Background Conditions Research Articles

The deep underground Bellotti Ion Beam Facility—status and perspectives

For more than three decades, accelerators are in use in the underground laboratories of the Laboratori Nazionali del Gran Sasso (LNGS), located in central Italy. The LUNA Collaboration has exploited the potential of the site’s low cosmic ray background to achieve important and often groundbreaking results in the field of nuclear astrophysics. This long success story stimulated the installation of accelerators in deep underground laboratories also in other countries, including the USA and China. Recently, LNGS took a major step forward with the activation of the Bellotti Ion Beam Facility, which will provide ion beams to the scientific community for research not only in nuclear astrophysics, but in all fields that can benefit from the low cosmic ray background conditions of the underground site.

Identifying emission sources of CH4 in East Asia based on in-situ observations of atmospheric δ13C-CH4 and C2H6

Methane (CH4) is the second most important greenhouse gas influenced by human activity. The increase in atmospheric CH4 concentrations contributed ~23 % to the anthropogenic radiative forcing (Saunois et al., 2020). The current anthropogenic CH4 emissions trajectory implies that large emissions reductions are needed to meet the target of the Paris Agreement (Nisbet et al., 2019). For effective regulation of CH4, it is important to identify spatiotemporal emission sources, in particular those from East Asia – one of the largest CH4 emitters. In this study, we present in-situ observations of atmospheric CH4 concentrations (i.e., dry air mole fractions in part per billion (ppb)) and carbon isotopic compositions of CH4 made during 2017–2020 at the Gosan station (GSN, 33.3°N, 126.2°E, 72 m a.s.l) which is representative of regional background conditions in East Asia. The annual growth rate of the observed CH4 baseline concentrations was 11 ± 1 ppb yr−1. The enhanced pollution concentrations of CH4 showed seasonally distinctive correlations with the corresponding δ13C-CH4. The CH4 source isotopic signature for winter derived based on both the Keeling and Miller-Tans approaches was −40.7 ± 3.4 ‰, suggesting dominant thermogenic sources (e.g., coal and/or gas combustion), whereas the source signature for summer was estimated as −54.1 ± 1.2 ‰, which seemed to represent both microbial sources (e.g., rice paddies) and fossil fuel sources of CH4 emissions. Based on the δ13C-CH4 source signatures, we were able to infer that the proportional contribution of microbial sources to CH4 summer emissions was ranges from 45 to 79 %. The finding indicates that microbial sources account for a substantial portion of CH4 summer emissions, consistent with estimates of 74–80 % derived from the observed correlation between CH4 and C2H6, which serves as a complementary tracer for fossil fuel sources.

Key role of initial interface on contact characteristics of Pd/p-GaN

Sequential three-layers of Pd/Pt/Au were deposited on p-GaN by magnetron sputtering under high vacuum (HV, ∼1.33 × 10−4 Pa) and ultra-high vacuum (UHV, ∼1.33 × 10−7 Pa) background conditions, respectively, for investigating the electrical contact properties. Linear I–V curves are observed in the samples deposited under the UHV conditions, whereas nonlinear I–V characteristics are obtained in the samples deposited under the HV vacuum. The depth profiles of X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) of the as-deposited samples were probed in detail. It is found that the amounts of O and OH as well as Pd oxide of the sample deposited under the HV background condition are larger than those of the sample deposited under the UHV conditions due to the higher residual gases such as water in the HV chamber. The oxide layer leads to an extra barrier, influencing the electrical characteristics of Pd/Pt/Au/p-GaN contact. This study demonstrates that metal deposition under the UHV environmental conditions can reduce and even prevent formation of oxide on p-GaN surface, and hence favor making a good ohmic contact on p-GaN.

