Miracle-dz/UniFAR-FTUs huggingface dataset:
Using CRL Push Delivery for Ecient Certicate Revocation Information Distribution in Grids CESNET Technical Report X/2007

AbstractChecking revocation information is necessary to prevent from using digital certi cateswhose contents become invalid. In current system either periodical retrieval of Certi cateRevocation Lists (CRLs) or the Online Certi cate Status Protocol (OCSP) are the mostcommon mechanisms to access revocation information issued by the certi cation authorities.As both these approaches pose problems we propose a new method based on a Push model,which is based on the Grid Monitoring Architecture. Using this approach we guarantee therevocation information is distributed in a robust and timely manner. We also describe a pilotimplementation of the service based on the proposed design. 1 Introduction Reliable use of the PKI is based on credible certi cates and trust in their authenticity. A revocationmechanism has been developed to inform relaying parties about stolen, invalid, or compromisedcerti cates known to the issuing Certi cation authorities (CA). Information about canceled cer-ti cates is published by each CA or specialized service using a speci c information channel as theonly way how the information can reach the relying parties (end users and services) that rely oncredibility of the certi cates. Such an approach obviously requires to check the latest revocationinformation as part of the standard veri cation of the certi cate because accepting a revokedcerti cate could potentially lead to unauthorized access or information leakage. CAs usually payattention to proper management of revocation information and make sure revocation informationis updated promptly and made available to the relying parties.The most usual mechanism for distribution of revocation information nowadays is using acerti cate revocation list (CRL) that the CA publishes regularly for the relying parties to download.While the certi cate validity check is made local (using the downloaded copy of the revocationlist), there may be too long delay between the time when a new CRL is published and when itis retrieved by all relying parties. Due to potentially large size of the CRLs they often cannotbe downloaded more faster so the window cannot be reduced. This limitation was dealt withthe Online Certi cate Status Protocol (OCSP), which allows to make a direct connection to theCA during certi cate veri cation to check if the certi cate is still valid. However, the OCSPalso su ers from few drawbacks, notably the dependency on a network connectivity to an OCSPresponder, which makes the certi cate veri cation last longer and be more fragile. Also, due tothe high computational cost the OCSP server may become a bottleneck if too many requests areissued simultaneously.There are also other solutions addressing the revocation problem. Despite some of them arevery e ective, support for such mechanisms is not implemented in the major tools used to build a
Considerations in the reliability and fairness audits of predictive models for advance care planning

Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend that models be audited for reliability and fairness. However, there is a gap of operational guidance for performing reliability and fairness audits in practice. Following guideline recommendations, we conducted a reliability audit of two models based on model performance and calibration as well as a fairness audit based on summary statistics, subgroup performance and subgroup calibration. We assessed the Epic End-of-Life (EOL) Index model and an internally developed Stanford Hospital Medicine (HM) Advance Care Planning (ACP) model in 3 practice settings: Primary Care, Inpatient Oncology and Hospital Medicine, using clinicians' answers to the surprise question (“Would you be surprised if [patient X] passed away in [Y years]? ”) as a surrogate outcome. For performance, the models had positive predictive value (PPV) at or above 0.76 in all settings. In Hospital Medicine and Inpatient Oncology, the Stanford HM ACP model had higher sensitivity (0.69, 0.89 respectively) than the EOL model (0.20, 0.27), and better calibration (O/E 1.5, 1.7) than the EOL model (O/E 2.5, 3.0). The Epic EOL model flagged fewer patients (11%, 21% respectively) than the Stanford HM ACP model (38%, 75%). There were no differences in performance and calibration by sex. Both models had lower sensitivity in Hispanic/Latino male patients with Race listed as “Other. ” 10 clinicians were surveyed after a presentation summarizing the audit. 10/10 reported that summary statistics, overall performance, and subgroup performance would affect their decision to use the model to guide care; 9/10 said the same for overall and subgroup calibration. The most commonly identified barriers for routinely conducting such reliability and fairness audits were poor demographic data quality and lack of data access. This audit required 115 person-hours across 8–10 months. Our recommendations for performing reliability and fairness audits include verifying data validity, analyzing model performance on intersectional subgroups, and collecting clinician-patient linkages as necessary for label generation by clinicians. Those responsible for AI models should require such audits before model deployment and mediate between model auditors and impacted stakeholders.
What is What in the Nanoworld A Handbook on Nanoscience and Nanotechnology Second, Completely Revied and Enlarged Edition

