bluuebunny/crossref_metadata_2025 huggingface dataset:
Examine the pedestrian road crossing behavior in Kuwait
Authors: AlKheder Sharaf , Alrashidi Ashwag , Zaqzouq Areej
Published: January 2022

This study sheds light on pedestrian and driver's behavior towards crossing in Kuwait. It examined the effect of different factors on pedestrian and vehicle violations at 10 locations in Kuwait including signalized, nonsignalized intersections, locations with/without pedestrian's bridge, and locations with/without pedestrian crossing signals. Four types of pedestrian's violations were observed; crossing outside the marked crossway, not obeying traffic signal, distracted crossing (eating, drinking, or using a smartphone while crossing), and not using pedestrian's bridge. For vehicle violations, two types were observed; not yielding to pedestrians and stopping over the marked crossway. The pedestrian and vehicle violations observed are considered as “violations” according to the existing Highway Code in Kuwait. A total of 24,393 pedestrian crossing violations were recorded over 40 days. Pedestrian violations were analyzed using a multinomial logit model while vehicle violations were analyzed by frequencies analysis. Video data were used for validation applying a trajectory‐based approach showing a high accuracy level of 98.85%. The results showed that all the studied factors; time of observation including weekdays and weekends, and peak and off‐peak hours, location of observation, demographic characteristics including pedestrian gender, and age, and site properties (with/without pedestrian's bridge and with/without pedestrian crossing signal) were significant and had a strong relationship with the type of violations. The results also indices the importance of providing pedestrian's bridge in locations with a high number of violations.
Impact of winding parameters on the fiber bandwidth in the cylindrical area of a hydrogen pressure vessel for generating a digital twin
Authors: Hopmann Christian , Magura Nadine , Müller Robert , Schneider Daniel , Fischer Kai
Published: January 2022

The wet winding process is an established manufacturing technology for the production of rotationally symmetrical components made of fiber‐reinforced plastic (FRP), such as type‐IV hydrogen pressure vessels. In this process, the FRP laminate is produced by continuously depositing an impregnated fiber band on the winding core in a winding pattern adapted to the component stress. Despite a long history of the process, there are shortcomings in the understanding of the process, without which further optimization in terms of cost and material efficiency will reach its limits in the future. Currently, the fiber band is assumed to have a constant rectangular cross‐section in most simulations, which neglects manufacturing influences such as fiber spreading. The investigations carried out aim to extend the process understanding by a realistic description of the fiber bandwidth when deposited on the winding core. The investigations show that the fiber spreading, as well as the absolute fiber bandwidth, depend on the resin loading and the fiber band tension, independent of the winding pattern in the cylindrical area of a pressure vessel shaped winding core. In addition, the fiber bandwidth changes by 15%–20% during circumferential winding, depending on the machine movement.
An introduction to the full random effects model
Authors: Yngman Gunnar , Bjugård Nyberg Henrik , Nyberg Joakim , Jonsson E. Niclas , Karlsson Mats O.
Published: January 2022

The full random‐effects model (FREM) is a method for determining covariate effects in mixed‐effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates. This approach is robust against issues that may cause reduced performance in methods based on estimating fixed effects (e.g., correlated covariates where the effects cannot be simultaneously identified in fixed‐effects methods). FREM covariate parameterization and transformation of covariate data records can be used to alter the covariate‐parameter relation. Four relations (linear, log‐linear, exponential, and power) were implemented and shown to provide estimates equivalent to their fixed‐effects counterparts. Comparisons between FREM and mathematically equivalent full fixed‐effects models (FFEMs) were performed in original and simulated data, in the presence and absence of non‐normally distributed and highly correlated covariates. These comparisons show that both FREM and FFEM perform well in the examined cases, with a slightly better estimation accuracy of parameter interindividual variability (IIV) in FREM. In addition, FREM offers the unique advantage of letting a single estimation simultaneously provide covariate effect coefficient estimates and IIV estimates for any subset of the examined covariates, including the effect of each covariate in isolation. Such subsets can be used to apply the model across data sources with different sets of available covariates, or to communicate covariate effects in a way that is not conditional on other covariates.
Tailoring Drug Mobility by Photothermal Heating of Graphene Plasmons
Authors: Phan Anh D. , Ngan Nguyen K. , Nga Do T. , Le Nam B. , Ha Chu Viet
Published: January 2022

A theoretical approach to quantitatively determine the photothermally driven enhancement of molecular mobility of graphene‐indomethacin mixtures under infrared laser irradiation is proposed. Graphene plasmons absorb incident electromagnetic energy and dissipate them into heat. The absorbed energy depends on optical properties of graphene plasmons, which are sensitive to structural parameters, and concentration of plasmonic nanostructures. By using theoretical modelling, temperature gradients of the bulk drug with different concentrations of graphene plasmons are calculated. From these, the temperature dependence of structural molecular relaxation and diffusion of indomethacin are determined and how the heating process significantly enhances the drug mobility is found out.
Extended prescribed performance control with input quantization for nonlinear systems
Authors: Gao Shigen , Xu Chaoan , Zheng Yue , Dong Hairong , Hu Xiaoming
Published: January 2022

