bluuebunny/crossref_metadata_2025 huggingface dataset:
Hierarchical Discriminative Learning for Visible Thermal Person Re-Identification
Authors: Ye Mang , Lan Xiangyuan , Li Jiawei , Yuen Pong
Published: June 2022

Person re-identification is widely studied in visible spectrum, where all the person images are captured by visible cameras. However, visible cameras may not capture valid appearance information under poor illumination conditions, e.g, at night. In this case, thermal camera is superior since it is less dependent on the lighting by using infrared light to capture the human body. To this end, this paper investigates a cross-modal re-identification problem, namely visible-thermal person re-identification (VT-REID). Existing cross-modal matching methods mainly focus on modeling the cross-modality discrepancy, while VT-REID also suffers from cross-view variations caused by different camera views. Therefore, we propose a hierarchical cross-modality matching model by jointly optimizing the modality-specific and modality-shared metrics. The modality-specific metrics transform two heterogenous modalities into a consistent space that modality-shared metric can be subsequently learnt. Meanwhile, the modality-specific metric compacts features of the same person within each modality to handle the large intra-modality intra-person variations (e.g. viewpoints, pose). Additionally, an improved two-stream CNN network is presented to learn the multi-modality sharable feature representations. Identity loss and contrastive loss are integrated to enhance the discriminability and modality-invariance with partially shared layer parameters. Extensive experiments illustrate the effectiveness and robustness of the proposed method.
Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification
Authors: Li Yi , Song Lingxiao , Wu Xiang , He Ran , Tan Tieniu
Published: June 2022

Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized non-makeup images for further verification. Two adversarial networks in BLAN are integrated in an end-to-end deep network, with the one on pixel level for reconstructing appealing facial images and the other on feature level for preserving identity information. These two networks jointly reduce the sensing gap between makeup and non-makeup images. Moreover, we make the generator well constrained by incorporating multiple perceptual losses. Experimental results on three benchmark makeup face datasets demonstrate that our method achieves state-of-the-art verification accuracy across makeup status and can produce photo-realistic non-makeup face images.
Robust Collaborative Discriminative Learning for RGB-Infrared Tracking
Authors: Lan Xiangyuan , Ye Mang , Zhang Shengping , Yuen Pong
Published: June 2022

Tracking target of interests is an important step for motion perception in intelligent video surveillance systems. While most recently developed tracking algorithms are grounded in RGB image sequences, it should be noted that information from RGB modality is not always reliable (e.g. in a dark environment with poor lighting condition), which urges the need to integrate information from infrared modality for effective tracking because of the insensitivity to illumination condition of infrared thermal camera. However, several issues encountered during the tracking process limit the fusing performance of these heterogeneous modalities: 1) the cross-modality discrepancy of visual and motion characteristics, 2) the uncertainty of degree of reliability in different modalities, and 3) large target appearance variations and background distractions within each modality. To address these issues, this paper proposes a novel and optimal discriminative learning framework for multi-modality tracking. In particular, the proposed discriminative learning framework is able to: 1) jointly eliminate outlier samples caused by large variations and learn discriminability-consistent features from heterogeneous modalities, and 2) collaboratively perform modality reliability measurement and target-background separation. Extensive experiments on RGB-infrared image sequences demonstrate the effectiveness of the proposed method.
Brute-Force Facial Landmark Analysis With a 140,000-Way Classifier
Authors: Li Mengtian , Jeni Laszlo , Ramanan Deva
Published: June 2022

We propose a simple approach to visual alignment, focusing on the illustrative task of facial landmark estimation. While most prior work treats this as a regression problem, we instead formulate it as a discrete K-way classification task, where a classifier is trained to return one of K discrete alignments. One crucial benefit of a classifier is the ability to report back a (softmax) distribution over putative alignments. We demonstrate that this distribution is a rich representation that can be marginalized (to generate uncertainty estimates over groups of landmarks) and conditioned on (to incorporate top-down context, provided by temporal constraints in a video stream or an interactive human user). Such capabilities are difficult to integrate into classic regression-based approaches. We study performance as a function of the number of classes K, including the extreme "exemplar class" setting where K is equal to the number of training examples (140K in our setting). Perhaps surprisingly, we show that classifiers can still be learned in this setting. When compared to prior work in classification, our K is unprecedentedly large, including many "fine-grained" classes that are very similar. We address these issues by using a multi-label loss function that allows for training examples to be non-uniformly shared across discrete classes. We perform a comprehensive experimental analysis of our method on standard benchmarks, demonstrating state-of-the-art results for facial alignment in videos.
Dictionary Learning Inspired Deep Network for Scene Recognition
Authors: Liu Yang , Chen Qingchao , Chen Wei , Wassell Ian
Published: June 2022

