This study aims to estimate the quantum Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy in curvilinear coordinates. We used the results to calculate the quantum binary and triplet distribution functions in curvilinear coordinates. The analytical form of the quantum distribution functions was obtained for dusty plasma in Saturn’s rings model. We use particles-in-cell (PIC) simulations to find a visualization of dusty three-component plasma phase space in curvilinear coordinates. Our results were compared with others.

This paper aims to obtain the thermodynamic variables (temperature, thermodynamic volume, angular velocity, electrostatic potential, and heat capacity) corresponding to the Schwarzschild black hole, Reissner-Nordstrom black hole, Kerr black hole and Kerr-Newman-Anti-de Sitter black hole. We also obtained the free energy for black holes by using three different methods. We obtained the equation of state for rotating Banados, Teitelboim and Zanelli black holes. Finally, we used the quantum correction of the partition function to obtain the heat capacity and entropy in the quantum sense.

NULL

The HTTP flooding attack is the hardest type of
DDoS attacks to detect since the malicious packets are hidden
in the huge amount of normal traffic. Most detection schemes
available up to now use similarity method of communication
attributes (i.e. fixed threshold for every attribute ) or machine
learning algorithms.it is notable,however,that attributes number
very dramatically according to the users activity. Also, using
machine learning need a large amount of data for training.In this
paper, we introduce a new detection scheme for HTTP flooding
attack that exhausting servers. the proposed detection scheme is
based on HTTP request/raspons protocol.During normal cases,
any server can measure various statistical attributes for its users
and their traffic. a server can keep the statistical attributes
as a reference profile. during the attack time, measuring some
attributes for every connection (i.e. Request number, Response
number, Not finished connections number, Number of TCP
packets, Number of UDP packets and Number of ICMP packets)
then computes distance between its attributes and statistical
attributes in normal cases. the proposed detection scheme uses
small amount of data to specify the score for a normal connection,
also it does not take into consideration a fixed threshold for
every attribute in normal connections. Extensive trace-driven
simulation has been conducted to demonstrate the efficiency of
the proposed scheme in terms of its detection rate, probability
of false positive and also average detection time.

The HTTP flooding attack is the hardest type of
DDoS attacks to detect since the malicious packets are hidden
in the huge amount of normal traffic. Most detection schemes
available up to now use similarity method of communication
attributes (i.e. fixed threshold for every attribute ) or machine
learning algorithms.it is notable,however,that attributes number
very dramatically according to the users activity. Also, using
machine learning need a large amount of data for training.In this
paper, we introduce a new detection scheme for HTTP flooding
attack that exhausting servers. the proposed detection scheme is
based on HTTP request/raspons protocol.During normal cases,
any server can measure various statistical attributes for its users
and their traffic. a server can keep the statistical attributes
as a reference profile. during the attack time, measuring some
attributes for every connection (i.e. Request number, Response
number, Not finished connections number, Number of TCP
packets, Number of UDP packets and Number of ICMP packets)
then computes distance between its attributes and statistical
attributes in normal cases. the proposed detection scheme uses
small amount of data to specify the score for a normal connection,
also it does not take into consideration a fixed threshold for
every attribute in normal connections. Extensive trace-driven
simulation has been conducted to demonstrate the efficiency of
the proposed scheme in terms of its detection rate, probability
of false positive and also average detection time.

The HTTP flooding attack is the hardest type of
DDoS attacks to detect since the malicious packets are hidden
in the huge amount of normal traffic. Most detection schemes
available up to now use similarity method of communication
attributes (i.e. fixed threshold for every attribute ) or machine
learning algorithms.it is notable,however,that attributes number
very dramatically according to the users activity. Also, using
machine learning need a large amount of data for training.In this
paper, we introduce a new detection scheme for HTTP flooding
attack that exhausting servers. the proposed detection scheme is
based on HTTP request/raspons protocol.During normal cases,
any server can measure various statistical attributes for its users
and their traffic. a server can keep the statistical attributes
as a reference profile. during the attack time, measuring some
attributes for every connection (i.e. Request number, Response
number, Not finished connections number, Number of TCP
packets, Number of UDP packets and Number of ICMP packets)
then computes distance between its attributes and statistical
attributes in normal cases. the proposed detection scheme uses
small amount of data to specify the score for a normal connection,
also it does not take into consideration a fixed threshold for
every attribute in normal connections. Extensive trace-driven
simulation has been conducted to demonstrate the efficiency of
the proposed scheme in terms of its detection rate, probability
of false positive and also average detection time.

