Average Rate Research Articles

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.

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.

Acne treatment using tea tree oil, aloe vera, lavender, and calendula: The perception of pharmacy students

Skin acne is a common skin condition that occurs when hair follicles become clogged with oil and dead skin cells. In this paper, the use tea tree oil, aloe vera, lavender, and calendula for skin acne is reviewed along with a survey of the views of first-year pharmacy students on these products. The survey included 5 knowledge-based and 5 opinion-based questions, focusing on skin acne remedies. The survey enrolled 10 male and 29 female participants. The response rate for the demographic questions was 100%. In the knowledge-based and opinion survey questions, 34 participated for a response rate of about 87%. For the knowledge-based questions, the correct average response rate was 75.9%, with one question scoring 14.7% correct rate. The highest correct response rate was 97.1%. Opinion-based questions revealed a positive attitude towards herbal skin acne remedies with approximately 37.4% expressing strong agreement. On average, over 93% of the respondents agreed with the positive aspects of the products. Nearly 53% strongly believed that lavender has therapeutic properties such as promoting relaxation and reducing stress. This survey provided valuable pharmacy students’ insights into the potential of herbal remedies for skin acne properties.

Canopy cover and microtopography control precipitation-enhanced thaw of ecosystem-protected permafrost

Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015–2020) to 56 ± 12 cm yr−1 (2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Evaluation of saline and magnetized water on emitter hydraulic performance and clogging in drip irrigation

The present investigation was carried out at the National Irrigation Laboratory of Agricultural Engineering Research Institute (AEnRI), Dokki, Giza, Egypt. This study was performed to investigate the hydraulic performance and clogging ratio of drip irrigation with magnetized water. Magnetized water was created by transferring water through a permanent magnet connected to a feed pipeline. Two main treatments of magnetized and non-magnetized water, as well as three sub-treatments of irrigation water salts, including fresh water (219 ppm) and the addition of 1000 and 2000 ppm to irrigation water with three replications were applied under different operating pressure (75, 100, 125 and 150 kPa). At the beginning of the experiment, results show that hydraulic parameters were almost the same for both the magnetized and the non-magnetized water and for all salinity levels. At the end of working time, the hydraulic parameters were improved for the magnetized water under salinity levels compared to the non-magnetized water. Average emitter discharge increased with roughly 2.7% and 5.6%, coefficient of variation (Cv) decreased by 0.6 and 0.91%, emission uniformity (Eu) increased about 1 and 1.1% and variation of average flow rate (qavr) decreased by 21.3 and 29.4% when 1000 and 2000 ppm were used, respectively. Magnetized water had slight effect on clogging at non-saline water at the end of experiment. At 1000 and 2000 ppm salinity levels, the clogging ratio decreased by 1.97 and 2.45% at different pressure, respectively. The results show that magnetized water treatment could effectively relieve and delay the occurrence time of clogging.

Optimum scaling relation for dynamic shear enhanced membrane filtration pilots in nutrient and mineral recovery from wastewater

Recovery of nutrients and minerals from wastewater by membrane filtration holds promise for sustainable water processing. While dealing with such processes that often result in substantial filtrate throughput decline due to membrane fouling, Dynamic Shear Enhanced Membrane Filtration Pilots (DSEMFPs) have proven to be an ideal contender. These devices generate feed flow-independent shear that triggers solute back-transport away from the membrane. Subsequent flux enhancement is a congruous merit. Power-law correlation between the flux and average shear rate has been so extensively used in several studies so that it became the ubiquitous scaling relation for DSEMFPs. However, in this article, we have demonstrated a novel and more expansive scaling between flux and a set of prototypical turbulent measures (i.e., turbulent kinetic energy, kinetic energy dissipation rate, and Kolmogorov scale), relative to shear rate, ranging over five different DSEMFPs. Flux data were collected from previous studies, whereas hydrodynamic variables were evaluated computationally. All turbulent measures exhibited better correlation compared to shear rate with the Kolmogorov scale being the best regressor (R2 > 0.96). The study demonstrates that turbulence has an equivalent, if not superior, role in upscaling flux in DSEMFPs against the conventional idea of shear generation being primary.

Influence of soil moisture conditions on severity of Botryosphaeria branch dieback caused by Lasiodiplodia iraniensis and impact of plant growth in macadamia

Australian macadamias experience outbreaks of Botryosphaeria branch dieback. Extreme environmental conditions are thought to be responsible for the episodic outbreaks of diseases caused by Botryosphaeriaceae species in horticultural ecosystems. However, studies on the interactive effects of biotic and abiotic stresses in macadamia are unknown. Knowledge of management practices that contribute to the severity of the disease would assist in decisions for Botryosphaeria branch dieback control in commercial macadamia orchards. Using an in planta stem wound inoculation assay with Lasiodiplodia iraniensis, we investigated the association of soil moisture conditions as a factor of plant stress with the severity of Botryosphaeriaceae infection in macadamia. Sap flow rate and soil water potential were monitored in 72 macadamia potted cv. HAES 344 plants (18-month-old and 12-month-old grown in pine bark-sand mixture) that were kept under three watering treatments, based on the amount of water used per day (Wd) at the start of the trial, at Wd, ½ of Wd and ¼ of Wd for 14 weeks. A significant differential effect of the watering regimes was observed on disease severity, revealing a strong (R2 > 0.87) negative linear relationship between disease severity and sap flow rates. Necrotic lesions were significantly (P < 0.001) longer in the lowest and highest watering regimes. The average sap flow rate of 0.4 kg h−1 was recorded in the plants under moderately watered conditions, ½Wd regime, whereas 50% and 75% reductions in sap flow rates were recorded at the lowest and highest watering treatments, respectively. Our results indicate that maintaining optimal soil water potential between −50 and −80 kPa through irrigation practices may reduce the risk of Botryosphaeria branch dieback outbreaks in macadamia orchards.