Scene recognition remains one of the most challenging problems in image understanding. With the help of fully connected layers (FCL) and rectified linear units (ReLu), deep networks can extract the moderately sparse and discriminative feature representation required for scene recognition. However, few methods consider exploiting a sparsity model for learning the feature representation in order to provide enhanced discriminative capability. In this paper, we replace the conventional FCL and ReLu with a new dictionary learning layer, that is composed of a finite number of recurrent units to simultaneously enhance the sparse representation and discriminative abilities of features via the determination of optimal dictionaries. In addition, with the help of the structure of the dictionary, we propose a new label discriminative regressor to boost the discrimination ability. We also propose new constraints to prevent overfitting by incorporating the advantage of the Mahalanobis and Euclidean distances to balance the recognition accuracy and generalization performance. Our proposed approach is evaluated using various scene datasets and shows superior performance to many state-of-the-art approaches.
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process
Authors: Zou Haosheng , Su Hang , Song Shihong , Zhu Jun
Published: June 2022

Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process based on various factors, such as the pedestrians' own destinations, interaction with nearby pedestrians and anticipation of upcoming events. In this paper, we propose a novel framework of Social-Aware Generative Adversarial Imitation Learning (SA-GAIL) to mimic the underlying decision-making process of pedestrians in crowds. Specifically, we infer the latent factors of human decision-making process in an unsupervised manner by extending the Generative Adversarial Imitation Learning framework to anticipate future paths of pedestrians. Different factors of human decision making are disentangled with mutual information maximization, with the process modeled by collision avoidance regularization and Social-Aware LSTMs. Experimental results demonstrate the potential of our framework in disentangling the latent decision-making factors of pedestrians and stronger abilities in predicting future trajectories.
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Authors: Yan Sijie , Xiong Yuanjun , Lin Dahua
Published: June 2022

Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.
A Cascaded Inception of Inception Network With Attention Modulated Feature Fusion for Human Pose Estimation
Authors: Liu Wentao , Chen Jie , Li Cheng , Qian Chen , Chu Xiao , Hu Xiaolin
Published: June 2022

Accurate keypoint localization of human pose needs diversified features: the high level for contextual dependencies and the low level for detailed refinement of joints. However, the importance of the two factors varies from case to case, but how to efficiently use the features is still an open problem. Existing methods have limitations in preserving low level features, adaptively adjusting the importance of different levels of features, and modeling the human perception process. This paper presents three novel techniques step by step to efficiently utilize different levels of features for human pose estimation. Firstly, an inception of inception (IOI) block is designed to emphasize the low level features. Secondly, an attention mechanism is proposed to adjust the importance of individual levels according to the context. Thirdly, a cascaded network is proposed to sequentially localize the joints to enforce message passing from joints of stand-alone parts like head and torso to remote joints like wrist or ankle. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both MPII and LSP benchmarks.
Progressive Cognitive Human Parsing
Authors: Zhu Bingke , Chen Yingying , Tang Ming , Wang Jinqiao
Published: June 2022

Human parsing is an important task for human-centric understanding. Generally, two mainstreams are used to deal with this challenging and fundamental problem. The first one is employing extra human pose information to generate hierarchical parse graph to deal with human parsing task. Another one is training an end-to-end network with the semantic information in image level. In this paper, we develop an end-to-end progressive cognitive network to segment human parts. In order to establish a hierarchical relationship, a novel component-aware region convolution structure is proposed. With this structure, latter layers inherit prior component information from former layers and pay its attention to a finer component. In this way, we deal with human parsing as a progressive recognition task, that is, we first locate the whole human and then segment the hierarchical components gradually. The experiments indicate that our method has a better location capacity for the small objects and a better classification capacity for the large objects. Moreover, our framework can be embedded into any fully convolutional network to enhance the performance significantly.
Diverse Beam Search for Improved Description of Complex Scenes
Authors: Vijayakumar Ashwin , Cogswell Michael , Selvaraju Ramprasaath , Sun Qing , Lee Stefan , Crandall David , Batra Dhruv
Published: June 2022

