Charge transfer electrostatic model of compositional order in perovskite alloys

We introduce an electrostatic model including charge transfer, which is shown to account for the observed B-site ordering in Pb-based perovskite alloys. The model allows charge transfer between A-sites and is a generalization of Bellaiche and Vanderbilt's purely electrostatic model. The large covalency of Pb^{2+} compared to Ba^{2+} is modeled by an environment dependent effective A-site charge. Monte Carlo simulations of this model successfully reproduce the long range compositional order of both Pb-based and Ba-based complex A(BB^{'}B^{''})O_3 perovskite alloys. The models are also extended to study systems with A-site and B-site doping, such as (Na_{1/2}La_{1/2})(Mg_{1/3}Nb_{2/3})O_3, (Ba_{1-x}La_{x})(Mg_{(1+x)/3}Nb_{(2-x)/3})O_3 and (Pb_{1-x}La_{x})(Mg_{(1+x)/3}Ta_{(2-x)/3})O_3. General trends are reproduced by purely electrostatic interactions, and charge transfer effects indicate that local structural relaxations can tip the balance between different B-site orderings in Pb based materials.Comment: 15 pages, 6 figure

Kinetic Monte Carlo Simulations of Crystal Growth in Ferroelectric Alloys

The growth rates and chemical ordering of ferroelectric alloys are studied with kinetic Monte Carlo (KMC) simulations using an electrostatic model with long-range Coulomb interactions, as a function of temperature, chemical composition, and substrate orientation. Crystal growth is characterized by thermodynamic processes involving adsorption and evaporation, with solid-on-solid restrictions and excluding diffusion. A KMC algorithm is formulated to simulate this model efficiently in the presence of long-range interactions. Simulations were carried out on Ba(Mg_{1/3}Nb_{2/3})O_3 (BMN) type materials. Compared to the simple rocksalt ordered structures, ordered BMN grows only at very low temperatures and only under finely tuned conditions. For materials with tetravalent compositions, such as (1-x)Ba(Mg_{1/3}Nb_{2/3})O_3 + xBaZrO_3 (BMN-BZ), the model does not incorporate tetravalent ions at low-temperature, exhibiting a phase-separated ground state instead. At higher temperatures, tetravalent ions can be incorporated, but the resulting crystals show no chemical ordering in the absence of diffusive mechanisms.Comment: 13 pages, 16 postscript figures, submitted to Physics Review B Journa

Efektivitas Implementasi Permainan Aung-carbon-card Pada Materi Pelajaran Senyawa Karbon

Tujuan dalam penelitian adalah mengetahui keefektifan, tingkat kemudahan dan tingkat ketertarikan siswa penggunaan media permainan Aung-Carbon-Card (ACC) dalam pembelajaran struktur senyawa karbon. Penelitian ini menggunakan jenis penelitian eksperimen. Sampel yang digunakan yakni siswa kelas X IPA-1dianggap sebagai kelompok konrol (KC), kelas X IPA-2 (E1), kelas X IPA-3 (E2) dan kelas IPA-4 (E3) dijadikan kelompok eksperimen. Sumber data penelitian ini diperoleh dari tes, angket dan observasi. Instrumen yang digunakan sebelumnya dilakukan uji instrumen untuk mengetahui validitas dan reliabilitas. Data yangdiperoleh dilakukan uji normalitas data. Uji hipotesis dilakukan dengan perhitungan uji statistik uji T two sampel independent menggunakan SPSS 17. Hasil penelitian diperoleh: 1). Pembelajaran senyawa hidrokarbon menggunakan media permainan kartu ACC lebih efektif dibuktikan pada hasil tes dengan uji T, nilai Thitung > Ttabel. 2) Kartu ACC menarik perhatian siswa dalam mempelajari pokok bahasan senyawa karbon dibuktikan dengan hasil angket persentase siswa yang menyatakan permainan kartu ACC hal yang menarik. Hasil persentase E1 88%, E2 85% dan E3 74%. Hasil perhitungan terhadap respon siswa masing-masing kelas eksperimen terhadap KC Thitung > Ttabel. 3). Media permainan kartu ACC dapat membantu mempermudah siswa dalam mempelajari pokok bahasan senyawa dengan hasil angket persentase siswa yang menyatakan bahwa lebih mudah memahami materi dengan menggunakan ACC E1= 76%, E2 = 85% dan E3 = 81%. 4). Media permainan kartu ACC sesuai dan tepat jika digunakan dibuktikan dengan observasi dengan nilai Thitung > Ttabel

Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks

Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement. This presents a particular challenge for embedded systems, such as mobile robots, where the computational resources tend to be far less than for workstations. As an alternative to standard, uniformly sampled images, we propose the use of foveated image sampling here to reduce the size of images, which are faster to process in a CNN due to the reduced number of convolution operations. We evaluate object detection and recognition on the Microsoft COCO database, using foveated image sampling at different image sizes, ranging from 416×416 to 96×96 pixels, on an embedded GPU – an NVIDIA Jetson TX2 with 256 CUDA cores. The results show that it is possible to achieve a 4× speed-up in frame rates, from 3.59 FPS to 15.24 FPS, using 416×416 and 128×128 pixel images respectively. For foveated sampling, this image size reduction led to just a small decrease in recall performance in the foveal region, to 92.0% of the baseline performance with full-sized images, compared to a significant decrease to 50.1% of baseline recall performance in uniformly sampled images, demonstrating the advantage of foveated sampling