For a class of MIMO nonlinear systems, comprised of interconnected subsystems in Brunovsky canonical form, with uncertain yet locally Lipschitz nonlinearities among subsystems and quantized inputs, the target is to form a closed‐loop system that exhibits extended prescribed performance on states tracking (accuracy determined by maximum overshoot, minimum convergence rate, maximum steady‐state error, and control gains), yet still a low‐complexity control structure, without requiring any identification, approximation, and filtering techniques, regardless of uncertainties. In this article, a static, decentralized, continuous, yet computationally inexpensive controller is designed, without requiring parameters of quantizers. Inheriting the merit of pioneering prescribed performance control (PPC) methodology, it is required that the reference signal isfunction only, while, an essential difference and new feature is that, the pioneering PPC guarantees that state tracking errors are constrained by boundary functions (also known as prescribed performance functions, PPFs) only, while, the proposed extend PPC scheme achieves that state tracking errors are constrained by boundary functions and control gains simultaneously, in other words, without “tighter” PPFs, state tracking errors can still be adjusted to arbitrarily small by choosing proper control gains. Finally, comparative simulation results are given to verify the theoretical findings.
Rivers—mouths—tides—memories: A creative, inter‐deep‐mapping of two river/tidal places. Love of place, memory and affect; movements, patterns, marks, and practices of care
Authors:   The River Severn Estuary UK,   Bahía Adair, Sonora, Mexico, Jones Owain , Green Heather
Published: January 2022

This article sets out the authors' attachments to, and memories of, two tidal river/channel landscapes and how these are being folded into collaborative work, which brings these places to attention through art practice and shared reflection. The rivers/channels in question are given voice in the introduction and ideas of the ecologies of mouths and river mouths, particularly those tidal, are explored in the following sections. Aspects of polyvocality and deep mapping are employed to reveal the unique characteristics of these places—particularly their tidal patterns and rhythms and our concerns for them. An art project,Tidal Timespace, lies at the heart of this article with the role of memories, shared memories, and our relationships to place as key themes. The descriptive accounts of the authors' respective place memories are set in the margins of the article in two columns. We conclude with considerations of expanded notions of ecology, and we add that loss of place, the passing of time, and wider senses of ecocide and extinction run like shadows through this narrative.
Dual‐Functional Photocatalysis for Cooperative Hydrogen Evolution and Benzylamine Oxidation Coupling over Sandwiched‐Like Pd@TiO2@ZnIn2S4 Nanobox
Authors: She Ping , Qin Jun‐sheng , Sheng Jiyao , Qi Yuanyuan , Rui Hongbang , Zhang Wei , Ge Xin , Lu Geyu , Song Xiaowei , Rao Heng
Published: January 2022

Photocatalytic hydrogen evolution (PHE) over semiconductor photocatalysts is usually constrained by the limited light‐harvesting and separation of photogenerated electron–hole pairs. Most of the reported systems focusing on PHE are facilitated by consuming the photoinduced holes with organic sacrificial electron donors (SEDs). The introduction of the SEDs not only causes the environmental problem, but also increases the cost of the reaction. Herein, a dual‐functional photocatalyst is developed with the morphology of sandwiched‐like hollowed Pd@TiO2@ZnIn2S4 nanobox, which is synthesized by choosing microporous zeolites with sub‐nanometer‐sized Pd nanoparticles (Pd NPs) embedded as the sacrificial templates. The ternary Pd@TiO2@ZnIn2S4 photocatalyst exhibits a superior PHE rate (5.35 mmol g−1 h−1) and benzylamine oxidation conversion rate (>99%) simultaneously without adding any other SEDs. The PHE performance is superior to the reported composites of TiO2 and ZnIn2S4, which is attributed to the elevated light capture ability induced by the hollow structure, and the enhanced charge separation efficiency facilitated by the ultrasmall sized Pd NPs. The unique design presented here holds great potential for other highly efficient cooperative dual‐functional photocatalytic reactions.
The Galapagos Chip Platform for High‐Throughput Screening of Cell Adhesive Chemical Micropatterns
Authors: Tuvshindorj Urandelger , Trouillet Vanessa , Vasilevich Aliaksei , Koch Britta , Vermeulen Steven , Carlier Aurélie , Alexander Morgan R. , Giselbrecht Stefan , Truckenmüller Roman , de Boer Jan
Published: January 2022

