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The potentiodynamic polarization of aluminium 6063 alloy reinforced with silicon carbide (SiC) and palm kernel shell ash (PKSA) particulates at various mixing ratios were investigated. Double stir casting method was adopted for the production of the hybrid reinforced composites. The existence of the reinforcements within the matrix alloy acted as active sites for corrosion initiation. Hence, pitting corrosion was observed. The range of values for Ecorr and Icorr obtained at 24 h in 1.0 M H2SO4 were between -627.74 and -644.46 mV, and between 423.81 and 860.23 µA/cm2, respectively. The Ecorr values ranged from -654 to -697.22 mV, and the Icorr ranged from 1075.65 to 3057.16 µA/cm2 at 72 h in 1.0 M H2SO4. The relative resistance to corrosion of the samples is dependent on the thin oxide film formed on the surface of the samples.

The study examined the microstructure and mechanical properties of Al-Mg-Si alloy reinforced with palm kernel shell ash (PKSA) and silicon carbide (SiC). The alloy matrix was reinforced with SiC (2 - 8 wt.%) and PKSA (2 wt.%). The double stir-casting method was used to prepare the hybrid composite. The mechanical properties of the produced samples were evaluated based on ASTM standards. Identification of phases present in the composite was done using a PANalytical Empyrean diffractometer, while the microstructural characterization was examined using a scanning electron machine with electron dispersive spectrometer attachment. The density values increase as the SiC contents in the composites increase. As the reinforcement particulates increase, the mechanical properties of the reinforced composite improved with hardness value (73 – 85.5 BHN); yield strength (81 – 102 MPa); and ultimate tensile strength (123 – 133 MPa) compared with the matrix alloy of 73 BHN, 79 MPa, and 116 MPa, respectively. However, the percentage elongation and the fracture toughness of the reinforced samples reduced to 34.2 and 40.11%, respectively. The phases identified in the composites were Al, SiO2, Fe3Si, MgO, and SiC. The synthesized hybrid composites would be applicable as building materials such as aluminium frames and roofing sheets.

The development of engineering materials is continuously attracting attention from scientists and engineers for numerous engineering applications. The physical properties and wear mechanism of aluminium (Al 6063) matrix reinforced with silicon carbide (SiC) and palm kernel shell ash (PKSA) particulates at different weight ratios ranging from 0 to 10 wt.% with 2 wt.% intervals were investigated. The liquid route of double stir casting was employed in synthesizing the composites. The wear experiment was conducted using the Taber-type wear abrasion machine. The worn surfaces were examined using scanning electron microscopy (SEM) with energy-dispersive x-ray spectroscopy (EDS), while the intermetallic phases were examined using the x-ray diffractometer (XRD). From the result, the increase in PKSA and SiC lowered and improved the density of the composites, respectively. The percentage porosity values (2 - 2.4%) obtained in this study were found to be within the acceptable limit of less than 4% for metal matrix composites castings. The mass loss and wear index increased owing to the rotating speed and applied load increase, resulting from the occurrence of mechanical mixing between the contacting surface of the sample disk and the machined disc. Adhesive and abrasive wear mechanisms were the major mechanisms observed in this study.

The corrosion behavior of metal matrix composites (MMCs) is accelerated by the inclusion of reinforcements. Hence, this study investigates the corrosion behavior of MMCs produced from Al 6063 matrix alloy with reinforcement particulates of silicon carbide (SiC) and palm kernel shell ash (PKSA) inclusion at different mix ratios. The MMCs were synthesized using the double stir casting technique. The corrosion behaviors of the composites in NaCl solutions were studied via gravimetric analysis and electrochemical measurements. The gravimetric analysis showed fluctuating dissolution rate of the samples in NaCl solution to indicate flawed film as well as corrosion product formation over the surface of the specimens. The observed corrosion mechanism of the samples was general and pitting corrosion. The presence of reinforcements within the Al6063 matrix acted as active sites for corrosion initiation. The range of values for Ecorr and Icorr obtained in 3.5% NaCl at 24 h was between −220.62 and −899.46 mV and between 5.45 and 40.87 µA/cm2, respectively, while at 72 h, the Ecorr values ranged from 255.88 to −887.28 mV, and the Icorr ranged from 7.19 to 16.85 µA/cm2. The Nyquist and Bode plots revealed the electrochemical corrosion behavior of the samples under investigation, with predominant reactions on the surface of the samples linked to charge transfer processes. The relative resistance to corrosion of the samples depends on the thin oxide film formed on the surface of the samples.

