Hasil untuk "Science (General)"

Aitor Lewkowycz, Anders Andreassen, David Dohan et al.

Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally struggled with tasks that require quantitative reasoning, such as solving mathematics, science, and engineering problems at the college level. To help close this gap, we introduce Minerva, a large language model pretrained on general natural language data and further trained on technical content. The model achieves state-of-the-art performance on technical benchmarks without the use of external tools. We also evaluate our model on over two hundred undergraduate-level problems in physics, biology, chemistry, economics, and other sciences that require quantitative reasoning, and find that the model can correctly answer nearly a third of them.

J. Gilmer, S. Schoenholz, Patrick F. Riley et al.

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.

L. Monostori, B. Kádár, T. Bauernhansl et al.

A. Miyake, N. Friedman

K. Lee, D. Mooney

B. T. T. Yeo, Fenna M. Krienen, J. Sepulcre et al.

Pamela Baxter, S. Jack

W. Wright

Wynne W. Chin, Barbara L. Marcolin, P. R. Newsted

W. Bodmer

R. Felder, L. Silverman

J. Driscoll, Aart C. Kraay

F. Keefe, M. Rumble, C. Scipio et al.

S. Radenkovic, M. Dugic, I. Radojevic

The answers on the current status and future development of Quantum Science and Technology are presented.

Società Italiana di Biologia Sperimentale

Lake Faro is a unique coastal meromictic ecosystem characterized by a permanent density stratification that prevents complete vertical mixing, resulting in a stable oxic-anoxic interface (chemocline). This study aims to characterize microbial diversity and community assembly along the water column, thanks also to the measure of the environmental parameters of the water nutrients which gave us great support to understand how steep environmental gradients shape niche partitioning. Microbial community composition was investigated using Illumina MiSeq sequencing of the 16S rRNA (using cDNA), revealing interesting insights on taxonomic and functional transitions from surface waters to the reduced bottom layers. The highlighted a pronounced vertical zonation of microbial communities, tightly coupled to sulfur and nitrogen biogeochemical cycles. The monimolimnion (0–12m) is dominated by typical marine aerobic groups, including oxygenic Cyanobacteria (mainly Synechococcus spp.), followed by Bacteroidota and Proteobacteria, which collectively sustain primary production in the nutrient-limited upper layers. Chemocline (12-15m) and Ipolimnion (15–25m), represent a metabolic hotspot where a shift in community composition occurs. Here, bacterial communities are dominated by Desulfobacterota, the second most abundant phylum after Bacteroidota, with Dissulfuribacteraceae, Desulfobulbaceae, Desulfuromonadaceae as most representative families, well adapted to low-light and high-sulfide conditions. Overall, the findings of this study suggests that below 15m depth a complex anaerobic food web develops, primarily structured by dissolved oxygen, sulfide concentration, and redox potential. This study provides a comprehensive baseline for understanding microbial dark matter in meromictic lagoons and highlights the chemocline as a critical ecological filter regulating carbon and sulfur cycling in coastal Mediterranean environments.

Huynh Hoang Khanh Thu, Yulia V. Ostankova, Alexander N. Shchemelev et al.

Vietnam faces a hyperendemic burden of hepatitis B virus (HBV) infection, but the prevalence of occult HBV infection (OBI) and its underlying molecular mechanisms in healthy populations remain poorly understood. This study aimed to characterize the serological and molecular HBV profile of a healthy Vietnamese adult cohort in Southern Vietnam. We assessed the prevalence of occult HBV infection (OBI) and HBsAg-positivity (serving as a proxy for probable chronic infection). In this cross-sectional study, 397 healthy adults from Southern Vietnam underwent serological screening for HBsAg, anti-HBs, and anti-HBc. All participants were screened for HBV DNA using a high-sensitivity PCR assay (LOD ≥ 5 IU/mL). For all viremic cases, the full Pre-S/S region was sequenced to determine genotype and characterize escape mutations. We uncovered a high prevalence of both HBsAg-positivity (17.6%) and OBI (9.3% HBsAg-negative, HBV DNA-positive). Serological analysis revealed a massive, age-dependent reservoir of past exposure (63.7% anti-HBc) characterized by a high and increasing prevalence of the anti-HBc only profile (31.5%), a key serological marker for OBI. This trend contrasted sharply with a steep age-related decline in protective anti-HBs. The viral landscape was dominated by genotypes B (73.8%) and C (26.2%), with sub-genotypes B4 and C1 being the most prevalent. Critically, individuals with OBI carried a significantly higher burden of S gene escape mutations compared to those with HBsAg-positivity (<i>p</i> < 0.001). Canonical escape variants, including sG145R (21.6%), sK141R/T/E/Q (24.3%), and sT116N/A/I/S (18.9%), were exclusively or highly enriched in the OBI group. A LASSO-logistic model based on this mutational profile successfully predicted occult infection with high accuracy (AUC = 0.83). A substantial hidden reservoir of occult HBV infection exists within the healthy adult population of Vietnam, driven by a high burden of S gene escape mutations. These findings highlight the significant limitations of conventional HBsAg-only screening. They also underscore the need for comprehensive molecular surveillance to address the true scope of HBV viremia, hopefully enabling a reduction in hidden transmission of clinically significant viral variants.