In vivo cells reside in a complex extracellular matrix (ECM) that presents spatially distributed biochemical and ‑physical cues at the nano‐ to micrometer scales. Chemical micropatterning is successfully used to generate adhesive islands to control where and how cells attach and restore cues of the ECM in vitro. Although chemical micropatterning has become a powerful tool to study cell–material interactions, only a fraction of the possible micropattern designs was covered so far, leaving many other possible designs still unexplored. Here, a high‐throughput screening platform called “Galapagos chip” is developed. It contains a library of 2176 distinct subcellular chemical patterns created using mathematical algorithms and a straightforward UV‐induced two‐step surface modification. This approach enables the immobilization of ligands in geometrically defined regions onto cell culture substrates. To validate the system, binary RGD/polyethylene glycol patterns are prepared on which human mesenchymal stem cells are cultured, and the authors observe how different patterns affect cell and organelle morphology. As proof of concept, the cells are stained for the mechanosensitive YAP protein, and, using a machine‐learning algorithm, it is demonstrated that cell shape and YAP nuclear translocation correlate. It is concluded that the Galapagos chip is a versatile platform to screen geometrical aspects of cell–ECM interaction.
Formalizing model transformation patterns
Authors: Rouhi Alireza , Kolahdouz Rahimi Shekoufeh , Lano Kevin
Published: January 2022

Model transformation has become an established field, and it is important to improve the quality of specifications written in transformation languages. Different transformation patterns have been introduced in the model‐driven engineering (MDE) community to improve the quality of transformation specifications. However, due to the different definitions of pattern concepts by different authors, it is difficult for practitioners to understand how to apply patterns in practice. Therefore, there is a need to unify transformation pattern concepts by presenting a generic metamodel and formalizing patterns in terms of this metamodel, to define the meaning of pattern application. In this research a general metamodel for definition of different design patterns in model transformation is provided. The metamodel presents clear description of common aspects of transformation patterns, which facilitates the application of patterns on model transformations by validating the application against the underlying formalism. Additionally, a unified and precise terminology for the application and verification of model transformation patterns by using a formal model of model transformation patterns in the Z notation is presented. To show the applicability of the proposed formalism, four well‐known model transformation patterns are specified.
Sparse Bayesian predictive modelling of tumour response using radiomic features
Authors: Golchi Shirin , Fu Jingyan , Liu Xiaoyang , Yu Eugene , Forghani Reza , Bhatnagar Sahir
Published: January 2022

We propose a sparse Bayesian hierarchical model for the analysis of data including radiomic features for characterization of head and neck squamous cell carcinoma. The proposed model facilitates radiomic feature selection, handling of missing values in key predictors as well as prediction in a unified framework. The fully Bayesian approach enables adequate incorporation of uncertainty arising from various aspects of the inference and prediction procedure. The prediction performance of the model is assessed via cross validation and compared with two frequentist methods.
Shear performance of the interfaces for precast concrete shear wall components by novel preparation method
Authors: Zhu Zhangfeng , Guo Zhengxing , Lu Hepeng
Published: January 2022

For emulating cast‐in‐place (CIP) precast concrete (PC) shear wall structures in China, the adjacent PC shear wall components are joined by vertical post‐cast concrete. Shear behavior of concrete‐to‐concrete interfaces is critical for structural integrity. A novel preparation method, as cast using bubble film (BF) form, is proposed. Shear performance experiments are conducted to obtain the shear capacity and deformation characteristic of the specimens by the preparation methods of hydro‐demolition (HD), as‐cast by patterned steel plate (PSP) form and BF. Concrete strength and shear reinforcement are also considered. By comparison with reference CIP specimens, HD specimens performs best, followed by BF specimens, and the worst are PSP specimens. The collected shear loads for HD and BF specimens are larger than calculated results under diagonal shear failure, assuring structural integrity. Concrete strength and shear reinforcements have effective influence on interface shear capacity. Shear reinforcements with small diameter and small spacing are recommended.
How do high school students' genetics progression networks change due to genetics instruction and how do they stabilize years after instruction?
Authors: Todd Amber , Romine William , Sadeghi Reza , Cook Whitt Kate , Banerjee Tanvi
Published: January 2022

Learning progressions (LPs) serve as frameworks for understanding the level of complexity of students' ideas within a domain. Multifaceted LPs contain multiple interrelated constructs where increased knowledge in one construct may increase knowledge of a separate construct. While it is challenging to identify and test these contingencies between constructs, our research group has used Bayesian structure learning methods to determine how mental models of genetics change with instruction. In this study, we follow a cohort of high school students for 3 years, measuring their genetics knowledge prior to instruction in 10th grade, immediately after genetics instruction in 10th grade, and at the end of 12th grade. We investigated how their idea networks change as a result of instruction and how they stabilize 2 years after instruction ended. We found students move from a phenotypic to a more molecular/mechanistic understanding of genetics immediately after instruction; students idea networks 2 years after instruction stabilized grounded within molecular explanations of diversity of life and how genetic diversity drives evolution. This study provides a robust mechanism for researchers to test connections between constructs in multifaceted LPs, illustrated by a genetics framework unable to be detected by previous methods.
Electrical Properties of Fresh Human Blood at 10 kHz–100 MHz
Authors: Takamatsu Reo , Higuchi Kenichi , Suzuki Tatsunori , Muramatsu Dairoku
Published: January 2022

