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Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests. [ABSTRACT FROM AUTHOR]

Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility. In contrast, bedside scores are simple to administer, but lack the granularity to capture subtle but relevant tremor features. We utilise the open-source computer vision pose tracking algorithm Mediapipe to track hands in clinical video recordings and use the resulting time series to compute canonical tremor features. This approach is compared to marker-based 3D motion capture, wrist-worn accelerometry, clinical scoring and a second, specifically trained tremor-specific algorithm in two independent clinical cohorts. These cohorts consisted of 66 patients diagnosed with essential tremor, assessed in different task conditions and states of deep brain stimulation therapy. We find that Mediapipe-derived tremor metrics exhibit high convergent clinical validity to scores (Spearman's ρ = 0.55–0.86, p≤.01) as well as an accuracy of up to 2.60 mm (95% CI [−3.13, 8.23]) and ≤0.21 Hz (95% CI [−0.05, 0.46]) for tremor amplitude and frequency measurements, matching gold-standard equipment. Mediapipe, but not the disease-specific algorithm, was capable of analysing videos involving complex configurational changes of the hands. Moreover, it enabled the extraction of tremor features with diagnostic and prognostic relevance, a dimension which conventional tremor scores were unable to provide. Collectively, this demonstrates that current computer vision algorithms can be transformed into an accurate and highly accessible tool for video-based tremor analysis, yielding comparable results to gold standard tremor recordings. [ABSTRACT FROM AUTHOR]

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide. [ABSTRACT FROM AUTHOR]

Detecting and monitoring dental plaque is an important issue in research and clinical practice. In this context, new digital imaging methods that permit permanent documentation of the clinical findings could be promising tools. The aim of the study was therefore to investigate whether disclosed plaque can be reliably visualised on 2D and 3D images captured with digital intraoral imaging devices. Clinical examination was the reference method. Twenty subjects (27.5±1.2 years) were included and plaque was measured at three different stages: habitual plaque (T1), after 72 h without oral hygiene (T2) and after a subsequent habitual brushing exercise (T3). At each time point, plaque was disclosed followed by the clinical examination and capturing the 2D and 3D images (intraoral-camera CS 1500 and intraoral-scanner CS 3600; Carestream Dental, Germany). Plaque amounts were recorded on oral and vestibular surfaces of the Ramfjord-teeth (16, 21, 24, 36, 41, 44) using the Rustogi-modified-Navy-Plaque-Index (RMNPI) and expressed as percentage of plaque-containing RMNPI areas of all RMNPI areas. At T1, percentages (mean±SD) obtained from the clinical examination, 2D and 3D images were 62.2±10.6, 65.1±10.0 and 64.4±10.6 resp. increasing to 76.9±8.0, 77.9±8.6 and 77.5±9.4 resp. at T2. After toothbrushing (T3), values decreased to 56.3±11.1, 58.2±12.1 and 61.2±10.8 resp. All methods were able to show statistically significant changes in plaque amounts at the different time points with in part statistically significant but minor differences between them. The Bland-Altmann analysis revealed a good agreement between values from both 2D and 3D images with the clinical examination. The agreement of the scores obtained with the both image-based methods for the single RMNPI areas with the clinical examination was mainly classified as substantial to almost perfect. Amounts of plaque can be reliably detected and monitored on 2D images from an intraoral camera and on 3D images from an intraoral scanner. [ABSTRACT FROM AUTHOR]

A limited number of gauging stations, especially for nested catchments, hampers a process understanding of the interaction between streamflow, groundwater and water usage during drought. Non‐commercial measurement devices can help overcome this lack of monitoring, but they need to be thoroughly tested. The Dreisam River in the South‐West of Germany was affected by several hydrological drought events from 2015 to 2020 during which parts of the main stream and tributaries fell dry. Therefore it provided a useful case study area for a flexible longitudinal water quality and quantity monitoring network. Among other measurements the setup employs an image‐based method with QR codes as fiducial marker. In order to assess under which conditions the QR‐code based water level loggers (WLL) deliver data according to scientific standards, we compared its performance to conventional capacitive based WLL. The results from 20 monitoring stations reveal that the riverbed was dry for >50% at several locations and even for >70% at most severely affected locations during July and August 2020, with the north western parts of the catchment being especially concerned. Highly variable longitudinal drying patterns of the stream reaches emerged from the monitoring. The image‐based method was found valuable for identification and validation of zero level occurrences. Nevertheless, a simple image processing approach (based on an automatic thresholding algorithm) did not compensate for errors due to natural conditions and technical setup. Our findings highlight that the complexity of measurement environments is a major challenge when working with image‐based methods. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyse the signal dependency of the camera-based coaxial monitoring system QMMeltpool 3D (Concept Laser GmbH, Lichtenfels, Germany) for laser powder bed fusion (LPBF) under the variation of process parameters, position, direction and layer thickness to determine the capability of the system. Because such and similar monitoring systems are designed and presented for quality assurance in series production, it is important to present the dominant signal influences and limitations. Design/methodology/approach: Hardware of the commercially available coaxial monitoring QMMeltpool 3D is used to investigate the thermal emission of the interaction zone during LPBF. The raw images of the camera are analysed by means of image processing to bypass the software of QMMeltpool 3D and to gain a high level of signal understanding. Laser power, scan speed, laser spot diameter and powder layer thickness were varied for single-melt tracks to determine the influence of a parameter variation on the measured sensory signals. The effects of the scan direction and position were also analysed in detail. The influence of surface roughness on the detected sensory signals was simulated by a machined substrate plate. Findings: Parameter variations are confirmed to be detectable. Because of strong directional and positional dependencies of the melt-pool monitoring signal a calibration algorithm is necessary. A decreasing signal is detected for increasing layer thickness. Surface roughness is identified as a dominating factor with major influence on the melt-pool monitoring signal exceeding other process flaws. Research limitations/implications: This work was performed with the hardware of a commercially available QMMeltpool 3D system of an LPBF machine M2 of the company Concept Laser GmbH. The results are relevant for all melt-pool monitoring research activities connected to LPBF, as well as for end users and serial production. Originality/value: Surface roughness has not yet been revealed as being one of the most important origins for signal deviations in coaxial melt-pool monitoring. To the best of the authors' knowledge, the direct comparison of influences because of parameters and environment has not been published to this extent. The detection, evaluation and remelting of surface roughness constitute a plausible workflow for closed-loop control in LPBF. [ABSTRACT FROM AUTHOR]