Electrostatic model of atomic ordering in complex perovskite alloys

We present a simple ionic model which successfully reproduces the various types of compositional long-range order observed in a large class of complex insulating perovskite alloys. The model assumes that the driving mechanism responsible for the ordering is simply the electrostatic interaction between the different ionic species. A possible new explanation for the anomalous long-range order observed in some Pb relaxor alloys, involving the proposed existence of a small amount of Pb^4+ on the B sublattice, is suggested by an analysis of the model.Comment: 4 pages, two-column style with 1 postscript figure embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/index.html#lb_orde

L1 norm based multiplication-free cosine similarity measures for big data analysis

The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector 'product' using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm. As a result, new cosine measure-like similarity measures are normalized by the ℓ1-norms of the vectors. They can be computed using the MapReduce framework. Simulation examples are presented. © 2014 IEEE

Heterovalent and A-atom effects in A(B'B'')O3 perovskite alloys

Using first-principles supercell calculations, we have investigated energetic, structural and dielectric properties of three different A(B'B'')O_3 perovskite alloys: Ba(Zn_{1/3}Nb_{2/3})O_3 (BZN), Pb(Zn_{1/3}Nb_{2/3})O_3 (PZN), and Pb(Zr_{1/3}Ti_{2/3})O_3 (PZT). In the homovalent alloy PZT, the energetics are found to be mainly driven by atomic relaxations. In the heterovalent alloys BZN and PZN, however, electrostatic interactions among B' and B'' atoms are found to be very important. These electrostatic interactions are responsible for the stabilization of the observed compositional long-range order in BZN. On the other hand, cell relaxations and the formation of short Pb--O bonds could lead to a destabilization of the same ordered structure in PZN. Finally, comparing the dielectric properties of homovalent and heterovalent alloys, the most dramatic difference arises in connection with the effective charges of the B' atom. We find that the effective charge of Zr in PZT is anomalous, while in BZN and PZN the effective charge of Zn is close to its nominal ionic value.Comment: 7 pages, two-column style with 2 postscript figures embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/index.html#lb_he

Pituitary Insufficiency and Hyperprolactinemia Associated with Giant Intra- and Suprasellar Carotid Artery Aneurysm

Pituitary insufficiency secondary to internal carotid artery (ICA) aneurysm is a very rare condition. Its prevalence is reported as 0.17% (Heshmati et al., 2001). We present a case of pituitary insufficiency and hyperprolactinemia secondary to suprasellar giant intracranial aneurysm. A 71-year-old man was admitted to our clinic with symptoms of hypopituitarism, hyperprolactinemia, and visual field defect. His pituitary MRI and cerebral angiography revealed a giant saccular aneurysm filling suprasellar cistern arising from the ophthalmic segment of the right ICA. Endovascular treatment was performed on the patient to decrease the mass effect of aneurysm and improve the hypophysis dysfunction. After treatment, his one-year follow-up showed the persistence of hypophysis insufficiency, decrease of prolactin (PRL) level, and normal visual field. An intracranial aneurysm can mimic the appearance and behavior of a pituitary adenoma. Intracranial aneurysms should be taken into consideration in the situation of hypopituitarism and hyperprolactinemia. It is important to distinguish them because their treatment approach is different from the others

The polarizability model for ferroelectricity in perovskite oxides

This article reviews the polarizability model and its applications to ferroelectric perovskite oxides. The motivation for the introduction of the model is discussed and nonlinear oxygen ion polarizability effects and their lattice dynamical implementation outlined. While a large part of this work is dedicated to results obtained within the self-consistent-phonon approximation (SPA), also nonlinear solutions of the model are handled which are of interest to the physics of relaxor ferroelectrics, domain wall motions, incommensurate phase transitions. The main emphasis is to compare the results of the model with experimental data and to predict novel phenomena.Comment: 55 pages, 35 figure

Biomedical knowledge graph-optimized prompt generation for large language models

MotivationLarge Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational overhead, requiring further domain-expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.5-Turbo and GPT-4, to generate meaningful biomedical text rooted in established knowledge.ResultsCompared to the existing RAG technique for Knowledge Graphs, the proposed method utilizes minimal graph schema for context extraction and uses embedding methods for context pruning. This optimization in context extraction results in more than 50% reduction in token consumption without compromising the accuracy, making a cost-effective and robust RAG implementation on proprietary LLMs. KG-RAG consistently enhanced the performance of LLMs across diverse biomedical prompts by generating responses rooted in established knowledge, accompanied by accurate provenance and statistical evidence (if available) to substantiate the claims. Further benchmarking on human curated datasets, such as biomedical true/false and multiple-choice questions (MCQ), showed a remarkable 71% boost in the performance of the Llama-2 model on the challenging MCQ dataset, demonstrating the framework's capacity to empower open-source models with fewer parameters for domain-specific questions. Furthermore, KG-RAG enhanced the performance of proprietary GPT models, such as GPT-3.5 and GPT-4. In summary, the proposed framework combines explicit and implicit knowledge of KG and LLM in a token optimized fashion, thus enhancing the adaptability of general-purpose LLMs to tackle domain-specific questions in a cost-effective fashion.Availability and implementationSPOKE KG can be accessed at https://spoke.rbvi.ucsf.edu/neighborhood.html. It can also be accessed using REST-API (https://spoke.rbvi.ucsf.edu/swagger/). KG-RAG code is made available at https://github.com/BaranziniLab/KG_RAG. Biomedical benchmark datasets used in this study are made available to the research community in the same GitHub repository.Supplementary informationSupplementary data are available at Bioinformatics online