Understanding the electrical properties of blood is essential for medical applications based on bioelectromagnetic responses. However, reference values in the commonly used database were taken from measurements of ovine blood at frequencies higher than 1 MHz and treated with anticoagulants. In this study, the parallel‐plate method was used to measure the relative permittivity and conductivity of untreated fresh human blood in the frequency range of 10 kHz–100 MHz. The results showed a significant difference in the conductivity compared with the previous reference data. The variation in conductivity due to coagulation was concluded to be sufficiently small when compared with other factors. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Introduction to Topologies of Digital Work
Authors: Will-Zocholl Mascha , Roth-Ebner Caroline
Published: January 2022

At the turn of the millennium, a “spatial turn” appeared, first in cultural studies and then increasingly in the social sciences. From today's perspective, it can be said that the reinvention of space as a category of analysis has led to a renewed focus on spatial issues. On this basis, we pursue spatial constellations, references and structures using the concept of “topologies”. This volume aims at broadening the basis of topological research, taking into account the current developments of the digital transformation, theoretical considerations and empirical evidence.
Quaternionic Kähler Metrics Associated to Special Kähler Manifolds with Mutually Local Variations of BPS Structures
Authors: Cortés Vicente , Tulli Iván
Published: January 2022

We construct a quaternionic Kähler manifold from a conical special Kähler manifold with a certain type of mutually local variation of BPS structures. We give global and local explicit formulas for the quaternionic Kähler metric and specify under which conditions it is positive-definite. Locally, the metric is a deformation of the 1-loop corrected Ferrara–Sabharwal metric obtained via the supergravity c-map. The type of quaternionic Kähler metrics we obtain is related to work in the physics literature by S. Alexandrov and S. Banerjee, where they discuss the hypermultiplet moduli space metric of type IIA string theory, with mutually local D-instanton corrections.
Effectiveness of a Constructed Wetland with Carbon Filtration in Reducing Pesticides Associated with Agricultural Runoff
Authors: McCalla Laura B. , Phillips Bryn M. , Anderson Brian S. , Voorhees Jennifer P. , Siegler Katie , Faulkenberry Katherine R. , Goodman Maurice C. , Deng Xin , Tjeerdema Ron S.
Published: January 2022

The Salinas Valley in Monterey County, California, USA, is a highly productive agricultural region. Irrigation runoff containing pesticides at concentrations toxic to aquatic organisms poses a threat to aquatic ecosystems within local watersheds. This study monitored the effectiveness of a constructed wetland treatment system with a granulated activated carbon (GAC) filter installation at reducing pesticide concentrations and associated toxicity toCeriodaphnia dubia,Hyalella azteca, andChironomus dilutus. The wetland was supplied with water pumped from an impaired agricultural and urban drainage. Across five monitoring trials, the integrated system’s average pesticide concentration reduction was 52%. The wetland channel and GAC filtration components individually provided significant treatment, and within each, pesticide solubility had a significant effect on changes in pesticide concentrations. The integrated treatment system also reduced nitrate by 61%, phosphate by 73%, and turbidity by 90%. Input water was significantly toxic toC. dubiaandH. aztecain the first trial. Toxicity toC. dubiapersisted throughout the system, whereas toxicity toH. aztecawas removed by the channel, but there was residual toxicity post-GAC. The final trial had significant input toxicity toH. aztecaandC. dilutus. The channel reduced toxicity toH. aztecaand removed toxicity toC. dilutus. GAC filtration reducedH. aztecatoxicity to an insignificant level. There was no input toxicity in the other three trials. The results demonstrate that a wetland treatment system coupled with GAC filtration can reduce pesticide concentrations, nutrients, suspended particles, and aquatic toxicity associated with agricultural runoff.
Decoding the dynamics of poleward shifting climate zones using aqua-planet model simulations
Authors: Yang Hu , Lu Jian , Wang Qiang , Shi Xiaoxu , Lohmann Gerrit
Published: January 2022

Growing evidence indicates that the atmospheric and oceanic circulation experiences a systematic poleward shift in a warming climate. However, the complexity of the climate system, including the coupling between the ocean and the atmosphere, natural climate variability and land-sea distribution, tends to obfuscate the causal mechanism underlying the circulation shift. Here, using an idealised coupled aqua-planet model, we explore the mechanism of the shifting circulation, by isolating the contributing factors from the direct CO$$_2$$

2

forcing, the indirect ocean surface warming, and the wind-stress feedback from the ocean dynamics. We find that, in contrast to the direct CO$$_2$$

2

forcing, ocean surface warming, in particular an enhanced subtropical ocean warming, plays an important role in driving the circulation shift. This enhanced subtropical ocean warming emerges from the background Ekman convergence of surface anomalous heat in the absence of the ocean dynamical change. It expands the tropical warm water zone, causes a poleward shift of the mid-latitude temperature gradient, hence forces a corresponding shift in the atmospheric circulation and the associated wind pattern. The shift in wind, in turn drives a shift in the ocean circulation. Our simulations, despite being idealised, capture the main features of the observed climate changes, for example, the enhanced subtropical ocean warming, poleward shift of the patterns of near-surface wind, sea level pressure, storm tracks, precipitation and large-scale ocean circulation, implying that increase in greenhouse gas concentrations not only raises the temperature, but can also systematically shift the climate zones poleward.
Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays
Authors: Paul Ashis , Basu Arpan , Mahmud Mufti , Kaiser M. Shamim , Sarkar Ram
Published: January 2022

Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients’ lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.
Identification of time series models using sparse Takagi–Sugeno fuzzy systems with reduced structure
Authors: Wiktorowicz Krzysztof , Krzeszowski Tomasz
Published: January 2022

Simplifying fuzzy models, including those for predicting time series, is an important issue in terms of their interpretation and implementation. This simplification can involve both the number of inference rules (i.e., structure) and the number of parameters. This paper proposes novel hybrid methods for time series prediction that utilize Takagi–Sugeno fuzzy systems with reduced structure. The fuzzy sets are obtained using a global optimization algorithm (particle swarm optimization, simulated annealing, genetic algorithm, or pattern search). The polynomials are determined by elastic net regression, which is a sparse regression. The simplification is based on reducing the number of polynomial parameters in the then-part by using sparse regression and removing unnecessary rules by using labels. A new quality criterion is proposed to express a compromise between the model accuracy and its simplification. The experimental results show that the proposed methods can improve a fuzzy model while simplifying its structure.
Complex Pathways Towards Emergent Pastoral Settlements: New Research on the Bronze Age Xindian Culture of Northwest China
Authors: Jaffe Yitzchak , Hein Anke , Womack Andrew , Brunson Katherine , d’Alpoim Guedes Jade , Guo Rongzhen , Zhou Jing , Ko Jada , Wu Xiaohong , Wang Hui , Li Shuicheng , Flad Rowan
Published: January 2022

The Xindian culture of northwest China has been seen as a prototypical example of a transition toward pastoralism, resulting in part from environmental changes that started around 4000 years ago. To date, there has been little available residential data to document how and whether subsistence strategies and community organization in northwest China changed following or in association with documented environmental changes. The Tao River Archaeology Project is a collaborative effort aimed at gathering robust archaeological information to solidify our baseline understanding of economic, technological, and social practices in the third through early first millennia BC. Here we present data from two Xindian culture residential sites, and propose that rather than a total transition to nomadic pastoralism—as it is often reconstructed—the Xindian culture reflects a prolonged period of complex transition in cultural traditions and subsistence practices. In fact, communities maintained elements of earlier cultivation and animal-foddering systems, selectively incorporating new plants and animals into their repertoire. These locally-specific strategies were employed to negotiate ever-changing environmental and social conditions in the region of developing ‘proto-Silk Road’ interregional interactions.
The optimal spacing interval between principal shelterbelts of the farm-shelter forest network
Authors: Sun Qinming , Zheng Bo , Liu Tong , Zhu Lekui , Hao Xiaoran , Han Zhiquan
Published: January 2022

The farm-shelter forest network is a complex grid protection system, with a windbreak that is distinctly different from that of the single shelterbelt. We selected the farm-shelter forest network of a jujube field in the Tarim Basin of northwest China and used a combination of field measurements and wind tunnel tests to determine the optimal spacing interval between principal shelterbelts. The wind speed reductive curve of the farm-shelter forest network showed a gradual wind speed tendency to stability. Therefore, a model was established based on the energy transfer balance between the upper and the lower airflows for a steady wind speed. The prediction error of the model was found to be < 1%. The model results indicated that increasing the spacing interval between principal shelterbelts from 10 to 20 H, where H is the shelterbelt height, maintained more than 70% of the windbreak effect of the farm-shelter forest network. If the spacing interval between principal shelterbelts were to be increased from 10 to 20 H, the jujube planting area would be increased by 0.54%. Therefore, a thorough consideration of the windbreak effect of each shelterbelt, the synergistic effects of shelterbelts, the windbreak effects of tall crops, and the effects of temperature and humidity in farm-shelter forest networks indicates that increasing the spacing interval will not only maintain the windbreak effect, but it will also reduce the side effects of shelterbelts, increase the planting area, favor mechanized operation, and improve planting efficiency.
Uncertainty and sensitivity analyses of operational errors in air handling units and unexpected user behavior for energy efficiency and thermal comfort
Authors: Lauss Lukas , Meier Andreas , Auer Thomas
Published: January 2022

