Apresentação do professor Pedro Grande, da seção UFRGS do Instituto Nacional de Engenharia de Superfície. Palestra convidada do Simpósio Engenharia de Superfície do X Encontro da SBPMAT. Realizada no dia 26 de setembro de 2011 em Gramado (RS).
Apresentação do professor Pedro Grande, da seção UFRGS do Instituto Nacional de Engenharia de Superfície. Palestra convidada do Simpósio Engenharia de Superfície do X Encontro da SBPMAT. Realizada no dia 26 de setembro de 2011 em Gramado (RS).
A Mechanism Of Electron Pairing Relating To SupperconductivityQiang LI
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Design, Simulation and Verification of Generalized Photovoltaic cells Model U...IDES Editor
This paper presents the implementation of a
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model applicable for mono crystalline, poly crystalline silicon,
thin film like CIS, CdTe, Amorphous silicon, polymer from
various manufacturers on Matlab/Simulink simulation
software platform using first principle method. This model is
known to have better accuracy estimation of electrical
behavior of the cell with respect to changes on environmental
parameter of temperature and irradiance. All inputs to the
model can be easily extracted from standard PV module
datasheet. The functioning of the proposed model is evaluated
by simulation. The accuracy of the simulation is verified by
comparing output current and power characteristics of PV cell
with datasheet provided by PV cell manufacturers
In this presentation on External Thermal Resistance we will look at the thermal resistance of various substrate types, thermal management with printed circuit boards, and examples that compare the thermal resistance of various substrate technologies.
A Mechanism Of Electron Pairing Relating To SupperconductivityQiang LI
Mechanism of superconductivity is based on a mechanism of electron pairing near EF with definite binding energy. A candidate mechanism of such electron pairing is described in this paper. Electron pairs are induced by EM wave modes generated by corresponding lattice wave modes. The pairs are formed between E(k) faces from different Brillouin zones, with definite binding energies. The binding energy of an electron pair is characterized by the frequency of the EM mode that induces the pairing.
Design, Simulation and Verification of Generalized Photovoltaic cells Model U...IDES Editor
This paper presents the implementation of a
generalized photovoltaic model of PV cell, module, and array
model applicable for mono crystalline, poly crystalline silicon,
thin film like CIS, CdTe, Amorphous silicon, polymer from
various manufacturers on Matlab/Simulink simulation
software platform using first principle method. This model is
known to have better accuracy estimation of electrical
behavior of the cell with respect to changes on environmental
parameter of temperature and irradiance. All inputs to the
model can be easily extracted from standard PV module
datasheet. The functioning of the proposed model is evaluated
by simulation. The accuracy of the simulation is verified by
comparing output current and power characteristics of PV cell
with datasheet provided by PV cell manufacturers
In this presentation on External Thermal Resistance we will look at the thermal resistance of various substrate types, thermal management with printed circuit boards, and examples that compare the thermal resistance of various substrate technologies.
Science Cafe Discovers a New Form of Alternative EnergyEngenuitySC
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[L'angolo del PhD] Alessandro Palma - XXII Ciclo - 2009accatagliato
The first part of this work describes how to use Z -> e+e- events in order to calibrate the CMS electromagnetic calorimeter, which makes use of scintillating crystals in order to precisely measure the energy of electrons and photons coming from the proton-proton interactions.
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The second part of this work concentrates on the misidentification of the electric charge of electrons/positrons in CMS. It will be shown how it is possible to extract the charge misidentification rate from the first CMS data, this time relying on the fact that electrons coming from the Z decay are always oppositely-charged.
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The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
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Paper: https://eprint.iacr.org/2023/1886
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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Building better applications for business users with SAP Fiori.