Objective: To investigate the relationship between imaging findings and S100A4 overexpression in pancreatic ductal adenocarcinoma (PDAC) and to determine imaging biomarkers of S100A4 overexpression from whole-tumor evaluation with MRI and texture analysis. Methods: A total of 60 patients with pathologically confirmed PDAC were included in the study. All patients underwent preoperative abdominal contrast-enhanced MRI examination with Magnetom Aera (Siemens Healthcare, Germany, 1.5 T) at our institute. Whole-tumor evaluation including texture analysis was performed. Sections of specimens were reviewed, and the S100A4 expression status was quantitatively evaluated. Univariate and multivariate logistic regression analyses were conducted to find imaging biomarkers that could predict S100A4 overexpression. Results: Twenty-four tumors (40.0%) had negative results for S100A4 overexpression, and 36 tumors (60.0%) exhibited overexpression. After univariate and multivariate analysis, distal pancreatic duct dilatation, T1WI_10th percentile and the enhancement rate difference between delayed phase (DP) and portal venous phase (PVP) were identified to predict S100A4 overexpression in PDAC independently (p = 0.009, 0.012 and 0.044), with odds ratios (ORs) of 0.102, 0.139 and 4.645, respectively. The area under the ROC curve (AUC) values were 0.715, 0.707 and 0.691. The AUC value of the proposed model was 0.877 with a sensitivity of 80.6% and specificity of 75.0%. Conclusion: A model including distal pancreatic duct dilatation, T1WI_10th percentile and the enhancement rate difference between the DP and PVP could predict S100A4 overexpression in PDAC as imaging biomarkers. [ABSTRACT FROM AUTHOR]

In advanced Rett syndrome (RTT), limited or complete loss of ambulation, nutritional problems and scoliosis are unfavorable factors for bone mineral density (BMD). Still, there are few data available in this research area. Spinal quantitative computed tomography (QCT) allows an exact measurement of the volumetric BMD (vBMD) in this patient group. Two examiners measured vBMD of thoracic and lumbar vertebrae on asynchronous calibrated CTs that were acquired prior to surgical scoliosis correction (n = 21, age 13.6 ± 2.5 years). The values were compared to age- and sex-matched healthy controls to additionally derive Z-scores (n = 22, age 13.8 ± 2.0 years). The results showed the most significant reduction of vBMD values in non-ambulatory RTT patients, with p < 0.001 and average BMD-Z-score −1.5 ± 0.2. In the subgroup comparison, non-ambulatory patients with valproate treatment had significant lower values (p < 0.001) than ambulatory patients without valproate therapy, with an average BMD-Z-score of −2.3 ± 0.2. Comparison of the Z-scores to critical BMD thresholds of 120 and 80 mg/cm3 showed normal Z-scores in case of the ambulatory RTT subgroup, as opposed to BMD-Z-scores of the non-ambulatory RTT subgroups, which were partially below osteopenia-equivalent values. Furthermore, valproate treatment seems to have a direct effect on vBMD in RTT patients and when combined with loss of ambulation, BMD-Z-scores are reduced to osteoporosis-equivalent levels or even further. [ABSTRACT FROM AUTHOR]

With its standardized MRI datasets of the entire spine, the German National Cohort (GNC) has the potential to deliver standardized biometric reference values for intervertebral discs (VD), vertebral bodies (VB) and spinal canal (SC). To handle such large-scale big data, artificial intelligence (AI) tools are needed. In this manuscript, we will present an AI software tool to analyze spine MRI and generate normative standard values. 330 representative GNC MRI datasets were randomly selected in equal distribution regarding parameters of age, sex and height. By using a 3D U-Net, an AI algorithm was trained, validated and tested. Finally, the machine learning algorithm explored the full dataset (n = 10,215). VB, VD and SC were successfully segmented and analyzed by using an AI-based algorithm. A software tool was developed to analyze spine-MRI and provide age, sex, and height-matched comparative biometric data. Using an AI algorithm, the reliable segmentation of MRI datasets of the entire spine from the GNC was possible and achieved an excellent agreement with manually segmented datasets. With the analysis of the total GNC MRI dataset with almost 30,000 subjects, it will be possible to generate real normative standard values in the future. [ABSTRACT FROM AUTHOR]

While aging is characterized by neurodegeneration, musical training is associated with experience-driven brain plasticity and protection against age-related cognitive decline. However, evidence for the positive effects of musical training mostly comes from cross-sectional studies while randomized controlled trials with larger sample sizes are rare. The current study compares the influence of six months of piano training with music listening/musical culture lessons in 121 musically naïve healthy elderly individuals with regard to white matter properties using fixel-based analysis. Analyses revealed a significant fiber density decline in the music listening/musical culture group (but not in the piano group), after six months, in the fornix, which is a white matter tract that naturally declines with age. In addition, these changes in fiber density positively correlated to episodic memory task performances and the amount of weekly piano training. These findings not only provide further evidence for the involvement of the fornix in episodic memory encoding but also more importantly show that learning to play the piano at an advanced age may stabilize white matter microstructure of the fornix. [ABSTRACT FROM AUTHOR]

This research captures local networks of German political parties and welfare agencies in regards to poverty. The article explores whether there are differences in regards to homophily and brokerage between the two studied groups using a dataset of 33 egonetworks in two German cities. The computer assisted drawn networks were collected in an interactive participative way together with the interviewed egonetworks. To achieve the theoretical aim of analysing homophily and brokerage between politicians and welfare workers, two hypotheses are examined, resting upon social capital theory. The hypotheses were quantified and explicated with different variables. The first hypothesis states that heterophile networks imply more social capital, which referred to different measurements (size, density, homophily). This could be partially validated since the analysed networks of association representatives (n=12) were denser and slightly more heterophile than those of party representatives (n=21). Second, it was assumed that politicians, because of their function as elected representatives, would be more likely to take on an interface function within the communities than representatives of civil society institutions. Results based on calculated EI-indices, subgraphs and brokerage show that party representatives do indeed have larger networks, but these networks split into fewer subgraphs than association representatives' networks. [ABSTRACT FROM AUTHOR]