The vegetation-soil relationships in the four major habitats of Kharga Oasis (farmlands, date- palm orchards, salinized lands
and the surrounding desert) in the Western Desert of Egypt are examined in this study. Altogether, 122 vascular plants
species distributed in 102 genera and thirty-five families were recorded. Poaceae (25.2 %), Asteraceae (11.9 %),
Brassicaceae (6.5 %), Cyperaceae (6.5 %), Amaranthaceae (5.4 %) and Euphorbiaceae (5.4 %) were the largest families.
With respect to the floristic composition, habitats varied from one to another: eighty-six species in farmlands, seventy-nine
species in date-palm orchards, seventy-three species in salinized lands and thirty-nine species in the surrounding desert
lands. About 22 % of the total flora was represented in the four habitats, while 37.7 % was found in one habitat. The
vegetation classificatory method of Two-Way Indicator Species Analysis yielded fourteen vegetation groups: four in both
farmlands and date-palm orchards, and three for both salinized lands and the surrounding desert habitats. The results of the
Canonical Correspondence Analysis (CCA) showed that water content, soil texture, organic matter and bicarbonates were
most related to the species distribution in the studied habitats. Other related variables included sulfates and phosphates in the
date-palm orchards and salinized lands, and electric conductivity in the surrounding desert. Farmlands had the highest
species richness, followed by the date-palm orchards and the salinized lands, whereas the desert outskirts were the lowest in
terms of species richness. The linear correlations (r) between the farmlands and palm orchards were highly significant (r =
0.703), and also occurred between salinized lands and the surrounding deserts (r = 0.764). These high correlations may be
attributed to the effect of concentric zonation of the habitat as each pair of the aforementioned habitats is adjacent to each
other.

The vegetation-soil relationships in the four major habitats of Kharga Oasis (farmlands, date- palm orchards, salinized lands
and the surrounding desert) in the Western Desert of Egypt are examined in this study. Altogether, 122 vascular plants
species distributed in 102 genera and thirty-five families were recorded. Poaceae (25.2 %), Asteraceae (11.9 %),
Brassicaceae (6.5 %), Cyperaceae (6.5 %), Amaranthaceae (5.4 %) and Euphorbiaceae (5.4 %) were the largest families.
With respect to the floristic composition, habitats varied from one to another: eighty-six species in farmlands, seventy-nine
species in date-palm orchards, seventy-three species in salinized lands and thirty-nine species in the surrounding desert
lands. About 22 % of the total flora was represented in the four habitats, while 37.7 % was found in one habitat. The
vegetation classificatory method of Two-Way Indicator Species Analysis yielded fourteen vegetation groups: four in both
farmlands and date-palm orchards, and three for both salinized lands and the surrounding desert habitats. The results of the
Canonical Correspondence Analysis (CCA) showed that water content, soil texture, organic matter and bicarbonates were
most related to the species distribution in the studied habitats. Other related variables included sulfates and phosphates in the
date-palm orchards and salinized lands, and electric conductivity in the surrounding desert. Farmlands had the highest
species richness, followed by the date-palm orchards and the salinized lands, whereas the desert outskirts were the lowest in
terms of species richness. The linear correlations (r) between the farmlands and palm orchards were highly significant (r =
0.703), and also occurred between salinized lands and the surrounding deserts (r = 0.764). These high correlations may be
attributed to the effect of concentric zonation of the habitat as each pair of the aforementioned habitats is adjacent to each
other.

NULL

To cope better with desert harsh conditions or to improve their growth magnitude and productivity, jojoba plants might need some aid. One week drought, over a period of two months, was stressful as it induced severe growth inhibition compared with maintained 50% field capacity. In addition, jojoba sustained growth throughout the whole experimental period (4 months) relying on a minimum of nitrate (tap water or 25% Hoagland concentration). Extra nitrate (1 25%) was stimulatory to growth in full hydration and may overcome growth inhibition in partial hydration but not in drought. The osmoregulants (soluble sugars, proline and amino acids), membrane properties (leakage of K, electrolytes and UV absorbing metabolites as well as lipid peroxidation) and antioxidants (phenolics, catalase, guiacol peroxidase and superoxide dismutase activity) were assessed to evaluate drought stress impacts. They did not respond identically to drought leading to confusion which is most-expressing manifestation of drought stress. From which, enhanced proline accumulation and lipid peroxidation with inhibited ascorbate peroxidase displayed a clear cut and reliable stress biomarkers. The antioxidant phenolics increased with increasing nitrate in water-stressed jojoba leaves (50% field capacity or one week drought). Nitrate may participate in scavenging electrons since lipid peroxidation was lowered at high nitrate levels.