Average Rate Research Articles

Experimental investigation of silica gel coated heat exchanger air conditioning system based on graphene nanofluid solar collector

ABSTRACT The solar driven desiccant air conditioning systems can play a vital role to decrease the dependency on conventional VCR system for air conditioning application. In present work, a solar-driven desiccant air conditioning system is developed. It consists of two silica gel coated fin tube heat exchangers (SGCHE) for continuous dehumidification and regeneration processes. A solar air heater based on parabolic trough collector integrated with heat pipe is used to regenerate the silica gel. A U-type heat exchanger is used for sensible cooling after dehumidification. The 0.1 vol. % concentration of graphene nanoparticle with ethylene glycol has been used in the heat pipe. The experimental results confirmed that use of graphene nanofluid instead of conventional fluid in heat pipe plays a vital role to supply hot air easily in short span of time and helps to reduce the cycle time. The present system performance has been analyzed in terms of moisture removal rate, regeneration rate, adsorption rate, and cooling capacity of system. The average regeneration and adsorption rate of SGCHE are 0.022 kg/hr and 0.033 kg/hr, respectively. The average cooling capacity of SGCHE and U-type heat exchanger are 117 W and 19 W, respectively.

Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing

In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users’ Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this article, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%.

Mapping the Landscape of Obesity Effects on Male Reproductive Function: A Bibliometric Study.

Due to changes in lifestyle and dietary habits, the global population with obesity is increasing gradually, resulting in a significant rise in the number of individuals having obesity. Obesity is caused by an imbalance between energy intake and consumption, leading to excessive fat accumulation, which interferes with normal human metabolism. It is also associated with cardiovascular disease, metabolic syndrome, male reproductive endocrine regulation disorders, systemic and local inflammatory reactions, excessive oxidative stress, and apoptosis. All these factors can damage the internal environment for sperm generation and maturation, resulting in male sexual dysfunction, a decline in sperm quality, and lower fertility. This study analyzes the trends and priorities of the effects of obesity on male reproductive disorders from a bibliometric perspective. This study uses the Web of Science as the statistical source, covering all time spans. Tools like Web of Science, VOSviewer, and CiteSpace are used to analyze countries, institutions, authors, journals, and keywords in the field. Total publications, total citations, and average number of citations are selected for statistics. The results show that the research on the impact of obesity on male reproductive function can be roughly divided into three stages: the initial stage, the slow development stage, and the rapid development stage. Our statistical scope includes 463 highly relevant articles that we have screened. We found that the journal with the most publications in this field is Andrologia, and the institution with the highest total citations is the University of Utah. The most influential countries, institutions, and authors in this field are the United States, the University of Utah, and Carrell, Douglas. Currently, research related to the impact of obesity on male reproduction focuses mainly on three aspects: biochemistry, molecular biology, and reproductive biology. The keyword explosion results indicate that sperm, obesity, and male reproduction are at the forefront and trends of future research in this field. There has been a shift from basic biochemical and molecular research to research on molecular mechanisms relying on omics technologies. However, we have observed that the number of papers published in 2022 is lower than in 2021, indicating a growth interruption during this period. Considering that this deviation may be due to the impact of the COVID-19 pandemic, it may hinder the progress of certain experiments in 2022. In recent years, China has rapidly developed research in this field. However, the average citation rate is relatively low, indicating the need for Chinese scholars to improve the quality of their articles further. Based on our research and in the context of global obesity, men are at risk of increased infertility. Addressing this issue relies on our continued research into the mechanisms of obesity-related male reproductive disorders. Over the past forty-three years, with the contributions of scientists worldwide, research in this field has flourished. The impact of obesity on male reproductive disorders has been extensively studied. Currently, research in this field primarily focuses on male sperm function, sperm quality, and the effects or mechanisms of cells on male reproduction. Future trends in this field should concentrate on the relationship between male fertility and energy metabolism, as well as the endocrine function of adipose tissue. This study comprehensively analyzes the current research status and global trends in obesity and male reproductive disorders. We also discuss the future developments in this field, making it easier for researchers to understand its developmental history, current status, and trends, providing valuable reference for effective exploration in this area.

