Performance of the CMS phase 1 pixel detector Akgün, Bora
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
04/2019, Letnik:
924
Journal Article
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Odprti dostop
It is anticipated that the LHC accelerator will reach and exceed the luminosity of L=2×1034 cm−2s−1 during the LHC Run 2 period until 2023. At this higher luminosity and increased hit occupancies the ...CMS phase-0 pixel detector would have been subjected to severe dead time and inefficiencies introduced by limited buffers in the analog read-out chip and effects of radiation damage in the sensors. Therefore a new pixel detector has been built and replaced the phase-0 detector in the 2016/17 LHC extended year-end technical stop. The CMS phase-1 pixel detector features four central barrel layers and three end-cap disks in forward and backward direction for robust tracking performance, and a significantly reduced overall material budget including new cooling and powering schemes. The design of the new front-end readout chip comprises larger data buffers, an increased transmission bandwidth, and low-threshold comparators. These improvements allow the new pixel detector to sustain and improve the efficiency of the current pixel tracker at the increased requirements imposed by high luminosities and pile-up. A new DAQ system has been developed based on a combination of custom and standard microTCA parts. This contribution gives an overview of the design and performance of the CMS phase-1 pixel detector.
The ϒ production cross sections are measured with the CMS detector using 36.7 ± 1.5 pb-1 of proton-proton collisions at special characters omitted = 7 TeV. The observed yields of the ϒ(1S), ϒ(2 S), ...and ϒ(3S) reconstructed in the di-muon decay channel are: 73569 ± 465, 23390 ± 364, and 13359 ± 130, respectively. Integrated over the rapidity range |y| ≤ 2.4, the product of the ϒ(nS) production cross section (unpolarized scenario) and the di-muon branching ratio are measured to be special characters omittedwhere the first uncertainty is statistical, the second is systematic, and the third is associated with the estimation of the integrated luminosity of the data sample. The cross sections are obtained assuming unpolarized-ϒ production. Assuming fully transverse or fully longitudinal polarization leads to variations in the cross sections by about 20%. The ϒ(1S), ϒ(2S), and ϒ(3 S) differential cross sections in transverse momentum and rapidity, along with respective cross section ratios, are presented. Comparison to theory predictions and previous experimental measurements are provided.
The production of J /{\psi} and {\Upsilon} mesons is studied in pp collisions
at \surds = 7 TeV with the CMS experiment at the LHC. The J /{\psi} measurement
is based on a dimuon sample corresponding ...to an integrated luminosity of 314
nb^{-1}. The J /{\psi} differential cross section is determined, as a function
of the J/{\psi} transverse momentum, in three rapidity ranges. A fit to the
decay length distribution is used to separate the prompt from the non-prompt (b
hadron to J/{\psi}) component. Integrated over the J /{\psi} transverse
momentum from 6.5 to 30 GeV/c and over rapidity in the range |y| < 2.4, the
measured cross sections, times the dimuon decay branching fraction, are 70.9
\pm 2.1(stat.) \pm 3.0(syst.) \pm 7.8(lumi.) nb for prompt J /{\psi} mesons,
assuming unpolarized production, and 26.0 \pm 1.4(stat.) \pm 1.6(syst.) \pm
2.9(lumi.) nb for J /{\psi} mesons from b-hadron decays. The {\Upsilon}
measurement is based on a dimuon sample corresponding to an integrated
luminosity 3.1 \pm 0.3 pb^{-1}. Integrated over the rapidity range |y| < 2, we
find the product of the {\Upsilon}(1S) production cross section and branching
fraction to dimuons to be 7.37 \pm 0.13(stat.)^{+0.61}_{-0.42}(syst.) \pm
0.81(lumi.) nb. This cross section is obtained assuming unpolarized
{\Upsilon}(1S) production. If the {\Upsilon}(1S) production polarization is
fully transverse or fully longitudinal, the cross section changes by about 20
%.
