romantic period has had a great influence, Beethoven in his life created a large number of excellent works. As for the creation of sonatas, Beethoven carried his compositional style through the Baroque period, the Classical period, and the Romantic period. His works fully interpreted his inner feelings, so that people can feel the strong emotional fluctuations. However, during the teaching process and students’ practice in class, some students fail to grasp the essence of the piece in terms of playing technique and emotional expression. This is due to a lack of mastery of the appropriate performance techniques and a lack of understanding of the composer’s message. Using the literature review method, this paper describes the problems that students have in playing piano works in terms of performance skills at this stage, and proposes solutions. Uses case studies to explore the use of performance techniques in the work and suggests ideas and methods of practice.

Large language models, such as ChatGPT, are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the internet. However, medical texts, such as clinical notes and diagnoses, require rigorous validation, and erroneous medical content generated by ChatGPT could potentially lead to disinformation that poses significant harm to health care and the general public. This study is among the first on responsible artificial intelligence-generated content in medicine. We focus on analyzing the differences between medical texts written by human experts and those generated by ChatGPT and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT. We first constructed a suite of data sets containing medical texts written by human experts and generated by ChatGPT. We analyzed the linguistic features of these 2 types of content and uncovered differences in vocabulary, parts-of-speech, dependency, sentiment, perplexity, and other aspects. Finally, we designed and implemented machine learning methods to detect medical text generated by ChatGPT. The data and code used in this paper are published on GitHub. Medical texts written by humans were more concrete, more diverse, and typically contained more useful information, while medical texts generated by ChatGPT paid more attention to fluency and logic and usually expressed general terminologies rather than effective information specific to the context of the problem. A bidirectional encoder representations from transformers-based model effectively detected medical texts generated by ChatGPT, and the F score exceeded 95%. Although text generated by ChatGPT is grammatically perfect and human-like, the linguistic characteristics of generated medical texts were different from those written by human experts. Medical text generated by ChatGPT could be effectively detected by the proposed machine learning algorithms. This study provides a pathway toward trustworthy and accountable use of large language models in medicine.

An effective marketing strategy is a critical contribution in any business including the tourism sector. Understanding marketing strategies increases business performance. The purpose of this study is to determine how service marketing strategies impact small town tourism business owners. Using an econometric model, the implementation of the 4P mixed strategy on the profitability and potential of service providers in Molise is investigated. Revenue, cost, and profit are the three areas analyzed. Results showed that service operators have to place emphasis on research and development, reflecting cultural identities and the adoption of modern media. Operators should emphasize marketing and sales promotions through websites and social media. Creating a quality brand and setting reasonable prices without exploiting consumers are essential strategies. In conclusion, strategies that emphasize knowledge management and team building should be put into practice. This study could help tourism businesses in small regions to be more sustainable. It is the first research study where Stan Shih’s innovation smiling curve has been used in an Italian region.

The metabolites that provide the aroma and flavor to wine are the products of several influences, such as grape cultivar, geographic location and associated environmental features, viticultural practices, and vinification techniques, which are central to production protocols, quality evaluation and development of wine regions. Accordingly, we initiated the requisite studies to investigate the differences in the dry red wine metabolites of different grape varieties. The proton-nuclear magnetic resonance technique ( 1 H-NMR) combined with multivariate statistical analysis was used to investigate the changes of metabolite levels in Cabernet Sauvignon, Merlot and Cabernet Gernischt dry red wines vinified in Changli, Hebei province, China, in 2017. The results showed that the types of metabolites in different varieties of dry red wines were similar, but the content was significantly different. The main contributors to the differences in Cabernet Sauvignon, Merlot and Cabernet Gernischt dry red wines were ethyl acetate, lactic acid, alanine, succinic acid, proline, malic acid, and gallic acid, indicating 1 H-NMR method combined with multivariate statistical analysis can distinguish these three types of dry red wines from each other. It provides a benchmark for further comparative study on wine quality and the verification of wine authenticity.

