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This paper presents a compact intelligent camera with traffic surveillance and enforcement as a primary application field. The camera's unique features are 1/ embedded object detection of user-defined objects and 2/ excellent image quality achieved by a custom HDR image acquisition and tone mapping. Intelligent cameras are essential parts of equipment in production, control, security and primarily traffic surveillance and enforcement. In many applications, they serve as a versatile source of images processed elsewhere, and reduction of required processing and communication achieved by embedded (pre)processing within the intelligent camera is always welcome. The key blocks of the camera are implemented in FPGA to reduce the CPU load and thus the power requirements. The control software uses an embedded CPU running Linux. The camera is designed to provide key images that are interesting from the application point of view and to provide metadata. On the camera prototype, we observed total power consumption of 8 W and object detection accuracy > 99%. Application designers can extensively modify the camera design and functionality to harness various applications, such as traffic surveillance and enforcement, quality control, etc. [ABSTRACT FROM AUTHOR]

The procedure of reducing noise before analyzing the time series is important to get accurate outcomes when building models. Wavelet Shrinkage, consisting of thresholded wavelets, is a powerful mathematical technique for reducing the amount of noise that can be exposed. It has time series observations and selects the cutoff threshold appropriate for removing most of the noise. This paper used data on natural gas production in Iraq during (1981-2019) that contained non-stationary data, so the researcher must treat this problem before starting the analysis. Therefore, the researcher selected the model of time series analysis using Box-Jenkins models (ARIMA (p, d, q) models) and the method of wavelet shrinkage and the comparison between them based on some statistical criteria including the Akaike information criterion, Schwarz Bayesian information criterion, Bayesian information criterion, and Hannan-Quinn information criterion. Statistical programs such as Statgraphics XVII-X64 and MATLAB were used to analyze the data. The paper reached the efficiency of wavelet shrinkage filtering in treating the noise problem and obtaining efficient estimated models, in particular the wavelet shrinkage filter (Daubechies (db8)) with a soft threshold estimated by the fixed-form method and the possibility of linear models with lower one orders and higher efficiency for the obtained filtered observations compared to the corresponding models estimated from the original observations, that is, the suitable model for the data is a model ARIMA (0,1,1) for the data paved in a Daubechies method, it is preferable to do the waveform analysis of the data before analyzing the time series concerning the observations of natural gas production in Iraq. The preference was given to the model estimated by Box and Jenkins ARIMA (0,1,1) model using wavelet shrinkage Daubechies (db8) wavelet reduction. [ABSTRACT FROM AUTHOR]

Crop forecasting is a formidable challenge. The national and state governments need such predictions before harvesting for various policy decisions relating to storage, distribution, pricing, marketing, importexport etc. In this paper, univariate forecasting models such as random walk, linear trend, quadratic trend, exponential trend, S-curve trend, simple exponential smoothing, Holt's linear exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are used to predict vegetable production in the United Arab Emirates. For empirical analysis, a set of 9 different vegetable groups have been considered, contingent upon availability of required data. Annual data from 1974-75 to 2018-19 was used to forecast the next five years since 2019. Suitable models were selected based on the lowest RMSE and minimum of AIC criterion. Model diagnostic checking was done through Runs above and below the median, Runs up and down and Ljung-Box tests on ACF and PACF of residual terms. For onions and green shallots linear trend model was selected as the best fit, whereas simple exponential smoothing model was most suitable in cauliflowers and broccoli, pumpkins, squash and gourds and spinach. The optimum model obtained for forecasting carrots and turnips was Holt's linear exponential smoothing model and ARIMA model was the best fit for the rest of vegetable groups. [ABSTRACT FROM AUTHOR]

Copyright of Archives of Criminology / Archiwum Kryminologii is the property of Polish Academy of Sciences, Institute of Legal Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PCN/Bi0-BWO/Zn(CH3COO)2-ACF composite was compounded for the removal of n-hexane in this paper. Characterization results show that the composite was successfully prepared and the light response ability of the catalyst was improved. The photocatalytic properties of the composite were also tested; the removal rate of PCN/Bi0-BWO/Zn(CH3COO)2-ACF composite can reach more than 85% in 360 min. The main reason for the improvement of the performance of the composites was that the addition of phosphorus and the formation of Z-scheme heterojunction increased the efficiency of electron–hole separation on the surface of the catalyst. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world's economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 46 out 48 metrics (in forecasting future values), i.e., on 95.8% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems. [ABSTRACT FROM AUTHOR]

