Dimensional Relationship Research Articles

Childhood language development and alexithymia in adolescence: an 8-year longitudinal study.

Alexithymia (difficulties identifying and describing feelings) predicts increased risks for psychopathology, especially during the transition from childhood to adolescence. However, little is known of the early contributors to alexithymia. The language hypothesis of alexithymia suggests that language deficits play a primary role in predisposing language-impaired groups to developing alexithymia; yet longitudinal data tracking prospective relationship between language function and alexithymia are scarce. Leveraging data from the Surrey Communication and Language in Education cohort (N = 229, mean age at time point 1 = 5.32 years, SD = 0.29, 51.1% female), we investigated the prospective link between childhood language development and alexithymic traits in adolescence. Results indicated that boys with low language function at ages 4-5 years, and those who later met the diagnostic criteria for language disorders at ages 5-6 years, reported elevated alexithymic traits when they reached adolescence. Parent-reported child syntax abilities at ages 5-6 years revealed a dimensional relationship with alexithymic traits, and this was consistent with behavioral assessments on related structural language abilities. Empirically derived language groups and latent language trajectories did not predict alexithymic traits in adolescence. While findings support the language hypothesis of alexithymia, greater specificity of the alexithymia construct in developmental populations is needed to guide clinical interventions.

Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders

BackgroundAutism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs).MethodsData came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16–35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL’s PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted.ResultsImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance.LimitationsDespite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities.ConclusionsThe lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs.

Cognitive Dysfunction in the Addictions (CDiA): A Neuron to Neighbourhood Collaborative Research Program on Executive Dysfunction and Functional Outcomes in Outpatients Seeking Treatment for Addiction.

Substance use disorders (SUDs) are pressing global public health problems. Executive functions (EFs) are prominently featured in mechanistic models of addiction. However, there remain significant gaps in our understanding of EFs in SUDs, including the dimensional relationships of EFs to underlying neural circuits, molecular biomarkers, disorder heterogeneity, and functional ability. To improve health outcomes for people with SUDs, interdisciplinary clinical, preclinical and health services research is needed to inform policies and interventions aligned with biopsychosocial models of addiction. Here, we introduce Cognitive Dysfunction in the Addictions (CDiA), an integrative team-science and translational research program, which aims to fill these knowledge gaps and facilitate research discoveries to enhance treatments for people living with SUDs. The CDiA Program comprises seven complementary interdisciplinary projects that aim to progress understanding of EF in SUDs and investigate new biological treatment approaches. The projects draw on a diverse sample of adults aged 18-60 (target N=400) seeking treatment for addiction, who are followed prospectively over one year to identify EF domains crucial to recovery. Projects 1-3 investigate SUD symptoms, brain circuits, and blood biomarkers and their associations with both EF domains (inhibition, working memory, and set-shifting) and functional outcomes (disability, quality of life). Projects 4 and 5 evaluate interventions for addiction and their impacts on EF: a clinical trial of repetitive transcranial magnetic stimulation and a preclinical study of potential new pharmacological treatments in rodents. Project 6 links EF to healthcare utilization and is supplemented with a qualitative investigation of EF-related barriers to treatment engagement for those with substance use concerns. Project 7 uses innovative whole-person modeling to integrate the multi-modal data generated across projects, applying clustering and deep learning methods to identify patient subtypes and drive future cross-disciplinary initiatives. The CDiA program has promise to bring scientific domains together to uncover the diverse ways in which EFs are linked to SUD severity and functional recovery. These findings, supported by emerging clinical, preclinical, health service, and whole-person modeling investigations, will facilitate future discoveries about cognitive dysfunction in addiction and could enhance the future clinical care of individuals seeking treatment for SUDs.

Autism Symptom Presentation and Hierarchical Models of Intelligence.

