A folded-sandwich polarization-entangled two-color photon pair source with large tuning capability for applications in hybrid quantum architectures

We demonstrate a two-color entangled pho ton pair source which can be adapted easily to a wide range of wavelengths combinations. A Fresnel rhomb as a geometrical quarter-wave plate and a versatile combination of compensation crystals are key components of the source. Entanglement of two photons at the Cs D1 line (894.3 nm) and at the telecom O-band (1313.1 nm) with a fidelity of $F = 0.753 \pm 0.021$ is demonstrated and improvements of the setup are discussed

Malignant Vascular Tumors of the Head and Neck—Which Type of Therapy Works Best?

Malignant vascular tumors of the head and neck are rare neoplasms with variable clinical presentation, wide age distribution, and variable clinical courses. The heterogeneous presentation of angiosarcomas and epithelioid hemangioendothelioma often leads to misdiagnosis and unsuitable treatment. While risk factors for angiosarcomas are previous radiation, chronic lymphedema, and exposure to arsenic, thorium oxide, or vinyl chloride, there are only limited and retrospective data available on prognostic factors in EHE. In both angiosarcomas and EHE, surgery is the mainstay of treatment. There is limited evidence regarding the role of radiotherapy in EHE, although EHE is considered relatively radiosensitive. In angiosarcomas, adjuvant radiotherapy is recommended according to retrospective case series. A standard medical therapy for metastasized malignant vascular tumors is lacking. Chemotherapy, which is effective in angiosarcoma, is mostly ineffective in EHE. Targeted therapy, antiangiogenetic drugs and immunotherapy have been studied as new treatment options. The goal of this review is to summarize the current data regarding malignant vascular tumors along with their diagnosis and management

Best Practice in Surgical Treatment of Malignant Head and Neck Tumors

Purpose of review: Defining the best practice of surgical care for patients affected by malignant head and neck tumors is of great importance. In this review we aim to describe the evolution of “best practice” guidelines in the context of quality-of-care measures and discuss current evidence on “best practice” for the surgical treatment of cancers of the sino-nasal tract, skull base, aero-digestive tract, and the neck. Recent findings: Current evidence based on certain structure and outcome indicators, but mostly based on process indicators already helps defining the framework of “Best practice” for head and neck cancer surgery. However, many aspects of surgical treatment still require in-depth research. Summary: While a framework of “Best practice” strategies already exists for the conduction of the surgical treatment of head and neck cancers, many questions still require additional research in particular in case of rare histologies in the head and neck region

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Global Determination of Snow Cover using Remote Sensing and a Near Real Time Processing Chain

Remote sensing offers the best prerequisites for obtaining comprehensive information on global snow cover. Although microwave remote sensing can provide limited information about the thickness of the snowpack and the water stored (i.e. snow water equivalent), its geometric resolution is not sufficient for an accurate spatial analysis. Optical remote sensing provides the required spatial resolution, but it is often compromised by clouds or at high geographical latitudes by the polar night. In order to obtain cloud-free information on daily snow cover from optical data, the German Aerospace Center developed the already established product Global SnowPack (GSP). It is based on the daily MODIS snow products originating from Terra and Aqua platforms and provided by NSIDC. With the help of sequential algorithms and additional data (digital elevation model, land cover classifications), pixels with clouds or polar night are continuously eliminated. While the Global SnowPack has so far only been calculated retrospectively for the entire hydrological year, there will now also be a near real time product (NRT-GSP). The latest MODIS data (these are available after approx. 2 days) are interpolated on a daily basis using the previous days. The product will be available in the future on the GeoService of the Earth Observation Center. We see an application of this product, for example, in the prediction of extreme hydrological events. In a recently published study, the development of the snow cover of various catchment areas of nival rivers derived from Global SnowPack was incorporated into a snowmelt runoff model. It was found that extreme high and low water events during the annual spring flood were reflected early in the development of the snow cover extent. With the help of the NRT-GSP product, such a development would be recognizable at an early stage and preparations could be made

Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt

The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed

Применение метода аналитических сетей для оптимизации процесса выбора стратегии развития пассажирского автотранспортного предприятия