Resource scarcity and anthropogenic climate change require the reduction of performance gaps in existing buildings. In addition to unexpected user behavior, performance gaps are primarily caused by the technical gap due to operational errors in building technology. The main objective of this paper is to quantify model input uncertainty incorporating uncertain boundary conditions in terms of operational errors using thermo-dynamic building performance simulations and to identify the most relevant input parameters for the performance gaps in air conditioning systems by means of sensitivity analyses. Model input uncertainty is stochastically determined using Monte-Carlo Simulations to calculate the target values “primary energy demand” as well as “over- and under-temperature degree hours” for an office building. Selected parameters are simulated in a specific uncertainty and sensitivity analyses using the Sobol’ and Jansen estimators, which distinguish between a direct influence on the target variables and interactions between the parameters. The methodology requires a selection process, which is carried out as part of relative uncertainty and relative sensitivity analyses. Furthermore, the operational errors are compared with construction factors as well as building physics inputs and design parameters for building technology systems to show their reciprocal effects as part of a comprehensive investigation. The main findings of this paper are that operational errors in air conditioning systems play an essential role in decreasing energy efficiency and thermal comfort, but do not warrant the significance of certain construction factors as well as setpoints in building technology. Moreover, the impact of operational errors on thermal overheating of the building investigated is minor compared to other targets that cause greater model input uncertainty.
Differential Brain Perfusion Changes Following Two Mind–Body Interventions for Fibromyalgia Patients: an Arterial Spin Labelling fMRI Study
Authors: Medina Sonia , O’Daly Owen G. , Howard Matthew A. , Feliu-Soler Albert , Luciano Juan V.
Published: January 2022

Objectives

Further mechanistic insight on mind–body techniques for fibromyalgia (FMS) is needed. Arterial spin labelling (ASL) imaging can capture changes in regional cerebral blood flow (rCBF) that relate to spontaneous pain.

Methods

We recruited FMS patients undergoing either mindfulness-based stress reduction training (MBSR,n = 14) or a psychoeducational programme (FibroQoL,n = 18), and a control FMS group with no add-on treatment (n = 14). We acquired whole-brain rCBF maps and self-report measures at baseline and following treatment and explored interaction effects in brain perfusion between the treatment group and session with a focus on the amygdala, the insula and the anterior cingulate cortex (ACC).

Results

We identified a significant interaction effect in the amygdala, which corresponded with rCBF decreases following FibroQoL specifically. At baseline, rCBF in the amygdala for the FibroQoL group correlated with pain catastrophizing and anxiety scores, but not after treatment, suggesting a decoupling between activity in the amygdala and negative emotional symptoms of FMS as a consequence of treatment. Baseline rCBF correlated positively with pain symptoms in the ACC and the anterior insula across all patients; moreover, the correlation between rCBF changes post intervention in the insula and pain improvement was negative for both treatments and significantly different from the control group. We suggest that there is disruption of the typical relationship between clinical pain and activity as a product of these two nonpharmacological therapies.

Conclusions

We have demonstrated that different mind-to-body treatments correspond to differential changes in clinical symptoms and brain activity patterns, which encourages future research investigating predictors of treatment response.

Trial Registration

 NCT02561416.
Comparative geomorphometric approach to understand the hydrological behaviour and identification of the Erosion prone areas of a coastal watershed using RS and GIS tools
Authors: Rautela Kuldeep Singh , Kumar Mohit , Khajuria Varun , Alam M. A.
Published: January 2022

Assessment of the geomorphometric parameters using Remote Sensing (RS) and Geographic Information System (GIS) tools forms an important part in routing the runoff and other hydrological processes. The current study uses a geospatial model based on geomorphometric parameters for the categorization of surface runoff and identification of the erosion-prone areas in the watershed of the Kuttiyadi River. The 4th order Kuttiyadi river is dominated by a dendritic to semi-dendritic drainage pattern in the subwatersheds. The linear aspect of the subwatersheds indicates towards the presence of permeable surface and subsurface materials with uniform lithology. The aerial and relief aspects of the subwatersheds shows fine drainage texture, gentle slopes, delayed peak flow, flatter hydrograph, and large concentration time which shows that subwatersheds are quite capable of managing flash floods during storm events. The estimated values of surface runoff (Q) and sediment production rate (SPR) are range from 2.13 to 32.88 km2-cm/km2 and 0.0004–0.017 Ha-m/100km2/year respectively and suggest that Subwatershed 1 (SW1) will generate more surface runoff and is prone to soil erosion followed by subwatershed 2 (SW2) in comparison to other subwatersheds. This paper aims to fill the knowledge gap regarding categorization of flow and erosion dynamics in a coastal river watershed. We believe that our work may work help in providing the crucial information for decision-makers and policymakers responsible for establishing suitable policies and sustainable land use practices for the watershed.
Brivaracetam – eine gute Alternative in der akuten Behandlung der Trigeminusneuralgie
Authors: Kneppe Katharina , Czell David
Published: January 2022

Zusammenfassung. Bei der Trigeminusneuralgie kommt es zu kurzen neuropathischen Schmerzen im Versorgungsgebiet des Nervus trigeminus. Unterschieden werden eine klassische, eine symptomatische, und eine idiopathische Trigeminusneuralgie. Für die Akutbehandlung gibt es nur wenige Medikamente. In der vorgestellten Fallserie berichten wir von fünf Personen mit einer Trigeminusneuralgie unterschiedlicher Genese, die gut auf die Behandlung mit Brivaracetam ansprachen. Brivaracetam bindet an das synaptische Vesikelprotein 2A, das auch an Nerven und Nervenwurzeln vorkommt. SV2 reguliert die exozytotische Freisetzung von Neurotransmittern, was die Wirkung von Brivaracetam auf neuropathische Schmerzen erklären könnte. Der Einsatz von Brivaracetam kann bei der kurzfristigen Therapie von paroxysmalen Schmerzen im Rahmen der Trigeminusneuralgie hilfreich sein. Es sind weitere Studien erforderlich, um diese Wirkung nachzuweisen und einen Placeboeffekt auszuschliessen.
Lehrveranstaltung zu interprofessionellen Versorgungsketten in der Schweiz
Authors: Schlegel Claudia , Stämpfli Dominik , Kut Elvan , Mang Georg , Moser Toni , Useini Mirdita , Goldhahn Jörg
Published: January 2022