There is a substantial history studying the relationship between general intelligence and the core symptoms of autism. However, a gap in knowledge is how dimensional autism symptomatology associates with different components of clinically-relevant hierarchical models of intelligence. We examined correlations between autism diagnostic symptom magnitude (Autism Diagnostic Observational Schedule; ADOS) and a hierarchical statistical model of intelligence. One autistic cohort was tested on the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV; N = 131), and another on the fifth edition (WISC-V; N = 83). We anticipated a convergent pattern of results between cohorts. On WISC-IV, ADOS scores were correlated significantly with g and three out of four intermediate factor scores, which was a broader pattern of correlations than anticipated from the literature. In the WISC-V cohort, only one intermediate factor correlated significantly with the ADOS; correlations with g and the other intermediate factors were less statistically certain. ADOS-factor correlations were larger in the WISC-IV than WISC-V cohort; this difference was significant at the 90% level. WISC-IV shows dimensional relationships with ADOS at multiple points in the hierarchical model of intelligence. Moreover, the current results provide evidence that relationship between core autism symptomatology and the construct of general intelligence may depend on how intelligence is measured. Known cohort effects in the relationship between categorical autism diagnosis and general intelligence have previously been attributed to changes in autism diagnostic practices. To our knowledge, this is the first evidence that differing versions of IQ tests may be implicated.

Design Optimization and Tradeoff Analysis of an Actuated Continuum Probe for Pulmonary Nodule Localization and Resection.

Pulmonary nodules are abnormal tissue masses in the lungs, typically less than 3.0 cm in diameter, commonly detected during imaging of the chest and lungs. While most pulmonary nodules are not cancerous, surgical resection may be required if growth is detected between scans. This resection is typically performed without the benefit of intraoperative imaging, making it difficult for surgeons to confidently provide appropriate margins. To enhance the efficacy of wedge resection, researchers have developed a modified ultrasound imaging approach that utilizes both multiple scattering (MS) and single scattering (SS) to enhance the accuracy of margin delineation. Clinical deployment of this novel ultrasound technology requires a highly maneuverable ultrasound probe, ideally one that could be deployed and actuated with minimal invasiveness. This study details the design optimization and tradeoff analysis of an actuated continuum probe for pulmonary nodule localization and resection. This device, deployed through intercostal ports, would enable the intraoperative imaging and precise mapping of nodules for improved margin delineation and patient outcomes. To achieve this objective, multiple objective genetic algorithms (MOGAs) and a design of experiments (DOE) study are used to explore the design space and quantify key dimensional relationships and their effects on probe actuation.

Formal method for selecting rough datum in part machining using graphs of dimensional relationships

We set out to determine formal conditions for selecting rough datum surfaces and rules for specifying the relationships between the surfaces of the workpiece and the part machined by metal-cutting equipment. The research was based on the geometry of non-ideal objects, which represents the geometric configuration of the part or workpiece in the form of a set of surfaces (S) arranged in strict accordance with a set of linear and angular dimensional relationships (V) in a sixdimensional geometric space. The representation of dimensional relationships in each of the six dimensions was performed using graph theory. The algorithm described in the works of V.G. Starostin and V.E. Lelyukhin was used for modeling the process of sequential surface machining. The question of selection of rough datum surfaces was considered. Here, the selection criterion is the possibility of automatic formation of machining sequence without recalculation of the dimension chains defined in the drawing. This criterion eliminates the problem of significant toughening of tolerances and additional

DC-BVM: Dual-channel information fusion network based on voting mechanism

Emotion recognition in conversations (ERC) has been challenging due to the dynamics and complexity of emotions in conversations. Most current emotion recognition studies have focused on modeling temporal dimensions, such as context-sensitive dependencies while ignoring the spatial dimensional relationships between discourses. In this paper, we propose a dual-channel information fusion network based on a voting mechanism, in which we model modal information in two dimensions: distance dependence is constructed in the spatial dimension, and context dependence is constructed in the temporal dimension. We aim to extract more comprehensive and accurate sentiment features to recognize verbal emotions from limited data. In addition, we discard the traditional modal fusion method and propose a fusion strategy based on the voting mechanism, which significantly accelerates the model convergence speed and recognition performance. We conducted experiments on two benchmark datasets, IEMOCAP and MELD, respectively, and the experimental results demonstrate the superiority of the designed network in emotion recognition.

