scholarly journals Spatial Distribution Characteristic of Antimony in Typical Paddy Soil of Eastern Hunan Province, China

2021 ◽  
Vol 9 ◽  
Author(s):  
ChenRan Wang ◽  
DaJuan Wan ◽  
XueYing Cao ◽  
Huan Wang ◽  
JiaQi Chen ◽  
...  
Keyword(s):  
Paddy Soil ◽  
Geometric Mean ◽  
Research Area ◽  
Hunan Province ◽  
Distribution Characteristics ◽  
Distribution Characteristic ◽  
Parent Materials ◽  
Profile Depth ◽  
Set Up ◽  
Spatial Distribution Characteristic

Considering the eastern part of Hunan Province as the research area, 34 sampling sites were set up, 198 samples were collected from representative paddy soil, the distribution characteristics of antimony (Sb) were studied. The results showed that: (1) The content of Sb on the surface of paddy soil ranging from 0.07 to 11.00 mg/kg and the geometric mean was 1.56 mg/kg. (2) The distribution of contents of Sb in paddy soil in different areas was shown as Yueyang > Changsha > Zhuzhou > Xiangtan. (3) Sb showed a strong migration in paddy soil in the research area and its content increased initially and then decreased or gradually decreased with the increase of profile depth. (4) The content of Sb in the substratum was significantly affected by parent materials.

scholarly journals Epiphytic mosses in urban sites and the possibility of their use in biomonitoring (Yuzhno-Sakhalinsk)

2021 ◽  
Vol 946 (1) ◽  
pp. 012039
Author(s):  
V V Kaganov
Keyword(s):  
Research Area ◽  
Urban Vegetation ◽  
Distribution Characteristics ◽  
Study Region ◽  
Study Results ◽  
Set Up ◽  
Urban Sites

Abstract The article presents the study results on epiphytic mosses. The research area is Yuzhno-Sakhalinsk and its surroundings. To assess the epiphytic cover the method of occurrence measuring on the tree trunks with the help of a frame 10×40 cm (400 cm2) was used at 15 locations within the study region. 15 species of epiphyte moss and their distribution characteristics were identified. Populus maximowiczii A. Henri was selected as a substrate because it predominates in urban vegetation and grows naturally in floodplain areas where control plots were set up.

scholarly journals Hybrid-specific nutrient and water use of maize on chernozem soil

2013 ◽  
pp. 51-54
Author(s):  
Lajos Karancsi
Keyword(s):  
Maximum Yield ◽  
Field Research ◽  
Research Area ◽  
Control Treatment ◽  
Chernozem Soil ◽  
Set Up ◽  
The University ◽  
Optimum Nutrition ◽  
Corn Hybrid ◽  
Yield Decrease

The field research was set up on chernozem soil at the Látókép AGTC KIT research area of the University of Debrecen. The study focused on yield, water utilization, nutrient reaction and the amount of yield per kg fertilizer of corn hybrid NX 47279 in 2011 and 2012. Based on the yield results it can be concluded that the largest yield in 2011 was 15 963 kg ha-1 at level N120+PK, while in 2012, the maximum yield amounted to 14 972 kg ha-1 at level N90+PK. Surplus yield per kg fertilizer proved that in 2011 level N30+PK resulted in the highest surplus yield (42.3 kg kg-1) compared to the control treatment. In 2012, yield growth was 18.0 kg kg-1 compared to the control treatment. We measured at level N60+PK 17,5 kg kg-1 compared to at level N30+PK, at the N90+PK 17,7 kg kg-1 compared to at level N60+PK. level N30+PK kg kg-1, 17.5 kg kg-1 at level N60+PK and 17.7 kg kg-1 at level N90+PK compared to the control treatment. Results of the regression analysis showed that the amount of nitrogen fertilizer was 117 kg ha-1 in 2011 and 111 kg ha-1 in 2012 in order to reach maximum yield. Doses of fertilizers above the amounts previously mentioned resulted in yield decrease. Our results indicated that in the drought year of 2012 the hybrid used available water more efficiently than in 2011. The hybrid produced 59 kg ha-1 yield in 2012 and 51.9 kg ha-1 in 2011 at an optimum nutrition level.

scholarly journals Speech-based human emotion recognition

2021 ◽  
Author(s):  
Talieh Seyed Tabtabae
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Speaker Identification ◽  
Recognition System ◽  
Research Area ◽  
Speech Signals ◽  
Spectral Features ◽  
Emotional States ◽  
Human Communication ◽  
Set Up