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
1. Evaluation of Mathematical Models
for X-ray Spectrum Generation suitable for Industrial
Radiography Applications
Nityanand Gopalika, A. V. K Satish, V. Manoharan
Industrial Imaging and Modeling Lab
Imaging Technologies
John F. Welch Technology Centre
Bangalore
2. Presentation Outline
Different X-Ray generation models
Validation approach:
Variation of Photon Fluence / mR with Average
Energy
Relationship between Average Energy and kVp for
different filters
Half Value Layer (HVL) for different cases
Dose validation with experiment
Summary
3. Birch and Marshall model
Intensity produced in a solid target
Governing Relationships dT
Nρ
T −1
v
Iv =
A ∫ dx
T0
Q
dT Physics
• Theoretical model
Effect of target absorption • Target absorption taken care
T = (T02 − Cxρ ) 0.5 (improvement over Kramer's theory)
Substituting the above gives
−1
Nρ
Tv
T0 dT µv
Iv = ∫ 1 + m0C 2
Q
dx exp( (T 2 − T02 ) cot θ ) dT
A T0 Cρ
Characteristic Intensity
I ch = K (U 0 − 1)1.63
Drawbacks
Applicable only from 30-150 kV.
Small target angles greater error.
Back Scatter not considered.
Suited for medical applications
4. Ellery Storm Model
Thick target energy loss as an integral of thin target energy loss:
E0
dE dE = Energy loss in thin strip of target
I E0 ,K = ∫ I E0 ,k ,E
E >k −dE /dx E = Initial electron energy at photon emission
Correction for electron backscatter losses, photon attenuation and target angle
E0
dE
I E0 ,k = ∫k
E>
I E0 ,k ,E (1−ηε E 0 ,k )*exp( −µ k x/ tanα )
−dE /dx
Emission per unit solid angle in the photon energy
−3 k
11 ( E0 −k )(1−e ) Ek 1.0
I E0 ,k ≅( Z ) f E0 ,k ,α
4π 1 E0
( k / E0 ) 3 (1−e Ek )
40 kV
60 kV
Photon Attenuation Correction Factor C E0 , k
f E0 ,k ,α ≅ exp(−0.2C E0 ,k ℜ E0 µ k / tan α )
100 kV
0.5
200kV
E0 Initial Electron Energy
Ek K Edge Energy 300 kV
K Photon Energy 0.0
10 20 40
Z Atomic Weight Photon Energy (keV)
Best suited for industrial applications
5. Photon Fluence /mR & Average Energy
Photon Fluence per Roentgen
Photon Fluence
4.E+10
3.E+10
/ mR
2.E+10
1.E+10
Photon Fluence /mR =
∫Θ( E )*E*dE
0.E+00 ∫Θ( E )*(µ ( E )/ ρ )*E*dE
0 50 100 150 200 250 300 350
Average Energy
Average energy
70
60
Average energy kev
no filter
Average Energy =
∫Θ( E )*dE 50
40
1 mm aluminium
2 mm aluminium
∫dE 30
20
3 mm aluminium
4 mm aluminium
10 5 mm aluminum
0
0 50 100 150
kVP
Literature data available below 150 kVp
6. Model Performance Verification
Average Energy Vs. kvp (Simulation) Photon Fluence per roentgen Vs.
Average Energy (Simulation)
140
120 3.00E+05
Average Energy
Thick = 1mm
Photon Fluence / mR
100 Thick = 2mm 2.50E+05
Thick = 1mm
80 Thick = 3mm 2.00E+05 Thick = 2mm
60 Thick = 4mm 1.50E+05 Thick = 3mm
40 Thick = 5mm 1.00E+05 Thick = 4mm
20 Thick = 5mm
5.00E+04
0 0.00E+00
0 100 200 300 400 500 0 20 40 60 80 100 120 140
kvp Average Energy
Average Energy Vs. kvp Photon Fluence / mR Vs. Average Energy
(Simulation & Liturature) (Simulation & Literature)
140 3.E+05
Photon Fluence / mR
120 3.E+05