The Association of County-level Prostate-specific Antigen Screening with Metastatic Prostate Cancer and Prostate Cancer Mortality

Background and objectiveThere exists ongoing debate about the benefits and harms of prostate-specific antigen (PSA) screening for prostate cancer. This study sought to evaluate the association of county-level PSA screening rates with county-level incidence of metastatic prostate cancer and prostate cancer mortality in the USA. MethodsThis ecological study used data from the 2004–2012 Behavioral Risk Factor Surveillance System (BRFSS) to build a multilevel mixed-effect model with poststratification using US Census data to estimate county-level PSA screening rates for all 3143 US counties adjusted for age, race, ethnicity, and county-level poverty rates. The exposure of interest was average county-level PSA screening rate from 2004 to 2012, defined as the proportion of men aged 40–79 yr who underwent PSA screening within the prior 2 yr. The primary outcomes were county-level age-adjusted incidence of regional/distant prostate cancer during 2015–2019 and age-adjusted prostate cancer mortality during 2016–2020. Key findings and limitationsA total of 416221 male BRFSS respondents aged 40–79 yr met the inclusion criteria and were used in the multilevel mixed-effect model. The model was poststratified using 63.4 million men aged 40–79 yr from all 3143 counties in the 2010 Decennial Census. County-level estimated PSA screening rates exhibited geographic variability and were pooled at the state level for internal validation with direct BRFSS state-level estimates, showing a strong correlation with Pearson correlation coefficients 0.77–0.90. A 10% higher county-level probability of PSA screening in 2004–2012 was associated with a 14% lower county-level incidence of regional/distant prostate cancer in 2015–2019 (rate ratio 0.86, 95% confidence interval [CI] 0.85–0.87, p < 0.001) and 10% lower county-level prostate cancer mortality in 2016–2020 (rate ratio 0.90, 95% CI 0.89–0.91, p < 0.001). Conclusions and clinical implicationsIn this population-based ecological study of all US counties, higher PSA screening rates were associated with a lower incidence of regional/distant prostate cancer and lower prostate cancer mortality at extended follow-up. Patient summaryUS counties with higher rates of prostate-specific antigen (PSA) screening had significantly lower rates of metastatic prostate cancer and prostate cancer mortality in subsequent years. These data may inform shared decision-making regarding PSA screening for prostate cancer.

Substantial Warming of the Atlantic Ocean in CMIP6 Models

Abstract The storage of anthropogenic heat in oceans is geographically inhomogeneous, leading to differential warming rates among major ocean basins with notable regional climate impacts. Our analyses of observation-based datasets show that the average warming rate of 0–2000-m Atlantic Ocean since 1960 is nearly threefold stronger than that of the Indo-Pacific Oceans. This feature is robustly captured by historical simulations of phase 6 of Coupled Model Intercomparison Project (CMIP6) and is projected to persist into the future. In CMIP6 simulations, the ocean heat uptake through surface heat fluxes plays a central role in shaping the interbasin warming contrasts. In addition to the slowdown of the Atlantic meridional overturning circulation as stressed in some existing studies, alterations of atmospheric conditions under greenhouse warming are also essential for the increased surface heat flux into the North Atlantic. Specifically, the reduced anthropogenic aerosol concentration in the North Atlantic since the 1980s has been favorable for the enhanced Atlantic Ocean heat uptake in CMIP6 models. Another previously overlooked factor is the geographic shape of the Atlantic Ocean which is relatively wide in midlatitudes and narrow in low latitudes, in contrast to that of the Indo-Pacific Oceans. Combined with the poleward migration of atmospheric circulations, which leads to the meridional pattern of surface heat uptake with broadly enhanced heat uptake in midlatitude oceans due to reduced surface wind speed and cloud cover, the geographic shape effect renders a higher basin-average heat uptake in the Atlantic.

Event-Based Intermittent Formation Control of Multi-UAV Systems Under Deception Attacks.

