bluuebunny/crossref_metadata_2025 huggingface dataset:
Search for Sub-Solar Mass Binaries with Einstein Telescope and Cosmic Explorer
Authors: Nunes Rafael
Published: February 2022

A possible detection of sub-solar mass ultra-compact objects would lead to new perspectives on the existence of black holes that are not of astrophysical origin and/or pertain to formation scenarios of exotic ultra-compact objects. Both possibilities open new perspectives for better understanding of our universe. In this work, we investigate the significance of detection of sub-solar mass binaries with components mass in the range: 10−2M⊙ up to 1M⊙, within the expected sensitivity of the ground-based gravitational waves detectors of third generation, viz., the Einstein Telescope (ET) and the Cosmic Explorer (CE). Assuming a minimum of amplitude signal-to-noise ratio for detection, viz., ρ=8, we find that the maximum horizon distances for an ultra-compact binary system with components mass 10−2M⊙ and 1M⊙ are 40 Mpc and 1.89 Gpc, respectively, for ET, and 125 Mpc and 5.8 Gpc, respectively, for CE. Other cases are also presented in the text. We derive the merger rate and discuss consequences on the abundances of primordial black hole (PBH), fPBH. Considering the entire mass range [10−2–1]M⊙, we find fPBH<0.70 (<0.06) for ET (CE), respectively.
Convincing 3D Face Reconstruction from a Single Color Image under Occluded Scenes
Authors: Zhao Dapeng , Cai Jinkang , Qi Yue
Published: February 2022

The last few years have witnessed the great success of generative adversarial networks (GANs) in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction works often pursue higher resolutions and ignore occlusion. We study the problem of detailed 3D facial reconstruction under occluded scenes. This is a challenging problem; currently, the collection of such a large scale high resolution 3D face dataset is still very costly. In this work, we propose a deep learning based approach for detailed 3D face reconstruction that does not require large-scale 3D datasets. Motivated by generative face image inpainting and weakly-supervised 3D deep reconstruction, we propose a complete 3D face model generation method guided by the contour. In our work, the 3D reconstruction framework based on weak supervision can generate convincing 3D models. We further test our method on the MICC, Florence and LFW datasets, showing its strong generalization capacity and superior performance.
TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling
Authors: Alotaibi Hamdah , Alsolami Fawaz , Abozinadah Ehab , Mehmood Rashid
Published: February 2022

DNA profiling involves the analysis of sequences of an individual or mixed DNA profiles to identify the persons that these profiles belong to. A critically important application of DNA profiling is in forensic science to identify criminals by finding a match between their blood samples and the DNA profile found on the crime scene. Other applications include paternity tests, disaster victim identification, missing person investigations, and mapping genetic diseases. A crucial task in DNA profiling is the determination of the number of contributors in a DNA mixture profile, which is challenging due to issues that include allele dropout, stutter, blobs, and noise in DNA profiles; these issues negatively affect the estimation accuracy and the computational complexity. Machine-learning-based methods have been applied for estimating the number of unknowns; however, there is limited work in this area and many more efforts are required to develop robust models and their training on large and diverse datasets. In this paper, we propose and develop a software tool called TAWSEEM that employs a multilayer perceptron (MLP) neural network deep learning model for estimating the number of unknown contributors in DNA mixture profiles using PROVEDIt, the largest publicly available dataset. We investigate the performance of our developed deep learning model using four performance metrics, namely accuracy, F1-score, recall, and precision. The novelty of our tool is evident in the fact that it provides the highest accuracy (97%) compared to any existing work on the most diverse dataset (in terms of the profiles, loci, multiplexes, etc.). We also provide a detailed background on the DNA profiling and literature review, and a detailed account of the deep learning tool development and the performance investigation of the deep learning method.
Processing at the Edge: A Case Study with an Ultrasound Sensor-Based Embedded Smart Device
Authors: Poza-Lujan Jose-Luis , Uribe-Chavert Pedro , Sáenz-Peñafiel Juan-José , Posadas-Yagüe Juan-Luis
Published: February 2022

