12 research outputs found
Automatic Conversion of CityGML to IFC
No full textThis study presents a methodology to convert from the most dominant 3D city model standard in 3D GIS to BIM. Namely, CityGML to IFC. IFC is chosen because it is the common open standard to exchange data in the BIM world. For the aim of this study, the two standards are divided into 5 comparable sub-parts; Semantics, Geometry, Geographical coordinates, Topology, and Encoding. The characteristics of each of these sub-parts are studied and a theoretical conversion method is proposed for it from the first standard to the other. This is done by performing a semantic and geometrical mapping between the two standards, converting the georeferencing from Global to local, converting the encoding that the two standards use from XML to STEP, deciding which topological relations are to be retained, and providing a basic implementation that is created using Python to combine the above tasks. The work presented in this thesis can provide a foundation for future work in converting CityGML to IFC. It provides an insight into the relationship between the two standards and a methodology for the conversion from one to the other, and the process of developing software to perform such conversion. This is done in a way that can be extended for future specific needs.Geomatic
Raising awareness of citizens by interactively providing environmental data: Pilot of a static sensor network in Delft
No full textThis synthesis project is focused on implementing an Internet of Things (IoT) network to measure environmental data in the city of Delft. This network consists of sensor platforms that are placed in the urban environment. Each sensor platform is mounted on fixed locations and it is not moved during the measurement time. The aim is to raise community’s environmental awareness to improve the quality of the environment.Recent developments in technology made it possible to fabricate small, efficient, and reliable sensors boards which are the base of these sensors platforms and making them efficient and reliable. Sensor boards like Arduino, Raspberry Pi, and LoPy are some examples of these small sensor boards. In this project, the LoPy is used which is a sensor board that is equipped with Bluetooth Low Energy, Wifi and a LoRa radio. This last one is a communication technology that makes longer communication distances possible.The sensor network measures four different environmental indicators that will be distributed to the public: temperature, humidity, noise and air quality. The network then communicates via LoRa this data to one centralized server where the data is stored, processed and sent back to the citizens. This data is made publicly accessible to academia, citizens and the stakeholders alike. The network is also made interactive, people who pass by can interact with the sensors and request specific environmental data in real time.The sensor network has been build and deployed in the city. During the uptime of the network it succeeded to provide the data to the citizens via the feedback mechanisms: a website with a dashboard and an automated twitter account. Local differences have been measured with temperature and humidity sensors. With regard to the noise sensor and air quality sensors no definitive conclusions could be drawn.Sensor City Delft - Geomatics Synthesis Project on IoTGeomatic
Reference study of CityGML software support: The GeoBIM benchmark 2019—Part II
No full textOGC CityGML is an open standard for 3D city models intended to foster interoperability and support various applications. However, through our practical experience and discussions with practitioners, we have noticed several problems related to the implementation of the standard and the use of standardized data. Nevertheless, a systematic investigation of these issues has never been carried out, and there is thus insufficient evidence for tackling the problems. The GeoBIM benchmark project is aimed at finding such evidence by involving external volunteers, reporting on various aspects of the behavior of tools (geometry, semantics, georeferencing, functionalities), analyzed and described in this article. This study explicitly pointed out the critical points embedded in the format as an evidence base for future development. A companion article (Part I) describes the results of the benchmark related to IFC, the counterpart of CityGML within building information modeling.Urban Data Scienc
Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset
No full textEmotion identification is an essential task for human–computer interaction systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition. So far, there have been several EEG-based emotion recognition datasets that the researchers have used to validate their developed models. Hence, we have used a new ICBrainDB EEG dataset to classify angry, neutral, happy, and sad emotions in this work. Signal processing-based wavelet transform (WT), tunable Q-factor wavelet transform (TQWT), and image processing-based histogram of oriented gradients (HOG), local binary pattern (LBP), and convolutional neural network (CNN) features have been used extracted from the EEG signals. The WT is used to extract the rhythms from each channel of the EEG signal. The instantaneous frequency and spectral entropy are computed from each EEG rhythm signal. The average, and standard deviation of instantaneous frequency, and spectral entropy of each rhythm of the signal are the final feature vectors. The spectral entropy in each channel of the EEG signal after performing the TQWT is used to create the feature vectors in the second signal side method. Each EEG channel is transformed into time–frequency plots using the synchrosqueezed wavelet transform. Then, the feature vectors are constructed individually using windowed HOG and LBP features. Also, each channel of the EEG data is fed to a pretrained CNN to extract the features. In the feature selection process, the ReliefF feature selector is employed. Various feature classification algorithms namely, k-nearest neighbor (KNN), support vector machines, and neural networks are used for the automated classification of angry, neutral, happy, and sad emotions. Our developed model obtained an average accuracy of 90.7% using HOG features and a KNN classifier with a tenfold cross-validation strategy. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG
Association of corneal endothelial cell morphology with neurodegeneration in mild cognitive impairment and dementia
No full textINTRODUCTION
Corneal confocal microscopy (CCM) detects neurodegeneration in mild cognitive impairment (MCI) and dementia and identifies subjects with MCI who develop dementia. This study assessed whether abnormalities in corneal endothelial cell (CEC) morphology are related to corneal nerve morphology, brain volumetry, cerebral ischemia, and cognitive impairment in MCI and dementia.