This article investigates the problem of event-based intermittent formation control for multi-UAV systems subject to deception attacks. Compared to the available research studies on multi-UAV systems with continuous control strategy, the proposed intermittent control strategy saves a large amount of computation resources. An average method is introduced in developing the event-triggered mechanism (ETM) such that the amount of unexpected triggering events induced by uncertain disturbances is greatly reduced. Moreover, such a mechanism can further decrease the average data-releasing rate, thereby alleviating the burden of network bandwidth. Sufficient conditions for multi-UAV systems with deception attacks to achieve the predefined formation are obtained with the aid of Lyapunov stability theory. Finally, the validity of the proposed theoretical results is demonstrated via a simulation example.

Absence of Monotony and Strain Effects on Referees' Physical Performance During International Basketball Federation World Cup Basketball Competition.

The study aimed to conduct a comparative analysis of physical performance indicators and assess the levels of performance monotony and strain experienced by basketball referees. The study involved the participation of 12 basketball referees (mean age: 40.0 [4.9]y) affiliated with the International Basketball Federation. The investigation was carried out throughout 2 density tournaments, wherein the maximum heart rate, average heart rate, performance monotony, and performance strain were documented for 3 variables. The findings indicated significant variations in the mean heart rate, maximum heart rate, total distance monotony, total distance strain, the total number of sprints monotony, the total number of sprints strain, calories monotony, and calories strain (P < .05). Decreasing the density of elite-level basketball competitions has been observed to reduce the monotony and strain experienced by referees. However, this action does not increase motion distance or speed zones. The environmental stressors experienced by senior-level athletes (World Cup) differ from those encountered by younger athletes (World Cup Under 19). Further investigation is required to ascertain the potential effects of competition monotony and strain on decision-making processes and the overall quality of refereeing.

Effects of water-nitrogen interactions on NH3 and N2O emissions and yield in winter wheat cropland

To investigate the effects of different irrigation and nitrogen application modes on nitrogen gaseous loss in winter wheat farmland, we conducted a field experiment at Changqing Irrigation Experiment Station in Shandong Province, with two irrigation levels (80%-90% θf(I1) and 70%-80% θf(I2)) and three nitrogen application levels (conventional nitrogen application of 240 kg·hm-2(N1), nitrogen reduction of 12.5% (N2), and nitrogen reduction of 25% (N3)). The results showed that ammonia volatilization and nitrous oxide emission rate peak appeared within 2-4 days after fertilization or irrigation. The ammonia volatilization rate during the chasing fertilizer period was significantly higher than that during the basal fertilizer period. Compared with other treatments, the ave-rage ammonia volatilization rate of I2N2 treatment during the chasing fertilizer period was reduced by 10.1%-51.6%, and the average nitrous oxide emission rate over the whole growth period was reduced by 15.4%-52.2%. The ammonia volatilization rate was significantly positively associated with surface soil pH value and ammonium nitrogen content, while the nitrous oxide emission rate was significantly positively associated with nitrate content in topsoil. The accumulation amount of soil ammonia volatilization and nitrous oxide emission ranged from 0.83-1.42 and 0.11-0.33 kg·hm-2, respectively. Moderate reduction of irrigation water and nitrogen input could effectively reduce cumulative amounts of ammonia volatilization and nitrous oxide emission from winter wheat farmland. The cumulative amounts of ammonia volatilization and nitrous oxide emission under I1N3 and I2N2 treatments were signi-ficantly lower than those under other treatments. The highest winter wheat yield (5615.6 kg·hm-2) appeared in I2N2 treatment. The irrigation water utilization efficiency of I2 was significantly higher than that of I1, with the maximum increase rate of 45.2%. Compared with N1 and N3 treatments, the maximum increase rate of nitrogen fertilizer productivity and agricultural utilization efficiency in N2 reached 15.2% and 31.8%, respectively. In conclusion, the treatment with 70%-80% θf irrigation level and 210 kg·hm-2 nitrogen input could effectively improve the utilization efficiency of irrigation water and nitrogen fertilization and reduce gaseous loss from winter wheat farmland.