In the current context of the Internet of Things, embedded devices can have some intelligence and distribute both data and processed information. This article presents the paradigm shift from a hierarchical pyramid to an inverted pyramid that is the basis for edge, fog, and cloud-based architectures. To support the new paradigm, the article presents a distributed modular architecture. The devices are made up of essential elements, called control nodes, which can communicate to enhance their functionality without sending raw data to the cloud. To validate the architecture, identical control nodes equipped with a distance sensor have been implemented. Each module can read the distance to each vehicle and process these data to provide the vehicle’s speed and length. In addition, the article describes how connecting two or more CNs, forming an intelligent device, can increase the accuracy of the parameters measured. Results show that it is possible to reduce the processing load up to 22% in the case of sharing processed information instead of raw data. In addition, when the control nodes collaborate at the edge level, the relative error obtained when measuring the speed and length of a vehicle is reduced by one percentage point.
Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm
Authors: Hameed Khurram , Khan Wasim , Abdalla Yasser S. , Al-Harbi Fatemah F. , Armghan Ammar , Asif Muhammad , Salman Qamar Muhammad , Ali Farman , Miah Md Sipon , Alibakhshikenari Mohammad , Dalarsson Mariana
Published: February 2022

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).
Fault-Tolerant Fuzzy Logic Control of a 6-Phase Axial Flux Permanent-Magnet Synchronous Generator
Authors: Bouyahia Omar , Betin Franck , Yazidi Amine
Published: February 2022

The objective of this paper is to propose the real-time implementation of a fault-tolerant strategy based on fuzzy logic controller (FLC) for a Six-Phase Axial Flux Permanent Magnet Synchronous Machine (6P-AFPMSM) for electrical energy production. This type of machine, suitable for high-power applications, is highly affected by the harmonics of the inductances and the electromotive force (emf) compared to the classical three-phase radial flux machine, which will influence the controller parameters of the machine. The proposed control strategy based on FLC is independent of the system model and guarantees the robustness of the process against disturbances and parameter variations of the model. An experimental comparison between FLC and a classical PI controller confirms the efficiency and the robustness of the proposed controller in healthy and faulty conditions with one open phase.
Artificial Intelligence Applications in Estimating Invisible Solar Power Generation
Authors: Wu Yuan-Kang , Lai Yi-Hui , Huang Cheng-Liang , Phuong Nguyen Thi Bich , Tan Wen-Shan
Published: February 2022

In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method.
The Shape Optimization and Experimental Research of Heave Plate Applied to the New Wave Energy Converter
Authors: Meng Zhongliang , Chen Yun , Li Shizhen
Published: February 2022

The development and utilization of wave energy is inseparable from the wave energy converter, and its stability is an important condition for operation. Heave is the biggest factor affecting the stable power generation of wave energy converters. The key method to solve this problem is to install a suitable heave plate. Therefore, the design of the heave plate is particularly important. Based on a new type of horizontal rotor wave energy converter, this paper proposes three different shapes of heave plate design schemes and completes the calculation and modeling of the engineering prototype. First, the three types of heave plate devices were numerically simulated using hydrodynamic calculation software to compare their stable performances and verify the feasibility of the scheme. Subsequently, an experimental model was made according to the parameters of the engineering prototype, and a tank experiment was carried out under the same working conditions to further study the influence of the heave plate installation distance on the stability of the wave energy generator. The results showed that when the distance was between 10 mm and 20 mm, the average amplitude change was large, and when the distance was between 20 mm and 30 mm, the average amplitude change was small. Therefore, the installation distance should be between 20 mm and 30 mm. In the case of the same heave plate area and installation distance, the average amplitude of the chamfered heave plate device was smaller than the other two types, indicating that its stability was better. The optimization of the shape and installation distance of the heave plate proposed in this study has obvious effects on improving the stability of the device and provides a reference for the design of the wave energy converter device.
Experimental Investigation of the Heat Transfer between Finned Tubes and a Bubbling Fluidized Bed with Horizontal Sand Mass Flow
Authors: Thanheiser Stefan , Haider Markus , Schwarzmayr Paul
Published: February 2022