Preface to the Second Edition. Preface to the First Edition. Source of Information. A. From Abbe's principle to Axbel' - Kaner cyclotron resonance. B. Grom B92 protocol to Burstein-Moss Shift. C. From cage compound to cyclotron resonance. D. From D'Alembert equation to Dzyaloshinskii-Moriya interaction. E. From (e,2e) reaction to Eyring equation. F. From Fabry-Perot resonator to FWHM (full width at half maximum). G. From gain-guided lasers to gyromagnetic frequency. H. From habit plane to hyperelastic scattering. I. From ideality factor to isotropy (of matter). J. From Jahn - Teller effect to Joule's law of electric heating. K. From Kane Model to Kuhn-Thomas-Reiche sum rule. L. From lab-on-a-chip to Lyman series. M. From Mach-Zender interferometer to Murrell-Mottram potential. N. From NAA (neutron activation analysis) to Nyquist-Shannon sampling theorem. O. From octet rule to oxide. P. From PALM (photoactivable localization microscopy) to pyrrole. Q. From Q-control to qubit. R. From Rabi flopping to Rydberg gas. S. From Saha equation to synergetics. T. From Talbot's law to type II superconductors. U. From ultraviolet photoelectron spectroscopy (UPS) to Urbach rule. V. From vacancy to von Neumann machine. W. From Waidner-Burgess standard to Wyckoff notation. X. From XMCD (X-ray magnetic circular dichroism) to XRD (X-ray diffraction). Y. From Yasukawa potential to Yukawa potential. Z. From Zeeman effect to Zundel ion. A list and a presentation of Scientific Journals which contain the stem Nano in their title. Abbreviations for the scientific journals which appear as sources in the text. Appendix - main properties of intrinsic (or lightly doped). Semiconductors.
Effect of Salmonella Typhimurium Colonization on Microbiota Maturation and Blood Leukocyte Populations in Broiler Chickens

Simple Summary Reducing Salmonella levels in chickens is vital to reducing the number of human Salmonella infections resulting from contact with contaminated chickens and poultry products. The intestinal tract of chickens is home to a diverse population of bacteria that serve as one of the first lines of defense against pathogenic microbes. Therefore, we sought to understand changes in the intestinal bacterial populations following Salmonella infection. Our results showed a clear change in the intestinal bacterial structure after Salmonella colonization. An inverse relationship between Salmonella and Lactobacillus and Escherichia was observed as well as an increase in Bacteroides. Additionally, we provide insight into the timing of the host immune response with monocytes and thrombocytes acting early following colonization followed by heterophils. In total, this data demonstrates the ability of Salmonella to change intestinal microbiota composition and highlights genera that may be useful as probiotics to fight Salmonella colonization. Abstract Reducing Salmonella in commercial chickens is vital to decreasing human salmonellosis infections resulting from contact with contaminated poultry and poultry products. As the intestinal microbiota plays an important role in preventing pathogen colonization, we sought to understand the relationship between Salmonella infection and the cecal microbiota and the host immune system. Day-of-hatch broiler chicks were assigned to three treatments: control, artificial (SA), and natural (SN) Salmonella infection. At seven days of age, control and SA birds were inoculated with PBS or Salmonella Typhimurium, respectively. Five SA birds were transferred to SN cages to facilitate natural infection. Cecal content and blood samples were collected at 0, 8, 14, and 21 days of age for microbiota and leukocyte analysis, respectively. A significant change in microbiota composition was observed in both groups as noted by a decrease in Lactobacillus and Escherichia and an increase in Bacteroides. Leukocyte analysis revealed a decrease in the percentage of circulating monocytes at 7 days post-infection while a decrease in thrombocyte and an increase in heterophil percentages were seen at 14 days post-infection. Taken together, these results demonstrate the ability of Salmonella to modulate the intestinal microbiota to facilitate colonization. Additionally, results indicated an early role of monocytes and thrombocytes during colonization, followed by heterophils.
A Study on Fine-Pitch Convertors for Radiation Detectors with Interposers as an Alternative to Through Silicon Via Technology