Fumos: Neural Compression and Progressive Refinement for Continuous Point Cloud Video Streaming.

Point cloud video (PCV) offers watching experiences in photorealistic 3D scenes with six-degree-of-freedom (6-DoF), enabling a variety of VR and AR applications. The user's Field of View (FoV) is more fickle with 6-DoF movement than 3-DoF movement in 360-degree video. PCV streaming is extremely bandwidth-intensive. However, current streaming systems require hundreds of Mbps bandwidth, exceeding the bandwidth capabilities of commodity devices. To save bandwidth, FoV-adaptive streaming predicts a user's FoV and only downloads point cloud data falling in the predicted FoV. But it is difficult to accurately predict the user's FoV even 2-3 seconds before playback due to 6-DoF. Misprediction of FoV or network bandwidth dips results in frequent stalls. To avoid rebuffering, existing systems would cause incomplete FoV and degraded experience, deteriorating the user's quality of experience (QoE). In this paper, we describe Fumos, a novel system that preserves interactive experience by avoiding playback stalls while maintaining high perceptual quality and high compression rate. We find a research gap in inter-frame redundant utilization and progressive mechaism. Fumos has three crucial designs, including (1) Neural compression framework with inter-frame coding, namely N-PCC, which achieves both bandwidth efficiency and high fidelity. (2) Progressive refinement streaming framework that enables continuous playback by incrementally upgrading a fetched portion to a higher quality (3) System-level adaptation that employs Lyapunov optimization to jointly optimize the long-term user QoE. Experimental results demonstrate that Fumos significantly outperforms Draco, achieving an average decoding rate acceleration of over 260×. Moreover, the proposed compression framework N-PCC attains remarkable BD-Rate gains, averaging 91.7% and 51.7% against the state-of-the-art point cloud compression methods G-PCC and V-PCC, respectively.

Design of an RFID-Based Self-Jamming Identification and Sensing Platform

Commodity RFID tags backscatter stored electronic product code (EPC) to the reader, but do not have sensing capability. Existing works have made much effort on designing RFID-based sensing platform. But most of them either need intricate hardware design or rely on modification of the tag, which increases the cost or constrains the sensing capability. In this paper, we design a self-jamming identification and sensing platform (SJISP) consisting of SJISP nodes and a commodity RFID reader. A subtle design of the SJISP node is the adoption of a jammer radio module with the same frequency as the reader, controlled by the micro control unit (MCU) to decide whether to interfere with the query process of the RFID reader. The RFID tag is not readable if the jammer is turned on to generate interference signals. Otherwise, it is readable when the jammer is turned off. The sensing data is thus modulated by switching the jammer on and off for transmitting bit 0 and bit 1, respectively. The reader demodulates the data through the compatible EPC UHF Gen2 air interface protocol. To further save the energy of the SJISP node, we propose a prefix codebook based data delivery scheme, which leverages the difference of energy consumption (DEC) between transmitting bit 0 and bit 1. Our proposed scheme can save more than 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the energy than common communication without codebook. Experimental results based on our prototyped system show that the designed SJISP can achieve an average packet reception rate (PRR) of over 99 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and is quite robust to environmental disturbance. Our designed platform provides a low-cost and compatible solution to extend the sensing capability of RFID system. A demo application with a temperature sensor and a light sensor embedded in two SJISP nodes respectively are developed to demonstrate how SJISP applies in real world scenario.

Investigation on distributed scheduling with lot-streaming considering setup time based on NSGA-II in a furniture intelligent manufacturing

Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5.

