Average Rate Research Articles

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.

Characterization of research trends and prospects on hepatic echinococcosis over the past forty years: a bibliometric analysis.

The distribution of hepatic echinococcosis (HE) is extensive, significantly impacting public health and economic development. Therefore, analyzing global collaboration networks and tracking developmental trends over the past four decades are crucial. This study aimed to demonstrate collaboration in the field of HE and explore key topics and future directions. Bibliometric analyses were conducted using CiteSpace, Bibliometrix package of R, and VOSviewer software on HE-related studies from the Web of Science Core Collection published before August 1, 2023. This study identified 2,605 records published in 196 journals by 9,860 authors from 2,607 institutes in 90 countries. Publications significantly notably increased in 2021. Developing countries like Turkey and China made notable contributions, while developed countries like the USA had higher average citation rates. The largest nodes in every cluster of the collaboration network were Hacettepe University, Tehran University, Xinjiang Medical University, Salford University, and the University of Pavia, and the top-producing authors were Wen H, Vuitton DA, Gottstein B, and Craig PS. Keyword co-occurrence analysis suggested that surgical techniques and novel drugs targeting combined immune checkpoints are the main therapeutic approaches in the future. Although developing countries had significantly contributed to publications on HE, the citation rate for individual articles from developed countries was significantly higher. Additionally, advancements in surgical techniques and novel drugs targeting combined immune checkpoints may emerge as the next research focus and developmental direction.

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.

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.

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.

Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning

In this paper, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.01% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 45.1% on average, and the average evasive rate is noticeably improved by up to 56.0%.

First report of Sclerotium hydrophilum causing leaf spot on Brasenia schreberi in central China.

Brasenia schreberi, commonly known as watershield and referred to as 'Chun Cai' in Chinese, is a worldwide aquatic vegetable. It has long been regarded as health- promoting vegetable due to production of mucilage in young shoots, and thus has gained popularity in China. In September 2022, a leaf spot disease was observed at the National Aquatic Vegetable Germplasm Resource Nursery located in Wuhan, Hubei province, China. The disease occurred on watershield leaves. It started with the formation of leaf spots surrounded by halos.These spots ranged in color from yellow to brown and in diameter from 1 to 10 mm. Subsequently, the smaller spots merged, ultimately causing the entire leaves to turn black. Small brown- to black-colored sclerotia were produced on the underside of the diseased leaves. Disease incidence was 30% on average, and yield loss was approximately 15% on average. To isolate the pathogen, leaf tissues at the disease-healthy border area were excised into 5 × 5 mm pieces, these segments were immersed in 75% ethanol for 30 s, followed by immersion in 0.5% sodium hypochlorite for 30 s, and then rinsed twice in sterile water. After air-drying, the leaf pieces were incubated on potato dextrose agar (PDA) in darkness at 28°C for 3 d. Mycelia from the leaf pieces were transferred to new PDA plates for purification, three sclerotia-forming fungal isolates (Whcc-1, Whcc-3, Whcc-4) were finally obtained. They were incubated on PDA at 28°C for 4 to 14 d for observation of colony morphology. At 4 d after incubation (DAI), they grew rapidly with the average growth rate of 2.2 cm/d and formed colonies with whitish substrate mycelia and well-developed aerial mycelia and small white to light brown-colored sclerotia. At 10 to 14 DAI, the sclerotia gradually turned to black, 0.2 to 0.4 mm in diameter (0.26 mm on average, n = 50). These morphological characteristics matched description of Sclerotium hydrophilum (Bashyal et al. 2021). Molecular identification was done to further clarify the species identity of this fungus. Genomic DNA was extracted from isolates Whcc-1, Whcc-3, and Whcc-4. The internal transcribed spacer region of the ribosomal DNA (ITS-5.8S rDNA) and the small subunit ribosomal RNA gene (ssrRNA) were amplified using universal primers ITS1/ITS4 and NS1/NS6, as described by White et al. (1990). Sequence analysis revealed a high degree of similarity between the ITS-5.8S rDNA sequences from Whcc-1, Whcc-3, and Whcc-4 (GenBank Acc. No. OP782030, PP035994, and PP035995, respectively) and those of S. hydrophilum strain CBS201.27 (GenBank Acc. No. FJ231396), with similarities of 99.25%, 99.4%, and 99.25%, respectively. The ssrRNA sequences from Whcc-1, Whcc-3, and Whcc-4 (GenBank Acc. No. PP238401, PP261342 and PP261345) were found to be identical, displaying 100% similarity to the ssrRNA sequences of S. hydrophilum strain ZH11 (GenBank Acc. No. KC354147). Based on both morphological and molecular evidence, it can be inferred that Whcc-1, Whcc-3, and Whcc-4 belong to the species S. hydrophilum. Pathogenicity tests were conducted by inoculation of the mycelial agar plugs of Whcc-1, Whcc-3 or agar plugs of fresh PDA (control) on floating leaves of two watershield plants, 4 leaves (replicates) for each treatment. After inoculation, the treated leaves were sealed in plastic bags to maintain humidity, and grown under natural conditions (18°C to 28°C, with 8 hours of light daily). At 7 DAI, while control leaves remained healthy, the leaves inoculated with Whcc-1 and Whcc-3 leaves formed yellow- to brown-colored spots similar to those observed in the field surveys. S. hydrophilum was re-isolated from the leaf spots, thus verifing Koch's postulates. S. hydrophilum has a wide host range, infecting at least 19 genera of plants, including common rice and wild rice (Johnson et al. 1976), and water lily (Kernkamp et al. 1977). Moreover, it has been reported to infect rice in China (Punter et al. 1984; Zhong et al. 2018). To our knowledge, this is the first report of S. hydrophilum on watershield leaves in central China.

Variation in Muskox (Ovibos moschatus) Guard Hair Growth Rates: Implications for Measuring Chronological Biomarkers.

Segmental analyses of hair may be useful for measuring biomarkers over several seasons to years from a single sample. To attribute hair segments to specific time periods, a known chronological marker, or a hair growth rate, is needed. We examined guard hair growth rates of captive muskoxen (Ovibos moschatus) in Fairbanks, Alaska, USA. We sought to determine if a general growth rate could be applied across muskox populations, thus facilitating the use of segmental analyses for various biomarkers. We used archived samples from 16 muskoxen that had guard hairs sampled at six, 14, and 30 wk after shaving. We measured the lengths of 10 guard hairs per sample, calculated weekly and annual growth rates, and then fitted linear mixed-effects models to assess the effect of different covariates on hair growth rate. The period in which hair had been grown had a significant effect (P<0.05) on growth rate. Extrapolated annual hair growth rates were 277±40 mm/yr (weeks 0-6), 248±47 mm/yr (weeks 7-14), and 165±36 mm/yr (weeks 15-30), with an overall average rate of 210±14 mm/yr. These rates were significantly faster than those of free-ranging Greenland muskoxen-78 mm/yr as measured by stable isotope analyses-and varied intra-annually. This suggests that a universal growth rate cannot be generalized across muskox populations and time.