In large data center, virtual machines are connected dynamically and data are moved between the physical machines and the virtual machines. With this environment, the Network Layer transfers the packets to the Transport Layer. To improve the security and to increase the speed of the packet transmission in the network layer, packet-filtering is carried out by firewalls based on the decision of routers namely Network addresses, Ports or Protocols. Earlier packet filtering techniques are IP data-gram, Queuing and Scheduling. In order to overcome the limitation in security mechanisms in the above filtering techniques, hashing algorithms are used for analyzing the elements in the packets that are stored in the Hash tables. The hashing algorithms are used in Quotient filter, Bloom filter, Cuckoo filter and Adaptive Cuckoo Filter (ACF). AMDS is termed as An Approximate Membership Data Structures were used to store the fingerprints in cuckoo hash tables. In this paper an attempt is made to improve the searching strategy in Cuckoo filter. Spring constant factor and Leivy Flight theorem is introduced in the searching strategy for best solution identification path. A suit of benchmark functions are employed to verify the performance of network with respect to time, speed and the memory space in the packets transmission. The performance of the Enhanced searching strategy gives better result when compared to ACF. [ABSTRACT FROM AUTHOR]

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection. [ABSTRACT FROM AUTHOR]

All kinds of vehicles have different ratios of width to height, which are called the aspect ratios. Most previous works, however, use a fixed aspect ratio for vehicle detection (VD). The use of a fixed vehicle aspect ratio for VD degrades the performance. Thus, the estimation of a vehicle aspect ratio is an important part of robust VD. Taking this idea into account, a new on-road vehicle detection system is proposed in this paper. The proposed method estimates the aspect ratio of the hypothesized windows to improve the VD performance. Our proposed method uses an Aggregate Channel Feature (ACF) and a support vector machine (SVM) to verify the hypothesized windows with the estimated aspect ratio. The contribution of this paper is threefold. First, the estimation of vehicle aspect ratio is inserted between the HG (hypothesis generation) and the HV (hypothesis verification). Second, a simple HG method named a signed horizontal edge map is proposed to speed up VD. Third, a new measure is proposed to represent the overlapping ratio between the ground truth and the detection results. This new measure is used to show that the proposed method is better than previous works in terms of robust VD. Finally, the Pittsburgh dataset is used to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Review of Policy Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the reliability of thermoplastic ACF in the process of OGS touch panel manufacturing was studied. The fourier transform infrared spectroscopy (FTIR), volume removal gel permeation chromatography (GPC) and weighing method research were used to characterize the structure of functional group, molecular weight and the change of absorption rate of ACF colloid materials under different curing condition. The thermal decomposition temperature and glass transition temperature were characterized by thermo gravimetric and differential thermal (TG-DSC) analysis. The effect of high temperature and high humidity on the bond strength of ACF was studied by the failure test of adhesion mode. The results showed that, under high temperature and high humidity environment, because of the erosion of water molecules, the plasticity of ACF materials became stronger, the glass transition temperature (Tg) decreased and the thermal stability became worse. As a result, the bond strength of the material was greatly influenced by the humidity. In addition, the comparison of different aging time pull out force test after the thermoplastic ACF sample scanning electron microscope (SEM) images could be indicated that the peeling effect of ACF with damp heat aging time was more and more obvious. [ABSTRACT FROM AUTHOR]

In intelligent transport systems, detection and identification of vehicle types enact a substantial role. In this context, this paper addresses the detection and pose classification of a specific vehicle type: auto-rickshaws which have been heavily neglected by the publicly available vehicle datasets, but remains the most commonly used and cheap form of transportation in south Asian countries. Here, we introduce a dataset for auto-rickshaws which consists of instances of varying shape, orientation, size, scale, colour, viewpoint and many more. Further, we carry out a detailed analysis on the performance of state-of-the-art detection algorithms based on both hand-designed and deep features on the proposed dataset. The introduction of pose classification along with the detection eventually results in better understanding of road scenes involving auto-rickshaws. As a matter of fact, we came up with revisions for the currently employed detection algorithms to achieve a low miss rate on the validation sets. It is evident that the findings of this study are tangible and enormously consequential to the road scene understanding and intelligent transportation of developing countries where auto-rickshaws play a pivotal role in public transportation. [ABSTRACT FROM AUTHOR]