Sustainable Income, Employment, and Income Distribution in Indonesia

Production and consumption activities in any economy have a direct impact on the environment. Although increased economic activity and population growth in developing countries continue to exert enormous pressure on their natural environments, the role of the environment is neglected in the estimation of national income. Such neglect at the macroeconomic level is at least in part, an important cause of environmental degradation in developing countries. Since the United Nations Conference on Environment and Development in 1992 at Rio and even as early as middle of the 1980s, a substantial literature had developed on methods to integrate the environment into the economic development process. The main assertion in this literature is that natural resources represent a form of capital that is analogous to the stock of manufactured capital. Sustainable income can be determined by allocating a portion of income to allow for the deprecation of natural capital [Ahmed, El Serafy, and Lutz (1989) and Solow (1992)]. Indonesia had average real GDP growth rates of more than five percent per year up to the early 1990s [World Bank (1994)]. But income inequality (measured by the Gini coefficient) has been high. Although inequality continues to be quite high, especially between rural and urban populations, Indonesia has been successful in poverty alleviation up to mid 1990s. In 1976 almost 40 percent of its population was below the poverty line, which in 1993 decreased to less than 14 percent [Todaro (1994)]. Income distributional consequences of economic growth would continue to be one of the main policy issues in Indonesia. This is due to its large population size, presence of different ethnic and religious groups, large diversity between rural and urban groups, variety of natural resources scattered over the country, huge distances and the effects of a far-flung archipelago [Akita, Lukman, and Yamada (1999)].

High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.

Improved ant colony optimization algorithms for multi-objective investment decision model based on intelligent fuzzy clustering algorithm

With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of which were controlled below 41%. Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making.

A machine learning pipeline for extracting decision-support features from traffic scenes1

Traffic systems play a key role in modern society. However, these systems are increasingly suffering from problems, such as congestions. A well-known way to efficiently reduce this kind of problem is to perform traffic light control intelligently through reinforcement learning (RL) algorithms. In this context, extracting relevant features from the traffic environment to support decision-making becomes a central concern. Examples of such features include vehicle counting on each queue, identification of vehicles’ origins and destinations, among others. Recently, the advent of deep learning has paved to way to efficient methods for extracting some of the aforementioned features. However, the problem of identifying vehicles and their origins and destinations within an intersection has not been fully addressed in the literature, even though such information has shown to play a role in RL-based traffic signal control. Building against this background, in this work we propose a deep learning pipeline for extracting relevant features from intersections based on traffic scenes. Our pipeline comprises three main steps: (i) a YOLO-based object detector fine-tuned using the UAVDT dataset, (ii) a tracking algorithm to keep track of vehicles along their trajectories, and (iii) an origin-destination identification algorithm. Using this pipeline, it is possible to identify vehicles as well as their origins and destinations within a given intersection. In order to assess our pipeline, we evaluated each of its modules separately as well as the pipeline as a whole. The object detector model obtained 98.2% recall and 79.5% precision, on average. The tracking algorithm obtained a MOTA of 72.6% and a MOTP of 74.4%. Finally, the complete pipeline obtained an average error rate of 3.065% in terms of origin and destination counts.

Epidemiology of Murine Typhus in Taiwan from 2013 to 2020.

Murine typhus is a flea-borne disease caused by Rickettsia typhi infection. The disease is a notifiable infectious disease in Taiwan. Specimens from suspected cases are required to be sent to the Taiwan Centers for Disease Control and Prevention for laboratory diagnosis. In this study, 204 cases of murine typhus were identified by bacterial isolation, real-time polymerase chain reaction, or indirect immunofluorescence assay between 2013 and 2020. The average incidence rate was 0.11/100,000 person-years (95% CI: 0.08-0.13). Murine typhus occurred throughout the year, but it was most prevalent in summer (May to August). The majority of patients were males (75%), residents of Kaohsiung city (31%), and worked in agriculture, forestry, fishing, and animal husbandry (27%). Fever was the most common symptom, present in 95.6% of patients, followed by headache (41%), myalgia (33%), and liver dysfunction (33%). Only 13% of patients had a rash. Up to 80% of cases were among hospitalized patients, and 43% of patients developed severe manifestations. Serological assays also indicated coinfection events. Seven patients showed a 4-fold increase in antibody titers against Orientia tsutsugamushi (N = 2), Coxiella burnetii (n = 2), and Leptospira (N = 3). In conclusion, murine typhus is an endemic and important zoonotic rickettsial disease in Taiwan that cannot be ignored. Further epidemiological surveillance and clinical characteristics should be continuously investigated to prevent and control murine typhus.

