Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Search for anomalous couplings in boosted WW/WZ -> l nu q(q)over-bar production in proton-proton collisions at root s=8TeV

Peer reviewe

Performance of the CMS muon trigger system in proton-proton collisions at √s = 13 TeV

The muon trigger system of the CMS experiment uses a combination of hardware and software to identify events containing a muon. During Run 2 (covering 2015-2018) the LHC achieved instantaneous luminosities as high as 2 × 10 cm s while delivering proton-proton collisions at √s = 13 TeV. The challenge for the trigger system of the CMS experiment is to reduce the registered event rate from about 40 MHz to about 1 kHz. Significant improvements important for the success of the CMS physics program have been made to the muon trigger system via improved muon reconstruction and identification algorithms since the end of Run 1 and throughout the Run 2 data-taking period. The new algorithms maintain the acceptance of the muon triggers at the same or even lower rate throughout the data-taking period despite the increasing number of additional proton-proton interactions in each LHC bunch crossing. In this paper, the algorithms used in 2015 and 2016 and their improvements throughout 2017 and 2018 are described. Measurements of the CMS muon trigger performance for this data-taking period are presented, including efficiencies, transverse momentum resolution, trigger rates, and the purity of the selected muon sample. This paper focuses on the single- and double-muon triggers with the lowest sustainable transverse momentum thresholds used by CMS. The efficiency is measured in a transverse momentum range from 8 to several hundred GeV

Impact of sensors on collision risk prediction for non-cooperative traffic in terminal airspace

The availability of off the shelf, easy to control, unmanned aerial systems (UAS) on the market has led to an increase in report of UAS incursion into terminal airspace. Such incursions often lead to airport shutdowns due to safety concern and could cause a cascading disruption to airline operations throughout the region. A better assessment tool for the collision risk between the existing air traffic and the intruder could help reduce unnecessary disruption to air traffic operations. Work has been done on the assessment of such risk using probabilistic UAS positions prediction based on Monte-Carlo simulations, under the assumption of a non-cooperative intruder with worst-case intention aiming at the flight corridor. Alert areas around the runway and the aircraft flight path could be constructed using the collision prediction method, albeit only valid under specific conditions. The accuracy of the predictions could be further improved with the incorporation of ground-based tracking equipment. This paper looks into how the availability of UAS tracking information could be used to complement the collision prediction algorithm, and how its inclusion affects the collision risk assessment.Accepted versio

Low band-gap diketopyrrolopyrrole-containing polymers for near infrared electrochromic and photovoltaic applications

10.1002/pola.27564Journal of Polymer Science, Part A: Polymer Chemistry53101287-129

Peroxiredoxin 3 regulates breast cancer progression via ERK-mediated MMP-1 expression

Abstract Peroxiredoxin 3 (PRDX3), a mitochondrial hydrogen peroxide scavenger, is known to be upregulated during tumorigenesis and cancer progression. In this study, we provide evidence for the first time that PRDX3 could regulate cellular signaling pathways associated with Matrix Metalloproteinase-1 (MMP-1) expression and activity in breast cancer progression. We show that shRNA-mediated gene silencing of PRDX3 inhibits cell migration and invasion in two triple-negative breast cancer cell lines. Reciprocal experiments show that PRDX3 overexpression promotes invasion and migration of the cancer cells, processes which are important in the metastatic cascade. Notably, this phenomenon may be attributed to the activation of MMP-1, which is observed to be upregulated by PRDX3 in the breast cancer cells. Moreover, immunohistochemical staining of breast cancer tissues revealed a positive correlation between PRDX3 and MMP-1 expression in both epithelial and stromal parts of the tissues. Further pathway reporter array and luciferase assay demonstrated that activation of ERK signaling is responsible for the transcriptional activation of MMP-1 in PRDX3-overexpressed cells. These findings suggest that PRDX3 could mediate cancer spread via ERK-mediated activation of MMP-1. Targeted inhibition of ERK signaling may be able to inhibit tumor metastasis in triple-negative breast cancer

Prenatal DEHP exposure predicts neurological disorders via transgenerational epigenetics

Abstract Recent experimental and observational research has suggested that childhood allergic asthma and other conditions may be the result of prenatal exposure to environmental contaminants, such as di-(2-ethylhexyl) phthalate (DEHP). In a previous epidemiological study, we found that ancestral exposure (F0 generation) to endocrine disruptors or the common plasticizer DEHP promoted allergic airway inflammation via transgenerational transmission in mice from generation F1 to F4. In the current study, we employed a MethylationEPIC Beadchip microarray to examine global DNA methylation in the human placenta as a function of maternal exposure to DEHP during pregnancy. Interestingly, global DNA hypomethylation was observed in placental DNA following exposure to DEHP at high concentrations. Bioinformatic analysis confirmed that DNA methylation affected genes related to neurological disorders, such as autism and dementia. These results suggest that maternal exposure to DEHP may predispose offspring to neurological diseases. Given the small sample size in this study, the potential role of DNA methylation as a biomarker to assess the risk of these diseases deserves further investigation