Pixel radiation detectors for X-rays and gammarays are commonly built by hybridization of sensor and readout application-specific integrated circuit (ASIC) chips using flip-chip bonding. A typical example is pixelated high-Z semiconductor sensor material (e.g. CdTe) bonded to a matching pixel array on an ASIC to form a hybrid module. Conventionally, the I/O connections of the ASIC for such hybrids are wire bonded to a Printed Circuit Board (PCB) which subsequently is connected to a data acquisition system. The wire bond pads and PCB connections require a considerable amount of the module's footprint and usually protrude at one side of the detector module by approximately 1.5mm. The disadvantage of this practice is that those modules are only suitable for close proximity tiling modules along three sides of the detector (3-side buttable) to form larger sensor areas. In recent years Through-Silicon Via (TSV) technology was introduced to overcome this disadvantage which permits the fabrication of potentially 4-side buttable modules. Commonly a TSV-last process step in the ASIC production is integrated. However, ASICs with TSV are expensive and the TSV-last process step is prone to failure of the TSV metallization. The consequences of the latter are missing I/O connections. Alternatively, 4-side buttable hybrid detector modules can be fabricated using interposers as pitch convertors. Here the sensor has a larger pixel-pitch than the ASIC and therefore the sensor covers a larger area than the ASIC. A Re-Distribution-Layer (RDL) on the interposer facilitates connections to the I/O of the ASIC. Therefore, ASIC and all other wire bonded connections can be placed under the sensor with all four sides of the sensor being the boundary of the full-sized module. This was demonstrated in the past with a conversion from 250um-pitch on the ASIC to a 500um-pitch on the sensor using a layered PCB. In order to achieve detectors with smaller sensor pixel pitch, the layout of pitch re-routing on the interposer has to be considerably reduced in size. As a consequence, the fabrication of such interposers becomes more difficult. This paper studies desirable designs and interposer materials and compares these with the limitations of current fine-pitch interposer fabrication methods and design rules for a pitch convertor interposer. In addition, the paper will also present necessary changes such as indium deposition on individual sensor die instead of wafer-level fabrication that have to be taken into consideration for sensor/ASIC hybridization of fine-pitch radiation detectors.
EXPERIMENTAL LIMIT ON THE PROTON ELECTRIC DIPOLE MOMENT.

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Optimization of heat exchanger enhanced surfaces under turbulent conditions