Glossopharyngeal neuralgia is a rare condition with neuralgic sharp pain in the pharyngeal and auricular region. Classical glossopharyngeal neuralgia is caused by neurovascular compression at the root entry zone of the nerve. Regarding the rare occurrence of glossopharyngeal neuralgia, we report clinical data and magnetic resonance imaging (MRI) findings in a case series of 19 patients, of whom 18 underwent surgery. Two patients additionally suffered from trigeminal neuralgia and three from additional symptomatic vagal nerve compression. In all patients, ipsilateral neurovascular compression syndrome of the IX cranial nerve could be shown by high-resolution MRI and image processing, which was confirmed intraoperatively. Additional neurovascular compression of the V cranial nerve was shown in patients suffering from trigeminal neuralgia. Vagal nerve neurovascular compression could be seen in all patients during surgery. Sixteen patients were completely pain free after surgery without need of anticonvulsant treatment. As a consequence of the operation, two patients suffered from transient cerebrospinal fluid hypersecretion as a reaction to Teflon implants. One patient suffered postoperatively from deep vein thrombosis and pulmonary embolism. Six patients showed transient cranial nerve dysfunctions (difficulties in swallowing, vocal cord paresis), but all recovered within 1 week. One patient complained of a gnawing and burning pain in the cervical area. Microvascular decompression is a second-line treatment after failure of standard medical treatment with high success in glossopharyngeal neuralgia. High-resolution MRI and 3D visualization of the brainstem and accompanying vessels as well as the cranial nerves is helpful in identifying neurovascular compression before microvascular decompression procedure. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine if micrographs produced with a colour charged couple device (CCD) camera were comparable in quality to micrographs obtained with a 35-mm colour film camera. The Axiocam (Zeiss, Jena, Germany) CCD camera produced images of 3900 × 3090 pixels.Each picture in a series of 50 pairs of film and digital micrographs was rated independently by each of three observers on the categories of colour, detail and overall impression. A total of 28 of the 50 pairs of micrographs were discarded due to colour differences.Digital micrographs were rated lower on colour and higher on detail compared to film micrographs. The film and digital micrographs were rated equally on overall impression. The difference in colour between film and digital micrographs can be compensated for using image processing software on the digital image. However, such manipulation was not carried out as part of the present study. The study originally included 50 digital micrographs and 50 film micrographs. The micrographs were paired and 22 of the 50 pairs of micrographs were assessed. Thus 28 pairs were not evaluated due to colour differences in the non-manipulated pictures. The interobserver difference on colour in the remaining 22 pairs given as mean square ± standard error of the mean was− 0.27 ± 0.10, 0.62 ± 0.10 and 0.90 ± 0.11. The interobserver difference on detail was− 0.48 ± 0.10, 0.95 ± 0.10 and 1.43 ± 0.10. The interobserver difference on overall impression was− 0.70 ± 0.10, 0.57 ± 0.10 and 1.27 ± 0.10. All the results differed significantly (p < 0.001).Digital micrographs are sufficient in detail, colour and overall impression to replace the traditional 35-mm film recordings for use in histopathology. [ABSTRACT FROM AUTHOR]

Purpose: Acute, isolated intracranial dissection (ICD) represents a rare and challenging cause of acute stroke. DSA is considered to be the gold standard imaging modality in patients with ICD. The role of novel, high-resolution (HR) compressed-sensing (CS) time-of-flight (TOF) MRA techniques in ICDs is unclear. Methods: A 22-year-old male patient with an isolated right ICA/MCA intracranial dissection underwent "conventional" 3-T TOF MRA, HR CS TOF MRA and also DSA including digital rotational angiography. Results: Unlike the "conventional" TOF MRA, HR CS TOF MRA provided comparable image quality to rotational angiography and a dissection membrane was clearly visible in both techniques. Conclusion: In this single case study, we demonstrated the feasibility of a novel HR CS TOF in a case of an acute isolated intracranial ICA/MCA dissection, which needs to be validated in a larger case series. [ABSTRACT FROM AUTHOR]

Conventional ultrasound (US) is the first-line imaging method for abdominal pathologies, but its diagnostic accuracy is operator-dependent, and data storage is usually limited to two-dimensional images. A novel tomographic US system (Curefab CS, Munich, Germany) processes imaging data combined with three-dimensional spatial information using a magnetic field tracking. This enables standardized image presentation in axial planes and a review of the entire examination. The applicability and diagnostic performance of this tomographic US approach was analyzed in an abdominal setting using conventional US as reference. Tomographic US data were successfully compiled in all subjects of a training cohort (20 healthy volunteers) and in 50 patients with abdominal lesions. Image quality (35% and 79% for training and patient cohort respectively) and completeness of organ visualization (45% and 44%) were frequently impaired in tomographic US compared to conventional US. Conventional and tomographic US showed good agreement for measurement of organ sizes in the training cohort (right liver lobe and both kidneys with a median deviation of 5%). In the patient cohort, tomographic US identified 57 of 74 hepatic or renal lesions detected by conventional ultrasound (sensitivity 77%). In conclusion, this study illustrates the diagnostic potential of abdominal tomographic US, but current significant limitations of the tomographic ultrasound device demand further technical improvements before this and comparable approaches can be implemented in clinical practice. [ABSTRACT FROM AUTHOR]

The mostly subterranean nature of district heating system pipelines makes pinpointing any occurring leakages a challenge. Airborne thermography offers a means for widespread monitoring, allowing thermal anomalies to be identified within multitudes of infrared images. This paper details a program developed to automate the entire image analysis process using mainly open-source software. Thermal images are acquired via unmanned aircraft system (UAS), pre-processed, and georeferenced individually or combined to orthomosaics. The search space is minimized to areas around the pipelines. Regions of interest are determined by image segmentation via tailored triangle histogram thresholding. The majority of resulting false alarms are removed by comparison with characteristic traits and results classified by their severity. The algorithm is applied to images newly acquired in Germany as part of a case study. The implemented methodology allows for a reduction of between 92 and 99% of thermal anomalies to a manageable amount of potential leakages for network operators to view. The use of orthomosaiking software in this context, though helpful in coalescing data, is found to lack robustness, precision and therefore reliability. Despite some limitations, the developed program is able to confidently detect and categorize leakages of varying severity and can be used directly by network operators. Future research will focus on further data pre-processing to eliminate thermal drift and remove the remaining false alarms, which mostly pertain to common urban features. [ABSTRACT FROM AUTHOR]

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF). [ABSTRACT FROM AUTHOR]

Presents an approach that combines the advantages of classification with linear spectral unmixing. Objectives of hyperspectral image analysis in the urban environment; Iterative linear spectral unmixing approach based on pixel-oriented endmember selection; Analysis of airborne hyperspectral data of the DAIS 7915 instrument for automated area-wide differentiation of urban surface cover types in Dresden, Germany.

Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms. [ABSTRACT FROM AUTHOR]

BackgroundPrevious studies hypothesized that neurodevelopmental risk factors may play a role in the pathogenesis of obsessive-compulsive disorder (OCD). Cortical folding has been shown to be a reliable indicator for normal and altered neurodevelopment, but in OCD it has barely been investigated up to now. The present study investigates whether alterations in gyrification are detectable in OCD and, if so, how these are associated with clinical characteristics.MethodWe compared the local Gyrification Index (lGI) between 75 OCD patients and 75 matched healthy subjects across the whole brain. In addition, for those regions exhibiting an altered lGI in patients we explored a potential relationship to symptom severity, age of onset, and influence of medication.ResultsOCD patients had a significantly decreased lGI in right parietal, precentral but also insula, temporal, pars triangularis and rostral middle frontal regions compared to healthy subjects. A positive association with age of onset was found but no association with symptom severity. There was no effect of co-morbidity or medication.ConclusionsThe reduced gyrification found in OCD confirms previous findings in other psychiatric disorders and suggests that alterations may already occur during early stages of brain development. Our findings support the idea that altered cortical folding might represent a trait characteristic of the disorder although longitudinal studies are needed to clarify the trajectory of this morphological measure in OCD. [ABSTRACT FROM PUBLISHER]

Coastal waters are one of the most vulnerable resources that require comprehensive investigation in space and time. One of the key factors for effective coastal monitoring is the use of remote sensing technologies. Since the Coastal Zone Color Scanner (CZCS) in 1978, a long list of space-borne missions had been successfully launched. However, those missions are limited to coastal waters applications. Despite a large number of missions, the existing systems are still facing similar challenges as four decades ago. Spatial and spectral data reconstruction and recovery a high resolution (HR) imagery data from a low resolution (LR) imaging is a challenging a task in many applications. The most promising technique in the field of digital image processing is known as Super Resolution (SR). Many techniques focus on reconstructing information at the sub-pixel level and dividing the original LR space into pixels corresponding to the HR space. Other methods assume that a series of LR images (in time) of a scene scanned from different perspectives (affine) will provide SR. Alternative methods use different data sources and proper image algorithms. In most cases, SR methods will perform a learning process in which the system will try to identify the inherent redundancy in the natural data in order to retrieve HR information from LR based on a spatial correlation between the original images. The learning process can be significantly efficient by using the Convolutional Neural Network (CNN). CNN submit to training through a large dataset that preserves scene's characteristics. The flexibility afforded by CNN is learning nonlinear relationships when reconstructing a spatial characteristic from an LR image to HR image. The main aim of this study is to identify spectral features related to the coastal water and inland water variations at different spatial and temporal scale and integrate them with a multi-scale information system. The main objectives of the study are developing of spatial-temporal-spectral fusion approach for multi-source data collected from the same geographical site; creating a new method for single image reconstruction from non-complementary information scene. The proposed method measures HR given LR by a downscaling process by turning HR into an LR. The deterministic process calculated using a Gaussian filter and by a photographic-focused distribution function. The correlation coefficient (at the LR-pixel level) used as an inverse ratio to upscaling. The proposed architecture is based on a three-convolutional network. In the first stage, the convolution is directly applied to the LR data, and then another sub-pixel convolution layer is subtracted to generate SR data from LR data through an upscaling process. This study performed in two sites, 1) a training site in Israel, 2) a test site in Germany. The training site is shallow seawaters around Oren River, Israel and the test site is Alfsee inland water in Germany. The results in both sites are SR imagery with full Sentinel 2 spectral resolution and spatial resolution of 0.3 m. [ABSTRACT FROM AUTHOR]

Background: Realignment osteotomies of valgus knee deformities are usually performed at the distal femur, as valgus alignment is considered to be a femoral-based deformity. This dogma, however, has not been proven in a large patient population. Valgus malalignment may also be caused by a tibial deformity or a combined tibial and femoral deformity. Purpose: The purposes of this study were (1) to analyze the coronal geometry of patients with valgus malalignment and identify the location of the underlying deformity and (2) to investigate the proportion of cases that require realignment osteotomy at the tibia, the femur, or both locations to avoid an oblique joint line. Study Design: Cross-sectional study; Level of evidence, 3. Methods: The analysis included 420 standing full-leg radiographs of patients with valgus malalignment (mechanical femorotibial angle [mFTA], ≥4°). A systematic analysis of the coronal leg geometry was performed including the mFTA, mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint-line convergence angle (JLCA). The localization of the deformity was determined according to the malalignment test described by Paley, and patients were assigned to 1 of 4 groups: femoral-based valgus deformity, tibial-based valgus deformity, femoral- and tibial-based valgus deformity, or intra-articular/ligamentary–based valgus deformity. Subsequently, the ideal osteotomy site was identified with the goal of a postoperative change of the joint line of two different maximum values, ±2° and ±4°, from its physiological varus position of 3°. Results: Measurements of the coronal alignment revealed a mean (±SD) mFTA of 7.4° ± 4.3° (range, 4°-28.2°). The mean mLDFA and mean mMPTA were 84.8° ± 2.4° and 90.9° ± 2.6°, respectively. The mean JLCA was 1.2° ± 3.1°. The majority (41.0%) of valgus deformities were tibial based, 23.6% were femoral based, 26.9% were femoral and tibial based, and 8.6% were intra-articular/ligamentary based. To achieve a straight-leg axis and an anatomic postoperative joint line with a tolerance of ±4°, the ideal site of a corrective osteotomy was tibial in 55.2% of cases and femoral in 19.5% of cases. A double-level osteotomy would be necessary in 25.2% of cases. With a tolerance of ±2°, the ideal osteotomy site was the proximal tibia in 41.0% of cases and the distal femur in 13.6% of cases; a double-level osteotomy would be necessary in 45.5% of cases. Conclusion: In contrast to the widespread belief that valgus malalignment is usually caused by a femoral deformity, this study found that valgus malalignment was attributable to tibial deformity in the majority of patients. In addition, a combined femoral- and tibial-based deformity was more common than an isolated femoral-based deformity. As a clinical consequence, varus osteotomies to treat lateral compartment osteoarthritis must be performed at the tibial site or as a double-level osteotomy in a relevant number of patients to avoid an oblique joint line. [ABSTRACT FROM AUTHOR]