A single image captures the appearance and position of multiple entities in a scene as well as their complex interactions. As a consequence, natural language grounded in visual contexts tends to be diverse---with utterances differing as focus shifts to specific objects, interactions, or levels of detail. Recently, neural sequence models such as RNNs and LSTMs have been employed to produce visually-grounded language. Beam Search, the standard work-horse for decoding sequences from these models, is an approximate inference algorithm that decodes the top-B sequences in a greedy left-to-right fashion. In practice, the resulting sequences are often minor rewordings of a common utterance, failing to capture the multimodal nature of source images. To address this shortcoming, we propose Diverse Beam Search (DBS), a diversity promoting alternative to BS for approximate inference. DBS produces sequences that are significantly different from each other by incorporating diversity constraints within groups of candidate sequences during decoding; moreover, it achieves this with minimal computational or memory overhead. We demonstrate that our method improves both diversity and quality of decoded sequences over existing techniques on two visually-grounded language generation tasks---image captioning and visual question generation---particularly on complex scenes containing diverse visual content. We also show similar improvements at language-only machine translation tasks, highlighting the generality of our approach.
Unravelling Robustness of Deep Learning Based Face Recognition Against Adversarial Attacks
Authors: Goswami Gaurav , Ratha Nalini , Agarwal Akshay , Singh Richa , Vatsa Mayank
Published: June 2022

Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries; (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, including OpenFace and VGG-Face, and two publicly available databases (MEDS and PaSC) demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. The proposed method is also compared with existing detection algorithms and the results show that it is able to detect the attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present several effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition.
Towards Automatic Learning of Procedures From Web Instructional Videos
Authors: Zhou Luowei , Xu Chenliang , Corso Jason
Published: June 2022

The potential for agents, whether embodied or software, to learn by observing other agents performing procedures involving objects and actions is rich. Current research on automatic procedure learning heavily relies on action labels or video subtitles, even during the evaluation phase, which makes them infeasible in real-world scenarios. This leads to our question: can the human-consensus structure of a procedure be learned from a large set of long, unconstrained videos (e.g., instructional videos from YouTube) with only visual evidence? To answer this question, we introduce the problem of procedure segmentation---to segment a video procedure into category-independent procedure segments. Given that no large-scale dataset is available for this problem, we collect a large-scale procedure segmentation dataset with procedure segments temporally localized and described; we use cooking videos and name the dataset YouCook2. We propose a segment-level recurrent network for generating procedure segments by modeling the dependencies across segments. The generated segments can be used as pre-processing for other tasks, such as dense video captioning and event parsing. We show in our experiments that the proposed model outperforms competitive baselines in procedure segmentation.
On the Tractability of SHAP Explanations
Authors: Van den Broeck Guy , Lykov Anton , Schleich Maximilian , Suciu Dan
Published: June 2022

SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on the prediction of a machine learning model. Despite a lot of recent interest from both academia and industry, it is not known whether SHAP explanations of common machine learning models can be computed efficiently. In this paper, we establish the complexity of computing the SHAP explanation in three important settings. First, we consider fully-factorized data distributions, and show that the complexity of computing the SHAP explanation is the same as the complexity of computing the expected value of the model. This fully-factorized setting is often used to simplify the SHAP computation, yet our results show that the computation can be intractable for commonly used models such as logistic regression. Going beyond fully-factorized distributions, we show that computing SHAP explanations is already intractable for a very simple setting: computing SHAP explanations of trivial classifiers over naive Bayes distributions. Finally, we show that even computing SHAP over the empirical distribution is #P-hard.
"In Which the Partners Undertook Reciprocal Duties of Support" – A Discussion of the Phrase as Used in Bwanya v Master of the High Court, Cape Town
Authors: Barratt Amanda
Published: June 2022

In December 2021 the Constitutional Court delivered judgment in Bwanya v Master of the High Court, Cape Town. The court ruled that survivors of life-partnerships "in which the partners undertook reciprocal duties of support" would be entitled to claim benefits under the Maintenance of Surviving Spouses Act (the MSSA) and the Intestate Succession Act (the ISA). This case note focusses on the phrase "in which the partners undertook reciprocal duties of support." It examines the jurisprudential development of the phrase through the case law. It considers whether reliance on the phrase is likely to present an obstacle to potential claimants or whether the phrase can be interpreted in a way that broadens the protection provided by the MSSA and ISA so as to ensure that all vulnerable partners can be protected. The note suggests that the optimal way in which to interpret the requirement that the life-partners had undertaken reciprocal duties of support would be to focus on the claimant's needs and financial dependence and to assess how the law can provide protection and redress to those who have incurred relationship-induced dependence as a consequence of the particular form and nature of the reciprocal support provided in the intimate relationship. Previous court judgments have noted the typically gendered nature of the contributions made by family members. The law must ensure that it furthers the constitutional goal of achieving substantive equality between men and women, while also acknowledging and responding to the intersectional forms that discrimination and disadvantage assumes.
Strategies to fight COVID-19: Beyond the difference between SARS-CoV-2 and Influenza virus
Authors: Huihui Lin , Hequan Li
Published: June 2022