Deconstructing co-occurring marine heatwave and phytoplankton bloom events in the Arkona Sea in 2018

Between May and August 2018, two separate marine heatwaves (MHWs) occurred in the Arkona Sea in the western Baltic Sea. These heatwaves bookended an extended period of phytoplankton growth in the region. Data from the Ocean and Land Colour Instrument (OLCI) on board the European Sentinel-3 satellite revealed an eddy-like structure containing high chlorophyll a (Chl-a) concentrations (ca. 25 mg.m-3) persisting for several days at the end of May in the Arkona Sea. Combining ocean colour observations, a coupled bio-optical ocean model and a particle tracking model, we examined the three dimensional relationship between these co-occurring MHW and phytoplankton bloom events. We find that the onset of the MHW in May provided the optimal conditions for phytoplankton growth, i.e. sufficient light and nutrients. Wind-driven surface eddy circulation, geostrophic eddy stirring and transient submesoscale dynamics along the edges of the eddy provided a transport path for nutrient fluxes and carbon export, and helped to sustain the phytoplankton bloom. The bloom may have indirectly had an enhancing effect on the MHW, through the impact of water constituent-induced heating rates on air-sea energy fluxes. The subsurface signature of the MHW plays a critical role in de-coupling surface and subsurface dynamics and terminating the phytoplankton bloom. Subsurface temperature anomalies of up to 8°C between 15 and 20 m depth are found to persist up to 15 days after the surface signature of the MHW has disappeared. The study reveals how surface and subsurface dynamics of MHWs and phytoplankton blooms are connected under different environmental conditions. It extends our knowledge on surface layer processes obtained from satellite data.

Venturi Injector Optimization for Precise Powder Transport for Directed Energy Deposition Manufacturing Using the Discrete Element Method and Genetic Algorithms.

Additive manufacturing technologies such as directed energy deposition use powder as their raw material, and it must be deposited in a precise and controlled manner. Venturi injectors could be a solution for the highly precise transport of particulate material. They have been studied from different perspectives, but they are always under high-pressure conditions and mostly fed by gravity. In the present study, an optimization of the different dimensional parameters needed for the manufacturing of a Venturi injector in relation to a particle has been carried out to maximize the amount of powder capable of being sucked and transported for a specific flow in a low-pressure system with high precision in transport. For this optimization, simulations of Venturi usage were performed using the discrete element method, generating different variations proposed by a genetic algorithm based on a preliminary design of experiments. Statistical analysis was also performed to determine the most influential design variables on the objective, with these being the suction diameter (D3), the throat diameter (d2), and the nozzle diameter (d1). The optimal dimensional relationships were as follows: a D3 34 times the particle diameter, a d2 26.5 times the particle diameter, a d1 40% the d2, a contraction angle alpha of 18.73°, and an expansion angle beta of 8.28°. With these proportions, an 85% improvement in powder suction compared to the initial attempts was achieved, with a maximum 2% loss of load.

Spanish validation of the pure procrastination scale: dimensional structure, internal consistency, temporal stability, gender invariance, and relationships with personality and satisfaction with life.

The objective of the current study was to adapt and validate the pure procrastination scale (PPS) for the Spanish adult population. Procrastination can have numerous consequences in daily life, making it essential to have reliable and valid instruments for measuring procrastination. Thus, this study was conducted to address this need. The sample consisted of 596 adults aged 18-83 years (M = 35.25, SD = 13.75). In addition to the PPS, participants completed two procrastination measures, namely the irrational procrastination scale and the decisional procrastination questionnaire, alongside the Big Five inventory and the satisfaction with life scale. The results of the confirmatory factor analysis revealed a three-factor structure of the PPS. The examination of the reliability of scores in terms of internal consistency and temporal stability showed satisfactory results for the PPS scores. Moreover, gender invariance was observed at the scalar level. Finally, the PPS scores correlated with other measures of procrastination, personality traits, and satisfaction with life in the expected direction and magnitude. In conclusion, the Spanish PPS offers valid and reliable scores when administered to adult population.

Robust and predictive 3D-QSAR models for predicting the activities of novel oxadiazole derivatives as multifunctional anti-Alzheimer agents.