Ligilactobacillus reuteri ZJ625 (ZJ625) and Limosilactobacillus salivarius ZJ614 (ZJ614) are important lactic acid bacteria with the potential for use as probiotics. To better understand the mechanisms of cell-to-cell interactions between ZJ625 and ZJ614 and elucidate their probiotics potentials, we studied the effects of co-culturing in defined media on the metabolomes. The strains were cultured singly (L. reuteri ZJ625 or L. salivarius ZJ614) and in co-cultures (L. reuteri ZJ625 plus L. salivarius ZJ614) and harvested at the mid-exponential phase. Single-phase extraction method was used to extract the metabolites from pre-weighed cells for the two-dimensional gas-column time-of-flight-mass spectrometry (GCxGC- TOFMS) analysis. The data was obtained using a Leco corporation ChromaTOF software and exported for statistical, pathway enrichment, and impact analyses using MetaboAnalyst 5.0. Principal component analysis (PCA) indicated an overlap of the samples from the two batches, indicating no significant batch effect. The data was then analyzed using the group allocations. This study revealed 1388 metabolites from L. reuteri ZJ625 and L. salivarius ZJ614 single and co-cultures in defined media, of which 18.87% were significantly expressed (p 1), of which 51.3% were annotated while 48.6% are currently not annotated in the databases. The whole metabolome of the strains in single and co-cultures revealed the expression of several important metabolites, including arabinofuranose, methyl-galactoside, N-acetyl-glutamic acid, phosphoric acid, decanoic acid, metyrosine, and deoxypentafuranose which play roles in different metabolic and biosynthetic pathways, hence confirming the probiotic potential as single- or multi-strain cultures. The co-culturing of the strains influenced the metabolites profiles of the strains showing additive and regulatory differential metabolites expressions. Pathway enrichment and impact analyses revealed the association of the differentially expressed metabolites with functional effects such as antihypertensive, antimicrobial, and microbiome stimulation.

Lactic acid bacteria are increasingly becoming important dietary supplements due to their health benefits when consumed in adequate quantity. The increasing attention on these important microbes has necessitated an in-depth understanding of their physiological processes, such as nutritional requirements and growth patterns, to better harness their probiotic potentials. This study was carried out to determine the nutritional requirements for the growth of L. salivarius ZJ614 and L. reuteri ZJ625 from a chemically defined medium and evaluate growth kinetics by fitting different sigmoidal growth models. The complete CDM contains 49 nutritional ingredients such as glucose, Tween 80®, mineral salts, buffers, amino acids, vitamins, and nucleotides at defined concentrations. In addition, the minimal nutritional requirements of the isolates were determined in a series of single-omission experiments (SOEs) to compose the MDM. Growth curve data were generated by culturing in an automated 96-well micro-plate reader at 37°C for 36 h, and photometric readings (optical density: OD600) were taken. The data were summarized in tables and charts using Microsoft Excel, while growth evaluation was carried out using open-source software (Curveball) on Python. The results revealed that omission of the amino acids, vitamins, and nucleotides groups resulted in 2.0, 20.17, and 60.24% (for L. salivarius ZJ614) and 0.95, 42.7, and 70.5% (for L. reuteri ZJ625) relative growths, respectively. Elimination of the individual CDM components also indicates varying levels of growth by the strains. The growth curve data revealed LogisticLag2 and Baranyi–Roberts models as the best fits for L. reuteri ZJ625 and L. salivarius ZJ614, respectively. All the strains showed appreciable growth on the CDM and MDM as observed in de Man–Rogosa–Sharpe (MRS) broth. We also described the growth kinetics of L. reuteri ZJ625 and L. salivarius ZJ614 in the CDM, and the best models revealed the estimated growth parameters.

Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We searched for papers using soft-computing and statistical learning models focused on T2DM published between 2010 and 2021 on three different search engines. Of 1215 studies identified, 34 with 136952 patients met our inclusion criteria. The pooled algorithm’s performance was able to predict T2DM with an overall accuracy of 0.86 (95% confidence interval [CI] of [0.82, 0.89]). The classification of diabetes prediction was significantly greater in models with a screening and diagnosis (pooled proportion [95% CI] = 0.91 [0.74, 0.97]) when compared to models with nephropathy (pooled proportion = 0.48 [0.76, 0.89] to 0.88 [0.83, 0.91]). For the prediction of T2DM, the decision trees (DT) models had a pooled accuracy of 0.88 [95% CI: 0.82, 0.92], and the neural network (NN) models had a pooled accuracy of 0.85 [95% CI: 0.79, 0.89]. Meta-regression did not provide any statistically significant findings for the heterogeneous accuracy in studies with different diabetes predictions, sample sizes, and impact factors. Additionally, ML models showed high accuracy for the prediction of T2DM. The predictive accuracy of ML algorithms in T2DM is promising, mainly through DT and NN models. However, there is heterogeneity among ML models. We compared the results and models and concluded that this evidence might help clinicians interpret data and implement optimum models for their dataset for T2DM prediction.