Background: Since the outbreak of COVID-19 emerged in Wuhan, China, in December 2019, the epidemic has spread worldwide and posed a great threat to society. Despite great achievements in COVID-19 research, few studies have focused on the similarities and differences between SARS-CoV-2 and influenza viruses. Results: Through a review of the literature on SARS-CoV-2 and influenza viruses, we found that influenza occurs every year, and influenza pandemics occur irregularly. The uncomplicated human influenza viruses primarily affect the larger airways and rarely the alveoli. However, SARS-CoV-2 mainly involves the deep airways and lungs and can cause DAD, leading to severe hypoxemia. In general, SARS-CoV-2 is no less infectious than the influenza virus. However, its destructive power to the lungs is no less than the avian influenza virus. There is currently no clinical vaccine and specific inhibitor against SARS-CoV-2. Conclusions: SASR-CoV-2 damages lung function more severely than the influenza virus, with higher morbidity, mortality, and severe disease rates. Controlling the source of infection, cutting off the route of transmission, and protecting susceptible populations are critical to the fight against SARS-CoV-2.
[RETRACTED] Myco Nootropic Brain Gummies Reviews (Newest Report) Does This Eliminate “Brain Fog Syndrome”? v1
Authors: not provided tomarhousin
Published: June 2022

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[RETRACTED] Myco Nootropic Brain Gummies Review – Is MycoMode Smart Gummy Scam or Legit? v1
Authors: not provided darnellbianco
Published: June 2022

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[RETRACTED] Condor CBD Gummies (ALERT FRAUD) Does This Condor CBD Gummies Formula Really Works? Read Shocking Report! v1
Authors: Health Supplements Working
Published: June 2022

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[RETRACTED] Condor CBD Gummies Reviews (Shocking Exposed 2022): Beware Scam!! v1
Authors: not provided markhussaion
Published: June 2022

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Authors: A H
Published: June 2022

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[RETRACTED] Keto Blast Gummy Bears Reviews[ Shark Tank] Read Shoking Scams v1
Authors: Blast Gummies Keto
Published: June 2022

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Roller damage detection method based on the measurement of transverse vibrations of the conveyor belt
Authors: Bortnowski Piotr , Król Robert , Ozdoba Maksymilian
Published: June 2022

The article presents the detection of damage to rollers based on the transverse vibration signal measured on the conveyor belt. A solution was proposed for a wireless measuring device that moves with the conveyor belt along of the route, which records the signal of transverse vibrations of the belt. In the first place, the research was conducted in laboratory conditions, where a roller with prepared damage was used. Subsequently, the process of validating the adopted test procedure under real conditions was performed. The approach allowed to verify the correctness of the adopted technical assumptions of the measuring device and to assess the reliability of the acquired test results. In addition, an LSTM neural network algorithm was proposed to automate the process of detecting anomalies of the recorded diagnostic signal based on designated time series. The adopted detection algorithm has proven itself in both laboratory and in-situ tests.
Del conocimiento a la violencia: la dimensión epistémica en el testimonio de la violencia sexual
Authors: Bustos Arellano Aurora Georgina
Published: June 2022

El propósito de este artículo es poner de relieve la dimensión epistémica presente en el testimonio de las víctimas de violencia sexual, la cual se manifiesta por medio de diversos mecanismos de injusticia epistémica; ya sea testimonial o hermenéutica. Para mostrar los efectos de la relación entre el daño epistémico y la violencia sexual, analizo dos casos de injusticia social en la sociedad mexicana. El estudio de estos casos me permite aseverar que la recurrencia sistemática de injusticias epistémicas y violencias sexuales conducen a una forma particular de deshumanización; a saber: una deshumanización epistémica
Injusticia hermenéutica: un ejercicio de precisión conceptual
Authors: Radi Blas
Published: June 2022