Background: Empathy is a basic human ability, and patients with schizophrenia show dysfunctional empathic abilities on the behavioural and neural level.Aims: These dysfunctions may precede the onset of illness; thus, it seems mandatory to examine the empathic abilities in individuals at clinical high risk for psychosis.Method: Using functional magnetic resonance imaging, we measured 15 individuals at clinical high risk of psychosis (CHR group) and compared their empathy performance with 15 healthy volunteers and 15 patients with schizophrenia.Results: Behavioural data analysis indicated no significant deficit in the CHR group. Functional data analysis revealed hyperactivation in a frontotemporoparietal network including the amygdala in the CHR group compared with the other two groups.Conclusions: Despite normal behavioural performance, the CHR group activated the neural empathy network differently and specifically showed hyperactivation in regions critical for emotion processing. This could suggest a compensatory mechanism reflecting emotional hypersensitivity or dysfunctional emotion regulation. Further investigations should clarify the role of these neural alterations for development and exacerbation of psychosis. [ABSTRACT FROM AUTHOR]

Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%–100%) and 60% (range 21.3%–91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%–91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%–95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. • A convolutional neural network (CNN) received enhanced training with 12,378 open-source dermoscopic images. • In a head-to-head comparison, the CNN outperformed 136 of 157 participating dermatologists. • The CNN was capable to outperform dermatologists of all hierarchical subgroups (from junior to chief physicians) in dermoscopic melanoma image classification. [ABSTRACT FROM AUTHOR]

The article reports on the 50th Photogrammetric Week that was held on September 5-9, 2005 at the University of Stuttgart, Germany. Tutorials on digital imaging in photogrammetry and photogrammetric low-level image processing, among other areas of interest preceded the conference. Those who presented the tutorials included Dieter Fritsch and Norbert Haala who are both senior members of the Institute of Photogrammetry at University of Stuttgart.

In 2008 the Association of German Engineers (VDI) published the first part of the VDI 4499 guideline for methods and tools for the digital factory. Part 1 of this guideline deals with the fundamentals of the system and will be expanded by the addition of further parts on specific topics within the digital factory. Soon to be published is Part 4 which covers human models as tools of the digital factory. It describes the state of the art of current digital human models with regards to the methods and procedures implemented in German speaking countries. The structure of this part of the guideline is based on the different sections of engineering mechanics and outlines the opportunities for ergonomic assessments and the evaluation of work systems. During the writing of the guideline, the restrictions of the existing procedures also became evident thus showing the requirement for further development of such procedures. This is illustrated in this paper using examples from commercial software systems. [ABSTRACT FROM AUTHOR]

Most strength tests are restricted to measuring joint torques only in main directions like flexion or pronation. However, for an advanced strength prediction model, information about maximum possible joint torques in intermediate directions also has to be known. This paper concentrates on obtaining functions for maximum joint torques in arbitrary directions in the elbow. Therefore, isometric joint torque measurements have been conducted with 20 young males applying joint torques in eight different directions in one posture (90 degrees elbow flexion, 0 degrees shoulder flexion, neutral forearm rotation). It turns out that the appearance of the resulting shape can be best predicted using ellipse segments. Furthermore, a standardized ellipse could be obtained for the measurement posture. This helps to predict maximum joint torques in any other direction by knowing only one of the main joint torques. [ABSTRACT FROM AUTHOR]

Described is a project, which was processed by the University Library of Frankfurt/Main together with the HeBIS network and the SRZ company (Berlin). The objective was to convert already digitized image catalogues into MAB structured records which could be directly inserted in the HeBIS database. An amount of 2,7million images should be processed on the basis of equivalent algorithms. After an intensive preparation phase the conversion via automatic processing delivered a well accepted result. [ABSTRACT FROM AUTHOR]

The article discusses the study that sees the application of information technology in analyzing archival documents, with manuscripts of Freidrich Carl von Savigny found at the Marburg University Library in Germany as subject. The data capture analysis paved a way in creating a database that detects the causes of the damage and explicate process that will preserve the documents. Comprehensive discussion of the different modules that were utilized in creating a clear structure is offered.

Objectives: To increase the chance for a cure, cancer must be detected as early as possible. This can be achieved with cytopathological diagnostic methods. For a further increase of the diagnostic accuracy of these methods we introduced the multimodal cell analysis, viz, cells on the slide have to be relocalized to enable successive analysis of identical cells in different stains. For practical reasons the relocalization step must be automated.Methods: For a fully automatic acquisition of successive cell images we use a passive autofocus that is adaptive to the material, i.e., to the cells, followed by a comparison of the scenes, i.e., the cell constellation, of two such obtained images from different stains. In case that no sub-scene match can be found the search is extended to the surrounding area. A set of 1556 scenes from seven specimens have been subject to our algorithm. The automatically relocalized and acquired images from a second stain have been manually compared to the images from a first stain.Results: An overall relocalization rate of 85.4% is achieved. 14.3% of the images could not be relocalized and are lost for the following diagnostic process, while the critical case of erroneously matched images was observed in only 0.3% of cases.Conclusions: We could show that it is possible to automatically acquire images of successive stains of identical cells on cytopathological specimens. The method presented achieves acceptable relocalization rates. Wrong image acquisitions are very rare and can mostly be ascribed to images with single cells, i.e., without scene information. [ABSTRACT FROM AUTHOR]

The article focuses on the various ways in which the Bavarian State Library (BSB) converted its Book Catalog and Alphabetical Catalog (the so-called "Quartkatalog") into a machine-readable form. The advancement of technology in the conversion process is discussed. The modernization of bibliographic retrieval and cataloging systems by linking the catalogs to digital images is explored.

The purpose of this study was to compare the in-built body coil of the 3.0-Tesla (T) scanner with a dedicated surface coil of a 1.5 T system regarding knee imaging. We performed an intraindividual prospective clinical trial on 17 patients with knee pain using magnetic resonance imaging (MRI) at 1.5 and 3.0 T systems equipped with identical gradient systems. Proton-density-weighted turbo spin echo sequences with the same spatial resolution and comparable contrast parameters were used. A quantitative measurement of signal to noise ratio (SNR), relative contrast (RC) and contrast to noise ratio (CNR) between muscle and bone marrow was performed, followed by a qualitative assessment of anatomic/pathologic structures and the extent of artefacts. At 3.0 T, 30 lesions (91%) compared to 33 lesions at 1.5 T were detected. The SNR/CNR/RC were moderately reduced at 3.0 T versus 1.5 T (muscle 42 vs 47 and bone 83 vs 112/46 vs 69/0.33 vs 0.43). Motion artefacts from the pulsating popliteal artery were significantly increased at 3.0 T. A visible and measurable signal loss occurred at 3.0 T using the built-in body coil compared with the dedicated 1.5 T knee coil, but nearly all clinically important information could be obtained. [ABSTRACT FROM AUTHOR]

