On the energetic origin of self-limiting trenches formed around Ge/Si quantum dots

At high growth temperatures, the misfit strain at the boundary of Ge quantum dots on Si(001) is relieved by formation of trenches around the base of the islands. The depth of the trenches has been observed to saturate at a level that depends on the base-width of the islands. Using finite element simulations, we show that the self-limiting nature of trench depth is due to a competition between the elastic relaxation energy gained by the formation of the trench and the surface energy cost for creating the trench. Our simulations predict a linear increase of the trench depth with the island radius, in quantitative agreement with the experimental observations of Drucker and coworkers

The prediction of academic performance of open admissions students at Virginia State University

The purpose of this study was to identify factors contributing to success, nonsuccess and, withdrawal of open admissions students at Virginia State University. The primary objective was to develop a means for predicting academic outcomes at the time of matriculation for each student in the open admissions program.;The students studied were entering freshmen who were underprepared to enter college. Ex post facto data used as predictors consisted of SAT scores, high school achievement, placement tests scores, and measures on student commitment, socioeconomic status, and student expectations regarding college life.;Using a total of 32 predictors and the criterion categories of successful persisters, unsuccessful persisters, and unsuccessful withdrawals, four separate discriminant analyses were performed on two freshmen groups. The objectives of these analyses were to determine the extent and manner in which the criterion categories could be differentiated by the predictors and to identify dimensions associated with the differentiated outcomes. Another purpose was to provide a means for properly classifying individual students in future freshman classes given the data required for the predictors.;The hypotheses tested emphasized the primacy of academic variables, and differences between males and females in both performance and persistence outcomes. The theoretical framework for the study consisted of Atkinson\u27s model regarding performance and Tinto\u27s theory of dropouts.;The results revealed a lack of uniformity among the groups analyzed and little evidence of discrimination. The primary predictor, anticipation of needing extra time to complete degree requirements, was nonacademic, but it was only a limited indication of the student\u27s expected degree of academic integration at college. The secondary predictor, reading ability, was only supportive in predictions. The results regarding prediction of performance did not conform to the majority of the findings in the literature nor to Atkinson\u27s model. The results on dropout, including actual outcomes, appear to be consistent with outcomes that could be expected from Tinto\u27s theory.;It was concluded that accurate predictions on open admissions outcomes for individual students could not be made at the time of matriculation. However, predictions of group outcomes could be made on the basis of actual outcomes determined in the study

Application of artificial neural networks for prokaryotic transcription terminator prediction

Artificial neural networks (ANN) to predict terminator sequences, based on a feed-forward architecture and trained using the error back propagation technique, have been developed. The network uses two different methods for coding nucleotide sequences. In one the nucleotide bases are coded in binary while the other uses the electron-ion interaction potential values (EIIP) of the nucleotide bases. The latter strategy is new, property based and substantially reduces the network size. The prediction capacity of the artificial neural network using both coding strategies is more than 95%

First-principles calculations of step formation energies and step interactions on TiN(001)

We study the formation energies and repulsive interactions of monatomic steps on the TiN(001) surface, using density functional total-energy calculations. The calculated formation energy of [100] oriented steps agree well with recently reported experimental values; these steps are shown to have a rumpled structure, with the Ti atoms undergoing larger displacements than the N atoms. For steps that are parallel to [110], our calculations predict a nitrogen (N) termination, as the corresponding formation energy is several hundred meV/\AA \ smaller than that of Ti-terminated steps

Unleashing shear: Role of intercellular traction and cellular moments in collective cell migration

In the field of endothelial biology, the term “shear forces” is tied to the forces exerted by the flowing blood on the quiescent cells. But endothelial cells themselves also exert physical forces on their immediate and distant neighbors. Specific factors of such intrinsic mechanical signals most relevant to immediate neighbors include normal (Fn) and shear (Fs) components of intercellular tractions, and those factors most relevant to distant neighbors include contractile or dilatational (Mc) and shear (Ms) components of the moments of cytoskeletal forces. However, for cells within a monolayer, Fn, Fs, Mc, and Ms remain inaccessible to experimental evaluation. Here, we present an approach that enables quantitative assessment of these properties. Remarkably, across a collectively migrating sheet of pulmonary microvascular endothelial cells, Fs was of the same order of magnitude as Fn. Moreover, compared to the normal components (Fn, Mc) of the mechanical signals, the shear components (Fs, Ms) were more distinctive in the cells closer to the migration front. Individual cells had an innately collective tendency to migrate along the axis of maximum contractile moment e a collective migratory process we referred to as cellular plithotaxis. Notably, larger Fs and Ms were associated with stronger plithotaxis, but dilatational moment appeared to disengage plithotactic guidance. Overall, cellular plithotaxis was more strongly associated with the “shear forces” (Fs, Ms) than with the “normal forces” (Fn, Mc). Finally, the mechanical state of the cells with fast migration speed and those with highly circular shape were reminiscent of fluid-like and solid-like matter, respectively. The results repeatedly pointed to neighbors imposing shear forces on a cell as a highly significant event, and hence, the term “shear forces” must include not just the forces from flowing fluid but also the forces from the substrate and neighbors. Collectively, these advances set the stage for deeper understanding of mechanical signaling in cellular monolayers.Osteopathic Medicin