The sandTES technology utilizes a fluidized bed counter current heat exchanger for thermal energy storage applications. Its main feature is an imposed horizontal flow of sand (SiO2) particles fluidized by a vertical air flow across a heat exchanger consisting of several horizontal rows of tubes. Past international research on heat transfer in dense fluidized beds has focused on stationary (stirred tank) systems, and there is little to no information available on the impact of longitudinal or helical fins. Previous pilot plant scale experiments at TU Wien led to the conclusion that the currently available correlations for predicting the heat transfer coefficient between the tube surface and the surrounding fluidized bed are insufficient for the horizontal sand flow imposed by the sandTES technology. Therefore, several smaller test rigs were designed in this study to investigate the influence of different tube arrangements and flow conditions on the external convective heat transfer coefficient and possible improvements by using finned tubes. It could be shown that helically finned tubes in a transversal arrangement, where the horizontal sand flow is perpendicular to the tube axes, allows an increase in the heat transfer coefficient per tube length (i.e., the virtual heat transfer coefficient) by a factor of 3.5 to about 1250 W/m2K at ambient temperature. Based on the literature, this heat transfer coefficient is expected to increase at higher temperatures. The new design criteria allow the design of compact, low-cost heat exchangers for thermal energy storage applications, in particular electro-thermal energy storage.
Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares
Authors: Li Zhongbing , Pang Wei , Liang Haibo , Chen Guihui , Duan Hongming , Jiang Chuandong
Published: February 2022

Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology.
Experiments with Pyrotechnic Compositions Based on a Mathematical Model—Part III: Comparative Analysis of Manufacturing Costs of Pyrotechnic Composition Producing an Acoustic Effect
Authors: Barański Krzysztof , Biegańska Jolanta
Published: February 2022

The article presents a comparison of the influence of price change of the components of pyrotechnic compositions producing an acoustic effect on their manufacturing costs. The analysis was based on the prices of raw materials in 1981 and 2021. The influence of price fluctuations of individual raw materials on the final cost of composites was determined. The impact of price changes on the profitability of pyrotechnic composition manufacturing was evaluated for the adopted models. The conducted economic analysis allowed for the determination of pyrotechnic compositions characterised by the lowest manufacturing costs, in terms of the unit cost, and for the assumed industrial models.
Hedging Wind Power Risk Exposure through Weather Derivatives
Authors: Masala Giovanni , Micocci Marco , Rizk Andrea
Published: February 2022

We introduce the industrial portfolio of a wind farm of a hypothetical company and its valuation consistent with the financial market. Next, we propose a static risk management policy originating from hedging against volumetric risk due to drops in wind intensity and we discuss the consequences. The hedging effectiveness firstly requires adequate modeling calibration and an extensive knowledge of these atypical financial (commodity) markets. In this hedging experiment, we find significant benefits for weather-sensitive companies, which can lead to new business opportunities. We provide a new financial econometrics approach to derive weather risk exposure in a typical wind farm. Our results show how accurate risk management can have a real benefit on corporate revenues. Specifically, we apply the spot market price simulation (SMaPS) model for the spot price of electricity. The parameters are calibrated using the prices of the French day-ahead market, and the historical series of the total hourly load is used as the final consumption. Next, we analyze wind speed and its relationship with electricity spot prices. As our main contribution, we demonstrate the effects of a hypothetical hedging strategy with collar options implemented against volumetric risk to satisfy demand at a specific time. Regarding the hedged portfolio, we observe that the “worst value” increases considerably while the earnings-at-risk (EaR) decreases. We consider only volumetric risk management, thus neglecting the market risk associated with electricity price volatility, allowing us to conclude that the hedging operation of our industrial portfolio provides substantial benefits in terms of the worst-case scenario.
Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power
Authors: Zhang Yangrui , Tao Peng , Wu Xiangming , Yang Chenguang , Han Guang , Zhou Hui , Hu Yinlong
Published: February 2022