Ali Sanaei, Ali Rajabzadeh

Large language models (LLMs) are increasingly utilized by researchers across a wide range of domains, and qualitative social science is no exception; however, this adoption faces persistent challenges, including interpretive bias, low reliability, and weak auditability. We introduce a framework that situates LLM usage along two dimensions, interpretive depth and autonomy, thereby offering a straightforward way to classify LLM applications in qualitative research and to derive practical design recommendations. We present the state of the literature with respect to these two dimensions, based on all published social science papers available on Web of Science that use LLMs as a tool and not strictly as the subject of study. Rather than granting models expansive freedom, our approach encourages researchers to decompose tasks into manageable segments, much as they would when delegating work to capable undergraduate research assistants. By maintaining low levels of autonomy and selectively increasing interpretive depth only where warranted and under supervision, one can plausibly reap the benefits of LLMs while preserving transparency and reliability.

Andre Massahiro Shimaoka, Renato Cordeiro Ferreira, Alfredo Goldman

This study explores the integration of eXtreme Programming (XP) and the Cross-Industry Standard Process for Data Mining (CRISP-DM) in agile Data Science projects. We conducted a case study at the e-commerce company Elo7 to answer the research question: How can the agility of the XP method be integrated with CRISP-DM in Data Science projects? Data was collected through interviews and questionnaires with a Data Science team consisting of data scientists, ML engineers, and data product managers. The results show that 86% of the team frequently or always applies CRISP-DM, while 71% adopt XP practices in their projects. Furthermore, the study demonstrates that it is possible to combine CRISP-DM with XP in Data Science projects, providing a structured and collaborative approach. Finally, the study generated improvement recommendations for the company.

WANG Jiawei(王佳薇), GONG Shuying(龚淑英), FAN Fangyuan(范方媛) et al.

Granular green tea is a significant category of famous high-quality green tea in Zhejiang Province. In this study, we conducted comparative experiments on key technical parameters, including moisture resurgence, second fixation and stir-frying techniques, during the primary processing of granular green tea and systematically analyzed their effects on sensory quality and chemical composition. The results indicated that appropriately controlling the rehumidification time (1.5 h), reducing the moisture content of the second fixed leaves (40%), decreasing the amount of tea leaves per frying pan (4 kg/pan), and selecting a frying pan with better air permeability (60-type) effectively enhanced the dry tea appearance and the emerald color of the tea liquor and improved the freshness of the aroma and taste. Moderate rehumidification and a lower moisture content in the second fixed leaves increased the content of catechins and some key umami amino acids, whereas a lower loading amount of tea leaves per frying pan increased the total amino acid content; a higher moisture content in the second fixed leaves increased the water extract content. Higher-quality samples with superior aroma and freshness had higher levels of floral compounds such as linalool, geraniol, and α-terpineol. Conversely, a longer rehumidification time, greater moisture content in the second fixed leaves, more tea leaves per frying pan, and lower air permeability of the stir-frying machine led to higher temperatures and moisture and increased the relative content of compounds such as n-hexadecanoic acid, heptanoic acid, and 6, 10, 14-trimethyl-2-pentadecanone in the aroma profile. In conclusion, this study identifies an optimal processing combination for improving the overall quality of granular green tea and provides a theoretical basis for refining its processing technology.(颗粒形绿茶是浙江省名优绿茶的重要品类。本研究针对颗粒形绿茶初制中回潮、二青、炒制等工序的关键技术参数开展对比试验，系统分析不同技术参数对颗粒形绿茶感官品质及化学组分的影响。结果表明，适当控制回潮时间（1.5 h）、降低二青叶含水率（40%）、适当减少炒制投叶量（4 kg/小锅）及选用透气性能较好的炒锅（60型），能够提升颗粒形绿茶外形及茶汤的翠绿色泽，并提高香气滋味的鲜爽性。适度回潮和较低的二青叶含水率有助于提升儿茶素含量及部分对鲜味具有重要贡献的氨基酸含量；较低的炒制投叶量可提升氨基酸总量；而较高的二青叶含水率则有利于提升水浸出物含量。在香气鲜爽性高、品质较优的样品中，芳樟醇、香叶醇、α-松油醇等具有花香特征的化合物的相对含量较高；而长时间回潮、较高的二青叶含水率、较高的炒制投叶量及较低透气性的锅型所引起的较高温度及含水率，则会促进正十六烷酸、庚酸、6，10，14-三甲基-2-十五烷酮等香气组分的积累。本研究明确了提升颗粒形绿茶综合品质的适宜工艺组合，为优化其加工技术提供了理论依据。)

Abdulrahman Alrumayh, Khaled Alhassoon, Fahd Alsaleem et al.

This paper presents a novel design of a continuum robot driven by electromagnets and springs, offering enhanced precision in multi-degree-of-freedom bending for diverse applications. Traditional continuum robots, while effective in navigating constrained environments, often face limitations in actuation methods, such as wire-based systems or pre-curved tubes. Our design overcomes these challenges by utilizing electromagnetically driven actuation, which allows each segment of the robot to bend independently at any angle, providing unprecedented flexibility and control. The technical challenges discussed emphasize the goals of this work, with the main aim being to develop a motion control system that uses electromagnets and springs to improve the accuracy and consistency of the robot’s movements. By balancing magnetic and spring forces, our system ensures predictable and stable motion in 3D space. The integration of this mechanism into multi-segmented robots opens up new possibilities in fields such as medical devices, search and rescue operations, and industrial inspection. Finite element method (FEM) simulations validate the efficiency of the proposed approach, demonstrating the precise control of the robot’s motion trajectory and enhancing its operational reliability in complex scenarios.