Why drug shortages are an ethical issue

Drug shortages are a growing problem in developed countries. To some extent they are the result of technical and organisational failures, but to view drug shortages simply as technical and economic phenomena is to miss the fact that they are also ethical and political issues. This observation is important because it highlights both the moral and political imperative to respond to drug shortages as vigorously as possible, and the need for those addressing shortages to do so in ethically and politically sophisticated ways. This brief article outlines the ethical issues that need to be considered by anyone attempting to understand or address drug shortages

A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs

The proliferation of personal artificial intelligence (AI) -assistant technologies with speech-based conversational AI interfaces is driving the exponential growth in the consumer Internet of Things (IoT) market. As these technologies are being applied to keyword spotting (KWS), automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS) applications, it is of paramount importance that they provide uncompromising performance for context learning in long sequences, which is a key benefit of the attention mechanism, and that they work seamlessly in polyphonic environments. In this work, we present a 25-mm $^2$ system-on-chip (SoC) in 16-nm FinFET technology, codenamed SM6, which executes end-to-end speech-enhancing attention-based ASR and NLP workloads. The SoC includes: 1) FlexASR, a highly reconfigurable NLP inference processor optimized for whole-model acceleration of bidirectional attention-based sequence-to-sequence (seq2seq) deep neural networks (DNNs); 2) a Markov random field source separation engine (MSSE), a probabilistic graphical model accelerator for unsupervised inference via Gibbs sampling, used for sound source separation; 3) a dual-core Arm Cortex A53 CPU cluster, which provides on-demand single Instruction/multiple data (SIMD) fast fourier transform (FFT) processing and performs various application logic (e.g., expectation–maximization (EM) algorithm and 8-bit floating-point (FP8) quantization); and 4) an always-on M0 subsystem for audio detection and power management. Measurement results demonstrate the efficiency ranges of 2.6–7.8 TFLOPs/W and 4.33–17.6 Gsamples/s/W for FlexASR and MSSE, respectively; MSSE denoising performance allowing 6 $\times$ smaller ASR model to be stored on-chip with negligible accuracy loss; and 2.24-mJ energy consumption while achieving real-time throughput, end-to-end, and per-frame ASR latencies of 18 ms

Coordination of Mobile Mules via Facility Location Strategies

In this paper, we study the problem of wireless sensor network (WSN) maintenance using mobile entities called mules. The mules are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and fix it. The mules must constantly optimize their collective deployment to account for occupied mules. The objective is to define the optimal deployment and task allocation strategy for the mules, so that the sensors' downtime and the mules' traveling distance are minimized. Our solutions are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location. Our empirical results demonstrate how cooperation enhances the team's performance, and indicate that a combination of k-Median based deployment with closest-available task allocation provides the best results in terms of minimizing the sensors' downtime but is inefficient in terms of the mules' travel distance. A k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc

Monolayer Stress Microscopy: Limitations, Artifacts, and Accuracy of Recovered Intercellular Stresses

In wound healing, tissue growth, and certain cancers, the epithelial or the endothelial monolayer sheet expands. Within the expanding monolayer sheet, migration of the individual cell is strongly guided by physical forces imposed by adjacent cells. This process is called plithotaxis and was discovered using Monolayer Stress Microscopy (MSM). MSM rests upon certain simplifying assumptions, however, concerning boundary conditions, cell material properties and system dimensionality. To assess the validity of these assumptions and to quantify associated errors, here we report new analytical, numerical, and experimental investigations. For several commonly used experimental monolayer systems, the simplifying assumptions used previously lead to errors that are shown to be quite small. Out-of-plane components of displacement and traction fields can be safely neglected, and characteristic features of intercellular stresses that underlie plithotaxis remain largely unaffected. Taken together, these findings validate Monolayer Stress Microscopy within broad but well-defined limits of applicability