In an open electricity market, increased accuracy and real-time availability of electricity price forecasts can help market parties participate effectively in market operations and management. As the penetration of clean energy increases, it brings new challenges to electricity price forecasting. An electricity price forecasting model is constructed in this paper for markets containing a high proportion of wind and solar power, where the scenario with a high coefficient of variation (COV) caused by the high frequency of low electricity prices is particularly concerned. The deep extreme learning machine optimized by the sparrow search algorithm (SSA-DELM) is proposed to make predictions on the model. The results show that wind–load ratio and solar–load ratio are the key input variables for forecasting in power markets with high proportions of wind and solar energy. The SSA-DELM possesses better electricity price forecasting performance in the scenario with a high COV and is more suitable for disordered time series models, which can be confirmed in comparison with LSTM.
Energy Savings in Hydraulic Hybrid Transmissions through Digital Hydraulics Technology
Authors: Azzam Israa , Pate Keith , Garcia-Bravo Jose , Breidi Farid
Published: February 2022

Hydraulic hybrid drivetrains, which are fluid power technologies implemented in automobiles, present a popular alternative to conventional drivetrain architectures due to their high energy savings, flexibility in power transmission, and ease of operation. Hydraulic hybrid drivetrains offer multiple environmental benefits compared to other power transmission technologies. They provide heavy-duty vehicles, e.g., commercial transportation, construction equipment, wagon handling, drilling machines, and military trucks, with the potential to achieve better fuel economy and lower carbon emissions. Despite the preponderance of hydraulic hybrid transmissions, state-of-the-art hydraulic hybrid drivetrains have relatively low efficiencies, around 64% to 81%. This low efficiency is due to the utilization of conventional variable displacement pumps and motors that experience high power losses throughout the drive cycle and thus fail to maintain high operating efficiency at lower volumetric displacements. This work proposes and validates a new methodology to improve the overall efficiency of hydraulic hybrid drivetrains by replacing conventional pump/motor units with their digital counterparts. Compared to conventional pump/motors, the digital pump/motor can achieve higher overall efficiencies at a wide range of operating conditions. A proof-of-concept digital pump/motor prototype was built and tested. The experimental data were integrated into a multi-domain physics-based simulation model of a series hydraulic hybrid transmission. The proposed methodology permits enhancing the overall efficiency of a series hydraulic hybrid transmission and thus allows for energy savings. Simulating the system at moderate load-speed conditions allowed achieving a total efficiency of around 89%. Compared to the average efficiency of the series hydraulic hybrid drivetrains, our simulation results reveal that the utilization of the state-of-the-art digital pump enables improving the total efficiency of the series hydraulic hybrid drivetrain by up to 25%.
Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform
Authors: Cen Senfeng , Yoo Jae Hung , Lim Chang Gyoon
Published: February 2022

Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms are employed to segment daily load curves and select the most appropriate algorithm. We experimented our method on the Manhattan dataset and the results indicated that clustering algorithms, combined with discrete wavelet transform, improve the clustering performance. Additionally, we discussed the clustering result and load pattern analysis of the dataset with respect to the electricity pattern.
Nanostructured Broadband Solar Absorber for Effective Photothermal Conversion and Electricity Generation
Authors: Zhang Shuai , Wu Zhenhua , Liu Zekun , Lv Yongbo , Hu Zhiyu
Published: February 2022

Photothermal conversion is an environmentally friendly process that harvests energy from the sun and has been attracting growing research interest in recent years. However, nanostructured strategies to improve light capture performance deserve further development, and the application of solar heating effects for clean energy needs to be explored. Herein, a multiscale nanomaterial was prepared by in situ polymerizing the polyaniline (PANI) nanoparticles into porous anodic aluminum oxide (AAO) membrane. As a result, the as-prepared PANI-AAO shows broadband solar absorption and provides a platform for efficient photothermal conversion. What is more, we introduced a typical thermoelectricity generator (TEG) with excellent output performance and combined it with PANI-AAO to prepare a solar thermoelectric generator (s-TEG). The s-TEG harvests solar energy and converts it into electricity, showing an outstanding power generation capability in outdoor conditions. Thus, the nanostructured broadband solar absorber and the integrated solar thermoelectric generator offer a promising candidate for a sustainable and green energy source in the future.
The Changes in Soil Microbial Communities across a Subalpine Forest Successional Series
Authors: Wang Zhihui , Bai Yi , Hou Jianfeng , Li Fei , Li Xuqing , Cao Rui , Deng Yuyue , Wang Huaibin , Jiang Yurui , Yang Wanqin
Published: February 2022