Abstract — The optimization of heat transfer enhancing surfaces for compact heat exchangers is dis-cussed. The heat transfer passages are parameterized through Bezier curves, and the optimum choice ofthe parameters is sought using both multi-objective and single objective optimization techniques. Theobjectives of the optimization are the maximization of the heat transfer rate and the minimization of thepressure drop encountered by the ﬂuid crossing the passage. The objectives are evaluated in nondimen-sional form in terms of the Nusselt number and the friction factor. The investigation is carried out bymeans of CFD analyses, the ﬂuid ﬂowing in the passages is air, and turbulent ﬂow is assumed. Two dif-ferent turbulence models (namely, k-and k-! ) are adopted in the optimization processes and the effectof the choice of the turbulence model on the optimization outcome is discussed. 1. Introduction Compact heat exchangers are an interesting topic for a wide range of industrial applications.The main requirement for the design of such devices is that they should be capable of trans-ferring a large amount of heat in a limited volume or with a reduced weight, while inducing alimited pressure drop on the heat-carrier ﬂuids. Compact heat exchangers can be liquid-to-liquid or liquid-to-air exchangers, usually madeof metal plates or ﬁns through which two non-mixing ﬂuids ﬂow. In many types of compactexchangers, as, for instance, road vehicle radiators, the hot ﬂuid ﬂows into straight pipes, andthese are connected to a series of thin metal plates stacked together, acting as ﬁns. The gapsbetween the plates are the channels through which the cooling air ﬂows. In this case, the heattransfer bottleneck is on the outer side, due to the low thermal performance of air. By modifyingthe shape of the channel walls so that air mixing and ﬂow impingement are promoted, a sub-stantial improvement of the channel performance can be achieved in terms of heat transfer rate.Of course, the price to pay is the acceptance of an enhanced pressure drop across the channel aswell. One of the easiest choices is to generate sinusoidal channels [1, 2, 3]. However, an inﬁnityof different shapes are possible, and sinusoidal channels are not necessarily the best option.An efﬁcient design of heat transfer enhancing surfaces can be achieved through multi-objecti-ve optimization. For a given engineering problem, described by a proper mathematical model,multi-objective optimization algorithms lead to a set of solutions lying on the Pareto front ofthe competing objectives. In the end, the designer can choose among the Pareto elements whichare trade-off solutions between the objectives.A number of authors address the design of heat transfer enhancing surfaces, making use ofcollaborative design software and optimization techniques. Hilbert et al. [4] optimize the shapeof the blade in a tube-bank heat exchanger using a multi-objective genetic algorithm. Laminarﬂow is assumed, and the blade shape is built through a
Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.
Hyperspectral Brain Tissue Classification using a Fast and Compact 3D CNN Approach

Glioblastoma (GB) is a malignant brain tumor and requires surgical resection. Although complete resection of GB improves prognosis, supratotal resection may cause neurological abnormalities. Therefore, intraoperative tissue classification techniques are needed to delineate infected tumor regions to remove reoccurrences. To delineate the affected regions, surgeons mostly rely on traditional magnetic resonance imaging (MRI) which often lacks accuracy and precision due to the brain-shift phenomenon. Hyperspectral Imaging (HSI) is a noninvasive advanced optical technique and has the potential to classify tissue cells accurately. However, HSI tumor classification is challenging due to overlapping regions, high interclass similarity, and homogeneous information. Additionally, HSI models using 2D Convolutional Neural Network (CNN) models works with spectral information eliminating spatial features and 3D followed by 2D hybrid model lacks abstract level spatial information. Therefore, in this study, we have used a minimal layer 3D CNN model to classify the GB tumor region from normal tissues using an intraoperative VivoHSI dataset. The HSI data have normal tissue (NT), tumor tissue (TT), hypervascularized tissue or blood vessels (BV), and background (BG) tissue cells. The proposed 3D CNN model consists of only two 3D layers using limited training samples (20%), which are further divided into 50% for training and 50% for validation and blind tested (80%) on the rest of the data. This study outperformed then state-of-the-art hybrid architecture by achieving an overall accuracy of 99.99%.
Evolutionary Algorithm with Phenotype Diversity for Virtual Network Embedding

With the diversification of applications using the Internet, network virtualization technologies that flexibly allocate network resources are attracting attention. In network virtualization, virtual network embedding is important to properly map the requirements of the virtual network to the physical network. However, it takes time to calculate the solution by optimization, and recalculation of the embedding becomes a problem when the environment of the virtual and physical networks changes. Therefore, a method of having multiple solution candidates in advance and switching the solution depending on the situation is considered, but the design and updating of the solution candidates themselves remain an issue. Such a relationship between solution candidates and solution selection is similar to the relationship between genotype and phenotype in biological evolution, and it is a shortcut to get hints from evolution. In biological evolution, the phenotype searches for short-term practical solutions while the genotype continues to search for optimal solutions. By introducing this mechanism into the network, it is possible to select a quasi-optimal solution in a fluctuating environment while continuing the search for a better solution candidate itself. In this paper, we propose a dynamic virtual network embedding method in which the solution candidates themselves can be dynamically updated based on the evolution of genotype and phenotype. In this method, candidate solutions are encoded as genotypes, and phenotypes are decoded by attractor selection using noise-induced fluctuations. Through evaluation, we show that the attractor selection by individuals leads to the discovery of appropriate solutions faster than when using neural networks.