Understanding policy change mechanisms has been a key question for scholars of public policy and collective action. However, policy scholarship mostly ignores civil society-based explanations of policy processes. In order to address this gap, this study combines the Advocacy Coalition Framework with networked collective action perspectives and analyzes a successful case of mobilization of women's rights organizations in Turkey to reverse a bill on child marriage. Study findings suggest that advocacy coalitions are not static entities. When different issues in a policy subsystem are invoked, the structure of inter-coalition networks can change substantially and these variations in inter-coalition interactions may have consequences for influencing policy change. Moreover, this paper argues that extensive street protests and online campaigns by civil society organizations have the capacity to boost the bargaining power of minority coalitions, especially in contexts that lack multiple formal venues for making policy claims. [ABSTRACT FROM AUTHOR]

Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage. Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries. Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings. [ABSTRACT FROM AUTHOR]

The Sun produces a fair amount of solar energy caused by the different solar activities that affect the terrestrial environment. The lowest part of the sun's atmosphere is photosphere gases that can be recognized by the disk of the sun and above parts are the chromospheres and corona. The three parts of the structure of the sun's atmosphere crucial to understand the nature of solar activities. The analysis of the study segregated monthly cyclic data of green line emission corona 530.03λ nanometers by appropriate stochastic models. After identifying stationary of each cycle along with total data is observed and generate autoregressive AR (p), MA (q) and ARIMA (p, d, q) models have revealed a linear difference and ARMA for a total span based on minimization of Akaike information criterion and Schwarz Beysian criterion. The parameters are examined by ACF and the PACF for different cyclic duration of Corona from 1944 to 2008. The models (0,1,1), (1,1,1) and (2,1,0) for cycles and (1,0,1) for total time series data were found the significance. The models obtained in this paper may be useful to understand and forecast the Coronal time series data. [ABSTRACT FROM AUTHOR]

The demand for water in the kingdom of Saudi Arabia increases due to population growth urbanization, industrialization and the expansion of irrigated agricultural lands. Analyzing the current situation of the water consumption and predicting water demand in the future are essential for the authorities to improve and manage production quantities. The objective of this paper is to find the best model for water consumption in Saudi Arabia and use the fitted model for forecasting. A data for water consumption between January 2010 and July 2017 were analyzed using a multiplicative seasonal autoregressive integrated moving average model (SARIMA). Through our research, we concluded that the best SARIMA model for fitting water consumption in Saudi Arabia is SARIMA (1,0,1) x (1,1,2)12. [ABSTRACT FROM AUTHOR]

Anisotropic conductive film (ACF) has been used for more than 30 years in flat panel display (FPD) module package. In this paper, the technical trends and development of ACF are described. Among all of them, ACF material design for flexible substrate bonding is critical for the development of flexible display. According to different process flow of flexible AMOLED, flexible substrate bonding technologies can be classified as 'PI/Glass' and 'Film type' substrate bonding. That is to say the flexible substrate bonding process can be implemented before and after PI substrate separation from the original carrier glass substrate. Based on the current results, soft conductive particles of ACF are developed and chosen in the PI/Glass substrate bonding. For the film type substrate bonding, especially in IC bonding, the design of ACF is very important in avoiding the insufficient conductive particle deformation issue induced by the sink of plastic substrate. Here, we present a feasible and simple flexible substrate bonding technology to package driver IC, FPC or COF for flexible AMOLED application. [ABSTRACT FROM AUTHOR]

