Hasil untuk "History"

Philip Scranton, A. Chandler

Timothy A Mousseau, D. Roff

H. Dubs, Fung Yu-lan, D. Bodde

Romila Thapar, Thomas George Percival Spear

Albina Hasa, Merita Xhetani, Mirela Lika et al.

Breast cancer is the leading cause of cancer among women and remains one of the most prevalent contributors to cancer-related mortality worldwide. This study aims to evaluate biomarker expression in women with breast cancer and the correlations between them and other prognostic parameters. A retrospective analysis of 252 subjects was conducted at the Oncology Department of Mother Teresa University Hospital in Albania between 2021 and 2024. The highest rate of the disease was observed in the over-45 age group (92.5%). A family history of cancer was reported in 13.9% of patients. The most common histological type identified was ductal carcinoma (81.3%), which predominantly affected the left breast (57.1%) and was most frequently stage 2 (69.8%). A significant association (p < 0.001) was found between disease stage and expression of ER (Estrogen Receptors), PR (Progesterone Receptors), and HER2 (Human Epidermal Growth Factor Receptor 2), as well as between patient age and expression of ER, PR, and HER2 receptors (p = 0.02). HER2 and Ki-67 expression were inversely associated with ER and PR. Ki-67 was significantly correlated with age (p = 0.008) and stage (p < 0.001). Nodal metastasis correlated with Ki-67 (p = 0.02) and ER (p = 0.01).

Jiale Zhou, Jiaxin Han

We investigate the role of the accretion history in shaping the depletion radius of dark matter halos using a large cosmological N-body simulation. We show that the inner depletion radius, rescaled by the virial radius, depends strongly on the recent mass accretion rate (MAR) measured over a dynamical timescale, while exhibiting only weak dependence on halo mass. While this dependence mirrors that of the splashback radius and the two radii are tightly correlated, the depletion radius exhibits a more nuanced response to the detailed accretion mode. Specifically, we find that the dependence on MAR steepens at lower redshifts, aligning with self-similar spherical collapse models yet contrasting with the behavior of the splashback radius. This redshift dependence is largely driven by dynamic events, as it diminishes significantly when halos undergoing recent major mergers are excluded. Furthermore, we identify a dichotomy in the drivers of the depletion radius. For slowly accreting halos, the MAR is the primary dependence, whereas for rapidly accreting halos, other properties (shape, spin, concentration, and formation time of the central subhalo) related to the anisotropic or perturbed accretion mode also play a significant role. These results establish the depletion radius as a sensitive physical probe of the detailed accretion history of dark matter halos, complementary to the splashback radius.

Salih Gülen, İsmail Dönmez, Fatma Betül Şengönül et al.

STEM education aims to develop 21st-century skills, support economic growth and promote gender equality in STEM fields. It is known that gender stereotypes play a significant role in the formation of STEM identity. The most important factor preventing some high school-level female students from pursuing STEM careers is their lack of participation in STEM activities. Female students in high schools have limited opportunities to explore or learn about STEM careers due to the emphasis on verbal and religious courses in their curriculum. However, it is known that women can work more autonomously in scientific activities compared to men. The current study examines the effect of robotics and coding education on the development of girls’ STEM careers. The study was conducted at an all-girls high school in Turkey, where the curriculum is predominantly centered on verbal and religious subjects. In the study, a pre-test and post-test experimental design with control group was used. A total of 76 volunteer female students (34 in the experimental group and 42 in the control group) participated in robotics and coding education over a period of 12 weeks. The data were collected using the validated STEM career, motivation and attitude scales and analyzed using t -tests, ANOVA and Pearson correlation. The findings revealed that robotics and coding training significantly improved the participants’ STEM career aspirations, attitudes and motivations. A strong positive correlation was found between career interest, attitude and motivation. The study also showed that STEM career scores are significantly higher among students who wish to become teachers compared to those considering a career in the fields of health or engineering. However, no significant correlation was found between the participants’ parents’ education levels, family income and STEM career aspirations.

احمد فرطوس حيدر

يناقش هذا البحث تلابوقا خان سادس حكام دولة مغول القبجاق تلك البلاد التي تعرف ايضاً باسم دولة مغول القبيلة الذهبية والغوص بسيرته الشخصية وحروبه الخارجية كالحرب التي دار رحاها مع الدولة الايلخانية تلك الدولة المغولية الاخرى التي اسسها هولاكو خان وقامت على انقاض الخلافة العباسية مروراً بحرب تلابوقا خان في بلاد الكرل (هنغاريا) .       كما تضمن البحث تأثير مقتل تلابوقا خان على اوضاع مسلمي القبجاق على يد خصمه طقطاي الذي كان يعتنق الديانة الشامانية المغولية إذ قطع مقتل هذا الخان سلسلة من الحكام المسلمين الذين تعاقبوا على زعامة بلاد القبجاق ,ام ان تاثير الدين يبقى محدودا في السياسة المغولية وفق الاسس والمبادئ التي وضعها جنكيزخان مؤسس الامبراطورية المغولية التي تقضي بالولاء السياسي المطلق للمغول ويترك مسالة اختيار الدين حرية شخصية من حكام ورعايا طالما التزموا بالقوانين المغولية 

Joop Niekus

We follow the history and development of Brouwer's use of individual choice sequences up to the discovery of a method to apply them successfully in 1927. With the principles we derive from this first use we analyze in detail Brouwer's work from that time onward. Our reconstruction uses only very basic principles. It aligns exactly with Brouwer's work after 1927 and, moreover, it gives a clear explanation of the proofs of his results and the terms he uses.