Abstract 17840: Impact of Age and Sex on Mortality Trends of Infective Endocarditis in High-Income Countries

Introduction: Incidence of infective endocarditis may relate to a variety of background conditions which may vary depending on age groups and sex in a country. Its national trends in each age group and sex among high-income countries are still unclear. Hypothesis: We hypothesized that mortality from infective endocarditis is without sex differences and has improved in all age groups in both sexes among high-income countries. Methods: The WHO mortality database was analyzed to determine trends in mortality from infective endocarditis in the UK, Germany, France, Italy, Japan, Australia, the USA, and Canada from 2000 to 2021. Age-standardized and age-specific (≤49, 50-64, 65-79, ≥80 years) mortality rates per 100,000 persons in either sex were compared by using male-to-female ratio. Trends were analyzed using joinpoint regression. Results: Age-standardized mortality rates ranged from 0.40 (95%CI, 0.38, 0.42) in Japan to 1.29 (95%CI, 1.23, 1.35) in France in the most recent year and showed increasing trends in all studied countries except for Japan with decreasing trends and the USA with constant trends. During the studied periods, age-standardized mortality was male-dominant in all countries (p<0.0001). In age-specific analysis, three age groups (≤49, 50-64, 65-79 years) showed male dominancy in all countries while age group ≥80 years showed no sex difference except for France. Different mortality trends were identified between the sexes. In the USA, female mortality rates showed increasing trends in the age group ≤49 years while there were decreasing trends in the other three age groups. Male mortality rates showed constant trends in age groups ≤49 and 50-64 years while there were decreasing trends in the age group 65-79 and ≥80 years. Conclusions: Mortality from infective endocarditis was male-dominant in age groups less than 80 years old in all studied high-income countries. The different mortality trends between sexes among age groups need to be contextualized.

Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms

Existing stored product insect monitoring methods are time-consuming, costly, and often require specialized equipment or training. This study proposed an integrated insect monitoring system that employs a simple RGB camera and data-driven deep-learning models to detect and identify stored product insect species in warehouses, food facilities, and retail environments. Top-down images of six common insect species were acquired with a setup simulating a conceptualized probe-type monitor under varying lighting and background conditions. These images were preprocessed and manually annotated, resulting in an insect dataset of 2630 images with 14,509 labeled insects. A state-of-the-art computer vision model from YOLO family was selected, and six YOLO variants (YOLOv5s/m/l and YOLOv5s/m/l) were trained and evaluated on the insect dataset. All trained YOLO models delivered an impressive performance in terms of high detection accuracy (above 76% for mAP@[0.50:0.95]) and fast inference time (12–36 ms range). Subsequently, the best-performing YOLOv8l model was integrated and deployed on a mobile device, achieving a good detection performance with average detection speeds of 16 and 29 fps on a desktop computer and smartphone, respectively. The study provided an end-to-end framework for automatic and real-time insect detection and identification in a stored product environment. The lightweight variant of YOLO can be deployed on low-cost edge hardware, mobile devices, or cloud computing. The proposed system is relatively fast, accurate, and inexpensive for insect monitoring and may prove an alternate solution to existing methods. The system would serve as a decision-making tool for stored product facilities managers and can be easily scaled and adapted in a variety of stored product environments.