Reinforcement Learning-Based Switching Controller for a Milliscale Robot in a Constrained Environment

This work presents a reinforcement learning-based switching control mechanism to autonomously move a ferromagnetic object (representing a milliscale robot) around obstacles within a constrained environment in the presence of disturbances. This mechanism can be used to navigate objects (e.g., capsule endoscopy, swarms of drug particles) through complex environments when active control is a necessity but where direct manipulation can be hazardous. The proposed control scheme consists of a switching control architecture implemented by two sub-controllers. The first sub-controller is designed to employs the robot's inverse kinematic solutions to do an environment search of the to-be-carried ferromagnetic particle while being robust to disturbances. The second sub-controller uses a customized rainbow algorithm to control a robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a desired position through a constrained environment. For the customized Rainbow algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN) algorithm and ResNet are employed. The proposed controller is first trained and tested in a real-time physics simulation engine (PyBullet). Afterward, the trained controller is transferred to a UR5 robot to remotely transport a ferromagnetic particle in a real-world scenario to demonstrate the applicability of the proposed approach. The experimental results show an average success rate of 98.86\% calculated over 30 episodes for randomly generated trajectories.

Two-stage HOG/SVM for license plate detection and recognition

Automatic license plate recognition (ALPR) is one of the technologies used in intelligent transport systems (ITS) to read vehicle license plates automatically. The extracted information has various potential applications, including but not limited to an electronic payment gateway, a system for paying parking fees, road surveillance, and managing traffic flow. In this paper, we propose an efficient method to detect and identify the Algerian license plate (LP). This method consists of a two-stage algorithm that combines the histogram of oriented gradients (HOG) with the support vector machine (SVM) classifier. The purpose of the first stage of HOG/SVM is the detection of the LP, while the recognition of the digits is accomplished by the second stage of HOG/SVM. As first contribution, a dataset of standard Algerian LP not available elsewhere is built (DZLP dataset), The second is a proposal of a very efficient pre-processing step for LP detection and digit recognition. Experimental results show that the proposed approach yields very high license plate and average digits recognition rates, which of 97.5% and 99.46%, respectively.

CO emission survey of asymptotic giant branch stars with ultraviolet excesses

Context. The transition from the spherically symmetric envelopes around asymptotic giant branch (AGB) stars to the asymmetric morphologies observed in planetary nebulae is still not well understood, and the shaping mechanisms are a subject of debate. Even though binarity is widely accepted as a promising option, it is limited by the complication of identifying binary AGB stars observationally. Recently, the presence of ultraviolet excesses in AGB stars has been suggested as a potential indicator of binarity. Aims. Our main goals are to characterise the properties of the circumstellar envelopes (CSEs) around candidate AGB binary stars, specifically those selected based on their UV excess emission, and to compare these properties with those derived from previous CO-based studies of AGB stars. Methods. We observed the 12CO (J=1–0) and 12CO (J=2–1) millimetre-wavelength emission in a sample of 29 AGB binary candidates with the IRAM-30 m antenna. We measured the systemic velocities and the terminal expansion velocities from their line profiles. Population diagrams were used to interpret the results, enabling the estimation of excitation temperatures (Tex), mass-loss rates (Ṁ), and the characteristic sizes of the envelope layers where the CO millimetre emission originates (Rs). We explored different trends between the envelope parameters deduced, multiwavelength flux measurements, and other properties of our sample, and compared them with those previously derived from larger samples of AGB stars found in the literature. Results. We detected 12CO emission in 15 sources, of which 5 are first detections. We found relatively low expansion velocities (3 km s−1 ≲ Vexp ≲ 20 km s−1) in our sample. We derived the average excitation temperature and column density of the CO-emitting layers, which we used to estimate self-consistently the average mass-loss rate (10−8 M⊙ yr−1 ≲ Ṁ ≲ 10−5 M⊙ yr−1) and the CO pho-todissociation radius (5 × 1015 cm ≲ RCO ≲ 2 × 1017 cm) of our targets. We find a correlation between CO intensity and IRAS 60 µm fluxes, revealing a CO-to-IRAS 60 µm ratio lower than for AGB stars and closer to that found for pre-planetary nebulae (pPNe). An anti-correlation is observed between 12CO (and IRAS 60 µm) and the near-ultraviolet (NUV), but no such correlation is observed with the far-ultraviolet (FUV). It is also worth noting that there is no correlation between bolometric luminosity and NUV or FUV. Conclusions. For the first time we have studied the mass-loss properties of UV-excess AGB binary candidates and estimated their main CSE parameters. Our sample of uvAGB stars shows similarities with the broader category of AGB stars, except for a distinct CO-to-IRAS 60 µm trend suggesting enhanced CO photodissociation. Our findings, based on single-dish low-J CO line emission observations, support the dust-driven wind scenario and indicate that alternative mass-loss mechanisms are not necessary (in principle) to explain the ~200–2000 yr old mass-loss ejecta in uvAGBs. The different relationships between 12CO and IRAS 60 µm, with NUV and FUV are consistent with an intrinsic origin of NUV emission, but potential dominance of an extrinsic process (e.g. presence of a binary companion) in FUV emission.