Accounting for Greenhouse Gas Emissions in the Agricultural System of China Based on the Life Cycle Assessment Method

Agricultural systems contribute nearly one-third of global anthropogenic GHG emissions and are an important source of GHGs globally. The clarification of the GHG emission pattern from agriculture is of paramount importance in the establishment of an agricultural emission reduction mechanism and the realization of China’s dual-carbon target. Based on the life cycle assessment method (LCA), this paper comprehensively quantifies the greenhouse gas emissions from the agricultural system in China, encompassing rice, wheat, and corn cultivation as well as animal husbandry including cows, horses, donkeys, mules, camels, pigs, and sheep. The analysis covers the period 2000–2020 and examines the spatial distribution, temporal trends, and structural changes in the greenhouse gas emissions within China’s agriculture sector. The main results are as follows: (1) from 2000 to 2020, China witnessed a consistent upward trajectory in its total agricultural GHG emissions, exhibiting an average annual growth rate of 0.73%. Notably, methane (CH4) emissions emerged as the largest contributor, displaying an overall fluctuating pattern. Carbon dioxide (CO2) emissions demonstrated intermittent increases with a noteworthy annual growth rate of 3%, signifying the most rapid expansion within this context. Conversely, nitrous oxide (N2O) emissions experienced decline over the specified period. (2) GHG emissions from cultivation demonstrate an upward trajectory, primarily driven by the CH4 emissions originating from rice cultivation and CO2 resulting from straw incineration. Conversely, GHG emissions stemming from animal husbandry declined, with the primary source being CH4 emissions arising from animal enteric fermentation. Agricultural N2O emissions predominantly arise due to manure management and nitrogen fertilizer application. (3) Agricultural greenhouse gas emissions exhibit significant variations in spatial distribution, gradually concentrating towards the North China Plain, the middle and lower reaches of the Yangtze River, and the northeast. Specifically, agricultural CH4 emissions are progressively concentrated in China’s pivotal rice-growing regions, encompassing the middle and lower reaches of the Yangtze River Plain, as well as livestock breeding areas like Inner Mongolia. Agricultural CO2 emissions primarily concentrate in dryland crop production zones such as North China and Northeast China. Meanwhile, Agricultural N2O emissions predominantly occur in Inner Mongolia and the North China Plain. China’s agricultural greenhouse gas emissions in 2020 show a significant spatial clustering effect, with hotspots primarily concentrated in Shandong, Anhui, Henan, and other regions and cold spots focused in the western and southern areas. The emission patterns of agricultural GHGs are closely intertwined with farming practices, regional development levels, and national policy; hence, tailored measures for emission reduction should be formulated based on specific crop types, livestock categories, agricultural production activities, and regional development characteristics.

Federated learning-based trajectory prediction for dynamic resource allocation in moving small cell networks

With the evolution of fifth generation (5G) of mobile communication, vehicular edge computing (VEC) and moving small cell (MSC) network are gaining attention because of their capability to provide improved quality-of-service (QoS) to vehicular users. The ultimate goal of VEC-enabled MSC network is to diminish the vehicular penetration effect and path loss resulting in improved network performance for vehicular users. In this paper, we explore distributed resource block (RB) allocation for fronthaul links of MSCs with probabilistic mobility in VEC-enabled MSC network. The proposed work exploits the computational power of road side units (RSUs) deployed with VEC servers present along the road sides to allocate the resources to MSCs in a distributed manner by exchanging limited information, with the objective of maximizing the data rate achieved by MSC network. Moreover, we propose federated learning (FL)-based position prediction of MSCs to predict the trajectory of MSCs in advance for efficient prediction of resource allocation in MSC network. Simulations results are presented to compare the position prediction dependent distributed and centralized time interval dependent interference graph-based resource allocation to MSCs in terms of RB utilization and average achievable data rate of MSC network. For comparison, we further investigated centralized as well as distributed threshold time dependent interference graph-based allocation of resources to MSC network.

Decadal evolution of fluvial islands and its controlling factors along the lower Yangtze River

Fluvial islands are vital from both morphological and ecological perspectives and consequently have been hotspots of morphodynamic research in large rivers around the world. This study selected 14 representative fluvial islands in the lower reaches of the Yangtze River and explored their spatial-temporal evolution, including their shape and area dynamics during 1945–2016, by interpreting remote sensing images and analyzing the hydrological data. Results indicated that the total area of the 14 fluvial islands showed a growing trend at an average rate of 0.30 km2 yr−1 during the 72 years. The island Fenghuangzhou experienced the largest change in area, while Xiaohuangzhou (XHZ) had the smallest change in area. Sediment discharge and flooding were assumed to be the primary natural factors controlling the island dynamics. Furthermore, dam construction and bank reinforcement also played a critical role in preventing shoreline collapse, improving channel conditions, and promoting the stability of fluvial islands. From 1976 to 2016, the maximum erosion occurred on the left XHZ, while the maximum accretion was found on the Qingjiezhou island. Almost the entire river section experienced an accretion process on the right bank, which was assumed to be caused by the construction of erosion control structures. Besides, the dynamics of the fluvial islands along the lower Yangtze River appears to follow the erosion processes of the river bank. Our findings can provide an important reference for sustainable utilization and management of fluvial islands.