First-order Model Research Articles

Testing the ability of water hyacinth for wastewater treatment and methane biogas production

This study aims to assess the potential of water hyacinth (WH) in treating wastewater and its viability for co-digestion with municipal solid waste to achieve zero waste treatment by generating methane biogas. A batch flow reactor treated wastewater, evaluating nine parameters (NO3, PO4, BOD5, Turbidity, Chromium, Cadmium, Lead, Calcium, and Magnesium). The highest removal efficiencies were observed for NO3 (94.13%), PO4 (75.85%), BOD5 (100%), Turbidity (93.86%), Chromium (94.3%), Cadmium (94.93%), Lead (91.33%), Calcium (41.42%), and Magnesium (43.13%). The pH ranged from 7.82 to 7.44. Methane biogas production was examined using anaerobic digesters with varying ratios of carbon-based waste and WH, along with pH, temperature, and total solid content variations. The optimal methane biogas production ratio was found to be 1:3 for WH and solid waste at 35°C, 10% total solids, and a pH of 7.5, resulting in the highest cumulative methane generation of 1039.80 mL/gm vs. The Gompertz model accurately described methane biogas generation with a yield of 1083.088 mL/gm vs., supported by a coefficient of determination (R2) of 0.999. The kinetics of the biodegradation process were evaluated using a first-order kinetic model. The negative value of k (-0.2364) suggests a rapid solid waste biodegradation, with a high correlation coefficient (R2) of 0.9971. Numerous correlations were employed to enhance the production of methane, yielding a correlation coefficient of 91.36%.

Phycocyanin-Loaded Alginate-Based Hydrogel Synthesis and Characterization.

Phycocyanin was extracted from Spirulina platensis using conventional extraction (CE), direct ultrasonic-assisted extraction (direct UAE), indirect ultrasonic-assisted extraction (indirect UAE), and microwave-assisted extraction (MAE) methods at different temperatures, extraction intervals, stirring rate, and power intensities while maintaining the same algae to solvent ratio (1:15 w/v). The optimization of the extraction parameters indicated that the direct UAE yielded the highest phycocyanin concentration (29.31 ± 0.33 mg/mL) and antioxidant activity (23.6 ± 0.56 mg TE/g algae), while MAE achieved the highest purity (Rp = 0.5 ± 0.002). Based on the RP value, phycocyanin extract obtained by MAE (1:15 w/v algae to solvent ratio, 40 min, 40 °C, and 900 rpm) was selected as active compound in an alginate-based hydrogel formulation designed as potential wound dressings. Phycocyanin extracts and loaded hydrogels were characterized by FT-IR analysis. SEM analysis confirmed a porous structure for both blank and phycocyanin loaded hydrogels, while the mechanical properties remained approximately unchanged in the presence of phycocyanin. Phycocyanin release kinetics was investigated at two pH values using Zero-order, First-order, Higuchi, and Korsmeyer-Peppas kinetics models. The Higuchi model best fitted the experimental results. The R2 value at higher pH was nearly 1, indicating a superior fit compared with lower pH values.

Rutin and Physalis peruviana Extract: Population Pharmacokinetics in New Zealand Rabbits

Background/Objectives: An extract of calyces from Physalis peruviana with hypoglycemic activity is being considered as a potential herbal medicine. Preclinical pharmacokinetics (PK) studies of the extract in rats, focusing on plasma concentrations of its main compound, rutin, and its metabolites, revealed PK interactions in the extract matrix that improved the absorption of rutin metabolites compared to the pure compound, among other PK effects. This research aimed to study the PK of rutin alone and in the extract and assess potential PK interactions in the extract matrix on the flavonoid and its metabolites in rabbits, a nonrodent species; Methods: Animals received pure rutin or extract orally and intravenously. The PK analysis used noncompartmental and population pharmacokinetics (popPK) methods, and simple allometry was applied to predict human PK parameters; Results: The rutin concentration–time profile fit a two-compartment model with first-order elimination, while its metabolites fit a double first-order absorption model. The extract matrix led to increased absorption, distribution, and elimination of rutin as well as increased bioavailability of its metabolites in rabbits; Conclusions: The popPK model defined the equations for PK parameters describing these findings, and the increased volume of distribution and clearance of rutin was maintained in human predictions. These results will support the development of a new herbal medicine.