Genetic improvement of goat breeds in growth and other traits (e.g. milk production) is limited by the demographics of the goat herds, extensive production system and the seemingly long-term nature of improvement through traditional genetics and breeding methods. We studied the genetic morphometry in Nigerian goats and South African Kalahari Red goat breeds. A total of 192 goats belonging to three Nigerian breeds (Red Sokoto (RS), Sahel (SH) and West African Dwarf (WAD)) and one South African Kalahari Red (KR) goat breed were analysed. Animals were classified into four age groups: A group – less than 1 year, B group – between 1 and 2 years, C group – between 2 and 3 years and C group – older than 3 years based on dentition. Analysis of variance, correlation matrix, regression and discriminant analyses were used to evaluate morphological variability. Results revealed that the effect of breed on the measured morphometric traits was significant (P < 0.05). The best prediction equation for body weight (BW) with R2 = 0.84 was obtained when body length (BL), withers height (WH) and chest depth (CD) were included in the model for KR goat. Growth traits were positively correlated with each other with the highest correlation coefficients found between BL and BW (r = 0.877, P < 0.01), WH and BW (r = 0.541, P < 0.01), WH and BW (0.661, P < 0.01) and CD and BW (0.738, P < 0.01) in KR, RS, SH and WAD goats, respectively, which are important for a conscious selection and breeding programme for desired traits. Stepwise discriminant procedure showed that WH, CD and BL were the most discriminating variables to separate KR, RS, SH and WAD goats. In accessing morphological diversity, efforts should be made to include phenotypic variables of at least ≥ 3 in order to minimize ambiguity in classification. Based on the pair-wise distances from the Discriminant function, the study provided informed decision, reference information on goat breeding and conservation strategy.

Teak wood is one of the prominently used raw material in the construction industry, thus contributing extremely to the biomass waste available in Nigeria. These wastes are usually used for energy generation that requires upgrade into better fuel before application. Hence, the present study evaluates the non-isothermal kinetic parameters for pyrolysis of teak wood using model-fitting techniques. Teak wood dust was subjected to proximate, ultimate and calorific value analyses based on different ASTM standards. The thermal degradation and decomposition behaviour of the teak wood dust was examined using a thermogravimetric analyzer. Pulverized teak (6.5 mg) was heated from 30 to 800 ºC at varying heating rates (5, 10 and 15 ºC) in an environment where 100 mL/min of nitrogen gas was charged in continuously to maintain an inert condition. Avrami-Erofeev, Ginstling-Broushtein (GB) and Mampel models were used to evaluate the kinetic parameters of the pyrolysis of teak wood dust. The teak wood dust contained 7.25 % moisture, 79.26 % volatile matter (VM), 1.74 % ash and 11.75 % fixed carbon. The calorific value of the wood dust was 18.72 MJ/kg. The results of the thermogravimetric analyses depicted that heating rate has no effect on weight loss during the reactive drying zone. However, as the thermal treatment progressed into the active pyrolysis and passive pyrolysis zones, the weight loss decreased with increase in heating rate. The devolatilization parameters also increased with heating rates except for the maximum conversion. The results of the kinetic parameters evaluation revealed that the GB model was best fit to evaluate the kinetic parameters of teak in the active pyrolysis zone while GB and Mampel models were considered most appropriate for the evaluation of the kinetic parameters in the passive pyrolysis zone. Model-fitting method has the capacity to capture a wide range of fractional conversion at a glance. HIGHLIGHTS Arrhenius parameters in terms of activation energy and pre-exponential factor for the pyrolysis of teak wood while comparing 4 different model-fitting techniques were obtained The α-temperature plot for solid state reaction of teak wood dust was a bell-shape (sigmoidal model) The Avrami-Erofeev and SSS models were unable to capture the overlapping multiple reactions that took place simultaneously at the active pyrolysis zone Higher energy input is needed for devolatilization of teak wood dust to give 10 - 80 % conversion due to higher activation energy at the active pyrolysis zone Ginstling-Broushtein was found to be the best model for evaluating the kinetic parameters at the active pyrolysis zone as it had the highest R2 value GRAPHICAL ABSTRACT

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

Proactive monitoring and control of indoor air quality in homes where there are pregnant mothers and infants is essential for healthy development and well-being of children. This is especially true in low income households where cooking practices and exposure to harmful pollutants produced by nearby industries can negatively impact on a healthy home environment. Interdisciplinary expert knowledge is required to make sense of dynamic and complex environmental phenomena from multivariate low level sensor observations and high level human activities to detect health risks and enact decisions about control. We have developed an ontology for indoor environmental quality monitoring and control based on an ongoing real world case study in Durban, South Africa. We implemented an Indoor Air Quality Index and a thermal comfort index which can be automatically determined by reasoning on the ontology. We evaluated the ontology by populating it with test sensor data and showing how it can be queried to analyze health risk situations and determine control actions. Our evaluation shows that the ontology can be used for real world indoor monitoring and control applications in resource constrained settings.