Climate change is a reality of today. Paleoclimatic proxies and climate predictions based on coupled atmosphere-ocean general circulation models provide us with temperature data. Using Detrended ...Fluctuation Analysis, we are investigating the statistical connection between the climate types of the present and these local temperatures. We are relating this issue to some well-known historic climate shifts. Our main result is that the temperature fluctuations with or without a temperature scale attached to them, can be used to classify climates in the absence of other indicators such as pan evaporation and precipitation.
COVID-19 or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared throughout the World and currently affected more than 9 million people and caused the death of around 470,000 ...patients. The novel strain of the coronavirus disease is transmittable at a devastating rate with a high rate of severe hospitalization even more so for the elderly population. Naso-oro-pharyngeal swab samples as the first step towards detecting suspected infection of SARS-CoV-2 provides a non-invasive method for PCR testing at a high confidence rate. Furthermore, proteomics analysis of PCR positive and negative naso-oropharyngeal samples provides information on the molecular level which highlights disease pathology. Samples from 15 PCR positive cases and 15 PCR negative cases were analyzed with nanoLC-MS/MS to identify the differentially expressed proteins. Proteomic analyses identified 207 proteins across the sample set and 17 of them were statistically significant. Protein-protein interaction analyses emphasized pathways like Neutrophil degranulation, Innate Immune System, Antimicrobial Peptides. Neutrophil Elastase (ELANE), Azurocidin (AZU1), Myeloperoxidase (MPO), Myeloblastin (PRTN3), Cathepsin G (CTSG) and Transcobalamine-1 (TCN1) were found to be significantly altered in naso-oropharyngeal samples of SARS-CoV-2 patients. The identified proteins are linked to alteration in the innate immune system specifically via neutrophil degranulation and NETosis.
This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version ...has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, <inline-formula> <tex-math notation="LaTeX">\tau _{1} </tex-math></inline-formula>- <inline-formula> <tex-math notation="LaTeX">\tau _{2} </tex-math></inline-formula>) and 3 outputs (acceleration, <inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {x}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {y}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {z}} </tex-math></inline-formula>) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals (<inline-formula> <tex-math notation="LaTeX">\tau _{1} </tex-math></inline-formula>- <inline-formula> <tex-math notation="LaTeX">\tau _{2} </tex-math></inline-formula>) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration (<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {x}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {y}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {z}} </tex-math></inline-formula>) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The <inline-formula> <tex-math notation="LaTeX">1^{\mathrm{st}} </tex-math></inline-formula> NN model is trained with 3 inputs (<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {x}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {y}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {z}} </tex-math></inline-formula>) and 2 targets (<inline-formula> <tex-math notation="LaTeX">\tau _{1} </tex-math></inline-formula>- <inline-formula> <tex-math notation="LaTeX">\tau _{2} </tex-math></inline-formula>) training data. The <inline-formula> <tex-math notation="LaTeX">2^{\mathrm{nd}} </tex-math></inline-formula> NN model is trained with 6 inputs (amplitudes and phases of <inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {x}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {y}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {z}} </tex-math></inline-formula>) and 2 targets (<inline-formula> <tex-math notation="LaTeX">\tau _{1} </tex-math></inline-formula>- <inline-formula> <tex-math notation="LaTeX">\tau _{2} </tex-math></inline-formula>) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The <inline-formula> <tex-math notation="LaTeX">3^{\mathrm{rd}} </tex-math></inline-formula> NN model is trained with 6 inputs (amplitudes and phases of <inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {x}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {y}} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">\text{a}_{\mathrm {z}} </tex-math></inline-formula>) and 4 targets (amplitudes and phases of <inline-formula> <tex-math notation="LaTeX">\tau _{1} </tex-math></inline-formula>- <inline-formula> <tex-math notation="LaTeX">\tau _{2} </tex-math></inline-formula>) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analysed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the <inline-formula> <tex-math notation="LaTeX">3^{\mathrm{rd}} </tex-math></inline-formula> NN, the actuator signals (<inline-formula> <tex-math notation="LaTeX">\tau _{1} </tex-math></inline-formula>- <inline-formula> <tex-math notation="LaTeX">\tau _{2} </tex-math></inline-formula>) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of <inline-formula> <tex-math notation="LaTeX">\tau _{1} </tex-math></inline-formula>- <inline-formula> <tex-math notation="LaTeX">\tau _{2} </tex-math></inline-formula>). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.