Knowledge regarding changes in soil microbial communities with forest succession is vital to understand soil microbial community shifts under global change scenarios. The composition and diversity of soil microbial communities across a subalpine forest successional series were therefore investigated in the Wanglang National Nature Reserve on the eastern Qinghai-Tibet Plateau, China. The calculated diversity indices of soil bacteria (8.598 to 9.791 for Shannon-Wiener, 0.997 to 0.974 for Simpson, 4131 to 4974 for abundance-based coverage estimator (ACE) and 3007 to 3511 for Species richness indices), and ACE (1323 to 921) and Species richness (1251 to 879) indices of soil fungi decreased from initial to terminal succession stages, but Shannon-Wiener and Simpson of soil fungi indices varied slightly with forest succession. Meanwhile, the composition and structure of soil microbial communities varied markedly with forest succession. The relative abundance of the dominant bacterial phyla (Acidobacteria, Firmicutes and Actinobacteria) and fungal taxa (Mortierellomycota, Rozellomycota and unassigned phylum clade GS01) varied considerably with forest succession. However, regardless of successional stage, Proteobacteria and Acidobacteria dominated soil bacterial communities and Ascomycota and Basidiomycota dominated soil fungal communities. Moreover, the changes in soil microbial diversity with forest succession were significantly affected by soil pH, soil organic carbon, soil temperature, altitude, and non-woody debris stock. Importantly, soil pH was the dominant driver of soil microbial community shift with forest succession. In conclusion, the forests at different succession stages not only conserve same microbial populations, but also nurse unique microbial diversity across the forest succession series; and the biodiversity of soil bacterial and fungal communities has differential responses to forest succession.
Reproducibility of Stress Wave and Electrical Resistivity Tomography for Tree Assessment
Authors: Rust Steffen
Published: February 2022

(1) Background: Tomography is widely used in tree risk assessment and forest ecology. Tomograms should be accurate, repeatable and comparable between consecutive measurements. This is the first longitudinal study addressing reproducibility over a period of several years and the key factors influencing it, from raw data to final tomograms. (2) Methods: Trees were either repeatedly measured by the same researcher over periods of hours to months, by different researchers using either the same or a new installation of the same equipment, or with different tomographs. (3) Results: Overall, differences between tomograms were small and without impact on the assessment of the safety of the tree. Between initial and follow-up measurements after 2 to 5 years, the coefficients of correlation of stress wave velocity ranged from 0.75 to 0.96, those of loss in section modulus estimated from tomograms ranged from 0.82 to 0.93. The coefficient of correlation of repeated sapwood area estimates with electrical resistivity tomography was 0.97. The major cause of deviations was variation in the sensor positions between repeated tomographies, and, for electrical resistivity tomography, variation of temperature. (4) Conclusion: Provided that sensor positions are accurately recorded and documented, tomograms can reliably be repeated by different assessors over periods of years. Assessments based on complex calculations like loss of section modulus should be treated with caution, because they accumulate and amplify all sources of variation.
Indigenous Forestry Tourism Dimensions: A Systematic Review
Authors: Salazar-Sepúlveda Guido , Vega-Muñoz Alejandro , Contreras-Barraza Nicolás , Zada Muhammad , Adsuar José Carmelo
Published: February 2022