Wenhan Lyu, Devashish Tyagi, Yihang Yang et al.

Long user history is highly valuable signal for recommendation systems, but effectively incorporating it often comes with high cost in terms of data center power consumption and GPU. In this work, we chose offline embedding over end-to-end sequence length optimization methods to enable extremely long user sequence modeling as a cost-effective solution, and propose a new user embedding learning strategy, multi-slicing and summarization, that generates highly generalizable user representation of user's long-term stable interest. History length we encoded in this embedding is up to 70,000 and on average 40,000. This embedding, named as DV365, is proven highly incremental on top of advanced attentive user sequence models deployed in Instagram. Produced by a single upstream foundational model, it is launched in 15 different models across Instagram and Threads with significant impact, and has been production battle-proven for >1 year since our first launch.

Kiyoshi Igusa, Gordana Todorov

Whereas exceptional sequences have a long history with many well-known connections to combinatorics, signed exceptional sequences are relatively recent. The authors introduced this concept in 2017 [19], although it was retroactively realized that the category of noncrossing partitions [24] is a special case of this construction. Buan and Marsh [4] have introduced the concept of $τ$-exceptional sequences to generalize the definitions and theorems to all finite dimensional algebras. This short paper is the story of the original concept of signed exceptional sequences for hereditary algebras and how it developed out of the two authors' study of algebraic K-theory, link invariants, and cluster combinatorics.

Yuwei Xia, Ding Wang, Qiang Liu et al.

Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based on given histories. Most recent graph-based models excel at capturing structural information within TKGs but lack semantic comprehension abilities. Nowadays, with the surge of LLMs, the LLM-based TKG prediction model has emerged. However, the existing LLM-based model exhibits three shortcomings: (1) It only focuses on the first-order history for prediction while ignoring high-order historical information, resulting in the provided information for LLMs being extremely limited. (2) LLMs struggle with optimal reasoning performance under heavy historical information loads. (3) For TKG prediction, the temporal reasoning capability of LLM alone is limited. To address the first two challenges, we propose Chain-of-History (CoH) reasoning which explores high-order histories step-by-step, achieving effective utilization of high-order historical information for LLMs on TKG prediction. To address the third issue, we design CoH as a plug-and-play module to enhance the performance of graph-based models for TKG prediction. Extensive experiments on three datasets and backbones demonstrate the effectiveness of CoH.

Junko Akiyama, Mina Imai, Keisuke Yamaguchi

Abstract A 56‐year‐old woman presented with a 2 months history of patulous eustachian tube. She had sudden weight loss after developing a cold, after which she had been experiencing disabling autophony and a sensation of blockage in the ear. She underwent stellate ganglion block in 8 months; her symptoms resolved subsequently.

Bogumiła Kaniewska

The topic of Central Europe is one of the key problems for contemporary Polish literature, as it defines the issues of social identity as well as both individual and collective self-identification. As a “middle country” chocked in between two major spheres of influence: Russia and Germany; the East and the West, Poland has been voided of its statehood ever since the second half of the 18th century and has been the place where these two spheres of influence kept clashing. The perception of Poland as the boundary between the influences, a historical bulwark of the West, the interwar bastion of western culture or a country from behind the Iron Curtain did not strengthen the bonds between its culture and Central Europe. The Iron Curtain bolstered the polarization of Central Europe after World War II and Polish literature observes this crack on two facets – chronological and spatial.

Ken'yo U, Masanori Kameyama, Masaki Ogawa

To understand the evolution of the Moon, we numerically modeled mantle convection and magmatism in a two-dimensional polar rectangular mantle. Magmatism occurs as an upward permeable flow of magma generated by decompression melting through the convecting matrix. The mantle is assumed to be initially enriched in heat-producing elements (HPEs) and compositionally dense ilmenite-bearing cumulates (IBC) at its base. Here, we newly show that magma generation and migration play a crucial role in the calculated volcanic and radial expansion/contraction history. Magma is generated in the deep mantle by internal heating for the first several hundred million years. A large volume of the generated magma ascends to the surface as partially molten fingers and plumes driven by melt-buoyancy to cause a volcanic activity and radial expansion of the planet with the peak at 3.5-4 Gyr ago. Eventually, however, the planet begins to radially contract when the mantle solidifies by cooling from the surface boundary. As the mantle is cooled, the activity of partially molten plumes declines but continues for billions of years after the peak because some basal materials enriched in the dense IBC components hold HPEs. The calculated volcanic and radial expansion/contraction history is consistent with the observed history of the Moon. Our simulations suggest a substantial fraction of the mantle was solid, and there was a basal layer enriched in HPEs and the IBC components at the beginning of the history of the Moon.

