Phase diagram and surface tension in the three-flavor Polyakov-quark-meson model

We obtain the in-medium effective potential of the three-flavor Polyakov-Quark-Meson model as a real function of real variables in the Polyakov loop variable, to allow for the study of all possible minima of the model. At finite quark chemical potential, the real and imaginary parts of the effective potential, in terms of the Polyakov loop variables, are made apparent, showing explicitly the fermion sign problem of the theory. The phase diagram and other equilibrium observables, obtained from the real part of the effective potential, are calculated in the mean-field approximation. The obtained results are compared to those found with the so-called saddle-point approach. Our procedure also allows the calculation of the surface tension between the chirally broken and confined phase, and the chirally restored and deconfined phase. The values of surface tension we find for low temperatures are very close to the ones recently found for two-flavor chiral models. Some consequences of our results for the early Universe, for heavy-ion collisions, and for proto-neutron stars are briefly discussed.Comment: 17 pages, 6 figures. V2: typos fixed, references adde

Perancangan Dan Pembuatan Sistem Informasi Administrasi Pada Toko Jaya Karya Berbasis Web

Toko Jaya Karya adalah sebuah toko yang bergerak dibidangpembelian dan penjualan barang seperti beras, gula, indomie, danrokok. Pada saat ini sistem pembelian dan penjualan pada TokoJaya Karya masih menggunakan sistem manual. Hal inimenyulitkan pemilik Toko untuk mengetahui keuntungan ataukerugian yang didapatkan.Berdasarkan analisis permasalahan yang dihadapi oleh toko JayaKarya, maka dilakukan pembuatan aplikasi untuk mendukungkinerja toko. Aplikasi diimplementasikan dapat memprosestransaksi pembelian, retur pembelian, penjualan, retur penjualan,stock opname, pembayaran hutang dan piutang. Untuk mendesainsistem baru DFD digunakan aplikasi Power Designer 6, dan ERDdigunakan aplikasi Power Designer 15.3. Aplikasi dibuat denganmenggunakan PHP dan MySQL sebagai penyimpan database.Aplikasi dapat melakukan penyimpanan data master dantransaksi, serta memberikan informasi berupa laporan yangdiperlukan termasuk perhitungan laba rugi dengan menggunakanmetode First In First Out (FIFO) yang mampu menghasilkan hasilyang akurat

Analysis of Neural Network based Proportional Myoelectric Hand Prosthesis Control

Objective: We show that state-of-the-art deep neural networks achieve superior results in regression-based multi-class proportional myoelectric hand prosthesis control than two common baseline approaches, and we analyze the neural network mapping to explain why this is the case. Methods: Feedforward neural networks and baseline systems are trained on an offline corpus of 11 able-bodied subjects and 4 prosthesis wearers, using the R2 score as metric. Analysis is performed using diverse qualitative and quantitative approaches, followed by a rigorous evaluation. Results: Our best neural networks have at least three hidden layers with at least 128 neurons per layer; smaller architectures, as used by many prior studies, perform substantially worse. The key to good performance is to both optimally regress the target movement, and to suppress spurious movements. Due to the properties of the underlying data, this is impossible to achieve with linear methods, but can be attained with high exactness using sufficiently large neural networks. Conclusion: Neural networks perform significantly better than common linear approaches in the given task, in particular when sufficiently large architectures are used. This can be explained by salient properties of the underlying data, and by theoretical and experimental analysis of the neural network mapping. Significance: To the best of our knowledge, this work is the first one in the field which not only reports that large and deep neural networks are superior to existing architectures, but also explains this result

Nanosecond Pulsed Electric Field Induced Cytoskeleton, Nuclear Membrane and Telomere Damage Adversely Impact Cell Survival

We investigated the effects of nanosecond pulsed electric fields (nsPEF) on three human cell lines and demonstrated cell shrinkage, breakdown of the cytoskeleton, nuclear membrane and chromosomal telomere damage. There was a differential response between cell types coinciding with cell survival. Jurkat cells showed cytoskeleton, nuclear membrane and telomere damage that severely impacted cell survival compared to two adherent cell lines. Interestingly, disruption of the actin cytoskeleton in adherent cells prior to nsPEF exposure significantly reduced cell survival. We conclude that nsPEF applications are able to induce damage to the cytoskeleton and nuclear membrane. Telomere sequences, regions that tether and stabilize DNA to the nuclear membrane, are severely compromised as measured by a pan-telomere probe. Internal pore formation following nsPEF applications has been described as a factor in induced cell death. Here we suggest that nsPEF induced physical changes to the cell in addition to pore formation need to be considered as an alternative method of cell death. We suggest nsPEF electrochemical induced depolymerization of actin filaments may account for cytoskeleton and nuclear membrane anomalies leading to sensitization

Deep learning a boon for biophotonics

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Magnetic substructure in the northern Fermi bubble revealed by polarized microwave emission

We report a correspondence between giant, polarized microwave structures emerging north from the Galactic plane near the Galactic center and a number of GeV gamma-ray features, including the eastern edge of the recently discovered northern Fermi Bubble. The polarized microwave features also correspond to structures seen in the all-sky 408MHz total intensity data, including the Galactic center Spur. The magnetic field structure revealed by the Wilkinson Microwave Anisotropy Probe polarization data at 23GHz suggests that neither the emission coincident with the Bubble edge nor the Galactic center Spur are likely to be features of the local interstellar medium. On the basis of the observed morphological correspondences, similar inferred spectra, and the similar energetics of all sources, we suggest a direct connection between the Galactic center Spur and the northern Fermi Bubble

Highly Sensitive Detection of the Antibiotic Ciprofloxacin by Means of Fiber Enhanced Raman Spectroscopy

Sepsis and septic shock exhibit a rapid course and a high fatality rate. Antibiotic treatment is time-critical and precise knowledge of the antibiotic concentration during the patients’ treatment would allow individual dose adaption. Over- and underdosing will increase the antimicrobial efficacy and reduce toxicity. We demonstrated that fiber enhanced Raman spectroscopy (FERS) can be used to detect very low concentrations of ciprofloxacin in clinically relevant doses, down to 1.5 µM. Fiber enhancement was achieved in bandgap shifted photonic crystal fibers. The high linearity between the Raman signals and the drug concentrations allows a robust calibration for drug quantification. The needed sample volume was very low (0.58 µL) and an acquisition time of 30 s allowed the rapid monitoring of ciprofloxacin levels in a less invasive way than conventional techniques. These results demonstrate that FERS has a high potential for clinical in-situ monitoring of ciprofloxacin levels