Tourism activities developed in forested areas are a non-wood forest exploitation method that contributes to sustainability objectives, even more so when they consider the participation of the community and the government in favor of its conservation. Under this context, this article will review the different investigations that relate to indigenous tourism, the conservation of the ecosystem and what attributes are important when measuring them. To do this, a scientometric meta-analysis was carried out, which extracts a set of articles that strictly refer to the themes of indigenous tourism in forests, considering two databases integrated into the Core Collection Web of Science, the selection process of which is aligned with the guidelines of the PRISMA methodology, establishing, with the PICOS tool, the eligibility criteria of the articles, which were applied to a qualitative systematic review. Finally, a model for measuring attributes in levels on indigenous tourism stands out, which incorporates the limit between the number of visitors to the tourist destination; the incorporation of tourists guides the identification of the necessary infrastructure facilities for an adequate experience and environmental conservation.
Whole-Tree Response of Non-Structural Carbohydrates, Carbon and Nitrogen Concentrations in Two Temperate Tree Species to 10-Year Nitrogen Fertilization
Authors: Wang Wenna , Wang Siyuan , Hoch Günter , Wang Yan , Gao Guoqiang , Gu Jiacun , Xu Haiwu
Published: February 2022

This study aimed to investigate the effects of long-term nitrogen fertilization on non-structural carbohydrates (NSC) and nitrogen (N) status and their interaction in mature trees at the whole-tree scale. Ten g N m−2 yr−1 of ammonium nitrate fertilizer were applied to 26-year-old Larix gmelinii Rupr. (larch) and Fraxinus mandschurica Rupr. (ash) trees in Northeastern China from 2002 to 2012. NSC, total carbon (C) and total N concentrations in different compartments were examined. For both species, concentrations of NSC and their components (soluble sugars and starch) tended to increase in aboveground organs but decrease in fine roots following N fertilization, with significant (p < 0.05) changes only observed in ash stems and larch roots. N fertilization increased N concentrations and decreased the C:N ratio in all organs, especially in foliage and roots, while the effects of fertilization on total C concentrations varied with tree species and organs. Concentrations of NSC (mainly reflected in soluble sugar) were generally negatively correlated with N concentration in fine roots but positively related to N concentration in aboveground woody organs in both control and fertilized treatments. However, fertilization strengthened this correlation in fine roots and weakened this relationship in aboveground organs. This study provides a decade-long insight into the effect of currently increasing N deposition on tree growth and function.
Bearing Witness in Analog and Digital Witness Films: Ethical Aesthetics in Shoah [1985] and Waltz with Bashir [2008]
Authors: Breitsohl Louis Levi
Published: February 2022

In the ongoing and passionate debates over the digitalization of visual media, many questions about the ontology or materiality of the new digital image have been raised. Analog representation is often thought of in terms of indexicality and sometimes a naive belief in the truthfulness of the photographic image, whereas the digital image is, in a way, no longer an image anymore, but a set of data in flux, superficially coded and easy to manipulate. The following article examines how this shift to digitalization affects the ethical genre per se: the witness film. In a film analytical close reading of Claude Lanzmann’s Shoah and Ari Folman’s Waltz with Bashir, resonances become visible to psychoanalysis, Deleuzian film philosophy and the debates over the materiality of the analog and digital image. Lanzmann draws on a specific kind of indirect indexicality, which is highly interested in the psychic and embodied realities of surviving, witnessing and the passing of time, whereas Folman develops a politics of the powers of the false: truthfulness, accessibility and memorability are abolished in favor of false images, screen-memories and traumatic mis/representation, which are staged noticeably digital and altered, revolving around the impossibility to grasp the ungraspable.
Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology
Authors: Kadlubowska Magda K. , Schrauwen Isabelle
Published: February 2022

During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.
Smith-Magenis Syndrome—Clinical Review, Biological Background and Related Disorders
Authors: Rinaldi Berardo , Villa Roberta , Sironi Alessandra , Garavelli Livia , Finelli Palma , Bedeschi Maria Francesca
Published: February 2022

Smith-Magenis syndrome (SMS) is a complex genetic disorder characterized by distinctive physical features, developmental delay, cognitive impairment, and a typical behavioral phenotype. SMS is caused by interstitial 17p11.2 deletions (90%), encompassing multiple genes and including the retinoic acid-induced 1 gene (RAI1), or by pathogenic variants in RAI1 itself (10%). RAI1 is a dosage-sensitive gene expressed in many tissues and acting as transcriptional regulator. The majority of individuals exhibit a mild-to-moderate range of intellectual disability. The behavioral phenotype includes significant sleep disturbance, stereotypes, maladaptive and self-injurious behaviors. In this review, we summarize current clinical knowledge and therapeutic approaches. We further discuss the common biological background shared with other conditions commonly retained in differential diagnosis.
Evaluating the Conservation Status of a North-Western Iberian Earthworm (Compostelandrilus cyaneus) with Insight into Its Genetic Diversity and Ecological Preferences
Authors: Marchán Daniel F. , Domínguez Jorge
Published: February 2022