Tri Nguyen, Chirag Modi, L. Y. Aaron Yung et al.

The mass assembly history (MAH) of dark matter halos plays a crucial role in shaping the formation and evolution of galaxies. MAHs are used extensively in semi-analytic and empirical models of galaxy formation, yet current analytic methods to generate them are inaccurate and unable to capture their relationship with the halo internal structure and large-scale environment. This paper introduces FLORAH, a machine-learning framework for generating assembly histories of ensembles of dark matter halos. We train FLORAH on the assembly histories from the GUREFT and VSMDPL N-body simulations and demonstrate its ability to recover key properties such as the time evolution of mass and concentration. We obtain similar results for the galaxy stellar mass versus halo mass relation and its residuals when we run the Santa Cruz semi-analytic model on FLORAH-generated assembly histories and halo formation histories extracted from an N-body simulation. We further show that FLORAH also reproduces the dependence of clustering on properties other than mass (assembly bias), which is not captured by other analytic methods. By combining multiple networks trained on a suite of simulations with different redshift ranges and mass resolutions, we are able to construct accurate main progenitor branches (MPBs) with a wide dynamic mass range from $z=0$ up to an ultra-high redshift $z \approx 20$, currently far beyond that of a single N-body simulation. FLORAH is the first step towards a machine learning-based framework for planting full merger trees; this will enable the exploration of different galaxy formation scenarios with great computational efficiency at unprecedented accuracy.

A. Camps-Fariña, P. Sánchez-Blázquez, S. Roca-Fàbrega et al.

Galaxies are expected to accrete pristine gas from their surroundings to sustain their star formation over cosmic timescales. Its lower abundance affects the metallicity of the ISM in which stars are born, leaving chemical imprints in the stellar populations. We measure the amount of pristine gas that galaxies accrete during their lifetime, using information on the ages and abundances of their stellar populations and a chemical evolution model. We also aim to determine the efficiency of star formation over time. We derived star formation histories and metallicity histories for a sample of 8523 galaxies from the MaNGA survey. We use the former to predict the evolution of the metallicity in a closed-box scenario, and estimate for each epoch the gas accretion rate required to match these predictions with the measured stellar metallicity. Using only chemical parameters, we find that the history of gas accretion depends on the mass of galaxies. More massive galaxies accrete more gas and at higher redshifts than less massive galaxies, which accrete their gas over longer periods. We also find that galaxies with a higher star formation rate at z = 0 have a more persistent accretion history for a given mass. The star formation efficiency shows similar correlations: early-type galaxies and higher-mass galaxies had a higher efficiency in the past, and it declined such that they are less efficient in the present. Our analysis of individual galaxies shows that compactness affects the peak star formation efficiency that galaxies reach, and that the slope of the efficiency history of galaxies with current star formation is flat. Our results support the hypothesis that a steady and substantial supply of pristine gas is required for persistent star formation in galaxies. Once they lose access to this gas supply, star formation comes to a halt.

Igor Manzura

The article discusses the problem of identification of prehistoric elites on the example of the Usatovo culture. Archaeological evidence allows us to discern in the culture three separate strata or classes which are conditionally designated as the Nobles, the Honoured, and the Commoners. Social elite represented a core within the noble class. It is distinguished by specific attributes which illustrate its attempts to resolve one of the most complex problems of any elite – to reconcile tension between “universalism” and “particularism”. The first quality is realized in ritual performance through manipulation of material objects whereas the second one is realized by means of sumptuary customs along with other methods. The emergence of the elites in the Usatovo culture became possible due to specific combination of objective factors which were successfully utilized by new social leaders.

Burigede Liu, Eric Ocegueda, Margaret Trautner et al.

This paper concerns the study of history dependent phenomena in heterogeneous materials in a two-scale setting where the material is specified at a fine microscopic scale of heterogeneities that is much smaller than the coarse macroscopic scale of application. We specifically study a polycrystalline medium where each grain is governed by crystal plasticity while the solid is subjected to macroscopic dynamic loads. The theory of homogenization allows us to solve the macroscale problem directly with a constitutive relation that is defined implicitly by the solution of the microscale problem. However, the homogenization leads to a highly complex history dependence at the macroscale, one that can be quite different from that at the microscale. In this paper, we examine the use of machine-learning, and especially deep neural networks, to harness data generated by repeatedly solving the finer scale model to: (i) gain insights into the history dependence and the macroscopic internal variables that govern the overall response; and (ii) to create a computationally efficient surrogate of its solution operator, that can directly be used at the coarser scale with no further modeling. We do so by introducing a recurrent neural operator (RNO), and show that: (i) the architecture and the learned internal variables can provide insight into the physics of the macroscopic problem; and (ii) that the RNO can provide multiscale, specifically FE2, accuracy at a cost comparable to a conventional empirical constitutive relation.

M. Kranzberg