In coal seam gas (CSG) wells, water is periodically removed from the wellbore in order to keep the bottom-hole flowing pressure at low levels, facilitating the desorption of methane gas from the coal bed. In order to calculate gas flow rate and further optimize well performance, it is necessary to accurately monitor the liquid level in real-time. This paper presents a novel method based on autocorrelation function (ACF) analysis for determining the liquid level in CSG wells under intense noise conditions. The method involves the calculation of the acoustic travel time in the annulus and processing the autocorrelation signal in order to extract the weak echo under high background noise. In contrast to previous works, the non-linear dependence of the acoustic velocity on temperature and pressure is taken into account. To locate the liquid level of a coal seam gas well the travel time is computed iteratively with the non-linear velocity model. Afterwards, the proposed method is validated using experimental laboratory investigations that have been developed for liquid level detection under two scenarios, representing the combination of low pressure, weak signal, and intense noise generated by gas flowing and leakage. By adopting an evaluation indicator called Crest Factor, the results have shown the superiority of the ACF-based method compared to Fourier filtering (FFT). In the two scenarios, the maximal measurement error from the proposed method was 0.34% and 0.50%, respectively. The latent periodic characteristic of the reflected signal can be extracted by the ACF-based method even when the noise is larger than 1.42 Pa, which is impossible for FFT-based de-noising. A case study focused on a specific CSG well is presented to illustrate the feasibility of the proposed approach, and also to demonstrate that signal processing with autocorrelation analysis can improve the sensitivity of the detection system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a constrained convex sets-based method for designing ultra-wideband (UWB) pulse. The UWB pulses designed using earlier proposed methods either do not fit well in UWB spectral mask or require extra processing circuitry for pulses to fit in the UWB spectral mask. The proposed constraint sets-based method is flexible and optimum for the UWB mask. The pulse designed by the proposed method is Federal Communication Commission compliant and has an autocorrelation function similar to the Gaussian pulse. The bit error rate performance of time hopping binary phase shift keying using the proposed method and some of the existing methods such as Norman and Lee pulses and sixth derivative Gaussian pulse is analyzed in additive white Gaussian noise and Saleh-Valenzuela channels. Pulse designed using the proposed method outperforms existing pulse design, and the same is verified through simulations. The proposed algorithm is flexible and can also be used to mitigate narrow band interference, multiband UWB pulse generation and any other Federal Communication Commission (or other regulatory body) compliant spectrum mask by modifying the parameter of the proposed constraint set(s). [ABSTRACT FROM AUTHOR]

Recently, drones have been regularly used to aid in search and rescue in places where it is normally to carry out some of the early forensic victim localization. There are many suitable human detectors for drone use, such as Histogram Oriented Gradient (HOG), You Only Looks Once (YOLO), and Aggregate Channel Features (ACF). In this paper, the height of the aerial images is analyzed for its effect on the accuracy of the detection. This works compares ACF, YOLO MobileNet, and YOLO ResNet50 using a different set of aerial images varying at 10m, 20m, and 30m heights. The results show that in a single-model test, with our proposed bounding-box standardization, YOLO MobileNet achieves significant increase in test precision (AP), with 0.7 AP recorded. For single-model test, YOLO MobileNet yield best AP using 20m training data where it obtained AP of 0.88 (10m test height), 0.82 (20m test height), and 0.91 (30m test height). [ABSTRACT FROM AUTHOR]

India is the major producer of Areca nut in the world. Volatile demand and price are the major challenges for the Areca nut growers in India. The use of time series models to manage the price risk has become the interest of academicians today. This paper deals with developing an appropriate model to predict the prices of a new variety of Areca nut in Karnataka using monthly price data for the period January 2009 to December 2018. Box Jenkins ARIMA methodology is used to develop the model. Along with ARIMA estimates, log-likelihood, Akaike's information criterion (AIC) and Bayesian (BIC) information criterion statistics are also estimated to decide on the appropriate model. ACF and PACF correlograms for residuals of ARIMA are used to do the diagnostic check of the selected ARIMA model. Appropriate model to forecast the new variety Areca nut price is ARIMA (3, 1, 3). [ABSTRACT FROM AUTHOR]