Además de abrir un campo fértil para la indagación en epistemología social analítica, la obra de Miranda Fricker ha aportado poderosas herramientas conceptuales que combinan capacidad descriptiva y potencia política. Por esta razón, en los últimos quince años, el repertorio conceptual introducido por la autora ha tenido una buena recepción tanto en ámbitos académicos como políticos. Por momentos los conceptos de injusticia testimonial y de injusticia hermenéutica adquieren dimensiones desmedidas en la literatura y eso atenta, por un lado, contra su precisión analítica y, por el otro, contra su utilidad. En este trabajo argumento contra la tesis del paralelismo estructural de Fricker y defiendo un tratamiento independiente de cada uno de estos conceptos. Sobre esta base, a los fines de contrarrestar la hiperinflación del concepto de injusticia hermenéutica, avanzo con un ejercicio de precisión conceptual. A tal efecto, identifico las condiciones que hacen que la injusticia hermenéutica sea injusta y que sea hermenéutica. Finalmente, presento razones teóricas y prácticas para alentar el uso riguroso de estos conceptos.
A Social Welfare State: A Concept, Essence and Types
Authors: Zabralova O. S.
Published: June 2022

The paper is devoted to the examination of the concept and essence of a social welfare state. The author studies the history of the formation of the concept of a social welfare state, correlates it with the state of prosperity, analyzes various approaches to the definition of the welfare state, classifies the models of the welfare state. The author criticizes the opinion that distinguishes between a paternalistic state and a welfare state and comes to the conclusion that a paternalistic state is an extreme form of a welfare state when the latter guarantees a high level of social safeguards. The paper concludes that the welfare state is directly related to such categories as social equality, social security, solidarity of society, social tension, social policy, as well as a number of other categories. At the same time, social justice is the central category, since it is the category that represents the goal for which the state conducts its social policy.
The Need for Legal Consolidation of Mechanisms for the Return to the Regional Operator of Funds Borrowed to Pay for Complete Repairs of an Apartment Building
Authors: Kartavov S. A.
Published: June 2022

The paper analyzes possible negative consequences of the use by a regional operator to pay for complete repairs of the apartment buildings of funds paid by the owners of the premises of other houses. The right of a regional operator to use such funds on a refundable basis is established by federal law and is actively applied in most regions of the Russian Federation. However, in practice, it is not always possible to return the funds borrowed in this way, which is a serious problem for the regional operator, undermining its financial stability. In addition. this requires the development of mechanisms to guarantee such a refund. The current requirements for regional capital repair programs, in conjunction with the established responsibilities of the regional operator, force the regional operator to ensure that major repairs are carried out even in those houses where repair is inappropriate. The subsequent exclusion of these houses from capital repair programs creates problems affecting the financial stability of the regional operator.
A Comprehensive Hazard Assessment of the Caribbean Region
Authors: Torpey Zimmerman Megan , Shen-Tu Bingming , Shabestari Khosrow , Mahdyiar Mehrdad
Published: June 2022

We present a probabilistic seismic hazard study for the Caribbean (CAR) that integrates global and regional historic earthquake catalogs, a comprehensive fault database, and geodetic data. To account for the heterogeneity of historic earthquake magnitude types (e.g., mb, mL), we developed regression relationships to convert non-moment magnitudes to moment magnitudes (Mw). We used a combination of areal sources and fault sources to model seismicity across the entire CAR domain capturing hazard from both shallow and deep earthquakes. Fault sources were modeled using both the characteristic earthquake model of Schwartz and Coppersmith (1984) and the Gutenberg and Richter (1954) exponential magnitude–frequency distribution models, accounting for single and multi-segment rupture scenarios, as well as balancing of seismic moments constrained by kinematic modeling results. Data from a Global Positioning System survey in conjunction with earthquake information were used to balance seismic moments for different source zones. We also incorporated time-dependent rupture probabilities for selected faults that have ruptured in recent large earthquakes. The complex tectonics of the CAR and lack of local strong-motion data necessitates the use of weighted logic trees of the most up to date ground motion prediction equations to account for uncertainty. We present our modeling methodology and hazard results for peak ground acceleration at key return periods, and compare them to recently published regional probabilistic seismic hazard analysis studies.
Active-Source Interferometry in Marine and Terrestrial Environments: Importance of Directionality and Stationary Phase
Authors: Plescia Steven M. , Sheehan Anne F. , Haines Seth S.
Published: June 2022