Zusammenfassung. Die Zunahme an chronischen und komplexen Erkrankungen fordert verschiedene Akteure des Gesundheitswesens heraus und betrifft das gesamte Gesundheitssystem der Schweiz. Durch einen interprofessionellen Austausch der Fachpersonen aus Medizin und Gesundheit kann besser auf die individuellen Bedürfnisse der Patientinnen und Patienten eingegangen werden, was sich positiv auf deren Behandlungen auswirkt. Damit Studierende des Bachelor Humanmedizin der ETH Zürich auf diese Herausforderungen vorbereitet werden, hat die ETH Zürich zusammen mit vier Bildungsinstitutionen eine spezifische Lehrveranstaltung konzipiert, die sich an den interprofessionellen Versorgungsketten orientieren, welche Patientinnen und Patienten durchlaufen. Ziel der interprofessionellen Lehrveranstaltung ist, dass Bachelor-Studierende der Medizin und die an der Lehrveranstaltung beteiligten Pharmazie- und HF Pflegestudierenden Wissen über die anderen Berufsgruppen sowie deren unterschiedlichen Aufgabengebiete und berufsspezifische Kompetenzen erwerben und gleichzeitig die Nahtstellen der Interprofessionellen Zusammenarbeit kennenlernen.
Chronische Müdigkeit: Wann liegt der Verdacht auf eine erbliche Stoffwechselerkrankung vor?
Authors: Tankeu Aurel T. , Tran Christel
Published: January 2022

Zusammenfassung. Chronische Müdigkeit ist ein unspezifisches Symptom, das bei erwachsenen ambulanten Patientinnen und Patienten häufig auftritt. Anhaltende körperliche Müdigkeit, deren Ätiologie nach Ausschluss der üblichen Ursachen unklar ist, sollte die Frage nach seltenen Krankheiten wie angeborene Stoffwechselstörungen (IEM) aufwerfen. Das Hauptmerkmal der chronischen Müdigkeit bei IEM ist ihr dynamischer Charakter, der durch Situationen verschlimmert wird, die den Stoffwechsel erhöhen, wie körperliche Anstrengung, Kälte, Fasten oder biologischer Stress. Angesichts solcher klinischer Anzeichen ist es wichtig, eine gezielte Diagnostik durchzuführen, um Patienten zu identifizieren, die wahrscheinlich eine unerwünschte Arzneimittelwirkung haben, um sie an ein spezialisiertes Zentrum zu überweisen. Zu den IEMs, die zu chronischer Müdigkeit führen, gehören metabolische Myopathien wie Muskelglykogenose, Fettsäure-Beta-Oxidationsstörungen und mitochondriale Erkrankungen. Spezifische biochemische und/oder molekulare Analysen ermöglichen es, die Diagnose zu stellen und ein multidisziplinäres Management einzurichten.
The Relationship Between Fear of COVID-19 and Depression, Anxiety,	and Stress
Authors: Erbiçer Eyüp Sabır , Metin Ahmet , Çetinkaya Ali , Şen Sedat
Published: January 2022

Abstract. The COVID-19 pandemic has had significant effects on public health. Previous studies showed that the fear of COVID-19 was positively correlated with depression, anxiety, and stress. This study aims to compile and meta-analyze the relationship between fear of COVID-19 and depression, anxiety, and stress among the general population. The studies published between March 2020 and June 2021 were scanned from various databases (Google Scholar, PubMed, Web of Science [ISI], and SCOPUS). As a result of the literature search, 746 studies were reached, 88 studies were found suitable, and 21 met the inclusion criteria. Comprehensive Meta-Analysis (CMA Version 3.0) and metafor package in R were used for analyses. A strong relationship was observed between the fear of COVID-19 and anxiety ( r = .55, p < .001); there were moderate relationships between the fear of COVID-19 and depression ( r = .34, p < .001) and stress ( r = .44, p < .001). All these relationships were significant. The results showed that the relationships between the fear of COVID-19 and depression, anxiety, and stress had a high level of heterogeneity. Meta-regression analyses revealed that the average of the sample age was a significant predictor of the relationship between fear of COVID-19 and anxiety. However, other covariates (female proportion and culture) were not statistically significant for the relationship between fear of COVID-19 and anxiety. None of these covariates was significant predictors of the relationship between fear of COVID-19 with depression and stress. Although the current meta-analysis has some limitations, the results revealed that the fear of COVID-19 is associated with mental health problems such as depression, anxiety, and stress.
Mental Health in Times of the COVID-19 Pandemic
Authors: Wirkner Janine , Christiansen Hanna , Knaevelsrud Christine , Lüken Ulrike , Wurm Susanne , Schneider Silvia , Brakemeier Eva-Lotta
Published: January 2022