METHODS
Participants with no cognitive impairment (NCI), MCI, and dementia underwent CCM to quantify corneal endothelial cell density (CECD) and area (CECA), corneal nerve fiber morphology, magnetic resonance imaging (MRI) brain volumetry, and severity of brain ischemia.
RESULTS
Of the 114 participants, 14 had NCI, 77 had MCI, and 23 had dementia. CECD (1971.3 ± 594.6 vs 2316.1 ± 499.5 cells/mm2, p < 0.05) was significantly lower in the dementia compared to the NCI group. CECD and CECA were comparable between the MCI and NCI groups (p = 0.13–0.65). Corneal nerve fiber density (CNFD) (31.7 ± 5.6 vs 24.5 ± 9.2 and 17.3 ± 5.3 fibers/mm2, p < 0.01), corneal nerve branch density (CNBD) (111.8 ± 58.1 vs 50.4 ± 36.4 and 52.7 ± 21.3 branches/mm2, p < 0.0001), and corneal nerve fiber length (CNFL) (24.6 ± 6.6 vs 16.5 ± 6.8 and 16.2 ± 5.0 mm/mm2, p < 0.0001) were lower in the MCI and dementia groups compared to the NCI group. Lower CECD partially mediated the impact of age and diabetes on CNFL reduction (p < 0.05), whereas CECA lost its significance after adjustment (p = 0.20). CEC morphology does not affect the association between corneal nerve fiber loss and MCI/dementia. CECD and CECA had no significant association with cerebral ischemic lesions (p = 0.21–0.47), dementia (p = 0.11–0.35), or cognitive decline (p = 0.37–0.38). However, lower CECD and higher CECA were associated with decreased cortical gray matter volume (p < 0.05–0.01).
DISCUSSION
CEC loss occurs in patients with dementia, and both endothelial cell loss and hypertrophy are associated with cortical gray matter atrophy. CNF loss occurs in individuals with MCI and dementia. Corneal nerve and endothelial cell abnormalities could act as biomarkers for neurovascular pathology in dementia
Diagnosing and managing diabetic somatic and autonomic neuropathy
No full textThe diagnosis and management of diabetic neuropathy can be a major challenge. Late diagnosis contributes to significant morbidity in the form of painful diabetic neuropathy, foot ulceration, amputation, and increased mortality. Both hyperglycaemia and cardiovascular risk factors are implicated in the development of somatic and autonomic neuropathy and an improvement in these risk factors can reduce their rate of development and progression. There are currently no US Food and Drug Administration (FDA)-approved disease-modifying treatments for either somatic or autonomic neuropathy, as a consequence of multiple failed phase III clinical trials. While this may be partly attributed to premature translation, there are major shortcomings in trial design and outcome measures. There are a limited number of partially effective FDA-approved treatments for the symptomatic relief of painful diabetic neuropathy and autonomic neuropathy
COVID-19 and neuropathy in type 2 diabetes
No full textThis study investigated the risk factors for COVID-19 and its impact on diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes (T2D). Patients with T2D underwent assessments with the NICE post-COVID questionnaire, DN4 questionnaire, vibration perception threshold (VPT), and corneal confocal microscopy (CCM) before and 11.0 ± 8.9 months after developing COVID-19. Of 76 participants with T2D, 35 (46.1%) developed COVID-19, of whom 8 (22.9%) developed severe COVID-19 and 9 (25.7%) developed long-COVID. The development of COVID-19 was associated with lower systolic blood pressure (P < 0.05). The presence and severity of DPN were not associated with developing COVID-19, severe COVID-19, or long-COVID (P = 0.42–0.94). Women were eight times more likely to develop long-COVID (P < 0.05) and elevated body weight, LDL, and VPT were associated with the development of long-COVID (P < 0.05 − 0.01). The long-COVID group exhibited significant changes in triglycerides and LDL (P < 0.05 for both) and body weight (P < 0.01) at follow-up. Their impact on clinical and neuropathy measures was comparable in patients with and without COVID-19 (P = 0.08–0.99). There was a significant reduction in corneal nerve measures (P < 0.05-0.0001) in patients with and without COVID-19. A low systolic blood pressure, altered lipids, body weight, higher VPT, and gender may determine the impact of COVID-19 in patients with T2D, but there was no evidence of an impact of COVID-19 on the development or progression of DPN