In recent years, Alzheimer disease (AD) as a neurodegenerative disorder has been increasing annually with the aging of the global population, therefore, development of novel anti-AD drugs is imperative. Studies have proven that glycogen synthase kinase-3β (GSK-3β) is a pivotal factor in the development of AD. Therefore, GSK-3β inhibitors would provide powerful means to treat the disorders, such as AD. To in-depth study the structure-activity relationship of a series of oxadiazole derivatives as multifunctional anti-Alzheimer agents, computational three dimensional quantitative structure-activity relationship (3D-QSAR) studies, molecular docking and molecular dynamics were conducted. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were conducted to build up the 3D-QSAR models, and exhibited significant results (R cv 2 = 0.692, R pred 2 = 0.6885/CoMFA, R cv 2 = 0.696, R pred 2 = 0.6887/CoMSIA). The accuracy of the 3D-QSAR models was validated by external validation and applicability domain analysis. The derived contour maps provided structural information for designing novel compounds to improve the inhibitory activities. Additionally, molecular docking and molecular dynamics were also employed to investigate the bonding interactions and stability of this series of inhibitors in the active site of GSK-3β, and the results revealed that the importance of residues Ile62, Asn64 Val70, Tyr128, Val129 and Leu182 for ligand binding to the receptor GSK-3β. All the results would be of great help for the discovery of new GSK-3β agents that can solve the problem of AD.

Music, cells and the dimensionality of nature

One of the foundational principles of recent developments in evolutionary biology has been the acknowledgement of homeostasis as an organising principle of cellular development from unicellular origins. Fundamentally, this concerns the balance between the inside of a biological entity and its environment. Given that the organ of balance is the ear, and that the evolutionary provenance of the vestibular system can be traced back to fish, music provides a rich foundation for evolutionary biological inquiry.This paper considers a specific dimensional relationship in sonic experience between noise, signal, redundancy and anticipation. Drawing on the physics of Bohm and more recent developments in Rowlands's nilpotent quantum mechanics, I argue that the relationship between these four parameters is not only that they represent aspects of sonic experience, but that they are dimensionally distinct, where noise can be considered to be scalar, a signal (or a note) is a vector (having magnitude and direction), redundancy is bi-vectorial (involving degrees of repetition of signals over time), and anticipation is tri-vectorial (involving reflexive consideration of different orders of redundancy).In outlining the dimensional distinction between these variables, an analysis is presented which considers the relationship between the Shannon entropy of different dimensions in music. This shows that the entropy of noise has a particular bearing on the entropy of the other dimensions. This dimensional relation is also reflected in biological evidence, where Torday has shown there to be a direct correlation between the effect of gravitational “noise” on cellular communication, and by extension the evolution of consciousness.

Dynamic responses of kerosene-filled riveted tanks subjected to hydrodynamic ram caused by ballistic impacts

The hydrodynamic ram (HRAM) is a hydrodynamic pressure phenomenon that occurs when high-speed projectiles impact a liquid-filled tank. During combat missions, aircraft are prone to HRAMs caused by projectile impacts, which can damage the structural integrity of riveted tanks, posing a threat to flight safety. In this study, kerosene-filled riveted tanks were chosen as the research object. The dynamic response histories of the rear panel were observed using the three-dimensional digital image correlation (3D-DIC) technique. Additionally, numerical simulations of the projectile's impact on the tank were performed using the fluid–structure interaction finite element method. The simulation results had a good fit with the experimental results. The spatial and temporal distribution of the HRAM and the effect of the projectile impact velocity on the deformation of the tank structure were further analyzed. The impulse exerted by the drag wave on the rear panel was greater than that of the pioneer wave. However, the drag wave's region was limited to a radius of 6 cm around the exit point with the impact velocity range of the projectile of 955 m·s−1–1607 m·s−1 in both the experiments and simulations. When the impact velocity of the projectile reached 955 m·s−1, radial plastic hinge lines appeared on the rear panel along the diagonal direction. When the impact velocity increased to 1607 m·s−1, radial plastic hinge lines along the diagonal direction, and traveling plastic hinges at the root of the central cross-shaped cracks appeared on the rear panel, accompanied by a petal-shaped hole. Based on the spatial distribution of the impulse on the rear panel, a dimensional relationship was established between the hole size and the impulse on the rear panel. The petal model for describing petal-shaped cracks was proposed based on the law of conservation of energy, which could predict the hole size in the case of projectile impact at high velocity. The research results are of great significance for the study of the damage of riveted tanks with HRAMs and provide support for the vulnerability analysis of aircraft tanks.