STOCKS OF POTENTIALLY TOXIC ELEMENTS IN THE GROUND COVER OF NORTHERN TAIGA PINE FORESTS UNDER AEROTECHNOGENIC POLLUTION

The paper presents the results of studying the distribution of stocks of potentially toxic elements (Ni, Cu) in subcrown spaces and “windows” of stands in medium-aged pine forests in the background area of the Kola Peninsula and that under environmental pollution by atmospheric emissions of the Severonickel Combine (Monchegorsk, Murmansk Region). On the basis of previously obtained data on the stocks of aboveground biomass of the components of the ground cover, mortmass of plant debris and forest litter and data of chemical analysis by means of atomic absorption spectrometry of leaves of dominant shrub species, aboveground parts of the moss Pleurozium schreberi (Brid.) Mitt, and lichen Cladonia stellaris (Opiz) Pouzar et Vězda, as well as plant waste and organogenic horizon (forest litter) of Al-Fe-humus podzols, Ni and Cu stocks in different components of forest ecosystems were calculated. It was found that the sharp decrease in atmospheric emissions by the Severonickel Combine over the last 20 years has not affected the level of habitat contamination, and the stock of heavy metals in the upper organogenic horizon of Al-Fe-humus podzols remains very high, especially in the impact zone, which does not even allow to start the process of restoration of the ground cover in this area. The stock of heavy metals in the plant waste consistently increases with the increase in the level of forest litter contamination when approaching the source of pollution, but the scale of this increase is significantly smaller in the mortmass compared to the litter. The pattern of distribution of heavy metal stocks in components of ground cover, plant waste and forest litter over the phytocenosis area is fundamentally different in background conditions and under aerotechnogenic pollution, which is due to differences in the accumulation of organic matter in different types of microsites. The ratio of Ni : Cu concentrations and stocks also differs in the soil-vegetation cover in pine forests of the background area and under the influence of aerotechnogenic pollution.

Dynamic Object Tracking Based on Triplet Relationship Guided Sampling Consensus Algorithm

Compared with the traditional methods based on the prediction of moving object trajectory, the method based on image registration has the advantage of not relying on the object motion equation to track the object. However, in the case of a large number of outliers, the image matching method has the defects of inadequate filtering of outliers or erroneous filtering of incomers due to the limitations of spatial constraints of feature matching pairs, which will make the tracking of flying objects inaccurate or impossible. During the flight, the object may be in a similar background and the image shooting angle is different, which makes the object imaging angle change, resulting in a large number of outlier interference. Because of the above situation, this paper adopted an improved TRESAC algorithm based on RANSAC, which used a feature matching method based on a triplet relationship to effectively filter outliers and then adopted an initial data subset selection strategy to increase its robustness. Experimental results show that the TRESAC algorithm can filter outliers quickly and accurately, and the object is still tracked effectively under the condition of similar background and pose change.

Background studies and normalization of signal events in the Mu2e experiment

The Mu2e experiment is currently being constructed at Fermilab to search for the neutrino-less conversion of negative muons into electrons in the field of an aluminum nucleus. The experiment aims at a sensitivity of four orders of magnitude higher than previous related experiments, which implies highly demanding accuracy requirements both in the design and during the operation. To achieve such a goal, two important tasks should be accomplished. First, it is essential to estimate precisely the particle yields and all the backgrounds that could mimic the monoenergetic conversion electron signal. Second, it is necessary to normalize the signal events accurately. The normalization of the signal events is planned to be done using a detector system made of an HPGe detector and a Lanthanum Bromide detector, which will measure the rate of muons stopped on the aluminum target by looking at the emitted characteristic X-and γ-rays of energies up to 1809 keV. Therefore, it is essential to evaluate the detector system's performance before the start of the actual experiment. In this study, the first task was addressed by an extensive campaign of Monte Carlo simulations to investigate the relevant parameters and their impact on the experiment's sensitivity. The second task was handled by taking advantage of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) pulsed Bremsstrahlung photon beam at the ELBE facility. The detector system was tested at the ELBE facility under timing and background conditions similar to the ones expected at the Mu2e experiment. The study presents and discusses the simulation results and the detector system testing campaign.

A method of convolutional neural network based on frequency segmentation for monitoring the state of wind turbine blades

Wind turbine blades are prone to failure due to high tip speed, rain, dust and so on. A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed. On the experimental measurement data, variational mode decomposition filtering and Mel spectrogram drawing are conducted first. The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network. Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients, considering the complexity of the real environment. The surfaces of Wind turbine blades are classified into four types: standard, attachments, polishing, and serrated trailing edge. The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%. In addition to support the differentiation of trained models, utilizing proper score coefficients also permit the screening of unknown types.