Microflora play an important role in the fermentation of blueberry wine, influencing the flavor and nutrient formation. Commercial yeasts give blueberry wines an average flavor profile that does not highlight the specific aroma and origin of the blueberry. In the present study, ITS1-ITS2 region sequencing analysis was performed using Illumina MiSeq high-throughput technology to sequence fermented blueberry wine samples of three Vaccinium ashei varieties, Gardenblue, Powderblue, and Britewell, from the Majiang appellation in Guizhou province to analyze the trends of fungal communities and the diversity of compositional structures in different periods of blueberry wine fermentation. The study’s results revealed that 114 genera from seven phyla were detected in nine samples from different fermentation periods of blueberry wine. The main fungal phyla were Ascomycota, Basidiomycota, Kickxellomycota, Chytridiomycota, and Olpidiomycota. The main fungal genera were Hanseniaspora, Saccharomyces, unidentified, Aureobasidium, Penicillium, Mortierella, Colletotrichum, etc. Hanseniaspora was dominant in the pre-fermentation stage of blueberry wine, accounting for more than 82%; Saccharomyces was the dominant genera in the middle and late fermentation stages of blueberry wine, with Saccharomyces accounting for more than 72% in the middle of fermentation and 93% in the late fermentation stage. This study screened indigenous flora for the natural fermentation of blueberry wine in the Majiang production area of Guizhou, improved the flavor substances of the blueberry wine, highlighted the characteristics of the production area, and made the blueberry wine have the characteristic flavor of the production area.

Background The purpose of this evaluation was to develop a novel framework for large language model (LLMs) iterative prompt optimization for the specialized domain of medication decision-making in the intensive care unit (ICU). This serves as a first step towards the use of LLMs as clinical decision support (CDS) tools capable of performing pharmacy related tasks. Methods Using a cohort of 1,000 adult patients managed in the ICU for greater than 24 h, an iterative optimization process in the GPT-4 framework was applied to enhance performance of various medication-related tasks, including patient disease state clustering by medication regimen, medication regimen generation, and outcome prediction with the intent to develop PharmacyGPT. Within this feedback loop, the input prompts were adjusted based on model performance in previous iterations. Results The iterative prompt optimization process housed within PharmacyGPT was able to develop meaningful disease state clusters based on input information, develop medication regimens with inclusion of drug, dose, and frequency, and predict mortality with an accuracy of 0.75, precision 0.37, and recall 0.70. Conclusion Iterative prompt optimization shows promise as a rapid means to improve LLM functionality to specific tasks, even in highly domain specific areas like medication management in the ICU. Such domain specific engineering may serve as a strategy to develop LLMs as viable CDS tools.

Large language models (LLMs) have shown the ability to diagnose complex medical cases, but only limited studies have evaluated the performance of LLMs in the development of evidence-based treatment plans. The purpose of this evaluation was to test four LLMs on their ability to develop safe and efficacious treatment plans on complex patients managed in the intensive care unit (ICU). Eight high-fidelity patient cases focusing on medication management were developed by critical care clinicians including history of present illness, laboratory values, vital signs, home medications, and current medications. Four LLMs [ChatGPT (GPT-3.5), ChatGPT (GPT-4), Claude-2, and Llama-2-70b] were prompted to develop an optimized medication regimen for each case. LLM generated medication regimens were then reviewed by a panel of seven critical care clinicians to assess safety and efficacy, as defined by medication errors identified and appropriate treatment for the clinical conditions. Appropriate treatment was measured by the average rate of clinician agreement to continue each medication in the regimen and compared using analysis of variance (ANOVA). Clinicians identified a median of 4.1-6.9 medication errors per recommended regimen, and life-threatening medication recommendations were present in 16.3%-57.1% of the regimens, depending on LLM. Clinicians continued LLM-recommended medications at a rate of 54.6%-67.3%, with GPT-4 having the highest rate of medication continuation among all LLMs tested (p < 0.001) and the lowest rate of life-threatening medication errors (p < 0.001). Caution is warranted using present LLMs for medication regimens given the number of medication errors that were identified in this pilot study. However, LLMs did demonstrate potential to serve as clinical decision support for the management of complex medication regimens given the need for domain specific prompting and testing.