Heart failure (HF) has been declared as global pandemic and current therapies are still ineffective, especially in patients that develop concurrent cardio-renal syndrome. Considerable attention has been focused on the nitric oxide (NO)/soluble guanylyl cyclase (sGC)/cyclic guanosine monophosphate (cGMP) pathway. In the current study, we aimed to investigate the effectiveness of sGC stimulator (BAY41-8543) with the same mode of action as vericiguat, for the treatment of heart failure (HF) with cardio-renal syndrome. As a model, we chose heterozygous Ren-2 transgenic rats (TGR), with high-output heart failure, induced by aorto-caval fistula (ACF). The rats were subjected into three experimental protocols to evaluate short-term effects of the treatment, impact on blood pressure, and finally the long-term survival lasting 210 days. As control groups, we used hypertensive sham TGR and normotensive sham HanSD rats. We have shown that the sGC stimulator effectively increased the survival of rats with HF in comparison to untreated animals. After 60 days of sGC stimulator treatment, the survival was still 50% compared to 8% in the untreated rats. One-week treatment with sGC stimulator increased the excretion of cGMP in ACF TGR (109 ± 28 nnmol/12 h), but the ACE inhibitor decreased it (-63 ± 21 nnmol/12 h). Moreover, sGC stimulator caused a decrease in SBP, but this effect was only temporary (day 0: 117 ± 3; day 2: 108 ± 1; day 14: 124 ± 2 mmHg). These results support the concept that sGC stimulators might represent a valuable class of drugs to battle heart failure especially with cardio-renal syndrome, but further studies are necessary. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interference of the background light. To reduce this kind of interference, we propose a novel method called annular convolution filtering (ACF). In our method, the region of interest (ROI) in the spot image is first searched by using the statistical properties of pixels. Then, the annular convolution strip is constructed based on the energy attenuation property of the laser and the convolution operation is performed in the ROI of the spot image. Finally, a feature similarity index is designed to estimate the parameters of the laser spot. Experiments on three datasets with different kinds of background light show the advantages of our ACF method, with comparison to the theoretical method based on international standard, the practical method used in the market products, and the recent benchmark methods AAMED and ALS. [ABSTRACT FROM AUTHOR]

Copyright of Politics & Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA model was analyzed and compared with autoregressive (AR) and autoregressive integrated moving average (ARIMA) for the data obtained from the NASA Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis. The statistical analysis of the autocorrelation function (ACF), partial autocorrelation function (PACF), normal quantile–quantile (normal Q-Q) plot, density of the residuals, and variance of normal variable w(t) shows that ARMA (1,1) fits better than AR (1) and ARIMA (1, d, 1) for d = 1, 2.

This paper attempts to investigate the markup's evolution at the firm level for Iran's economy for the period 2003–2013, using Iran's manufacturing plant panel data of firms with ten or more workers. Following the ACF approach, a translog production function has been estimated for each industry. Then, after estimating each firm's labor elasticity trend, we used the DLW approach to estimate and analyze the distributional variation of a markup over time and among Iran's industries. We plot the kernel density of markups for the first and last year in the data and show that the last year's markup distribution has become fattened in its upper tail compared to the first year. Subsequently, it is demonstrated that markups' distribution considerably differs from one industry to another, and each industry has a different weighted average markup trend. Finally, it is illustrated that exporting firms have charged more markup compared to domestic ones, and sanctions have positively affected this markup premium.

The CO2 emitted by coal-fired power plants is the main factor leading to global warming, and the capture of CO2 in the flue gas of power plants is still the main task at this stage. Many adsorbents have been developed to capture CO2 in high-temperature flue gas, but some materials are complicated to synthesize or the cost is too high. Here, we used low-cost raw materials activated carbon fiber and PEI, and used green synthesis to synthesize new adsorbents in order to capture CO2 in high-temperature flue gas of a power plant. To improve the performance of highly porous activated carbon fiber (ACF) in CO2 capture and separation, an organic polymer polyethylenimine (PEI) was loaded successfully into the oxidized ACF. The modified adsorbent was tested by FT-IR, XRD and SEM, and the CO2 adsorption capacity and CO2/N2 selectivity were analyzed. The results showed that the as-synthesized PEI-modified adsorbent has a CO2 adsorption capacity of 2.5 mmol/g, which is 1.7 times better than that of the pristine ACF adsorbent (1.5 mmol/g), at 1 bar and 333 K, and it has excellent CO2/N2 selectivity, as calculated by ideal adsorption solution theory (IAST). These data indicate that PEI was loaded successfully into the oxidized ACF. In addition, the dual site Langmuir isotherm equation and Langmuir isotherm equation can be in good agreement with the adsorption curves of CO2 and N2. In comparison with other composite adsorbents, the preparation process of the present new adsorbent is highly environmentally friendly, the synthesis method is simple and the cost is low, which demonstrates potential applications in the separation of CO2 from the flue gas of power plants. [ABSTRACT FROM AUTHOR]