Automatic Emotion Recognition (AER) is an emerging research area in the Human-Computer Interaction (HCI) field. As Computers are becoming more and more popular every day, the study of interaction between humans (users) and computers is catching more attention. In order to have a more natural and friendly interface between humans and computers, it would be beneficial to give computers the ability to recognize situations the same way a human does. Equipped with an emotion recognition system, computers will be able to recognize their users' emotional state and show the appropriate reaction to that. In today's HCI systems, machines can recognize the speaker and also content of the speech, using speech recognition and speaker identification techniques. If machines are equipped with emotion recognition techniques, they can also know "how it is said" to react more appropriately, and make the interaction more natural. One of the most important human communication channels is the auditory channel which carries speech and vocal intonation. In fact people can perceive each other's emotional state by the way they talk. Therefore in this work the speech signals are analyzed in order to set up an automatic system which recognizes the human emotional state. Six discrete emotional states have been considered and categorized in this research: anger, happiness, fear, surprise, sadness, and disgust. A set of novel spectral features are proposed in this contribution. Two approaches are applied and the results are compared. In the first approach, all the acoustic features are extracted from consequent frames along the speech signals. The statistical values of features are considered to constitute the features vectors. Suport Vector Machine (SVM), which is a relatively new approach in the field of machine learning is used to classify the emotional states. In the second approach, spectral features are extracted from non-overlapping logarithmically-spaced frequency sub-bands. In order to make use of all the extracted information, sequence discriminant SVMs are adopted. The empirical results show that the employed techniques are very promising.

scholarly journals Examination of the Nutrient and Water Utilization of Different Corn Genotypes in the Hajdúság

2014 ◽  
pp. 55-59
Author(s):  
Lajos Karancsi
Keyword(s):  
Research Area ◽  
Control Treatment ◽  
Chernozem Soil ◽  
Corn Hybrids ◽  
Water Utilization ◽  
Yield Production ◽  
Set Up ◽  
The University

The research was set up on chernozem soil at the Látókép MÉK research area of the University of Debrecen in Hungary. We examined the following factors of the hybrid P9494, P9578, PR37N01 and PR37M81 in 2013.Yield, yield production per 1 kg fertilizer, water utilization and nutrient reaction. We found that the best yield results were achieved at level N120+PK in case of hybrid P9494 (17 132 kg ha-1) P9578 (16 838 kg ha-1) and PR37N01 (17 476 kg ha-1) and at level N150+PK for hybrid PR37M81 (16 754 kg ha-1). Results of yield per 1 kg NPK studies indicate that the most intense yield growth occurred at level N30+PK compared to the control treatment. This means that yield production per 1 kg NPK was 39.2 kg kg-1 in the case of hybrid P9494, 54.2 kg kg-1 in the case of P9578, 17.6 kg kg-1 for PR37N01 and 44.2 kg kg-1 in the case of PR37M81. After comparing corn hybrids’ water utilization, our conclusion was that the control treatment achieved the worst results in the case of each hybrid (P9494: 20.8 kg mm-1, P9578: 21.0 kg mm-1, PR37N01: 26.2 kg mm-1, PR37M81: 19.5 kg mm-1). For hybrid P9494, P9578 and P37N01, the best water utilization results were measured at level N120+PK (31.5 kg mm-1, 31,0 kg mm-1 and 32.1 kg mm-1), while PR37M81 reached the highest values at level N150+PK (30.8 kg mm-1). We determined the hybrids’ nutrient reaction and its optimum fertilizer values. The best nutrient reaction results were achieved by hybrid PR37N01, while that of hybrid PR37M81 proved to be the weakest.

scholarly journals Internet-Based Administration of Shared Instruments with Facility Online Manager

2006 ◽  
Vol 14 (6) ◽  
pp. 36-39
Author(s):  
Shu-You Li ◽  
Vinayak P. Dravid
Keyword(s):  
Resource Sharing ◽  
Research Council ◽  
Scientific Research ◽  
National Research Council ◽  
Research Area ◽  
Research Groups ◽  
Principal Investigators ◽  
Materials Research ◽  
Set Up ◽  
Large Research

Resource sharing has become an absolute necessity for modern scientific research because of the increasing expense and complexity of instruments, and the ever changing funding paradigm which often requires sharing of major instrumentation resource across multiple disciplines. Many universities and even large companies have set up centralized facilities to serve researchers from different departments, divisions and units to minimize expenses and maximizing usage.Just in the materials research area alone, it is estimated that there are more than 500 mid-size facilities existing nationwide, as reported in 2005 by the Committee on Smaller Facilities of National Research Council. Resource sharing is also common in large research groups, between Principal Investigators, as well as in corporate/industrial laboratories.