Abstract. The COVID-19 pandemic is one of the most serious health and economic crises of the 21st century. From a psychological point of view, the COVID-19 pandemic and its consequences can be conceptualized as a multidimensional and potentially toxic stressor for mental health in the general population. This selective literature review provides an overview of longitudinal studies published until June 2021 that have investigated the impact of the COVID-19 pandemic on mental health in the European population. Risk and protective factors identified in the studies are summarized. Forty-two studies that met inclusion and search criteria ( COVID-19, mental health, longitudinal, and Europe) in PubMed, PsycInfo, and Web of Science databases indicate differential effects of the pandemic on mental distress, depression, and anxiety, depending on samples and methods used. Age-specific (e.g., young age), social (e.g., female, ethnical minority, loneliness), as well as physical and mental health-related factors (e.g., pre-pandemic illness) were identified as risk factors for poor mental health. The studies point to several protective factors such as social support, higher cognitive ability, resilience, and self-efficacy. Increasing evidence supports the assumption of the pandemic being a multidimensional stressor on mental health, with some populations appearing more vulnerable than others, although inconsistencies arise. Whether the pandemic will lead to an increase in the prevalence of mental disorders is an open question. Further high-quality longitudinal and multi-national studies and meta-analyses are needed to draw the complete picture of the consequences of the pandemic on mental health.
OSPREI: A Coupled Approach to Modeling CME‐Driven Space Weather With Automatically Generated, User‐Friendly Outputs
Authors: Kay C. , Mays M. L. , Collado‐Vega Y. M.
Published: January 2022

Coronal mass ejections (CMEs) drive space weather activity at Earth and throughout the solar system. Current CME‐related space weather predictions rely on information reconstructed from coronagraphs, sometimes from only a single viewpoint, to drive a simple interplanetary propagation model, which only gives the arrival time or limited additional information. We present the coupling of three established models into OSPREI (Open Solar Physics Rapid Ensemble Information), a new tool that describes Sun‐to‐Earth CME behavior, including the location, orientation, size, shape, speed, arrival time, and internal thermal and magnetic properties, on the timescale needed for forecasts. First, Forecasting a CME's Altered Trajectory (ForeCAT) describes the trajectory that a CME takes through the solar corona. Second, ANother Type of Ensemble Arrival Time Results simulates the propagation, including expansion and deformation, of a CME in interplanetary space and determines the evolution of internal properties via conservation laws. Finally, ForeCAT In situ Data Observer produces in situ profiles for a CME's interaction with a synthetic spacecraft. OSPREI includes ensemble modeling by varying each input parameter to probe any uncertainty in their values, yielding probabilities for all outputs. Standardized visualizations are automatically generated, providing easily accessible, essential information for space weather forecasting. We show OSPREI results for a CMEs observed in the corona on 22 April and 09 May 2021. We approach these CME as a forecasting proof‐of‐concept, using information analogous to what would be available in real time rather than fine‐tuning input parameters to achieve a best fit for a detailed scientific study. The OSPREI “prediction” shows good agreement with the arrival time and in situ properties.
Guanidyl-implanted UiO-66 as an efficient catalyst for the enhanced conversion of carbon dioxide into cyclic carbonates
Authors: Gao Aijia , Li Fangfang , Xu Zhi , Ji Changchun , Gu Jing , Zhou Ying-Hua
Published: January 2022

The guanidyl-modified UiO-66 exhibited great activity for epoxide-CO2 cycloaddition with Bu4NBr cocatalyst, attributed to the synergistic effect of guanidyl groups as hydrogen-bond donors and Zr centers as Lewis-acidic sites.
Research on image sentiment analysis technology based on sparse representation
Authors: Jin Xiaofang , Wu Yinan , Xu Ying , Sun Chang
Published: January 2022

Many methods based on deep learning have achieved amazing results in image sentiment analysis. However, these existing methods usually pursue high accuracy, ignoring the effect on model training efficiency. Considering that when faced with large‐scale sentiment analysis tasks, the high accuracy rate often requires long experimental time. In view of the weakness, a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed, and singular value decomposition (SVD) is used to find the sparse feature of the image, which are sparse vectors with strong discriminativeness and effectively reduce redundant information; The authors propose the Fast Dictionary Learning algorithm (FDL), which can combine neural network with sparse representation. This method is based on K‐Singular Value Decomposition, and through iteration, it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy. Moreover, the effectiveness of the proposed method is evaluated on the FER2013 dataset. By adding singular value decomposition, the accuracy of the test suite increased by 0.53%, and the total experiment time was shortened by 8.2%; Fast Dictionary Learning shortened the total experiment time by 36.3%.