3D-QSAR and Pharmacophoric study on 2,6-Disubstituted Thiazolo [4,5-b] Pyridines as H3 Receptor Antagonists

Histamine H3 receptor antagonist (H3RA) is a promising therapeutic for CNS disorders including attention deficit hyperactivity disorder [ADHD], sleep disorders, epilepsy, schizophrenia and obesity. 2,6-Disubstituted thiazolo[4,5-b]pyridines reported for their H3 receptor antagonistic activity were selected for three dimensional quantitative structure activity relationship (3D-QSAR) and pharmacophoric study in order to establish structure activity relationship quantitatively and essential structural features. In the current study, VLife Molecular Design Suite software (VlifeMDS) was used for QSAR and biophore studies. Pharmagist (web based server) was used for pharmacophoric study. Partial least square regression (PLSR) analysis showed r2= 0.7902, q2=0.6449 and pred_r2= 0.6650. In this model steric [S_138, S_826] and electrostatic descriptors [E_243, E_652] are involved to play an important role in eliciting biological activity. It showed good internal and external prediction. The contour plots provided further insight of the relationship between structural features of substituted thiazolo[4,5-b]pyridine derivatives and their activities which should be applicable to design newer potential H3R inhibitors. In addition with these studies, pharmacophoric models were also produced using Molsign (VLifeMDS) and Pharmagist (web based server). The identified pharmacophore features are two aromatic and two hydrogen bond acceptor with Molsign whereas common pharmacophoric features with Pharmagist are two aromatic, two hydrophobic and four hydrogen bond acceptors. The present work may be useful for further lead optimization and designing of potent H3 receptor antagonists.

A Meta-Analysis of Pyschological Capital and Psychological Well-Being: Testing on Relationship between Self-Efficacy and Self-Acceptance

Psychological well-being is variable that's considered suitable to discuss happiness because psychological well-being belongs to the realm of positive psychology that requires the development of positive emotions to ensure individuals have optimal functions and experiences. This study aims to deeply understand the psychological capital and psychological well-being relationships by conducting meta-analysis testing. Data collection in this study followed the Preferred Reporting for Systematic Reviews and Meta-Analysis (PRISMA). The sources or databases of research articles used are Proquest, Psycnet, Emerald, and Google Scholar. The search is carried out from April to May 2021. These results showed the relationship between psychological capital and psychological well-being variables in the moderate and firm positive categories. The effect size included in the moderate and significant positive groups through the analysis process. When viewing the dimensional relationship on both variables, the dimension of self-acceptance in psychological well-being has the strongest relationship with the psychological capital variable. To consider the existence of moderation or intervening variables that affect the relationship of the two variables. Meanwhile, for organization management, the need to pay attention to the psychological capital variables of employees that can be a predictor for their psychological well-being to produce optimal productivity and performance results.

Evolutionary divergence of body size and wing and leg structure in relation to foraging mode in Darwin’s Galapagos finches

Abstract The wings, legs, and tail in Darwin’s finches show many clear adaptations to different types of locomotion used during foraging. We use size scaling to analyse how various characters vary with body mass to clarify dimensional relationships. The selective advantage of a character is judged in terms of energy savings. The wing aspect ratio (4.6–4.9) is very low, so the energy costs for flight are high. Low body mass, low wing loading, and short arm wings in the warbler finch, small tree finch, and small ground finch promote agility and manoeuvrability among vegetation, along with short wings in the warbler finch. Evolution towards a shorter arm wing seems to be favoured in the smaller finch species. Long legs, long toes, and long curved claws are adaptations for climbing/clinging locomotion without tail support (woodpecker finch, small and large tree finches, cactus finch but having short legs). Selection for longer legs seems to act towards a lengthening of the tarsometatarsus. The climbing technique in the woodpecker finch is described. We discuss how the diversification in the beaks relates to the locomotion organs.