Regulating European Clouds

2016 ◽  
pp. 42-60
Author(s):  
Szilvia Varadi
Keyword(s):  
Cloud Computing ◽  
Data Protection ◽  
New Technology ◽  
Research Area ◽  
Third Party ◽  
The European Union ◽  
General Data Protection Regulation ◽  
Goods And Services ◽  
Data Protection Directive ◽  
Set Up

Cloud Computing is a diverse research area that encompasses many aspects of sharing software and hardware solutions, including computing and storage resources, application runtimes or complex application functionalities. In the supply of any goods and services, the law gives certain rights that protect the consumer and provider, which also applies for Cloud Computing. This new technology also moves functions and responsibilities away from local ownership and management to a third-party provided service, and raises several legal issues, such as data protection, which require this service to comply with necessary regulation. In this chapter the author investigates the revised legislation of the European Union resulting in the General Data Protection Regulation, which will be used to set up the new European Data Protection Framework. The author gathers and summarizes the most relevant changes this regulation brings to the field of Clouds, and draws relations to the previous legislation called the Data Protection Directive currently in force.

scholarly journals A Multi-Objective Permanent Basic Farmland Delineation Model Based on Hybrid Particle Swarm Optimization

2020 ◽  
Vol 9 (4) ◽  
pp. 243 ◽  
Author(s):  
Hua Wang ◽  
Wenwen Li ◽  
Wei Huang ◽  
Ke Nie
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Research Area ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Model Based ◽  
Hybrid Particle Swarm Optimization ◽  
General Rules ◽  
Set Up

The delimitation of permanent basic farmland is essentially a multi-objective optimization problem. The traditional demarcation methods cannot simultaneously take into account the requirements of cultivated land quality and the spatial layout of permanent basic farmland, and it cannot balance the relationship between agriculture and urban development. This paper proposed a multi-objective permanent basic farmland delimitation model based on an immune particle swarm optimization algorithm. The general rules for delineating the permanent basic farmland were defined in the model, and the delineation goals and constraints have been formally expressed. The model introduced the immune system concepts to complement the existing theory. This paper describes the coding and initialization methods for the algorithm, particle position and speed update mechanism, and fitness function design. We selected Xun County, Henan Province, as the research area and set up control experiments that aligned with the different targets and compared the performance of the three models of particle swarm optimization (PSO), artificial immune algorithm (AIA), and the improved AIA-PSO in solving multi-objective problems. The experiments proved the feasibility of the model. It avoided the adverse effects of subjective factors and promoted the scientific rationality of the results of permanent basic farmland delineation.

scholarly journals Keanekaragaman Makroarthropoda Tanah di Lahan Persawahan Padi Organik dan Anorganik, Desa Bakalrejo Kecamatan Susukan Kabupaten Semarang

2015 ◽  
Vol 17 (1) ◽  
pp. 21
Author(s):  
Roma Witriyanto ◽  
Mochamad Hadi ◽  
Rully Rahadian
Keyword(s):  
Organic Farming ◽  
Negative Impact ◽  
Research Area ◽  
Paddy Fields ◽  
Post Harvest ◽  
Rice Growth ◽  
Synthetic Chemicals ◽  
Residual Hazard ◽  
Set Up ◽  
The Impact

Inorganic paddies system has a negative impact as pests become resistant, environmental pollution and residual hazard. People who are aware of the impact of the application of synthetic chemicals have been applying organic farming. This research aimed to compare the abundance, diversity and dominance of soil macroarthropods  in the organic and inorganic paddies field in Village Bakalrejo, Susukan District of Kabupaten Semarang. Sampling of soil macroarthropods method used pit fall traps . Fifteen pit fall traps were set up in the research area and they were divided into three line whith respective line are 5 pit fall traps. Soil macroarthropods  which has been found were identified up to family taxon. The results of the research shows abundance of soil macroarthropods organic rice in paddy fields higher (297) than in inorganic paddy fields (236). Diversity of soil macroarthropods in every phase of rice growth (vegetative, reproductive, ripening and post-harvest) was higher in organic paddy fields (1,479; 1,644; 1,561; 1,019) than inorganic paddy fields (1.391; 0.595; 1.286; 1.002). Based on the relative abundance index, soil macroarthropods family dominated organic paddy fields in every phase of rice growth (vegetative, reproductive, ripening, and post-harvest) was lower (Gryllidae: 39.4%; Carabidae: 46.7%; Formicidae: 44.1 %; Formicidae: 48.8%) than in the inorganic paddy fields (Carabidae: 35.5%; Carabidae: 57.4%; Formicidae: 56.3%; Formicidae: 77.6%). Key words :  biodiversity, soil macroarthropods, organic farming.

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