We utilize active-source seismic interferometry with dense seismic arrays both offshore and onland to explore the utility of this method to create virtual sources and reveal body-wave reflections in these two different environments. We first utilize data from an ocean-bottom cable (OBC) array in the Gulf of Mexico with equal numbers of sources (160 airgun shots) and receivers (160 ocean-bottom four-component sensors). We next use data from a geophone array across the Bighorn Mountains of Wyoming with many receivers (1300 vertical-component geophones) but a small number of sources (14 borehole active-source shots). We find that the OBC virtual source results, which produce strong reflections from sub-seafloor structures, are far superior to the onland results which lack usable reflections, and we explore reasons for these differences through a set of selective stacking approaches. We present techniques to account for the direction the seismic waves travel (directionality) and stationary phase and show that improvements can be made when incorporating these corrections. Although interferometric methods are based on assumptions of large numbers of widely distributed actual sources, we find that selective exclusion of potentially problematic source–receiver pairs can yield improved results. These geometric adjustments to active-source interferometry methods have utility for dense-nodal-array surveys that are now common in academic studies, but that often suffer from sparse source geometry.
Seismic Background Noise Levels across the Continental United States from USArray Transportable Array: The Influence of Geology and Geography
Authors: Anthony Robert E. , Ringler Adam T. , Wilson David C.
Published: June 2022

Since 2004, the most complete estimate of background noise levels across the continental United States was attained using 61 broadband seismic stations to calculate power spectral density (PSD) probability density functions. To improve seismic noise estimates across the United States, we examine vertical component seismic data from the EarthScope USArray Transportable Array seismic network that rolled across the United States and southeastern Canada between 2004 and 2015 and form a large (10 TB) PSD database from 1679 stations that contains no smoothing or binning of the spectral estimates. Including station outages, our database has a mean of 98.9% data completeness, and we present maps showing the spatial and temporal variability of seismic noise in six bands of interest between 0.2 and 75 s period. At 0.2 s period, seismic noise across the eastern United States is predominantly anthropogenically generated and may be subsequently amplified more than 20 decibels in the sandy and water-saturated sediments of the southeastern U.S. Coastal Plain and Mississippi Embayment. In these sediments, 1 s noise shows similar amplification and is generated through a variety of mechanisms across the United States including cultural activity throughout Kentucky and the southeastern Appalachian Mountains, lake waves around the Great Lakes, and ocean waves throughout New England, the Pacific Northwest, and Florida. Both 0.2 and 1 s noise levels are the lowest in the Intermountain West portion of the United States. We attribute this to a combination of installations on crystalline rocks and reduced population density. Finally, we find that sensors emplaced in sandy, water-saturated sediments observe median, diurnal variations in vertical component power at 18–75 s period, which we infer arise through local deformation driven by pressure variations. Ultimately, our results underscore that for shallow (<5 m depth) sensor installation, bedrock provides superior broadband noise performance compared to unconsolidated sediments.
DESENVOLVIMENTO DE UM APLICATIVO PARA AUXÍLIO NA TOMADA DE DECISÃO PARA PEQUENOS AGRICULTORES
Authors: Solera Lara Moccio Breim , Goncalves Damaris Barbosa , Ribeiro Flávia Noronha Dutra , Steinert Laura , Nakano Fabio
Published: June 2022

In Brazil, small farmers are responsible for a significant amount of agricultural production. These farmers can benefit from deploying meteorological sensors and the analysis of these data to improve decisions regarding their crops. Soil moisture is a particularly important variable. Considering this, the present project developed and implemented an information system using low-cost sensors and free data repositories to generate and publicize information regarding meteorological data. The collected data are combined with public data on rainfall from a meteorological station in the region of the farms, generating soil water balance information which includes indicators of water surplus and deficit, that are shared through a website or an application for mobile devices. The results of the measured variables allow a deeper knowledge of the local microclimate. The farmer, once given the information, can use it as an element for decisions on crop irrigation. Therefore, the participation of farmers in the development of technological solutions is important. The relevance of an interdisciplinary team approach is evident in the creation of technological solutions that help sustainable environmental management
Network evolution in theory and as observed, the case of the Industrial Knowledge Bank of Latin America
Authors: Baquero Cruz Gesny Yadira , Florez Torres Greys M , Hanappi Gerhard
Published: June 2022