2D unified atmosphere and wind simulations of O-type stars

Context. Massive and luminous O-type star (O star) atmospheres with winds have been studied primarily using one-dimensional (1D), spherically symmetric, and stationary models. However, observations and theory have suggested that O star atmospheres are highly structured, turbulent, and time-dependent. As such, when making comparisons to observations, present-day 1D modeling tools require the introduction of ad hoc quantities such as photospheric macro- and microturbulence, wind clumping, and other relevant properties. Aims. We present a series of multi-dimensional, time-dependent, radiation-hydrodynamical (RHD) simulations for O stars that encapsulate the deeper sub-surface envelope (down to T ~ 450 kK), as well as the supersonic line-driven wind outflow in one unified approach. Our overarching aim is to develop a framework that is free from the ad-hoc prescriptions that plague present-day 1D models. Here, we start with an analysis of a small set of such multi-dimensional simulations and then compare them to atmospheric structures predicted by their 1D counterparts. Methods. We performed time-dependent, two-dimensional (2D) simulations of O star atmospheres with winds using a flux-limiting RHD finite volume modelling technique. Opacities are computed using a hybrid approach combining tabulated Rosseland means with calculations (based on the Sobolev approximation) of the enhanced line opacities expected for supersonic flows. The initial conditions and comparison models were derived using similar procedures as those applied in standard 1D stationary model atmosphere with wind codes. Results. Structure starts appearing in our simulations just below the iron-opacity peak at ~200 kK. Local pockets of gas with radiative accelerations that exceed gravity then shoot up from these deep layers into the upper atmosphere, where they interact with the line-driven wind outflow initiated around or beyond the variable photosphere. This complex interplay creates large turbulent velocities in the photospheric layers of our simulations, on the order of ~30–100km s−1, with higher values for models with higher luminosity-to-mass ratios. This offers a generally good agreement with observations of large photospheric ‘macroturbulence’ in O stars. When compared to 1D models, the average structures in the 2D simulations display less envelope expansion and no sharp density-inversions, along with density and temperature profiles that are significantly less steep around the photosphere, and a strong anti-correlation between velocity and density in the supersonic wind. Although the wind initiation region is complex and highly variable in our simulations, our average mass-loss rates agree well with stationary wind models computed by means of full co-moving frame radiative transfer solutions. Conclusions. The different atmospheric structures found in 2D and 1D simulations are likely to affect the spectroscopic determination of fundamental stellar and wind parameters for O stars as well as the empirical derivation of their chemical abundance patterns. To qualitatively match the different density and temperature profiles seen in our multi-dimensional and 1D models, we need to add a modest amount of convective energy transport in the deep sub-surface layers and a large turbulent pressure around the photosphere to the 1D models.