Glucose‐lowering medication associated with weight loss may limit the progression of diabetic neuropathy in type 2 diabetes
No full textAim: Obesity is a major risk factor for diabetic peripheral neuropathy (DPN) in type 2 diabetes (T2D). This study investigated the effect of glucose lowering medication associated with weight change on DPN. Methods: Participants with T2D were grouped based on whether their glucose lowering medications were associated with weight gain (WG) or weight loss (WL). They underwent clinical, metabolic testing and assessment of neuropathic symptoms, vibration perception threshold (VPT), sudomotor function and corneal confocal microscopy (CCM) at baseline and follow‐up between 4 and 7 years. Results: Of 76 participants, 69.7% were on glucose lowering medication associated with WG, and 30.3% were on glucose lowering medication associated with WL. At baseline, participants in the WG group had a significantly longer duration of diabetes (p < .01), higher douleur neuropathique en 4 (DN4) score (p < .0001) and VPT (p = .01) compared with those in the WL group. Over a 56‐month period, participants in the WG group showed no significant change in body weight (p = .11), HbA1c (p = .18), triglycerides (p = .42), DN4 (p = .11), VPT (p = .15) or Sudoscan (p = .43), but showed a decline in corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD) and corneal nerve fiber length (CNFL) (p < .0001). Participants in the WL group showed a reduction in weight (p = .01) and triglycerides (p < .05), no change in DN4 (p = .30), VPT (p = .31) or Sudoscan (p = .17) and a decline in the corneal nerve branch density (p < .01). Conclusions: Participants treated with glucose lowering medication associated with weight gain had worse neuropathy and greater loss of corneal nerves during follow‐up, compared to patients treated with medication associated with weight loss
Progressive loss of corneal nerve fibers is associated with physical inactivity and glucose lowering medication associated with weight gain in type 2 diabetes
No full textAims/Introduction: Limited studies have identified risk factors linked to the progression of diabetic peripheral neuropathy (DPN) in type 2 diabetes. This study examined the association of risk factors with change in neuropathy measures over 2 years. Materials and Methods: Participants with type 2 diabetes (n = 78) and controls (n = 26) underwent assessment of clinical and metabolic parameters and neuropathy using corneal confocal microscopy (CCM), vibration perception threshold (VPT), and the DN4 questionnaire at baseline and 2 year follow-up. Results: Participants with type 2 diabetes had a lower corneal nerve fiber density (CNFD), branch density (CNBD), and fiber length (CNFL) (P ≤ 0.0001) and a higher VPT (P ≤ 0.01) compared with controls. Over 2 years, despite a modest reduction in HbA1c (P ≤ 0.001), body weight (P ≤ 0.05), and LDL (P ≤ 0.05) the prevalence of DPN (P = 0.28) and painful DPN (P = 0.21) did not change, but there was a significant further reduction in CNBD (P ≤ 0.0001) and CNFL (P ≤ 0.05). CNFD, CNBD, and CNFL decreased significantly in physically inactive subjects (P < 0.05–0.0001), whilst there was no change in CNFD (P = 0.07) or CNFL (P = 0.85) in physically active subjects. Furthermore, there was no change in CNFD (P = 0.82), CNBD (P = 0.08), or CNFL (P = 0.66) in patients treated with glucose lowering medication associated with weight loss, whilst CNBD (P = 0.001) decreased in patients on glucose lowering medication associated with weight gain. Conclusions: In participants with type 2 diabetes, despite a modest improvement in HbA1c, body weight, and LDL there was a progressive loss of corneal nerve fibers; except in those who were physically active or on glucose lowering medication associated with weight loss