Average Energy
100 2.E+05
80 Thick = 5mm, Simulation 2.E+05
60
Thick = 5mm, Literature 1.E+05 Thick = 5mm, Simulation
40 Thick = 5mm, Experimental
5.E+04
20
0 0.E+00
0 20 40 60 80 100 120 140
0 50 100 150 200 250 300 350 400 450
kvp Average Energy
High accuracy in the range of 30 – 150 kVp
7. HVL Study: Comparison with NIST Data
Tube HVL Dose Dose
Potential Inherent (mm) - Before After
Cases (KvP) Filter Added Filter Object NIST Object Object % Error
1 100 1 mm Be % Difference in HVL
1.98 mm Al Al 2.77 12.668 6.744 -6.475
2 100 3 mm Be 5 mm Al Al 5.02 6.080 3.043 -0.106
3 100
8 3 mm Be 4 mm Al + 5.2 mm Cu Al 13.5 0.025 0.012 2.393
4 100 6 3 mm Be 4 mm Al + 5.2 mm Cu Cu 1.14 0.025 0.012 3.496
5 120 3 mm Be 6.87 mm Al Al 6.79 6.887 3.440 0.100
6 150 4 3 mm Be 5 mm Al + 0.25 mm Cu Al 10.2 8.405 4.177 0.610
% Difference
7 150 3 mm Be 5 mm Al + 0.25 mm Cu Cu 0.67 8.405 4.125 1.851
8 150 2 3 mm Be 4 mm Al + 4 mm Cu + 1.51 mm Sn Al 17 0.187 0.092 2.147
9 150 3 mm Be 4 mm Al + 4 mm Cu + 1.51 mm Sn Cu 2.5 0.187 0.087 7.005
10 200 0 3 mm Be 4.1 mm Al + 1.12 mm Cu Al 14.9 9.782 4.831 1.217
11 200 3 mm Be 4.1 mm Al + 1.12 mm Cu Cu 1.69 9.782 4.785 2.169
12 200
-2 3 mm Be 4 mm Al + 0.6 mm Cu + 4.16 mm Sn + 0.77 mm Pb Al 19.8 0.141 0.070 1.665
13 200 -4 3 mm Be 4 mm Al + 0.6 mm Cu + 4.16 mm Sn + 0.77 mm Pb Cu 4.1 0.141 0.068 4.188
14 250 3 mm Be 5 mm Al + 3.2 mm Cu Al 18.5 9.624 4.740 1.504
15 250 -6 3 mm Be 5 mm Al + 3.2 mm Cu Cu 3.2 9.624 4.695 2.425
16 250 3 mm Be 4 mm Al + 0.6 mm Cu + 1.04 mm Sn + 2.72 mm Pb Al 22 0.206 0.101 2.119
17 250 -8 3 mm Be 4 mm Al + 0.6 mm Cu + 1.04 mm Sn + 2.72 mm Pb Cu 5.2 0.206 0.102 0.613
18 300 3 mm Be
75 125 4 mm Al + 6.5 mm Sn
175 225 Al
27522 4.395 2.169
325 1.280
19 300 3 mm Be 4 mm Al + 6.5 mm Sn Cu 5.3 4.395 2.201 -0.149
20 300 3 mm Be
kVp
4.1 mm Al + 3 mm Sn + 5 mm Pb Al 23 0.164 0.083 -0.839
21 300 3 mm Be 4.1 mm Al + 3 mm Sn + 5 mm Pb Cu 6.2 0.164 0.086 -4.415
% Difference in HVL between NIST and simulation is within +/- 8%
8. Experimental Dose Measurement
Experimental Conditions: Simulation Conditions:
X-Ray Tube: Target Voltage : 20 < kV < 420 kVp
Current : 1 mA
1. KM16010E-A MicroFocus Target Material – W
2. Seifert ISOVOLT 420/10
Dose = 1.828*10-11∑φ(E).(µ(E)/ρ) air.E.dE
Dosimeter: Keithley 35050A Dosimeter
% Difference in Dose : Kevex, SDD = 1m % Difference in Dose: Seifert, SDD = 1m
(Experimental and Simulated) (Experimental and Simulated)
6 8
0.4 mm Cu Filter
4 6
9 mm Al Filter
4
% Difference
2
% Difference
2
0
0
-2
-2
-4 0.4 mm Cu Filter -4
-6 9 mm Al Filter -6
-8 -8
30 50 70 90 110 130 150 170 30 130 230 330 430
Tube Potential (kvp)
Tube Potential (kvp)
KM16010E-A MicroFocus Seifert ISOVOLT 420/10
Less than 7% difference is observed between simulation and experiments
9. Summary
Ellery Storm Model best suited for X-ray Spectrum Generation
Model performance metrics:
Accuracy for Photon Fluence / mR > 95%
Error in Average Energy < 5%
Deviation in HVL < 8%
Simulated Dose is in good agreement with Experiments