The accuracy of coronary calcium scoring using 16-row MSCT comparing 1- and 3-mm slices was assessed. A thorax phantom with calcium cylinder inserts was scanned applying a non-enhanced retrospectively ECG-gated examination protocol: collimation 12 x 0.75 mm; 120 kV; 133 mAs(eff). Thirty-eight patients were examined using the same scan protocol. Image reconstruction was performed with an effective slice thickness of 3 and 1 mm. The volume score, calcium mass and Agatston score were determined. Image noise was measured in both studies. The volume score and calcium mass varied less than the Agatston score. The overall measured calcium mass compared to the actual calcium mass revealed a relative difference of +2.0% for 1-mm slices and -1.2% for 3-mm slices. Due to increased image noise in thinner slices in the patient study (26.1 HU), overall calcium scoring with a scoring threshold of 130 HU was not feasible. Interlesion comparison showed significantly higher scoring results for thinner slices (all P<0.001). A similar accuracy comparing calcium scoring results of 1- and 3-mm slices was shown in the phantom study; therefore, the potentially necessary increase of the patient's dose in order to achieve assessable 1-mm slices with an acceptable image-to-noise-ratio appears not to be justified. [ABSTRACT FROM AUTHOR]

We assessed the diagnostic performance of magnetic resonance (MR) arthrography in the diagnosis of articular-sided partial-thickness and full-thickness rotator cuff tears in a large symptomatic population. MR arthrograms obtained in 275 patients including a study group of 139 patients with rotator cuff tears proved by arthroscopy and a control group of 136 patients with arthroscopically intact rotator cuff tendons were reviewed in random order. MR imaging was performed on a 1.0 T system (Magnetom Expert, Siemens). MR arthrograms were analyzed by two radiologists in consensus for articular-sided partial-thickness and full-thickness tears of the supraspinatus, infraspinatus, and subscapularis tendons. At arthroscopy, 197 rotator cuff tears were diagnosed, including 105 partial-thickness (93 supraspinatus, nine infraspinatus, three subscapularis) and 92 full-thickness (43 supraspinatus, 20 infraspinatus, 29 subscapularis) tendon tears. For full-thickness tears, sensitivity, specificity, and accuracy were 96%, 99%, and 98%, respectively, and for partial tears 80%, 97%, and 95%, respectively. False negative and positive assessments in the diagnosis of articular-sided partial-thickness tears were predominantly [78% (35/45)] observed with small articular-sided (Ellman grade1) tendon tears. MR arthrography is highly accurate in the diagnosis of full-thickness rotator cuff tears and is accurate in the diagnosis of articular-sided partial-thickness tears. Limitations in the diagnosis of partial-thickness tears are mainly restricted to small articular-sided tears (Ellman grade 1) due to difficulties in differentiation between fiber tearing, tendinitis, synovitic changes, and superficial fraying at tendon margins. [ABSTRACT FROM AUTHOR]

Our aim was to conduct a prospective study to evaluate staging accuracy of a new coil concept for endoluminal magnetic resonance imaging (MRI) on ex vivo gastric carcinomas. Twenty-eight consecutive patients referred to surgery with a clinically proven primary gastric malignancy were included. Surgical specimens were examined with a foldable and self-expanding loop coil (8-cm diameter) at 1.5 Tesla immediately after total gastrectomy. T1- and T2-weighted and opposed-phase sequences (axial, frontal sections; 3- to 4-mm slice thickness) were acquired. Investigators blinded to any patient information analyzed signal intensity of normal gastric wall, gastric tumor, and lymph nodes. Findings were compared with histopathological staging. On surgical specimens, 2-5 gastric wall layers could be visualized. All gastric tumors (26 carcinomas, two lymphomas) were identified on endoluminal MR data (100%). Overall accuracy for T staging was 75% (18/24); sensitivity to detect serosal involvement was 80% and specificity 89%. N staging correlated in 58% (14/24) with histopathology (N+ versus N-). The endoluminal coil concept is feasible and applicable for an ex vivo setting. Endoluminal MR data provided sufficient detail for gastric wall layer differentiation, and therefore, identification of T stages in gastric carcinoma is possible. Further investigations in in vivo settings should explore the potential of our coil concept for endoluminal MR imaging. [ABSTRACT FROM AUTHOR]

At the new heavy ion tumor therapy facility of the Gesellschaft für Schwerionenforschung at Darmstadt positron emission tomography (PET) has been implemented for in-beam and in-situ therapy control, i.e. during the tumor irradiation. The components necessary for this dedicated PET-imaging and their integration into the framework of therapy planning and quality assurance of heavy ion cancer treatments are presented. Results of the first application of this PET-method to patient treatments are reported. [ABSTRACT FROM AUTHOR]

Purpose: Seizures related to tumor growth are common in glioma patients, especially in low-grade glioma patients this is often the first tumor manifestation. We hypothesize that there are associations between preoperative seizures and morphologic features (e.g., tumor size, location) and histogram features in patients with glioblastoma (GB). Methods: Retrospectively, 160 consecutive patients with initial diagnosis and surgery of GB (WHO IV) and preoperative MRI were analyzed. Preoperative MRI sequences were co-registered (T2-FLAIR, T1-contrast, DTI) and tumors were segmented by a neuroradiologist using the software ITK-snap blinded to the clinical data. Tumor volume (FLAIR, T1-contrast) and histogram analyses of ADC- and FA-maps were recorded in the contrast enhancing tumor part (CET) and the non-enhancing peritumoral edema (FLAIR). Location was determined after co-registration of the data with an atlas. Permutation-based multiple-testing adjusted t statistics were calculated to compare imaging variables between patients with and without seizures. Results: Patients with seizures showed significantly smaller tumors (CET, adj. p = 0.029) than patients without preoperative seizures. Less seizures were observed in patients with tumor location in the right cingulate gyrus (adj. p = 0.048) and in the right caudate nucleus (adj. p = 0.009). Significant differences of histogram analyses of FA in the contrast enhancing tumor part were observed between patients with and without seizures considering also tumor location and size. Conclusion: Preoperative seizures in GB patients are associated with lower preoperative tumor volume. The different histogram analyses suggest that there might be microstructural differences in the contrast enhancing tumor part of patients with seizures measured by fractional anisotropy. Higher variance of GB presenting without seizures might indicate a more aggressive growth of these tumors. [ABSTRACT FROM AUTHOR]

A calendar of science and technology conferences for 2007 is presented. The Circuits, Signals, and Systems (CSS) 2007 will be held in Banff, Alberta on July 2-4. The Signal and Image Processing (SIP) 2007 will take place in Honolulu, Hawaii on August 20-22. Telematics 2007 will be held in Würz burg, Germany on August 29-31.