In this study, the fermentation mash of Cabernet Sauvignon, Cabernet Franc, and Matheran from Linfen, Shanxi Province, was sequenced using the Illumina MiSeq high-throughput sequencing platform to analyze the structural diversity of fungal communities in different samples. The results showed that a total of 10 phyla, 125 families, and 187 genera were detected in the nine samples of this study. The main fungal phyla were Ascomycota, Basidiomycota, and Mortierellomycota. The main fungal genera are Hanseniaspora, Mortierella, Sclerotinia, Aureobasidium, Saccharomyces, Aspergillus, Clavulina, Candida, etc. Hanseniaspora was the dominant genus in the pre-fermentation stage, accounting for more than 70%; Saccharomyces was the dominant genus in the middle and late fermentation stage, accounting for more than 75% in the middle fermentation stage and up to 90% in the late fermentation stage. This study provides a theoretical basis for monitoring and optimizing winemaking processes and introducing wine grape varieties in the Linfen region of Shanxi.

Background Extensile lateral approach had been recognized as the gold standard technique for displaced intra-articular calcaneus fractures (DIACFs) while sinus tarsi approach had been increasingly valued by surgeons and comparative clinical outcome was shown in both techniques. Appropriate decisions could be made by the clinicians with the help of cost-utility analysis (CUA) about optimal healthcare for type II/III calcaneus fracture. Method A single-center, retrospective study was conducted in which basic characteristics, clinical outcomes, and health care costs of 109 patients had been obtained and analyzed. Changes in health-related quality of life (HRQoL) scores, validated by EuroQol five-dimensional-three levels (EQ-5D-3L), were used to enumerate quality-adjusted life years (QALYs). Cost-effectiveness was determined by the incremental cost per QALY. Results One hundred nine patients were enrolled in our study including 62 in the ELA group and 47 in the STA group. There were no significant differences between these two groups in mean total cost, laboratory, and radiographic evaluation expense, surgery, anesthesia, and antibiotic expense. The expense of internal fixation materials ($3289.0 ± 543.9) versus ($2630.6 ± 763.7) and analgesia ($145.8 ± 85.6) versus ($102.9 ± 62.7) in ELA group were significantly higher than in the STA group ( P < .001, P = .008, respectively). Visual Analogue Scale (VAS) scores showed significant difference at postoperative 3 and 5 days ( P < .001). American Orthopaedic Foot and Ankle Society (AOFAS) ankle-hindfoot scores and the Bohlers’ and Gissane angle showed no significant differences between the two groups before and after the operation. The cost-effectiveness ratios of ELA and STA were $8766.8 ± 2835.2/QALY and $7914.9 ± 1822.0/QALY respectively, and incremental cost-effectiveness ratio (ICERs) of ELA over STA was $32110.00/QALY, but both showed no significant difference. Conclusion Both ELA and STA techniques are effective operative procedures for the patients with calcaneus fracture. Moreover, STA seems to be more reasonable for its merits including less postoperative pain, and less expense of analgesia as well as internal fixation materials. Level of evidence 5

Osteosarcoma (OS) is the most common primary malignant bone tumour in children and adolescents. The long‐term survival rate of OS patients is stubbornly low mainly due to the chemotherapy resistance. We therefore aimed to investigate the antitumoral effects and underlying mechanisms of proanthocyanidin B2 (PB2) on OS cells in the current study. The effect of PB2 on the proliferation and apoptosis of OS cell lines was assessed by CCK‐8, colony formation, and flow cytometry assays. The target gene and protein expression levels were measured by qRT‐PCR and Western blotting. A xenograft mouse model was established to assess the effects of PB2 on OS proliferation and apoptosis in vivo. Results from in vitro experiments showed that PB2 inhibited the proliferation and induced apoptosis of OS cells, and also increased the expression levels of apoptosis‐related proteins. Moreover, PB2 induced OS cell apoptosis through suppressing the PI3K/AKT signalling pathway. The in vivo experiments further confirmed that PB2 could inhibit OS tumour growth and induce its apoptosis. Taken together, these results suggested that PB2 inhibited the proliferation and induced apoptosis of OS cells through the suppression of the PI3K/AKT signalling pathway.