In spite of the high conservation value of soil fauna, the evaluation of their conservation status has usually been neglected. This is more evident for earthworms, one of the most important ecosystem service providers in temperate habitats but rarely the subject of conservation research. These studies have not been developed in Western Europe, which comprises high diversity and several early-branching, relic genera. One potentially menaced representative of this fauna is Compostelandrilus cyaneus; this risk can be assessed by implementing potential distribution modeling and genetic diversity monitoring to their known populations. Genetic barcoding was performed in representatives of four populations (three of them newly sampled) in order to estimate genetic diversity and population genetics parameters. Ensemble species distribution models were built by combining several algorithms and using the five more relevant bioclimatic and soil variables as predictors. A large amount of genetic diversity was found in a small area of less than 20 km2, with populations located in less managed, better-preserved habitats showing higher genetic variability than populations isolated from natural habitats and surrounded by anthropic habitats. Potential distribution appears to be strongly restricted at a regional scale, and suitable habitats within the extent of occurrence appear fragmented and relatively limited. In addition, the main variables determining the ecological niche of C. cyaneus suggests a vulnerability to climate change and increasing soil compaction. Based on this knowledge, this species was assessed as Critically Endangered following the IUCN Red List of Threatened Species criteria, and some potential conservation actions are suggested.
Support Networks and Family Empowerment in Early Intervention
Authors: Martínez-Rico Gabriel , Simón Cecilia , Cañadas Margarita , Mcwilliam Robin
Published: February 2022

Despite the importance of empowerment and the support network of families who receive early intervention (EI) with a family-centered approach, there is little evidence of a relationship between these two variables and family characteristics that might influence this relationship. This study analyzes the correlations between the perception of empowerment of the families, the family supports used, and the socio-demographic factors of both the child and the family. The study consisted of 44 families who received family-centered EI services. Our results show that families mainly used formal supports, followed by informal supports, and, to a lesser extent, intermediate supports. This indicates that families with children who receive EI preferably use the support network based on EI programs, schools, and professionals. Along with this formal support network, primary caregivers rely on their partners, parents, or friends—that is, the informal support network. Family empowerment was not correlated with age, diagnosis, or the reason for referral to EI; on the other hand, it was related to the supports where the families with the lowest empowerment scores were those who made greater use of formal support over informal support. Early intervention professionals must know, from the first encounter, the type and level of support of each family to enhance the development of the child and promote empowerment in families.
Ageist Communication Experienced by Middle-Aged and Older Canadians
Authors: Chasteen Alison L. , Tagliamonte Sali A. , Pabst Katharina , Brunet Samantha
Published: February 2022

Ageism has been well-documented in the United States, but ageism experiences in Canada remain less well-known. To address this gap, in the current research middle-aged and older Canadians completed a conversational interview in which they described their ageism experiences. Their descriptions were coded for life domain, perpetrator, and type of ageist communication. The most common domain in which ageist communication occurred was the public sphere, with perpetrators most often being strangers. Ageist communication most often involved age-based social or physical assumptions about the participant. In combination, these findings detail how ageism manifests in the everyday lives of Canadians and contribute to understanding the nuances of the expression of ageism in North America.
Understanding the Role of Persistent Organic Pollutants and Stress in the Association between Proximity to the World Trade Center Disaster and Birth Outcomes
Authors: Spratlen Miranda J. , Perera Frederica P. , Sjodin Andreas , Wang Yuyan , Herbstman Julie B. , Trasande Leonardo
Published: February 2022