This paper provides an evaluation of a theoretical approach of network theory that describes network evolution by comparing it to an observed evolution of an industrial cooperation network connecting different countries. The theoretical approach considered is the well-known preferential attachment mechanism leading to power-law distributions developed by Albert Barabási. The empirical phenomenon studied is the evolution of the Industrial Knowledge Bank (IKB) maintained by The United Nations Industrial Development Organization (UNIDO). By fitting a discrete power law to the empirical data collected during the 4 years of operation of the IKB project and testing the goodness-of-fit using the bootstrapping procedure, it is shown that the theoretical framework in its most elementary form is adequate to grasp the essential features of the observed case. The comparison also leads to an improved understanding of very influential social factors like language and geographical distance, thus can give valuable insight for the further theoretical advances in theoretical work on network evolution and its applications to Social, Industrial, and Economic Development.
Biogeoaccumulation of zinc in hybrid rice (Oryza sativa L.) in an Inceptisol amended with soil zinc application and its bioavailability to human being
Authors: MOHAPATRA Kiran Kumar , SİNGH Satish Kumar , PATRA Abhik , JATAV Surendra Singh , RAJPUT Vishnu D. , POPOVA Victoria , PUZİKOVA Olesya , NAZARENKO Olga , SUSHKOVA Svetlana
Published: June 2022

Soil Zn amended is an efficient agronomical Zn biofortification approach in rice. However, it is still need to know if higher rate of Zn over recommended dose can influence other essential nutrient uptake, high accumulation of Zn in soils and health risk for human consumption. This study was conducted by taking ten treatments (T1: control, T2: RDF, T3: RDF + 1.25 mg kg-1, T4: RDF + 2.5 mg kg-1, T5: RDF + 3.75 mg kg-1, T6: RDF + 5 mg kg-1, T7: RDF + 6.25 mg kg-1, T8: RDF + 7.5 mg kg-1, T9: RDF + 8.75 mg kg-1, T10: RDF + 10 mg kg-1) on hybrid rice in Zn (1.20 mg kg-1) enriched soil. The findings have shown that 6.25 mg kg-1 Zn application significantly increased crop growth and grain concentrations of N, K, Zn, Cu and Fe by 71.4, 125, 78.9, 28.5 and 2.4%, respectively. Nutrient harvest index was significantly affected by ranged between 29.1–36.4%. Application of Zn at 6.25 mg kg-1 (T7) recorded the highest Zn concentration in grain (28.2 mg kg-1) and bioavailability of the fortified Zn (2.05 mg Zn day-1). The lowest phytatic acid concentration in grain was recorded in T8 (RDF + Zn at 7.5 mg kg-1) and after that a significant increase was observed. Transfer coefficient was inversely behaving with Zn application and ranged between 6.03–18.0 grain. The average daily intake of Zn was ranged between 0.075–0.118 mg-1 kg-1 day. Across different treatments the Zn build-up factor, geo-accumulation index and soil enrichment factor was ranged between 0.98–4.90, -0.61–1.70 and 0.24–1.82, respectively in post-harvest soil. In conclusion, agronomic biofortification of Zn through soil applications at 6.25 mg Zn kg-1 was a sustainable way to improving growth and grain Zn, N, K, Cu and Fe uptake of hybrid rice to meet human recruitment.
Relative potential of Rhizobium sp for improving the rice-wheat crop in the semi-arid regions
Authors: QURESHİ M. Amjad , IQBAL M. Zaffar , RAHMAN Sajid Ur , ANWAR Javed , TANVEER M. Hammad , SHEHZAD Armghan , ALİ M. Asif , AFTAB Muhammad , SALEEM Usama , EHSAN Shabana
Published: June 2022

Soil Microbiologists have been concentrating on manipulation of rhizosphere microbes in cereals, but many researchers have reported that rhizobia can act as plant growth promoting rhizobacteria (PGPR). Rhizobium species impacted the crop ontogeny by root / endophytic colonization, producing phytohormones, efficient nutrient use and nutrient solubilization / mineralization. Field studies were performed at Soil Bacteriology Section and Soil Chemistry Section, Faisalabad to assess the comparative potential of Rhizobium species for promoting the growth, yield of wheat and rice. Auxin biosynthesis potential of isolates of Rhizobium species (mung (Vigna radiata), berseem (Trifolium alexandrinum), chickpea (Cicer arietinum), lentil (Lens culinaris) and peanut (Arachis hypogaea)) was determined and isolates of each species having higher values were used for field experiments. Assay for root / shoot elongation, root colonization in plates were carried out under controlled conditions. The rhizosphere soil of wheat and rice were assayed for the Indole Acedic Acid (IAA) content 15 and 30 days after germination / transplanting, respectively. Results revealed that significant increase was observed in the yield parameters of wheat and rice. Highest wheat grains were produced i.e., 4917 kg ha-1 with Rhizobium sp of mungbean (Mb3) followed by 4823 with Rhizobium sp of berseem (Br3) than control i.e., 4500 kg ha-1. Similarly, the maximum paddy yield i.e., 4667 kg ha-1 with Rhizobium sp of mungbean (Mb3) followed by 4625 Rhizobium sp of berseem (Br3) inoculation was obtained as compared to control i.e., 4208 kg ha-1. Other physical parameters of wheat and rice also showed positive response to inoculation and have elevated levels of IAA in the rhizosphere of inoculated treatments. Results clearly demonstrated that Rhizobium species increased the yield of rice and wheat.
Contrasting rice management systems – Site-specific effects on soil parameters
Authors: MAFTUKHAH Rizki , NGADİSİH Ngadisih , MURTİNİNGRUM Murtiningrum , MENTLER Axel , KEİBLİNGER Katharina Maria , MELCHER Andreas Helmut , ZEHETNER Franz , KRAL Rosana Maria
Published: June 2022