The early Paleocene (Danian) climate of Svalbard based on palaeobotanical data

AbstractThe early Paleocene (Danian) Barentsburg flora from the Firkanten Formation, Svalbard, provides a valuable insight into the environment and climate of the Arctic early in the warm Palaeogene prior to the Paleocene–Eocene Thermal Maximum (PETM) event. The flora includes c. 50 species of ferns, ginkgos, conifers and angiosperms. Angiosperms predominate and are represented by the families Platanaceae, Cercidiphyllaceae, Trochodendraceae, Hamamelidaceae, Fagaceae, Betulaceae, Nyssaceae and several taxa of uncertain taxonomic position. Palaeoclimate parameters, calculated using a new calibration of the Climate Leaf Analysis Multivariate Program (CLAMP), indicate that Svalbard in the early Paleocene was characterized by a temperate, maritime, humid, seasonal climate with cool mild winters and warm summers. Humidity was highest during winter months. Short‐term winter snow cover and light frosts were likely to have been frequent. The CLAMP analysis yields a mean annual temperature of 10.1 ± 1.98°С, a warmest month mean temperature of 19.2 ± 2.49°C and a coldest month mean temperature of 1.7 ± 3.24°С. The growing season lasted c. 6 months. Precipitation during this growth period was 572.4 ± 296.50 mm, while vapour pressure deficit and potential evapotranspiration indicate very high humidity year‐round, with summer being only slightly drier than winter. Taken together with data from other early Paleocene circum‐Arctic sites, these results point to highly uniform, temperate and extremely humid conditions around the margins of the early Cenozoic Arctic Ocean. This condition represents the warm background condition that dominated in the Arctic prior to the anomalous extreme warmth of the early Eocene.

Diversity, plasticity and asynchrony of actuarial and reproductive senescence in the Collembola Folsomia candida (Willem, 1902)

Flourishing recent comparative studies on senescence have revealed an uncovered diversity across the tree of life of the shapes of the age trajectories of mortality (actuarial senescence) and to a lesser extent of reproduction (reproductive senescence). Evolutionary theories have been called up to explain why some species suffer from positive senescence while others benefit from negligible or even negative senescence. We still know little about how, within a species, the shapes of the age trajectories of different traits are linked to each other and how they vary or covary depending on the genetic background and environmental conditions. We report here the results of an experimental study whose aim was to describe the actuarial and reproductive senescence in various genetically distinct lineages of a Collembola, a hexapod with indeterminate growth. We compared the age trajectories of individuals raised under two food regimes to study if and how the shapes of these age trajectories are plastically modified by environmental conditions. We found clear evidence of actuarial and reproductive senescence, especially when the springtails were fully fed. Clutch size increased as female become older and then declined progressively after reproduction reached a maximum. This age decline in fertility went along with a progressive slowing down of the pace of the egg-laying, a reduction of egg quality (more sterile eggs), while egg size undergoes little change with age. We found that the onset of reproductive decline occurred before the beginning of actuarial senescence, and show that escaping senescence is physiologically possible for certain lineages under dietary restricted conditions.

Between Traditionalism and Revisionism: Estlund and Renzo on the Obligation to Obey Orders to Fight in Unjust Wars

AbstractDavid Estlund and Massimo Renzo argue that, given the right background conditions, combatants are obligated to obey orders to fight in unjust wars, a thesis they put forward even as they recognize that this involves committing serious moral wrongs. Their views, then, fall between traditionalism and revisionism in the theory of just war. We argue that both Estlund and Renzo fail to adequately distinguish between binding and nonbinding serious morally wrong orders, that their views are incompatible with their assumed fact‐relative understanding of morality, and that they fail to establish that combatants are obligated to obey orders to fight in unjust wars. We conclude that just as untenable as these two middle‐ground positions is the traditionalist view.

Multiple interactions in new media sound art: A case study of ASMR

<p>The constant renewal and development of emerging technologies and new media offer more possibilities for the innovation and development of sound art. ASMR (autonomous sensory meridian response), as a new product of auditory culture, is an example of the innovative development of literature and art in the context of the fusion of new technologies and new media. The dependence on the medium is a prominent feature of this kind of auditory art, and the background conditions and unique aesthetic implications of ASMR auditory art offer the possibility of a shift towards auditory culture.</p>

Ventilation in a Residential Building Brings Outdoor NOx Indoors with Limited Implications for VOC Oxidation from NO3 Radicals.

