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•Machine learning models estimates manure temperature accurately during storage.•Manure temperature during storage is linked to depth, time, and weather parameters.•Machine learning ...models predicts accurate manure temperature with past weather data.
There is no standard method to predict manure temperature during storage. So, decision support tools, on-farm nutrient cycling models, and life cycle assessment tools to assess the sustainability of agricultural production systems that include manure typically use ambient air temperature as a surrogate for manure temperature. This study explores the application of machine learning algorithms' unique abilities to predict manure temperature based on measured data. The data was collected from two on-farm manure storages (clay pit and concrete tank) instrumented with sensors to acquire manure temperature at various depths during the storage period. The local weather data (ambient air temperature, wind speed, wind direction, solar radiation, relative humidity, and rainfall) were recorded by stations installed at each farm. The data were subjected to four machine learning algorithms gradient boosted trees, bagged tree ensembles, random forest ensembles, and neural networks using the supervised learning approach. The weather data and two additional parameters, time (month) and the manure depth above a sensor, were derived and used as inputs for the machine learning algorithms. Further, the developed machine learning algorithms were challenged with parameters from historical weather data (1990 to 2020) to assess their suitability to predict manure temperature where local weather is not available.
The results showed that, in general, the stored manure temperature lagged but followed a similar trend as the ambient air temperature and solar radiation. The average manure temperature was higher than the ambient air temperature for most of the year. Depth influenced the manure temperature; manure in the top layers had a higher temperature during warm periods than the bottom layers, and vice versa during cold seasons. The ensemble models performed better than the neural networks by predicting manure temperatures closer to the measured values and predictions during the scenario analysis. The random forests and bagged tree ensembles were the best performers. Models tended to make better predictions as the depth of manure above a sensor increased. This work will provide added value for developing better decision support tools and models for assessing nutrient cycling on farms. It also informs our knowledge to develop emission mitigation strategies during manure storage, leading to more sustainable manure management practices.
Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky ...and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.
Manure management on dairy farms impacts how farmers maximize its value as fertilizer, reduce operating costs, and minimize environmental pollution potential. A persistent challenge on many farms is ...minimizing ammonia losses through volatilization during storage to maintain manure nitrogen content. Knowing the quantities of emitted pollutants is at the core of designing and improving mitigation strategies for livestock operations. Although process-based models have improved the accuracy of estimating ammonia emissions, complex systems such as manure storage still need to be solved because some underlying science still needs work. This study presents a novel physics-informed long short-term memory (PI-LSTM) modeling approach combining traditional process-based with recurrent neural networks to estimate ammonia loss from dairy manure during storage. The method entails inverse modeling to optimize hyperparameters to improve the accuracy of estimating physicochemical properties pertinent to ammonia's transport and surface emissions. The study used open data sets from two on-farm studies on liquid dairy manure storage in Switzerland and Indiana, U.S.A. The root mean square errors were 1.51 g m−2 h−1 for the PI-LSTM model, 3.01 g m−2 h−1 for the base compartmental process-based (Base-CPBM) model, and 2.17 g m−2 h−1 for the hyperparameter-tuned compartmental process-based (HT-CPBM) model. In addition, the PI-LSTM model outperformed the Base-CPBM and the HT-CPBM models by 20 to 80 % during summer and spring, when most annual ammonia emissions occur. The study demonstrated that incorporating physical knowledge into machine learning models improves generalization accuracy. The outcomes of this study provide the scientific basis to improve policymaking decisions and the design of suitable on-farm strategies to minimize manure nutrient losses on dairy farms during storage periods.
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•Including process physics in machine learning models improves prediction accuracy.•Physics-informed LSTM predicts ammonia losses matching observed values better.•Physics-based parameter tuning enhances process-based model performance.•Physics-informed LSTM can estimate ammonia losses across different manure storages.
Using the Roemmich‐Gilson Argo data set, this study investigates variability of the Subtropical Underwater (STUW) and eastern Subtropical Mode Water (ESTMW) in the South Pacific during 2004–2020. The ...STUW volume decreased during 2004–2013 and increased during 2013–2020, while the volume of the ESTMW shows the opposite phase. On interannual time scales, there is also a significant negative correlation in volume between the STUW and ESTMW. This anti‐phase relationship is attributed to changes in their volumetric subduction rates, which are in turn closely related to variability in the mixed layer depth (MLD). ENSO directly contributes to variability of the subduction rates by modifying the MLD. Equatorward propagation of spiciness anomalies is identified along isopycnal surfaces of the STUW and ESTMW cores. These spiciness anomalies in the downstream region are correlated with changes in volume of both water masses, and significant spiciness anomalies can reach the tropical Pacific.