Fetal growth is affected by exposure to both prenatal stress and environmental contaminants. The attacks on the World Trade Center (WTC) resulted in exposure to chemicals and psychological stress amongst New York City residents. We measured prenatal maternal stress and exposure to persistent organic pollutants (polybrominated diphenyl ethers, polychlorinated biphenyls, and polychlorinated dibenzo-p-dioxins (PCDDs)) in 108 participants from a Columbia University WTC birth cohort. Principal component (PC) analyses were conducted to characterize the mixture of exposure to the three groups of chemicals. We evaluated the associations between geographical exposures (proximity to the WTC disaster) and both chemical exposures (PCs) and stress (demoralization). We then evaluated the effect these exposures (PCs and stress) had on previously reported associations between geographical WTC exposure and birth outcomes (birth weight and birth length) in this study population to understand their individual roles in the observed associations. Geographical exposure via proximity to the WTC was associated with the PC reflecting higher PCDD exposure (PC3) (β = 0.60, 95% CI: 0.03, 1.18 for living/working within 2 miles of the WTC; and β = 0.73, 95% CI = 0.08, 1.38 for living within 2 miles of WTC). Previously reported reductions in birth weight and length associated with WTC proximity (β = −215.2, 95% CI: −416.2, −14.3 and β = −1.47, 95% CI: −2.6, −0.34, respectively) were attenuated and no longer significant for birth weight (β = −156.4, 95% CI: −358.2, 45.4) after adjusting for PC3, suggesting that PCDDs may act as partial mediators in this previously observed association. The results of this study can help focus future research on the long-term health effects of these prenatally exposed populations.
Organization of Post-Transplant Care and the 5-Year Outcomes of Kidney Transplantation
Authors: Szymańska Agnieszka , Mucha Krzysztof , Kosieradzki Maciej , Nazarewski Sławomir , Pączek Leszek , Foroncewicz Bartosz
Published: February 2022

The outcomes of kidney transplantation depend on numerous factors and vary between transplant centers. The aim of this study is to assess the relationship between selected organizational factors, comorbidities, and patient and graft survival. This is a retrospective analysis of 438 renal transplant recipients (RTR) followed for 5 years. Patient and graft survival were evaluated in relation to hospitalization length, distance from the patient’s residence to the transplant center, the frequency of outpatient transplant visits, and the number and type of comorbidities. Five-year patient and graft survival rates were 93% and 90%, respectively. We found significant associations of patient survival with the prevalence of pre-transplant diabetes, cardiovascular diseases, malignancies, the number of comorbidities, and the first post-transplant hospitalization length. The incidence of infections, cardiovascular diseases, and transplanted kidney diseases was 60%, 40%, and 33%, respectively. As many as 41% of RTR had unknown etiology of primary kidney disease. In conclusion, the organization of post-transplant care needs to be adapted to the multi-morbidity of contemporary RTR and include multi-specialist care, especially in the context of current problems related to the COVID-19pandemic. The high proportion of patients with undetermined etiology of their primary renal disease carry the risk for additional complications during their long-term follow-up.
Feelings of Loneliness: Understanding the Risk of Suicidal Ideation in Adolescents with Internet Addiction. A Theoretical Model to Answer to a Systematic Literature Review, without Results
Authors: Khatcherian Eugénie , Zullino Daniele , De Leo Diego , Achab Sophia
Published: February 2022

The Internet has become an essential tool for adolescents. It is part of their social integration within peers and supports their identity construction. The Internet can also become a source of addiction, especially when used as a coping strategy towards unpleasant life situations. Addiction to the Internet is often linked with an increase in the feeling of loneliness. The feeling of loneliness is an emotion present during adolescence. However, in excess, it can lead to suicidal ideation. Addiction to the Internet is also linked to an increased suicide risk. We questioned ourselves on the impact of the feeling of loneliness on the link between an excessive use of the Internet by adolescents and the risks of suicidal ideation. We attempted to find an answer to this question by performing a systematic review of the literature. We found no result matching our search criteria. We noted the absence of studies with regards to the interaction between the feeling of loneliness, addiction to the Internet and the risk of suicidal ideation amongst adolescents. We established a theoretical model which could be used as a lead for future research. We insist on the importance that studies need to be conducted in this domain, in order to enable us to establish efficient preventive measures for the risks of suicidal ideation amongst adolescents.