В статье обозначена проблема снижения рентабельности пассажирских автотранспортных предприятий и связанные с этим сложности по реализации процедуры стратегического прогнозирования и управления предприятием. Обосновано использование метода аналитических сетей в модели поддержки принятия решений при управлении стратегией автотранспортного предприятия, представленной в работе [1], в качестве инструмента, позволяющего формализовать экспертные знания на основных этапах оценки и выбора проектов стратегического развития. Описаны основные шаги и приведены результаты расчета алгоритма метода аналитических сетей в рамках данной модели.The article outlines the problem of reducing the profitability of passenger motor transport enterprises and the associated difficulties in implementing the procedure of strategic forecasting and enterprise management. The use of the method of analytical networks in the model of decision support in managing the strategy of a trucking enterprise presented in [1] is substantiated as a tool that allows to formalize expert knowledge at the main stages of evaluation and selection of projects for strategic development. The main steps and calculations of the algorithm algorithm for analytical networks within the framework of this model are described

Use of near real-time cloud-free MODIS snow cover data from DLR's Global SnowPack for the early forecast of extreme hydrological events

The MODIS sensor on the NOAA Terra satellite has been providing daily information on global snow cover with a nominal spatial resolution of 500 m since February 2000. Since July 2022, this sensor is also located on NOAA's Aqua Satellite in orbit. The daily snow cover product of both platforms constitutes the basis for the DLR Global SnowPack (GSP) processor. In the course of the GSP processing, the daily data of both MODIS sensors are merged and data gaps (e.g., clouds or polar night) are interpolated over 3 days. From a digital elevation model, the snow height (elevation above which only snow occurs), as well as the snow-free height (elevation below which no snow occurs) are determined. Heights above or below these thresholds are filled accordingly. Finally, remaining gaps are gradually filled by the values of preceding days. Since the year 2022, the daily cloud free GSP data has been made available in near real time (3 days delay due to the preprocessing of the NSIDC) via the GeoService Portal of the Earth Observation Center (EOC). The rapid provision of the information on global snow coverage allows completely new applications of time-critical questions. These include hydrological estimates to what extent the snow conditions in the catchment area influence the drainage behavior. In addition to the satellite data, meteorological and hydrological data of the past 20 years are used to estimate the impact of a changing snow cover on the runoff. In the course of climate change, a delayed onset of snow cover and an earlier snowmelt is likely. Warmer winters also increase the risk of Rain-on-Snow events, which cause a strong increase in the outflow and have more dramatic ecological effects. We will present results for selected river catchment areas with a special focus on hydrological extreme events (droughts and floods), and when their occurrence has been shown early in the development of seasonal snow coverage. Our goal is to provide an automatic early warning system based on near real time GSP for large river catchments with nival-influenced drainage regimes

Course of Self-Reported Dysphagia, Voice Impairment and Pain in Head and Neck Cancer Survivors

Background: Head and neck cancer (HNC)-specific symptoms have a substantial impact on health-related quality of life. The aim of this study was to determine whether self-reported dysphagia, voice problems and pain of HNC patients changed over time and whether specific clinical or sociodemographic variables were associated with these symptoms. Methods: HNC patients (n = 299) in an outpatient setting answered questionnaires (Eating Assessment Tool-10; questions from the EORTC QLQ-C30 and EORTC H&N35) on dysphagia, voice problems and pain, collected with the software “OncoFunction” at three different timepoints (t1–t3) after diagnosis. The mean score changes from t1 to t3 were expressed in terms of effect sizes d. The impact of sociodemographic and clinical factors on the course of the variables was tested with multivariate analyses of variance. Results: Dysphagia, voice impairment and pain in HNC survivors significantly improved over a period of approximately 14 months after diagnosis. Tumor site, stage, treatment modality, occupational state and ECOG state were significantly correlated with self-reported functional outcome. The pain level of the HNC patients was rather low. Conclusions: Patients suffer from functional impairments after HNC treatment, but an improvement in self-reported symptoms could be demonstrated within this time period