Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Electrochemically deposited Fe2O3 nanorods on carbon nanofibers for free-standing anodes of lithium-ion batteries

Fe₂O₃ nanorod/carbon nanofiber (CNF) composites were prepared by the electrochemical deposition of Fe₂O₃ on a web of CNFs, which was then used as a free-standing anode. The conductive, three-dimensional structure of the CNF web allowed for the electrodeposition of the Fe₂O₃ nanorods, while its high conductivity made it possible to use the composite as a free-standing electrode in lithium-ion batteries. In addition, it was easy and cheap to fabricate by a simplification of a process of cell preparation. The nanorod-like Fe₂O₃ structures could only be electrodeposited on the CNFs; flake-like Fe₂O₃ was formed on flat conductive glass substrates. It can be attributed to the different growth mechanism of Fe₂O₃ on the CNFs because of the large number of reaction sites on the CNFs, differences in the precursor concentration and diffusivity within the CNF web. The formation of aggregates of the Fe₂O₃ particles on thicker CNFs also indicated that the CNFs had determined the Fe₂O₃ growth mechanism. The synthesised Fe₂O₃/CNF composite electrode exhibited stable rate capacities at different current densities. This suggested that CNF-based composite did not exhibit the intrinsic disadvantages of Fe₂O₃. Finally, carbon coatings were deposited on the Fe₂O₃/CNF composites to further improve their electronic conductivity and rate capability.     

Model checking temporal knowledge and commitments in multi-agent systems using reduction

Though modeling and verifying Multi-Agent Systems (MASs) have long been under study, there are still challenges when many different aspects need to be considered simultaneously. In fact, various frameworks have been carried out for modeling and verifying MASs with respect to knowledge and social commitments independently. However, considering them under the same framework still needs further investigation, particularly from the verification perspective. In this article, we present a new technique for model checking the logic of knowledge and commitments (CTLKC⁺). The proposed technique is fully-automatic and reduction-based in which we transform the problem of model checking CTLKC⁺ into the problem of model checking an existing logic of action called ARCTL. Concretely, we construct a set of transformation rules to formally reduce the CTLKC⁺ model into an ARCTL model and CTLKC⁺ formulae into ARCTL formulae to get benefit from the extended version of NuSMV symbolic model checker of ARCTL. Compared to a recent approach that reduces the problem of model checking CTLKC⁺ to another logic of action called GCTL¹, our technique has better scalability and efficiency. We also analyze the complexity of the proposed model checking technique. The results of this analysis reveal that the complexity of our reduction-based procedure is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approach is P-complete with regard to the size of the model and length of the formula, which makes it efficient. Finally, we implement our model checking approach on top of extended NuSMV and report verification results for the verification of the NetBill protocol, taken from business domain, against some desirable properties. The obtained results show the effectiveness of our model checking approach when the system scales up.     

Enhanced properties for visible-light-driven photocatalysis of Ag nanoparticle modified Bi2MoO6 nanoplates

The visible light driven Bi₂MoO₆ photocatalyst doped with different contents of Ag nanoparticles was successfully synthesized by a combination of hydrothermal and sonochemical methods. The as-synthesized samples were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning and transmission electron microscopy (SEM and TEM) and UV–visible spectroscopy to investigate crystalline structure, morphology, composition and photocatalytic properties. XRD patterns and TEM images of the samples revealed pure phase orthorhombic Bi₂MoO₆ nanoplates without any detection of Ag dopant due to its low concentration and very tiny particle size. TEM images showed that Ag nanoparticles with the size of 10–15 nm were dispersed randomly on the surface of Bi₂MoO₆. The XPS analysis of Ag/Bi₂MoO₆ nanocomposites revealed the presence of additional metallic Ag. Photocatalytic activities of the Ag/Bi₂MoO₆ nanocomposites were evaluated by determining the degradation of rhodamine B (RhB) under visible light radiation. In this research, the 10 wt% Ag/Bi₂MoO₆ nanocomposites showed the best photocatalytic activity. The results suggest that the dispersion of Ag nanoparticles on the surface of Bi₂MoO₆ significantly enhances its photocatalytic activity.     

Flame characteristics of premixed H2-air mixtures explosion venting in a spherical container through a duct

The explosion venting duct can effectively reduce the hazard degree of a gas explosion and conduct the venting energy to the safe area. To investigate the flame quantitative propagation law of explosion venting with a duct, the effects of hydrogen fraction and explosion venting duct length on jet flame propagation characteristics of premixed H₂-air mixtures were analyzed through experiment and simulation. The experiment results under initial conditions of room temperature and 1 atm show that when hydrogen fraction was high enough, part of the unburned hydrogen was mixed with air again to reach an ignitable concentration, resulting in the secondary combustion was easier produced and the duration of the secondary flame increased. With the increase of venting duct length, the flame front distance and propagation velocity increased. Meanwhile, the spatial distribution of pressure field and temperature field, and the propagation process and mechanism of the flame venting with a duct were analyzed using FLUENT software. The variation of the pressure wave and the pressure reflection oscillation law in the explosion venting duct was captured. Therefore, in the industrial explosion venting design with a duct, the hazard caused by the coupling of venting pressure and venting flame under different fractions should be considered comprehensively.     

Rip currents near coastal structures in Lake Michigan: Characterization and assessment for warnings

Rip currents near coastal structures commonly occur in Lake Michigan in the Great Lakes region of the United States. Lack of timely warning due to undocumented characteristics of rip currents and no assessment tool can contribute to tragic drownings incidents. In this paper, we characterized rip current occurrences near breakwater structures and developed an assessment tool for providing timely rip current warnings to beachgoers at the study site, City of Port Washington, WI. Characteristics of rip currents near the structure were observed from field measurements or visual images. Deflection rip currents had speeds of ～ 0.2 m/s and lasted for several hours. The rip current occurrences were associated with environmental proxies. It was found that rip currents can occur even when the water appears calm near the structure. A Structure Rip Checklist and Assessment Matrix (SRiCAM) with a four-tiered risk was developed and validated using observations. Furthermore, the SRiCAM was integrated into cyberinfrastructure with a data contingency plan to provide real-time warnings to the public. The applicability of the SRiCAM to other locations across Lake Michigan was further tested and results are promising. Overall, the SRiCAM has the potential to be widely extended to foster recreational water safety and resilience to rip current hazards in the Great Lakes.     

A zirconium-free glaze system for sanitary ceramics with SiO2-CaCO3-TiO2 composite opacifier containing anatase: Effect of interface combination among SiO2, CaCO3 and TiO2

TiO₂ is an ideal substitute to ZrSiO₄ ceramic opacifier, yet it is limited to application because of the undesirable yellowing resulting from rutile formation. Herein, the SiO₂-CaCO₃-TiO₂ composite opacifier (Si-Ca-Ti) was constructed. The glaze used Si-Ca-Ti presents a superior opacification performance than ZrSiO₄ opacified glaze without causing yellowing, showing L*, a*, b* values of 94.81, -0.67 and 3.23. By comparison, the glaze using SiO₂, CaCO₃, and TiO₂ mixture shows lower opacification and yellowish surface with L* and b* values of 92.99 and 5.36. It is revealed that there is a close interface bonding among SiO₂, CaCO₃ and TiO₂ in Si-Ca-Ti, which promotes their combination reaction to generate opacification phase titanite and inhibit rutile formation when sintering, resulting in the white surface and opacification improvement of the glaze. This study proposes a green and efficient strategy to achieve white and highly opacified glaze for sanitary ceramics, exhibiting good application prospect.     

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.