Energy-efficient residential building standards require the use of mechanical ventilation systems that replace indoor air with outdoor air. Transient outdoor pollution events can be transported indoors via the mechanical ventilation system and other outdoor air entry pathways and impact indoor air chemistry. In the spring of 2022, we observed elevated levels of NOx (NO + NO2) that originated outdoors, entering the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility through the mechanical ventilation system. Using measurements of NOx, ozone (O3), and volatile organic compounds (VOCs), we modeled the effect of the outdoor-to-indoor ventilation of NOx pollution on the production of nitrate radical (NO3), a potentially important indoor oxidant. We evaluated how VOC oxidation chemistry was affected by NO3 during NOx pollution events compared to background conditions. We found that nitric oxide (NO) pollution introduced indoors titrated O3 and inhibited the modeled production of NO3. NO ventilated indoors also likely ceased most gas-phase VOC oxidation chemistry during plume events. Only through the artificial introduction of O3 to the ventilation duct during a NOx pollution event (i.e., when O3 and NO2 concentrations were high relative to typical conditions) were we able to measure NO3-initiated VOC oxidation products, indicating that NO3 was impacting VOC oxidation chemistry.

The filtration system of localization and glocalization for brand voice and brand communication

For over half a century, there has been ongoing debate among communication professionals and theorists about the need for localization or globalization in brand communication. Both sides have argued about the most effective strategy for global brands in the global market, with no clear consensus on the optimal approach. This paper aims to examine the impact of localization and glocalization on brand voice. A qualitative study design was used, based on a content analysis of case studies involving globalization, localization, and glocalization. The study is exploratory in nature and uses inductive reasoning. Through case study analysis, it became apparent that localized and glocalized brand communication and brand voice are influenced by various factors such as cultural background, religion, national beliefs, political environment, regional languages, media policies, political restraints, and socioeconomic conditions. These factors act as "filters" that may edit, clarify, censor, change, or leave brand communication unchanged. The author introduces the concept of a "filtration system" for localization and glocalization, which can be applied by multinational companies entering new markets to better fit the culture, habits, and values of the local area.

Aftershock Forecasting

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine-learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing but do not yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine-learning forecasts shows promise, and new machine-learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts. ▪Several countries issue real-time aftershock forecasts following significant earthquakes, providing information to aid response and recovery.▪Statistical models based on past aftershocks are used to compute aftershock probability as a function of space, time, and magnitude.▪Aftershock forecasting is advancing through better statistical models, constraints on physical triggering mechanisms, and machine learning.▪Large high-resolution earthquake catalogs provide an opportunity to advance physical, statistical, and machine-learning aftershock models.

Cloud microphysical response to entrainment and mixing is locally inhomogeneous and globally homogeneous: Evidence from the lab

Entrainment of dry air into clouds strongly influences cloud optical and precipitation properties and the response of clouds to aerosol perturbations. The response of cloud droplet size distributions to entrainment-mixing is examined in the Pi convection-cloud chamber that creates a turbulent, steady-state cloud. The experiments are conducted by injecting dry air with temperature (Te) and flow rate (Qe) through a flange in the top boundary, into the otherwise well-mixed cloud, to mimic the entrainment-mixing process. Due to the large-scale circulation, the downwind region is directly affected by entrained dry air, whereas the upwind region is representative of the background conditions. Droplet concentration (Cn) and liquid water content (L) decrease in the downwind region, but the difference in the mean diameter of droplets (Dm) is small. The shape of cloud droplet size distributions relative to the injection point is unchanged, to within statistical uncertainty, resulting in a signature of inhomogeneous mixing, as expected for droplet evaporation times small compared to mixing time scales. As Te and Qe of entrained air increase, however, Cn, L, and Dm of the whole cloud system decrease, resulting in a signature of homogeneous mixing. The apparent contradiction is understood as the cloud microphysical responses to entrainment and mixing differing on local and global scales: locally inhomogeneous and globally homogeneous. This implies that global versus local sampling of clouds can lead to seemingly contradictory results for mixing, which informs the long-standing debate about the microphysical response to entrainment and the parameterization of this process for coarse-resolution models.