Оценка технологического потенциала деталей

The article describes a methodology for assessing the technological potential of parts, based on the sociophysical theory of potentials and dimensional analysis of the technological process. The technological potential of a part acts as a system characteristic of a complex technical system. The program A'PROPOS was chosen as a tool for calculating the technological potential. A hypothesis has been put forward that the technological potential of the part is associated with the complexity of the technological process of manufacturing this part. It is possible to evaluate it using dimensional analysis using CAD tools. Methods and materials. The emergence and creation of fundamentally new technologies is considered a historical process of development of the designed technical system, in this case, the DSE. The technological potential of the part is formed as the technological process is implemented. The value and structure of the potential are determined and consistent with the properties of the designed part. Based on the results of the analysis of the technological process of manufacturing a part, the construction of a sketch of a part in A'PROPOS., the technological potential is calculated using a mathematical model. The potential is calculated on the basis of the initial data of the enterprise for the object of study. Result. Based on the constructed model, the technological process is optimized. The result is a new machining plan containing the compositions and accuracy of the surfaces machined in the stages and the minimum allowance. Based on the model with variations of dimensional relationships, the dimensional structure is improved to expand the tolerance fields with guaranteed maintenance of technological parameters. Based on this, the deviation in the recommended labor intensity is calculated compared to the real one. Next, the accumulated technological potential of the part was calculated. Conclusions. The system makes it possible to iteratively perform technological calculations, as well as integration with CAD systems in the enterprise. When calculating the technological potential, instead of using the initial data of the enterprise, it is interpreted by the adequacy of the model.

A dynamic data prepossessing method for parameter identification in power distribution network

AbstractThe methods of building a model like Markov chain Monte‐Carlo (MCMC) and sequential model‐based global optimization (SMBO) in power distribution network (PDN) have achieved parameter identification successfully without extra measurement devices. However, the data processing focused on the feeder data is not concerned yet. In this study, the authors present a dynamic data prepossessing method for parameter identification in PDN to successfully obtain a more accurate result. This method considers the similarities of feeder data in both spatial relationship and statistical theory, and then realizes a dynamic aggregation process for new coming data and obtains a set of data with tighter higher dimensional relationship for following identification task. In experiments, the authors applied this data processing method to the actual feeder data with no adjustment of the other condition; identification results with the authors’ processing achieve a 5.3% improvement in accuracy at most.

Micro gadolinium oxide dispersed flexible composites developed for the shielding of thermal neutron/gamma rays

In this study, a series of flexible neutron/gamma shielding composites are fabricated through the doping of Gd2O3 into the matrix of SEBS with (MGd2O3: MSEBS) % from 5% to 100%. Neutron transmittance test shows an exponential attenuation with the increase of areal density of Gd, in which the transmittance T ranges from 59.1440% to 35.3026%, with standard deviation less than 2.2743%, mass attenuation coefficient μm from 0.3194 cm2/g to 0.4999 cm2/g, and half value layer-HVL value from 2.4530 mm to 1.1313 mm. Shielding efficiency of the Gd2O3/SEBS composites is basically improved in comparison with that of B4C/SEBS. The transmittance T, mass/linear attenuation coefficient μm and μ, HVL and effective atomic number Zeff for the shielding of γ rays (39 keV, 59 keV and 122 keV) are measured and calculated with XCOM as well as MCX programs. Finally, plots of the three dimensional relationships between transmittance, doping amount and thickness are provided to the guidance for engineering shielding design. In summary, the Gd2O3/SEBS composite is proved to be an effective flexible neutron/low energy γ rays shielding material, which could be of potential applications in the field of nuclear technology and nuclear engineering.

Inferring the Properties of Snow in Southern Ocean Shallow Convection and Frontal Systems Using Dual-Polarization C-Band Radar

Abstract We use dual-polarization C-band data collected in the Southern Ocean to examine the properties of snow observed during a voyage in the austral summer of 2018. Using existing forward modeling formalisms based on an assumption of Rayleigh scattering by soft spheroids, an optimal estimation algorithm is implemented to infer snow properties from horizontally polarized radar reflectivity, the differential radar reflectivity, and the specific differential phase. From the dual-polarization observables, we estimate ice water content qi, the mass-mean particle size Dm, and the exponent of the mass–dimensional relationship bm that, with several assumptions, allow for evaluation of snow bulk density, and snow number concentration. Upon evaluating the uncertainties associated with measurement and forward model errors, we determine that the algorithm can retrieve qi, Dm, and bm within single-pixel uncertainties conservatively estimated in the range 120%, 60%, and 40%, respectively. Applying the algorithm to open-cellular convection in the Southern Ocean, we find evidence for secondary ice formation processes within multicellular complexes. In stratiform precipitation systems we find snow properties and infer processes that are distinctly different from the shallow convective systems with evidence for riming and aggregation being common. We also find that embedded convection within the frontal system produces precipitation properties consistent with graupel. Examining 5 weeks of data, we show that snow in open-cellular cumulus has higher overall bulk density than snow in stratiform precipitation systems with implications for interpreting measurements from space-based active remote sensors.