Conventional rice production systems (CRPS) with continuous flooding demand much water. While population growth increases the demand for rice and, consequently, water consumption, agricultural production needs to reduce its water demand. The System of Rice Intensification (SRI) is promoted as an alternative cropland management strategy to sustainably maintain rice yields while optimizing water use. Here, we aimed at investigating whether different management translates into differences in soil parameters. To this end, the two contrasting rice production systems were compared on the same soil types, at four different study sites of D.I. Yogyakarta Province, Indonesia. Crop yields were estimated, and soils were analysed for soil total soil organic carbon (TOC), total nitrogen (TN), dissolved organic carbon (DOC), macro-aggregate stability, and a fungal biomarker (ergosterol) indicative of oxidative soil conditions. Rice yields in the study area were between 6.7 and 9 t ha-1. For TOC, the combined effect of management and site was significant; in particular, in Kulonprogo and Bantul, SRI significantly exceeded CRPS’ TOC values. However, a significant management effect was observed for ergosterol and DOC concentrations. Significantly higher ergosterol concentrations in SRI vs CRPS were found in Sleman and Bantul. DOC was significantly higher under SRI compared to CRPS only in Sleman. DOC and ergosterol were most responsive to management and were improved in SRI systems. The observed site-specific effects suggest the importance to consider the prevailing site conditions for adapting management strategies.
Asbestos: caught between technological development and the threat of civil liability. The Italian experience and an approach to Colombian law
Authors: Gamarra-Amaya Laura Cecilia , Woolcott Olenka
Published: June 2022

En Colombia, la Ley 1968 de 2019 prohibió el uso de asbesto en el territorio nacional. Esta ley no abordó la imputación de responsabilidad contra quienes  expusieron a otros al asbesto ni la evaluación de daños para las víctimas. Este artículo se ubica en el área de derecho civil e intenta responder a la pregunta de responsabilidad civil por asbesto basada en la experiencia Italiana y mirar los pequeños pasos jurisprudenciales tomados por los tribunales colombianos para poder determinar si se ha dispuesto la responsabilidad civil por asbesto. Al hacerlo, se identifican algunas teorías que conciernen al vínculo causal en la responsabilidad civil tradicional y al criterio de imputación o atribución de la responsabilidad objetiva aplicable a las víctimas de asbesto. Los resultados de la investigación aconsejan adaptar los elementos tradicionales de la responsabilidad a las demandas por daños relacionados con el asbesto en los casos contemporáneos teniendo en cuenta el recurso a las presunciones desde la perspectiva probatoria.
Developing Communicative Professional Competence in Future Economic Specialists in the Conditions of Postmodernism
Authors: Zahorodna Oksana , Saienko Volodymyr , Tolchieva Hanna , Tymoshchuk Nataliia , Kulinich Tetiana , Shvets Natalya
Published: June 2022

A detailed analysis of psycho-pedagogical, methodological and linguistic sources in the article has allowed one to justify and build a holistic model of developing communicative professional competence in economics students. This author’s model includes the interrelated components of communicative professional activities (motivation and goals; orientation and cognition; functions and activities; evaluation and correction), criteria for their assessment and levels of communicative professional competence in economics students (high, average, low). The following pedagogical conditions have been presented in the context of the research: modelling probable professional communicative situations in the educational process; ensuring communicative professional orientation of information and communication technologies; strengthening the subjective position of students during communicative professional training. Their effective use in the educational process contributes to developing the motivational sphere of students, improving their knowledge of languages ​​and other academic courses, enhancing their abilities to apply the obtained knowledge in practice and adequately assess themselves as specialists. The international relevance of the article lies in offering guidelines for educational institutions that strive to enhance the educational processes in a postmodern society.