Children with chronic neurological diseases, including cerebral palsy (CP), are especially susceptible to vaccine-preventable infections and face an increased risk of severe respiratory infections ...and decompensation of their disease. This study aims to examine age-appropriate immunization status and related factors in the CP population of our country. This cross-sectional prospective multicentered survey study included 18 pediatric neurology clinics around Turkey, wherein outpatient children with CP were included in the study. Data on patient and CP characteristics, concomitant disorders, vaccination status included in the National Immunization Program (NIP), administration, and influenza vaccine recommendation were collected at a single visit. A total of 1194 patients were enrolled. Regarding immunization records, the most frequently administrated and schedule completed vaccines were BCG (90.8%), hepatitis B (88.9%), and oral poliovirus vaccine (88.5%). MMR was administered to 77.3%, and DTaP-IPV-HiB was administered to 60.5% of patients. For the pneumococcal vaccines, 54.1% of children received PCV in the scope of the NIP, and 15.2% of children were not fully vaccinated for their age. The influenza vaccine was administered only to 3.4% of the patients at any time and was never recommended to 1122 parents (93.9%). In the patients with severe (grades 4 and 5) motor dysfunction, the frequency of incomplete/none vaccination of hepatitis B, BCG, DTaP-IPV-HiB, OPV, and MMR was statistically more common than mild to moderate (grades 1–3) motor dysfunction (
p
= 0.003,
p
< 0.001,
p
< 0.001,
p
< 0.00, and
p
< 0.001, respectively). Physicians’ influenza vaccine recommendation was higher in the severe motor dysfunction group, and the difference was statistically significant (
p
= 0.029).
Conclusion
: Children with CP had lower immunization rates and incomplete immunization programs. Clinicians must ensure children with CP receive the same preventative health measures as healthy children, including vaccines.
What is Known:
• Health authorities have defined chronic neurological diseases as high-risk conditions for influenza and pneumococcal infections, and they recommend vaccines against these infections.
• Children with CP have a high risk of incomplete and delayed immunization, a significant concern given to their increased healthcare needs and vulnerability to infectious diseases.
What is New:
• Influenza vaccination was recommended for patients hospitalized due to pneumonia at a higher rate, and patients were administered influenza vaccine more commonly.
• Children with CP who had higher levels of motor dysfunction (levels 4 and 5) were more likely to be overdue immunizations.
Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss and cognitive impairment. The diagnosis of Alzheimer's disease according to symptomatic events is still a ...puzzling task. Developing a biomarker-based, low-cost, and high-throughput test, readily applicable in clinical laboratories, dramatically impacts the rapid and reliable detection of the disease.
This study aimed to develop an accurate, sensitive, and reliable screening tool for diagnosing Alzheimer's disease, which can significantly reduce the cost and time of existing methods.
We have employed a MALDI-TOF-MS-based methodology combined with a microaffinity chromatography enrichment approach using affinity capture resins to determine serum kappa (κ) and lambda (λ) light chain levels in control and patients with AD.
We observed a statistically significant difference in the kappa light chain over lambda light chain (κLC/λLC) ratios between patients with AD and controls (mean difference -0,409; % 95 CI:- 0.547 to -0.269; p<0.001). Our method demonstrated higher sensitivity (100.00%) and specificity (71.43%) for discrimination between AD and controls.
We have developed a high-throughput screening test with a novel sample enrichment method for determining κLC/λLC ratios associated with AD diagnosis. Following further validation, we believe our test has the potential for clinical laboratories.
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