Objective. To monitor size and shape changes of bone erosions and changes in BMD in the vicinity of the erosion and in the periarticular trabecular compartment of patients with RA using high-resolution peripheral quantitative CT (HR-pQCT) imaging and to compare an automated three-dimensional (3D) image processing technique with manual measurements of erosion width and depth. Methods. The shape of 40 bone erosions and composition of bone around the erosions were analysed in the MCP joints of 22 RA patients both manually and by semi-automated 3D image processing at two different time points. Periosteal segmentation was performed using volume growing and morphological operations. Image registration was applied for transfer of baseline segmentations to follow-up datasets. Results. Eight erosions decreased in size, 6 increased and 28 remained stable. Increasing erosions were more spherical and smaller at baseline compared with decreasing or stable erosions. BMD in the vicinity of shrinking erosions increased, while it decreased next to expanding erosions. There was moderate agreement in the determination of erosion volume between semi-automated and manual measurements, but agreement was poor when assessing changes in volume over time. Conclusion. Longitudinal changes in erosion size and shape and of BMD in the vicinity of an erosion can be measured. BMD changes are associated with progression and regression of erosions. However, the semi-automated and manual approaches did not classify longitudinal changes of erosion volume in the same way. Further research is necessary to define the nature of these differences. [ABSTRACT FROM AUTHOR]

A calendar of events related to geoinformatics in 2010 is presented which includes the 2nd International Conference on Digital Image Processing (ICDIP 2010) in Singapore, the XVI International Course on Engineering Surveying in Germany, and the Space-Time Modelling and Analysis Workshop in California.

SUMMARY Passive seismic interferometry is a new promising methodology for seismic exploration. Interferometry allows information about the subsurface structure to be extracted from ambient seismic noise. In this study, we apply the cross-correlation technique to approximately 25 hr of recordings of ambient seismic noise at the Ketzin experimental CO2 storage site, Germany. Common source gathers were generated from the ambient noise for all available receivers along two seismic lines by cross-correlation of noise records. This methodology isolates the interstation Green's functions that can be directly compared to active source gathers. We show that the retrieved response includes surface waves, refracted waves and reflected waves. We use the dispersive behaviour of the retrieved surface waves to infer geological properties in the shallow subsurface and perform passive seismic imaging of the subsurface structure by processing the retrieved reflected waves. [ABSTRACT FROM AUTHOR]

A new semi-automated method is proposed in this study that detects joint traces from digital images and calculates the length distribution through three-dimensional data structuring. The method comprises detecting the pixels corresponding to the joint trace in digital images and calculating the length distribution through three-dimensional data structuring of the pixels detected as the joint trace. Semantic segmentation based on deep learning techniques is applied to the detection of joint trace pixels to overcome the limitations of rule-based image processing techniques. For training a deep learning network, various rock joint images were sampled and labeled, following which the data sets for training, validation, and testing were prepared through classification and augmentation. Using the prepared datasets, the performance of the classifiers based on widely used semantic segmentation networks, namely U-Net (University of Freiburg, Germany) network and DeepLabV3+ (Google, USA) network, was compared. The classifier based on the DeepLabV3+ network was ultimately selected and applied. The trained classifier successfully detected pixels corresponding to the joint trace in images of both flat and uneven rock surfaces, indicating that the deep learning technique could be applied effectively to joint trace detection from digital images. Further, a data structuring technique is proposed to calculate the joint trace length using pixelwise data detected through the trained classifier. This technique uses point cloud data obtained by photogrammetry, and comprises two-dimensional thinning and segmentation, three-dimensional projection, segmentation, and segment linking. The entire proposed method, from detecting the joint trace pixels to three-dimensional data structuring, was verified by applying it to both simple flat and uneven rock surface models. • New technique for joint trace detection using deep learning network is proposed. • New data structuring technique for pixelwise data is proposed. • Semi-automatic calculation of the trace length is possible using the two techniques. • Proposed techniques is verified by applying it to both flat and uneven rock surfaces. [ABSTRACT FROM AUTHOR]

Although several papers have shown that bike ridership is affected by actual weather conditions, this is the first study to comprehensively investigate the impact of forecasted weather conditions on bike ridership. The results show that both actual and forecasted weather conditions can be used as useful explanatory variables for predicting bicycle usage. Even incorrect weather forecasts can impact on bike ridership, which underlines the importance of weather forecast effects for traffic planners; for example, forecasted rain can reduce bike traffic by 3.6% in periods that turn out to be rain-free. Additionally, a digital image-processing method is used to calculate the darkness of the cloud coverage displayed on weather forecast maps. The results imply that bike ridership is significantly smaller in regions with darker forecasted clouds. It is also shown that weather forecasts have a stronger impact on recreational bike traffic than on utilitarian traffic. Furthermore, various lagging and leading effects of rain forecasts are outlined. Morning rain forecasts can, for example, reduce bike ridership in midday and afternoon hours that were predicted to be rain-free. To derive these results, hourly bicycle counts from 188 automated counting stations in Germany are collected for the years 2017 and 2018. They are linked to actual weather data from Germany's National Meteorological Service and with historical weather forecasts that are deduced from weather maps of Germany's most-watched television news program. Log-linear and negative binomial regression models are then used to estimate the weather forecast effects. [ABSTRACT FROM AUTHOR]

We describe a method to determine the daytime visibility at a station that is located at the University of Karlsruhe, Germany. A comparison of visibilities determined by applying our new method and visibilities observed with standard methods at a nearby station of the German Weather Service (DWD) demonstrate the successful operation of our method. The highest differences occur for very low and very high visibilities. For visibilities >10km, the correlation between visibilities of both methods is 0.73. Additionally, at low sun zenith angles of <30°, an overestimation by the visibility determined with digital photographs (VISIDIP) method of up to 20% has been observed. Partially, the bias could be explained by the different measurement heights and different local conditions which have an effect especially under certain meteorological conditions as fog or precipitation events. In contrast to human observations, the new method presented in this feasibility study is completely automatic and objective. [Copyright &y& Elsevier]