A comparison of annual streamflow sensitivities to vegetation change and climate variability in fourteen large watersheds along climate zones in China

Extensive studies have revealed the impacts of climate variability and vegetation change on annual streamflow. However, the response intensity of annual streamflow varies among watersheds owing to differences in climate and watershed properties, which have rarely been assessed, especially for streamflow sensitivity to forest change. This calls for studies to evaluate annual streamflow sensitivity, defined as the annual streamflow response intensity (%) per unit of external change (%), to better interpret variations in annual streamflow responses among watersheds as well as their associated driving mechanisms. In this study, we estimated and compared annual streamflow sensitivities to vegetation change (Sv) and climate variability (Sc) by applying a five-step framework in 14 large watersheds in China and disentangled the contributions of their associated key drivers. We found that Sv was significantly greater than Sc, suggesting that annual streamflow was more sensitive to vegetation change than to climate variability in 11 watersheds. Alpine watersheds have the lowest Sv because of water plays a minor supportive role in vegetation growth under low effective precipitation and leaf area index, whereas higher topsoil salinity, which largely promotes water movement, contributes to a high Sv in temperate watersheds. Temperate watersheds also have a large Sc because their climate is dry and the resilience of their topography to climate variability is low. The relative contributions of the associated key drivers showed that Sv was determined by topography (36.35%), soil (33.68%), landscape patterns (12.22%), vegetation background (9.26%), and climatic conditions (8.49%). In contrast, Sc was mostly controlled by topography (54.39%) and climatic conditions (33.08%), followed by vegetation background (12.53%). This study highlights that the management paradigm for maintaining sustainable water resources should focus on both climate and vegetation changes considering the differences in watershed properties in a changing environment.

The quality of social interactions in young girls with nomophobia syndrome

ObjectiveNomophobia, as a growing phenomenon, has gradually affected the quality of social interactions and people's communication and has had considerable consequences on the quality of their real-world relationships. The present study aimed to identify the quality of social interactions and the factors underlying their formation based on the experiences of girls with nomophobia syndrome. MethodsThis qualitative study was conducted using the grounded theory approach. The participants were 20 girls with nomophobia symptoms, who were selected through purposive sampling from among all unmarried girls aged 20–30 living in Babol, Iran in 2022. The participants were screened using the Nomophobia Questionnaire (NMP-Q) and open-ended questions asked by the researcher. The data were collected through semi-structured interviews and participant observation. Data analysis was performed using Strauss and Corbin's (1998) approach until the data saturation point. ResultsThe data revealed 103 open codes, 24 axial codes, and 10 selective codes clustered into five main categories: Contextual conditions (family background and socio-economic conditions), causal conditions (individual and personality factors and individual's internal experiences from online interactions), intervening conditions (external facilitators and inhibitors), action and reaction (alternative strategies in social interactions), and consequences (attachment consequences, mobile loss consequences, and interactional consequences of online communication). ConclusionThis study identified five main categories of themes that can account for the process of nomophobia and understand how people with nomophobia interact socially. Following the developed model and the related factors, related professionals can make more accurate evaluations and then conduct more effective interventions with a focus on reducing and eliminating drivers and forces underlying, perpetuating, and reinforcing nomophobia and preventing and treating it based on individual characteristics of the affected people. Moreover, some suggestions were offered for further research and interventions in this field.

Efficient background subtraction method based on fast independent component analysis in video-surveillance

Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the de-mixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the de-mixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and post-processing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change detection datasets CDnet 2012 and CDnet 2014 using performance parameters such as recall, precision and experimental results show that our algorithm can detect effectively and accurately the moving objects in several background and foreground conditions compared to other methods in literature with real-time frame rate.