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p < 0:05) indicating that the previous day value of the stochastic term also had a significant effect on the present value of meteorological parameters in the environment. It was observed that in all the fitted models except for wind direction in Conakry and rainfall in Abidjan have all their Autoregressive term of order 1 significant (p < 0:05) which implies that previous day value of these meteorological parameter had a significant effect on the present day value of the parameters. Therefore, the forecast model indicates that maximum temperature are expected in February, March, April, and June while minimum temperatures in January, August, December. Although, the selected models cannot forecast the precise air temperature, this can also provide information that can be of help to create tactics for appropriate preparation of farming which can be used as tools for effective environmental preparation and policymaking. [ABSTRACT FROM AUTHOR]

India is the major producer of Areca nut in the world. Volatile demand and price are the major challenges for the Areca nut growers in India. The use of time series models to manage the price risk has become the interest of academicians today. This paper deals with developing an appropriate model to predict the prices of a new variety of Areca nut in Karnataka using monthly price data for the period January 2009 to December 2018. Box Jenkins ARIMA methodology is used to develop the model. Along with ARIMA estimates, log-likelihood, Akaike’s information criterion (AIC) and Bayesian (BIC) information criterion statistics are also estimated to decide on the appropriate model. ACF and PACF correlograms for residuals of ARIMA are used to do the diagnostic check of the selected ARIMA model. Appropriate model to forecast the new variety Areca nut price is ARIMA (3, 1, 3)

This paper presents a comparative performance study of several time domain features for voiced/non-voiced classification of speech. Five classification schemes have been developed by combining one or two features amongst: Energy (E), Zeros Crossing Rate (ZCR), Autocorrelation Function (ACF), Average Magnitude Difference Function (AMDF), Weighted ACF (WACF), and the Discrete Wavelet Transform (DWT). The development of these classifiers was based on the selection of the lowest number of time domain features which allow voicing decision without the need of any frequency transformation or pre processing approaches. The performance of the classifiers has been evaluated on speech data extracted from the TIMT database. Two different noise types: White and babble, taken from the NOISEX92 database have been incorporated to validate the developed classification schemes in noisy environments. An overall ranking of these classifiers for high and low Signal to Noise Ratios (SNRs) have been established based on the average value of the Percentage of classification accuracy (Pc). [ABSTRACT FROM AUTHOR]

Using the Agent-based Computational Finance (ACF) method, we build an artificial stock market with heterogeneous adaptive investors and investigate the evolutionary and interacting relationship between rational investors and irrational investors. We find that with strategy switching, there is a symbiosis among the three kinds of investors in the ACF experiments, although the rational investors are often dominant in the market. And our main findings are robust with agents' scale. When the initial values change, the market ecology achieves new equilibrium. [ABSTRACT FROM AUTHOR]

Non-Governmental Organizations/Nonprofit Organizations (NGOs/NPOs) are progressively becoming an essential source for government agencies in education worldwide. Developing countries such as Ghana have opened the way through regulations and policies for NGOs/NPOs to contribute to the educational system. Nevertheless, perceived threats and confrontations hinder their ability to support the Ministry of Education (MoE) and partner leaders to fulfill their duties in assisting the government in realizing comprehensive universal primary education. This qualitative case study responded to stakeholders' perceptions of NGOs/NPOs influence on Ghana's educational system to better comprehend the dynamic and power of NGOs/NPOs on education in Ghana's rural communities. The study used the Advocate Coalition Framework; advocacy coalition theory brings together sub-systems and political actors interested in policy changes or shifts for a common goal to organize effective ways for the changes or shifts. Ten volunteers participated in the interview focusing on schools in rural Ghana. The findings indicated NGOs/NPOs are a complementary authority to MoE and partner leaders. According to the data analysis and findings, MoE and partner leaders need to form an intentional coalition with NGOs/NPOs for interventions to define the role descriptions and functions of programs and projects to achieve the interventions. NGOs/NPOs resources for MoE and partner leaders are necessary to improve the quality of teaching and learning in schools. Decision-making bodies of MoE and partner leaders may benefit from this study in policymaking for interventions of programs and projects supervision, implementation, and monitoring and evaluation for positive social change. [ABSTRACT FROM AUTHOR]

