Phytopathogenic organisms and mycotoxigenic fungi: Why do we control one and neglect the other? A biological control perspective in Malaysia

In this review, we present the current information on development and applications of biological control against phytopathogenic organisms as well as mycotoxigenic fungi in Malaysia as part of the integrated pest management (IPM) programs in a collective effort to achieve food security. Although the biological control of phytopathogenic organisms of economically important crops is well established and widely practiced in Malaysia with considerable success, the same cannot be said for mycotoxigenic fungi. This is surprising because the year round hot and humid Malaysian tropical climate is very conducive for the colonization of mycotoxigenic fungi and the potential contamination with mycotoxins. This suggests that less focus has been made on the control of mycotoxigenic species in the genera Aspergillus, Fusarium, and Penicillium in Malaysia, despite the food security and health implications of exposure to the mycotoxins produced by these species. At present, there is limited research in Malaysia related to biological control of the key mycotoxins, especially aflatoxins, Fusarium‐related mycotoxins, and ochratoxin A, in key food and feed chains. The expected threats of climate change, its impacts on both plant physiology and the proliferation of mycotoxigenic fungi, and the contamination of food and feed commodities with mycotoxins, including the discovery of masked mycotoxins, will pose significant new global challenges that will impact on mycotoxin management strategies in food and feed crops worldwide. Future research, especially in Malaysia, should urgently focus on these challenges to develop IPM strategies that include biological control for minimizing mycotoxins in economically important food and feed chains for the benefit of ensuring food safety and food security under climate change scenarios.     

Design of a highly ordered and uniform porous structure with multisized pores in film and particle forms using a template-driven self-assembly technique

A highly ordered arrangement of pores with multiple sizes in film and particle forms was successfully prepared using the dip-coating and spray-drying methods, respectively. The template-driven self-assembly technique was effective when a combination of 5 nm silica (as a model of an inorganic nanoparticle) and two different sizes of monodispersed polystyrene (PS) spheres (as models of the template) were self-assembled to produce a composite silica/PS template. Heat treatment was then used to remove the PS, which produced the porous particle. A material with spherical pores in an “art-design” organization was produced. The size of the pores (large pore: 100–1000 nm; small pore: 30–200 nm) could be controlled simply by adjusting the PS size. Sufficient numbers of large/small pores made it possible to produce a material of ultralow density with an ultralow refractive index, because the multiple pores allocated free space, all of which was confirmed by calculation.     

The extractability of winged bean (Psophocarpus tetragonolobus) seed trypsin inhibitors

The extractability of winged bean (Psophocarpus tetragonolobus) seed trypsin inhibitors by aqueous salt solution varies with pH as well as the ionic strength of the extraction medium. With a high ionic strength extraction medium, the extractability of the seed trypsin inhibitors increases with rising pH. With a low ionic strength extraction medium, however, a region of minimum extractability of trypsin inhibitors was observed at pH 4. At pH above 7, the same maximum amount of trypsin inhibitors (1.47 million inhibitor units (i.u.) per 100 g of seed meal) was extracted with both high and low ionic strength extraction media. The solution characteristics of winged bean seed proteins exhibited similar trends. Ninety-five per cent of the extracted seed trypsin inhibitors could be precipitated from 30–70% saturated ammonium sulphate solutions. The winged bean seed trypsin inhibitors isolated by ammonium sulphate fractionation lost all inhibitory activity when heated at 100°C for 10 min at alkaline pH.     

Two limits of melting temperatures of nanocrystals

It is demonstrated that the melting temperature of nanocrystals embedded in a matrix exhibit two asymptotic limits as the size of the nanocrystal reaches its smallest value. The lower limit of melting temperatures is related to the disappearance of size-dependent entropy of melting and is considered as the lowest glass transition temperature which is located between Kauzmann temperature and glass transition temperature. The upper limit of a nanocrystal embedded in a matrix is determined by the ratio between the bulk melting temperature of the embedded nanocrystal and that of the matrix. The predicted thermodynamic melting temperature range and the lowest glass transition temperature are supported by available experimental evidences.     

Performance and performance modeling of a parallel algorithm for solving the neutron transport equation

A parallel algorithm is derived for solving the discrete-ordinates approximation of the neutron transport equation, based on the naturally occurring decoupling between the mesh sweeps in the various discrete directions during each iteration. In addition, the parallel Source Iteration (SI) algorithm, characterized by its coarse granularity and static scheduling, is implemented for the Nodal Integral Method (NIM) into the Parallel Nodal Transport (P-NT) code on Intel's iPSC/2 hypercube. Measured parallel performance for solutions of two test problems is used as evidence of the parallel algorithm's potential for high speedup and efficiency. The measured performance data are also used to develop and validate a parallel performance model for the total, serial, parallel, and global-summation time components per iteration as a function of the spatial mesh size, the problem size (number of mesh cells and angular quadrature order), and the number of utilized processors. The potential for high performance (large speedup at high efficiency) for large problems is explored using the performance model, and it is concluded that present applications in three-dimensional Cartesian geometry will benefit by concurrent execution on parallel computers with up to a few hundred processors.Research sponsored by the U.S. Department of Energy, managed by Martin Marietta Energy Systems, Inc., under contract No. DE-AC05-84OR21400.     

Mathematical simulation of nitrogen interactions in soils

Four mathematical models were evaluated for their ability to describe the fate of nitrogen (N) in the soil environment. The first model is a general one which accounts for convective-dispersive N transport under transient water flow conditions with active N uptake by plants. Model II considers N transport to be only of the convective type, whereas model III considers N uptake as a passive process. In contrast, model IV considers N transport under conditions of steady water flow in the soil. Experimental data from lysimeter studies for two soils showed that the convective model (II) and the steady state model (IV) are inferior in describing N transport in comparison to models I and III. It is concluded that transient water flow in the soil system as well as the convective dispersive transport mechanisms must be considered for reliable simulation of N behavior in the soil environment.     

Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.     

Effect of X-ray energy and ionization time on the charging performance and nanoparticle formation of a soft X-ray photoionization charger

The present study is a detailed analysis of the charging performance of aerosol nanoparticles using soft X-ray photoionization. The study includes methods used to suppress the formation of unnecessary nanoparticles during ionization. Using a soft X-ray ionizer, the charging performance was evaluated at different emitting energies (3, 4 and 9.5 keV) and for ionization time and chamber type. The results were compared with the charging performance of an Am-241 α-ray ionizer. The experimental results showed that the total charging ratio for monodispersed nanoparticles is similar between X-ray and α-ray ionizers. However, a considerable amount of nanometer-sized particle formation caused by X-ray photoionization also was detected. Particle generation was significantly dependent on gas residence time in the ionization chamber, which was caused by ion-induced nucleation. Overall, the soft X-ray photoionizer is a more suitable bipolar diffusion charger because of a superior capacity for charging large numbers of dense aerosol particles in a relatively short residence time, which can be controlled in the ionization chamber.