This survey aimed to evaluate the cyber-security culture readiness of organizations from different countries and business domains when teleworking became a necessity due to the COVID-19 crisis. A targeted questionnaire was designed and a web-based survey was conducted addressing employees while working from home during the COVID-19 spread over the globe. The questionnaire contained no more than 23 questions and was available for almost a month, from 7th April 2019 until 3rd May 2019. During that period, 264 participants from 13 European countries spent approximately 8 minutes to answer it. Gathered data are being hosted in this dataset along with their analysis and graphical representation.
Contributors:Marc Nicolas Bienz, Hans Knecht, Sabine Mai
Oncogenic events of cutaneous T-cell lymphomas (CTCLs) progression remain elusive. Telomere remodeling, a manifestation of genetic instability, is associated with progression of some malignancies. We aim to characterize the three-dimensional (3D) telomeric organization in CTCLs. We performed 3D telomeric quantitative FISH (3D Telo-q-FISH) of skin tissue of 9 patients with CTCL and control lymphocytes. Reported parameters included telomeres of low intensity (TLI), number and intensity of telomeric signals, telomere aggregates and nuclear volume. Stratification was based on CD30 expression and clinical stage. CTCLs had more TLIs than controls (27% vs 16%, p<0.0001). TLI proportion was higher in CD30-high cells than CD30-low cells (34% vs 22%, p<0.0001) and in the advanced group compared to the early group (30% vs 24%, p<0.0001). CD30 expression and advanced stage were associated with larger nuclei and more telomeres and advanced stage cells had significantly more aggregates. We show that 3D Telo-QFISH is feasible in small skin biopsies with limited tissue, and we report evidence that advanced CTCLs are associated with an increased proportion of TLIs, a hallmark feature in many tumor cells. Our analysis suggests that CTCL cells undergo in a first step telomere shortening and loss compared to healthy controls. In a second step further telomeric shortening associated with chromosomal rearrangements and Breakage-fusion-Bridge cycles may be involved in the progression of CTCL.
Using 182 prefecture- and higher-level cities in China as the research focus, we intended to examine the effect of changes in commodity housing prices on the spatial agglomeration of the financial industry between 2006 and 2013. The spatial agglomeration index of the financial industry was calculated based on the number of employees at the end of each year provided by the China City Statistical Yearbook. The data of controlled and mediating variables were extracted from the China Population Statistics Yearbook and the Statistical Yearbook of Chinese Investment in Fixed Assets. The relevant price index data were extracted from the China Statistical Yearbook.
When free convection is to be calculated for very thin wires in rarefied atmospheres, the Rayleigh number becomes very small. At these small Rayleigh numbers, available correlations are no longer applicable and there is also a lack of published experimental data. Therefore, new tests were performed using 12.7 and 25 um horizontal wires at low pressures. The wires were heated using electrical current and the temperature of the wires was calculated using the electrical resistance temperature coefficient. The heat transfer parameters and non-dimensional numbers were obtained using usual theoretical analysis. The gathered experimental data show that the Knudsen number must be incorporated in order to reliably describe the heat transfer in above-mentioned conditions.
Data are in CSV format with utf-8 encoding.
The header of each columns is the variable's name as follows:
Gr: Grashof number
Pr: Parndtl number
Nu: Nusselt number
Kn: Knudsen number
h: heat transfer coefficient ( W/(m**2 K) )
Tw: wire's temperature (K)
Ti: air's temperature (K)
p: pressure (Pa)
d: wire's diameter (m)
L: wire's length (m)
The dataset contains multiple instances of 9 Activities of Daily Living (ADL)-related actions namely Walk, Sit Down, Stand Up, Open Door, Close Door, Pour Water, Drink Glass, Brush Teeth and Clean Table. Each of the 10 volunteers performed each activity at least 14 times, with the notable exception of the walking activity that has been performed 40 times, in different sequences and alternating the used hand. For each volunteer, the dataset contains 7 CSV files, i.e., one file for each of the 6 IMU sensors worn by the volunteer on different body parts, as described in Figure 1, namely left lower arm (lla.csv), left upper arm, (lua.csv), right lower arm (rla.csv), right upper arm (rua.csv) and right thigh (rt.csv). Each file contains the overall sequence recorded during the experiment. The first column contains a label "qags" indicating the type of recorded data (quaternions, acceleration, angular velocity). The next column is the time-stamp in milliseconds elapsed from 00:00:0.000 AM (with a 30 milliseconds sampling time). The next four columns are the quaternions (with a resolution of 0.0001 ). Following them, we have three columns with the accelerations along the x, y and z axes (with a 0.1 mG resolution). The last three columns refer to the angular velocity about the x, y and z axes (with a 0.01 dps resolution). The last CSV file (annotation.csv) contains the data labelling. The first two columns of this file contain the time in the format hh.mm.ss.000 (current day time) and in milliseconds elapsed from 00:00:0.000 AM. All the remaining columns are organised as couples where the first element represents the scope of the labelling and the second indicates whether the labelled activity is starting or ending. In the annotation file, there are four different labelling scopes.
• “BothArms”: all instances of each activity are labelled independently of which arm has been used;
• “RightArm”: are labelled the activity instances using only the right arm, or in case they belong to the Walk, Sit Down or the Stand Up activities;
• “LeftArm”: are labelled the activity instances using only the left arm, or in case they belong to the Walk, Sit Down or the Stand Up activities;
• “Locomotion”: in this scope are labelled only the instances of Walk, Sit Down and Stand Up.
Finally, the last two columns report a session ID. There are four different sessions characterised by the order in which the activities are performed, and by the used arm (see also Table 3), and whether the session starts or ends. The videos recorded during the experiments have been only used for labelling purposes, and they are not published.
Together with the dataset, we provide a MATLAB script named TimeStampExtraction.m that extract from the annotation and the data files, for each volunteer and for each sensor, the time stamp associated with the start and end of each ADL.