Abstract: Frequency stepping techniques are commonly used in modern radar system to get high range resolution with the disadvantage that its autocorrelation function (ACF) yield undesirable “grating lobes”. Wider mainlobe deteriorates the range resolution capability of the waveform and higher peak sidelobe either hides the small targets or causes the false target detection. Several techniques have been used to choose the parameters of linear frequency modulated (LFM) pulse train to suppress the grating lobes without paying much attention to the mainlobe width and peak sidelobe level. In this paper a multiobjective optimization (Nondominated Sorting Genetic Algorithm-II (NSGA-II)) approach is proposed to optimize the parameters of the LFM pulse train to achieve reduced grating lobes, low peak sidelobe level and narrow mainlobe width. The optimization problem has been studied in two different ways: first one is associated with the reduction of grating lobes and the minimization of peak sidelobe level of the ACF with constraints and second one is related to the minimization of the peak sidelobe level and mainlobe width of the ACF with constraints. Simulation studies have been carried out to justify the potentiality of the proposed approach. [Copyright &y& Elsevier]

Using a time series model to mimic an observed time series has a long history. However, with regard to this objective, conventional estimation methods for discrete-time dynamical models are frequently found to be wanting. In fact, they are characteristically misguided in at least two respects: (i) assuming that there is a true model; (ii) evaluating the efficacy of the estimation as if the postulated model is true. There are numerous examples of models, when fitted by conventional methods, that fail to capture some of the most basic global features of the data, such as cycles with good matching periods, singularities of spectral density functions (especially at the origin) and others. We argue that the shortcomings need not always be due to the model formulation but the inadequacy of the conventional fitting methods. After all, all models are wrong, but some are useful if they are fitted properly. The practical issue becomes one of how to best fit the model to data. Thus, in the absence of a true model, we prefer an alternative approach to conventional model fitting that typically involves one-step-ahead prediction errors. Our primary aim is to match the joint probability distribution of the observable time series, including long-term features of the dynamics that underpin the data, such as cycles, long memory and others, rather than short-term prediction. For want of a better name, we call this specific aim feature matching. The challenges of model misspecification, measurement errors and the scarcity of data are forever present in real time series modeling. In this paper, by synthesizing earlier attempts into an extended-likelihood, we develop a systematic approach to empirical time series analysis to address these challenges and to aim at achieving better feature matching. Rigorous proofs are included but relegated to the Appendix. Numerical results, based on both simulations and real data, suggest that the proposed catch-all approach has several advantages over the conventional methods, especially when the time series is short or with strong cyclical fluctuations. We conclude with listing directions that require further development. [ABSTRACT FROM AUTHOR]

Abstract: The microimage of activated carbon fibers (ACFs) obtained from SEM or TEM has low signal to noise ratio (SNR). And the objects in image overlapped together. Image processing methods based on clustering analysis and watershed transform were applied to explore the macropores morphology structure of ACFs. The microimage shows that abundant round-shaped macropores with pore size around 50–200nm, some of which cluster in the form of ring, distribute on ACF surface. Through clustering analysis, we calculated the surface area of macropores and their distributions. The results showed that the pores characterize within three regions, (1) the pore objects smaller than 30 pixels distributing nearly uniformly; (2) those with pixels between 30 and 165 peaking at lower region and (3) those larger than 165 pixels having fewer numbers and provide the main part of clusters. According to watershed transform segmentation analysis, the overlapped pores in one cluster were separated from each other. The number and other morphology parameters of macropores are calculated automatically and accurately in this paper. [Copyright &y& Elsevier]