Plain Language Summary
The Subtropical Underwater (STUW) and eastern Subtropical Mode Water (ESTMW) are two important water masses and have experienced significant changes in the upper South Pacific during the past decades. The formation of the two water masses is part of the subtropical overturning cells and is believed to play a role in transferring properties from subtropical region to the equatorial thermocline where water properties can affect the atmosphere. We found that the STUW volume decreased during 2004–2013 and increased after 2013, while the ESTMW volume changed oppositely. This anti‐phase relationship between the two water masses is due to changes in their volumetric subduction rates, which in turn mainly result from the varying mixed layer depth (MLD). Further, El Niño‐Southern Oscillation (ENSO) is found to play a role in modifying the MLD in the South Pacific Ocean. Causes and effects of these variabilities are discussed.
Key Points
The STUW volume decreased during 2004–2013 and increased after 2013, while the ESTMW shows an opposite phase
The anti‐phase relationship is attributed to changes in subduction rate which are closely related to variability in the mixed layer depth
Both the STUW and ESTMW volume anomalies are positively correlated with downstream spiciness anomalies
Driven by the need for delivering sustainable water purification solutions for the removal of heavy metals from water, electrospun PVC membranes were functionalised with triethylenetetramine (TETA) ...and were used to remove lead(
ii
) ions selectively from water. The membranes were characterised and their adsorption behavior towards the removal of lead from water was investigated. The incorporation of TETA on the membrane's surface significantly improved the removal efficiency of lead(
ii
) up to 99.8% in 30 minutes and under ambient conditions, with the lowest concentration of 50 ppm. The adsorption mechanism was investigated and kinetic data showed a better correlation with the pseudo-second-order model. Similarly, the equilibrium data best fitted with the Langmuir adsorption isotherm model with a relatively high maximum adsorption capacity of 1250 mg g
−1
for lead(
ii
) ions, larger than recently reported adsorption capacities for similar membranes. The functionalised membrane also showed high selectivity to lead(
ii
) in a mixed solution containing lead(
ii
), mercury(
ii
), cadmium(
ii
), arsenic(
iii
), copper(
ii
), and zinc(
ii
). The functionalised membrane was regenerated, where desorption of lead(
ii
) was achieved, under mildly acidic conditions. The removal efficiency of the regenerated membrane after six cycles of adsorption/desorption was maintained at a high level of 98%. The proposed design offers a simple yet effective, sustainable, and environmentally friendly solution for water treatment.
Driven by the need for delivering sustainable water purification solutions for the removal of heavy metals from water, TETA functionalised electrospun PVC membranes were fabricated and used to remove lead(
ii
) ions selectively from water.
Systemic lupus erythematosus (SLE) is a systemic chronic disease initiated by an abnormal immune response to self and can affect multiple organs. SLE is characterized by the production of ...autoantibodies and the deposition of immune complexes. In regard to the clinical observations assessed by rheumatologists, several chemokines and cytokines also contribute to disease progression. One such chemokine and adhesion molecule is CX
3
CL1 (otherwise known as fractalkine). CX
3
CL1 is involved in cell trafficking and inflammation through recognition by its receptor, CX
3
CR1. The CX
3
CL1 protein consists of a chemokine domain and a mucin-like stalk that allows it to function both as a chemoattractant and as an adhesion molecule. In inflammation and specifically lupus, the literature displays contradictory evidence for the functions of CX
3
CL1/CX
3
CR1 interactions. In addition, the gut microbiota has been shown to play an important role in the pathogenesis of SLE. This review highlights current studies that illustrate the interactions of the gut microbiota and CX
3
CR1 in SLE.
An improvement in resistive switching (RS) characteristics of CeO2-based devices has been reported by charge transfer through Al metal as a dopant. Moreover, density functional theory (DFT) ...calculations have been performed to investigate the role of Al-layer sandwiched between CeO2 layers by the Vienna ab initio simulation package (VASP). Total density of states (TDOS) and partial electron density of states (PDOS) have been calculated and analyzed with respect to resistive switching. It is established that the oxygen vacancy based conductive filaments are formed and ruptured in the upper region of CeO2 layer, because of the fact that maximum transport of charge takes place in this region by Al and Ti (top electrode), while the lower region revealed less capability to generate conductive filaments because minimum charge transfer takes place in this region by Al and/or Pt (bottom electrode). The effect of Al and Al2O3 on both the electronic charge transfer from valence to conduction bands and the formation stability of oxygen vacancies in conductive filament have been discussed in detail. Experimental results demonstrated that the Ti/CeO2:Al/Pt sandwich structure exhibits significantly better switching characteristics including lower forming voltage, improved and stable SET/RESET voltages, enhanced endurance of more than 104 repetitive switching cycles and large memory window (ROFF/RON > 102) as compared to undoped Ti/CeO x /Pt device. This improvement in memory switching behavior has been attributed to a significant decrease in the formation energy of oxygen vacancies and to the enhanced oxygen vacancies generation within the CeO2 layers owing to charge transferring and oxygen gettering ability of Al-dopant.