• Automatic Plant disease diagnosis for early disease symptoms. • Novel image processing algorithm in combination with machine learning methods. • A testing on wheat was performed with 7 mobile devices over a period of 4 seasons. • Obtained results reveal AuC metrics higher than 0.80 for all the analyzed diseases. Fungal infection represents up to 50 % of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014 , 2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany. [ABSTRACT FROM AUTHOR]

We present here a new method to predict cloud concentration five minutes in advance from all-sky images using the Artificial Neural Networks (ANN). An autoregressive neural network with backpropagation (Ar-BP) was created and trained with four years of all-sky images as inputs. The pictures were taken with a hemispheric sky imager fixed on the roof at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Firstly, a statistical method is presented to obtain key information of the pictures. Secondly, a new image-processing algorithm is suggested to optimize the cloud detection process starting with the Haze Index. Finally, the cloud concentration five minutes in advance at the IMUK is forecasted using machine learning methods. A persistence model forecast to provide a reference for comparison was generated. The results are quantified in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The new algorithm reduced both the RMSE and the MAE of the prediction by approximately 30% compared to the reference persistence model under diverse cloud conditions. The new algorithm could be used as a tool for the stable maintenance of the network for the transmission system operators, i.e., the primary control reserve (within 30 seconds) and the secondary control reserve (within 5 minutes). [ABSTRACT FROM AUTHOR]

Highlights • Time-related factors and distances-related factors affect the drivers' deceleration rate choice in Vehicle-Pedestrian conflict have been analyzed. • The drivers' effective decelerating zone with sight angle has been proposed with the consideration of longitudinal & lateral conflict behavior. • Intercultural comparison has been made to address the significant differences in driver behavior in China and Germany. Abstract To analyse the drivers' decelerating behavior in conflict avoidance process under Vehicle-Pedestrian conflict situation is accordingly a surrogate method for the guidance of driver evasive manoeuvre and improving the active driving safety. Field observation data has been collected by video recording and image processing at urban unsignalized midblock crosswalks in Beijng, China and Munich, Germany. The relationship between deceleration rate and vehicle speed has been statistically described. By analyzing how the time-related measures (Vehicle Time to Conflict Point and Pedestrian Time to Conflict Point) and the distance-related measures (Vehicle longitudinal distance to Conflict Point and Pedestrian Lateral distance to Conflict Point) affect the deceleration rate choice, the drivers' effective decelerating zone with sight angle has been proposed with the consideration of longitudinal & lateral conflict behavior. Minimum Vehicle-Pedestrian conflict distance is calculated and a 2 m minimum distance is suggested as the threshold for collision discrimination. Comparisons which address the needs to make the intercultural adaptation when introducing Driver Assistance Systems to developing countries have also been made to show the significant differences between China and Germany. The whole work could hopefully be the groundwork in the target of conflict pre-scanning by decelerating zones under various conflict situations and the design of vehicle active safety systems. [ABSTRACT FROM AUTHOR]

Germany's climate is moderate and has generally no longer periods of cold or hot weather. North western and coastal Germany has a maritime influenced climate which is characterized by warm summers and mild cloudy winters. It is a temperate country with warm summers and cold winters - prolonged periods of frost or snow are rare, rain falls throughout the year, with much of Germany experiencing its maximum rainfall over the high summer months. Apart from all the floods and bad weather already mentioned, earth quakes also happen in Germany every now and then. However, buildings usually are solid enough so damage is limited - there hasn't been an earth quake disaster for hundreds of years. I have conducted many comprehensive studies on the Germany climate and natural hazards. Keeping in view of all the above facts of climate and natural hazards of the Germany, I have conducted many comprehensive studies on the Germany climate and natural calamities combined with my researches and proposed the Germany Monsoon Time Scale, and Germany National Geoscope Project, Irlapatism-A New Hypothetical Model of Cosmology etc which can help to estimate the impending weather conditions and natural hazards of the country in advance to take mitigation measures and save the people, crops and other assets. For example-By setting up the Germany National Geoscope Project and maintain, the country can be predicted the impending earthquakes, volcanic hazards(and storm surges, tsunamis etc consequence secondary hazards due to the earthquakes occur in the womb that means underground of the sea or ocean if the country have the chances of occurring of these disasters) in advance. By setting up the Germany National Geoscope Project and maintain, the country can be predicted the earth's underground resources like metallic resources such as iron, gold, silver, tin, copper, nickel, aluminum, chromium etc mine sites and non-metallic resources like sand gravel, gypsum, halite, uranium, dimension stones, etc. can be found by inserting many kinds of super high remote sensing technology in the area of sensor physics, signal processing used specially image processing, electromagnetic detection technology and geophysical deep underground detectors and mineral exploration equipments, natural gas sensors etc in the underground of the Germany through the Geoscope. Setting up the Germany National Geoscope Project and maintain will also be useful in emerging industries such as geothermal and geo-sequestration etc. By establishing the Germany Monsoon Time Scale and maintain, the country can be estimated the impending weather conditions and natural calamities like rains, floods, landslides, avalanches, blizzard, droughts, extreme winter conditions, heavy rainfall, mudflows, extreme weather, cyclones, cloud bursts, sand storms, hails, and winds etc in advance. Surface water resources in advance. [ABSTRACT FROM AUTHOR]

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Rooftop solar photovoltaics can significantly contribute to global energy transitions by providing clean, decentralized energy without the need for new land, thereby avoiding land-use conflicts. It serves as a valuable complement to other renewable-energy sources and is expected to play a crucial role in future electricity systems. Due to the spatiotemporal variability in the solar radiation on roof surfaces, it is essential to determine the potentials of the rooftop photovoltaics and its variations in specific regions. In Germany, this potential was assessed in 5 km × 5 km zones, as well as at the federal-state and national levels. High-quality satellite and reanalysis data were used to determine the power output of the solar photovoltaics. Additionally, high-resolution (2 m × 2 m) European Settlement Map data, calibrated with solar cadastre data, were utilized to evaluate different scenarios. The results show that the potential is concentrated in larger urban areas and the southwestern part of Germany due to the availability of rooftop space and solar radiation. Overall, the national rooftop areas are substantial across all scenarios, ranging from 2100 to 4500 km2. The applied methods and scenarios provide a straightforward way to reveal the spatiotemporal variability and define realistic ranges of the solar photovoltaic potential without requiring detailed information about each building. However, assessing the rooftop photovoltaic potential remains challenging and uncertain due to the lack of large-scale, high-resolution data on building characteristics and the complexity of the solar radiation distribution in urban environments. [ABSTRACT FROM AUTHOR]