861 research outputs found
Heat capacity of the generalized two-atom and many-atom gas in nonextensive statistics
We have used the generalized two-atom ideal gas model in Tsallis statistics
for the statistical description of a real gas. By comparing the heat capacity
with the experimental results for the two-atom molecule gases such as N2, O2
and CO, we find that these gases appear extensive at normal temperature, but
they may be nonextensive at the lower temperature. Furthermore, we study the
heat capacity of the generalized many-atom gas model. We conclude that, for the
many-atom gas with a high degree of freedom, a weak nonextensivity of 1-q<0 can
lead to the instability.Comment: 12 pages, 2 figures, 1 table, 32 reference
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Do employees truly value their brand values? Examining the specificity of employee-brand value fit for service brands
The alignment between employee performance and brand values is crucial in realizing a differentiated and meaningful service brand. However, previous studies that examine this alignment tend to adopt only reflective measures that do not acknowledge the pluralistic nature of brand values. To fully demonstrate the specificity of employee-brand value fit, we proposed and tested a Multiple Indicators Multiple Causes (MIMIC) measurement model with both reflective indicators and formative indicators. This approach not only reveals a general employee-brand value fit level, but also yields insight with respect to what brand values are the most important in informing employee-brand value fit and their subsequent brand performance. Two empirical studies were conducted to test and re-test this new approach. Both studies strongly supported the superiority of this MIMIC model approach. With this statistical advancement, theoretical and managerial implications are provided accordingly
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Learning accurate cross-domain preference mappings in the absence of
overlapped users/items has presented a persistent challenge in Non-overlapping
Cross-domain Recommendation (NOCDR). Despite the efforts made in previous
studies to address NOCDR, several limitations still exist. Specifically, 1)
while some approaches substitute overlapping users/items with overlapping
behaviors, they cannot handle NOCDR scenarios where such auxiliary information
is unavailable; 2) often, cross-domain preference mapping is modeled by
learning deterministic explicit representation matchings between sampled users
in two domains. However, this can be biased due to individual preferences and
thus fails to incorporate preference continuity and universality of the general
population. In light of this, we assume that despite the scattered nature of
user behaviors, there exists a consistent latent preference distribution shared
among common people. Modeling such distributions further allows us to capture
the continuity in user behaviors within each domain and discover preference
invariance across domains. To this end, we propose a Distributional
domain-invariant Preference Matching method for non-overlapping Cross-Domain
Recommendation (DPMCDR). For each domain, we hierarchically approximate a
posterior of domain-level preference distribution with empirical evidence
derived from user-item interactions. Next, we aim to build distributional
implicit matchings between the domain-level preferences of two domains. This
process involves mapping them to a shared latent space and seeking a consensus
on domain-invariant preference by minimizing the distance between their
distributional representations therein. In this way, we can identify the
alignment of two non-overlapping domains if they exhibit similar patterns of
domain-invariant preference.Comment: 9 pages, 5 figures, full research paper accepted by ICDM 202
Use of anchorchip-time-of-flight spectrometry technology to screen tumor biomarker proteins in serum for small cell lung cancer
<p>Abstract</p> <p>Background</p> <p>The purpose of this study is to discover potential biomarkers in serum for the detection of small cell lung cancer (SCLC).</p> <p>Methods</p> <p>74 serum samples including 30 from SCLC patients and 44 from healthy controls were analyzed using ClinProt system combined with matrix-assisted laser desorption/ionization time-of-flight masss spectrometry (MALDI-TOF-MS). ClinProt software and genetic algorithm analysis selected a panel of serum markers that most efficiently predicted which patients had SCLC.</p> <p>Results</p> <p>The diagnostic pattern combined with 5 potential biomarkers could differentiate SCLC patients from healthy persons, with a sensitivity of 90%, specificity of 97.73%. Remarkably, 88.89% of stage I/II patients were accurately assigned to SCLC.</p> <p>Conclusions</p> <p>Anchorchip-time-of-flight spectrometry technology will provide a highly accurate approach for discovering new biomarkers for the detection of SCLC.</p
Modeling craniofacial development reveals spatiotemporal constraints on robust patterning of the mandibular arch
How does pattern formation occur accurately when confronted with tissue growth and stochastic fluctuations (noise) in gene expression? Dorso-ventral (D-V) patterning of the mandibular arch specifies upper versus lower jaw skeletal elements through a combination of Bone morphogenetic protein (Bmp), Endothelin-1 (Edn1), and Notch signaling, and this system is highly robust. We combine NanoString experiments of early D-V gene expression with live imaging of arch development in zebrafish to construct a computational model of the D-V mandibular patterning network. The model recapitulates published genetic perturbations in arch development. Patterning is most sensitive to changes in Bmp signaling, and the temporal order of gene expression modulates the response of the patterning network to noise. Thus, our integrated systems biology approach reveals non-intuitive features of the complex signaling system crucial for craniofacial development, including novel insights into roles of gene expression timing and stochasticity in signaling and gene regulation
Continuous Finite-Time Terminal Sliding Mode IDA-PBC Design for PMSM with the Port-Controlled Hamiltonian Model
Finite-time control scheme for speed regulation of permanent magnet synchronous motor (PMSM) is investigated under the port-controlled Hamiltonian (PCH), terminal sliding mode (TSM), and fast TSM stabilization theories. The desired equilibrium is assigned to the PCH structure model of PMSM by maximum torque per ampere (MTPA) principle, and the desired Hamiltonian function of state error is constructed in the form of fractional power structure as TSM and fast TSM, respectively. Finite-time TSM and fast TSM controllers are designed via interconnection and damping assignment passivity-based control (IDA-PBC) methodology, respectively, and the finite-time stability of the desired equilibrium point is also achieved under the PCH framework. Simulation results validate the improved performance of the presented scheme
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