A new series of hydrogels was successfully prepared by incorporating various substituted bisuracil (R-BU) linkages between chitosan Schiff's base chains (R-BU-CsSB) and between chitosan chains ...(R-BU-Cs). After protection of the amino groups of chitosan by benzaldehyde, yielding chitosan Schiff's base (CsSB), the reaction with epichlorohydrin was confined on the -OH on C6 to produce epoxy chitosan Schiff's base (ECsSB), which was reacted with R-BU to form R-BU-CsSB hydrogels, and finally, the bioactive amino groups of chitosan were restored to obtain R-BU-Cs hydrogels. Further, some R-BU-Cs-based ZnO nanoparticle (R-BU-Cs/ZnONPs) composites were also prepared. Appropriate techniques such as elemental analysis, FTIR, XRD, SEM, and EDX were used to verify their structures. Their inhibition potency against all the tested microbes were arranged as: ZnONPs bio-composites > R-BU-Cs hydrogels > R-BU-CsSB hydrogels > Cs. Their inhibition performance against Gram-positive bacteria was better than Gram-negative ones. Their minimum inhibitory concentration (MIC) values decreased as a function of the negative resonance effect of the substituents in the aryl ring of R-BU linkages in the hydrogels. Compared with
, the ZnONPs bio-composites showed superior inhibitory effects against most of the tested Gram-negative bacteria, all inspected Gram-positive ones, and all investigated fungi.
Efficient allocation of resources to intervene against malaria requires a detailed understanding of the contemporary spatial distribution of malaria risk. It is exactly 40 y since the last global map ...of malaria endemicity was published. This paper describes the generation of a new world map of Plasmodium falciparum malaria endemicity for the year 2007.
A total of 8,938 P. falciparum parasite rate (PfPR) surveys were identified using a variety of exhaustive search strategies. Of these, 7,953 passed strict data fidelity tests for inclusion into a global database of PfPR data, age-standardized to 2-10 y for endemicity mapping. A model-based geostatistical procedure was used to create a continuous surface of malaria endemicity within previously defined stable spatial limits of P. falciparum transmission. These procedures were implemented within a Bayesian statistical framework so that the uncertainty of these predictions could be evaluated robustly. The uncertainty was expressed as the probability of predicting correctly one of three endemicity classes; previously stratified to be an informative guide for malaria control. Population at risk estimates, adjusted for the transmission modifying effects of urbanization in Africa, were then derived with reference to human population surfaces in 2007. Of the 1.38 billion people at risk of stable P. falciparum malaria, 0.69 billion were found in Central and South East Asia (CSE Asia), 0.66 billion in Africa, Yemen, and Saudi Arabia (Africa+), and 0.04 billion in the Americas. All those exposed to stable risk in the Americas were in the lowest endemicity class (PfPR2-10 < or = 5%). The vast majority (88%) of those living under stable risk in CSE Asia were also in this low endemicity class; a small remainder (11%) were in the intermediate endemicity class (PfPR2-10 > 5 to < 40%); and the remaining fraction (1%) in high endemicity (PfPR2-10 > or = 40%) areas. High endemicity was widespread in the Africa+ region, where 0.35 billion people are at this level of risk. Most of the rest live at intermediate risk (0.20 billion), with a smaller number (0.11 billion) at low stable risk.
High levels of P. falciparum malaria endemicity are common in Africa. Uniformly low endemic levels are found in the Americas. Low endemicity is also widespread in CSE Asia, but pockets of intermediate and very rarely high transmission remain. There are therefore significant opportunities for malaria control in Africa and for malaria elimination elsewhere. This 2007 global P. falciparum malaria endemicity map is the first of a series with which it will be possible to monitor and evaluate the progress of this intervention process.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background Migrant farmworkers are at risk for heat-related illness (HRI) at work. Purpose The purpose of this study was to determine which risk factors could potentially reduce the prevalence of HRI ...symptoms among migrant farmworkers in Georgia. Methods Trained interviewers conducted in-person interviews of adults who attended the South Georgia Farmworker Health Project clinics in June 2011. The analysis was conducted in 2011–2012. Population intervention models were used to assess where the greatest potential impact could be made to reduce the prevalence of HRI symptoms. Results In total, 405 farmworkers participated. One third of participants had experienced three or more HRI symptoms in the preceding week. Migrant farmworkers faced barriers to preventing HRI at work, including lack of prevention training (77%) and no access to regular breaks (34%); shade (27%); or medical attention (26%). The models showed that the prevalence of three or more HRI symptoms ( n =361, 34.3%) potentially could be reduced by increasing breaks in the shade (−9.2%); increasing access to medical attention (−7.3%); reducing soda intake (−6.7%); or increasing access to regular breaks (−6.0%). Conclusions Migrant farmworkers experienced high levels of HRI symptoms and faced substantial barriers to preventing these symptoms. Although data are cross-sectional, results suggest that heat-related illness may be reduced through appropriate training of workers on HRI prevention, as well as regular breaks in shaded areas.
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