Biogenic fabrication of NiO-ZnO-CaO ternary nanocomposite using Azadirachta Indica (Neem) leaves extract with proficient photocatalytic degradation of rhodamine B dye

In the current study, a ternary nanocomposite (NiO–ZnO–CaO) was successfully synthesized through the co-precipitation method using Azadirachta Indica (neem) leaves extract as green approach. The characterization of prepared nanocomposite was conducted using FT-IR, XRD, SEM and EDS to determine the metal oxides absorption bands, structural properties, elemental composition, and surface morphology of the nanocomposite. FT-IR analysis revealed characteristic vibrational peaks at 621, 684, and 721 cm−1, corresponding to Ni-O, Zn-O, and Ca-O bond vibrations, respectively. XRD patterns showed distinct diffraction peaks, indicative of the hexagonal structure of ZnO and the cubic structures of NiO and CaO. EDS analysis confirmed the presence of calcium, nickel, zinc, and oxygen with weight percentages of 9.2%, 14.4%, 15.1%, and 20.9%, respectively. SEM images displayed an irregular cube like morphology with noticeable clustering within the nanocomposite. The ternary nanocomposite was employed as photocatalyst and exhibited degradation efficiency (97%) against rhodamine B dye under 180 min of irradiation time. The kinetic studies of dye on the photocatalyst surface were accurately explained by a first-order kinetics model, with all R2 > 95, signifying a strong correlation between time and dye concentration. The potocatalytic degradation mechanism of ternary nanocomposite was proposed based on band positions of of NiO, ZnO and CaO forming double type II heterojunction. Furthermore, species trapping experiments employing different scavengers were carried out, revealing the active participation of OH● and O2●─ radicals in the degradation mechanism.

Comparison of Higher-Order Beam Theory and First-Order Beam Theory Models on FGM Beam Element for Static Analysis

HOBT-Higher Order Beam Theory is the developing theory in the Finite Element Method (FEM), which considers the higher-order variation of transverse shear strain; therefore, the shear correction factor is not required. This paper uses beam elements and implements them to higher order beam theory developed by Vo et al., with two independent variables of bending and shear displacement, using third-order polynomial functions. This paper shows the contribution of the shear strain part as one of the independent variables. Static analyses are presented to obtain a comparative result with the First Order Beam Theory (FOBT) application on the imposition of different boundary conditions to ensure reliable results.

Dissipation of AM Nitrification Inhibitor in Soil Supplemented with Organic Amendments

ABSTRACT To examine the effect of organic amendments on the dissipation and nitrification inhibition activity of 2-amino 4-chloro 6-methyl pyrimidine (AM), a laboratory study was conducted on a sandy loam soil (Typic Hapludoll) receiving no organic amendments (Control) or application of farmyard manure (FYM) or pine needles biochar (PNB) @ 5 t ha−1. Dissipation of AM was initially faster during the first 7 d, and then it slowed down. Dissipation kinetics of AM conformed to the first-order model (R2 values = 0.973 to 0.981, significant at p ≤ .01). The conventionally estimated time for the dissipation of half the amount of initially detected AM (half-life) was 15.9, 23.6, and 12.1 d for the control, FYM, and PNB treatments, respectively. All treatments receiving AM had numerically lower NO3 −–N concentrations compared to no AM but significantly lower NO3 −–N concentrations were recorded under, especially the PNB + AM treatment at 15, 30, 45, 60 and 90 d. The higher effectiveness of AM under PNB could be attributed to the sorption of AM in the micro-pores of PNB with slower release. Percent distribution of sorbed AM onto FYM and PNB revealed that desorption of sorbed AM from PNB was comparatively very limited. FTIR spectra revealed that both FYM and PNB strongly retained AM. Application of AM @1 mg kg−1 soil (equivalent to 2.24 kg ha−1) seems to effectively retard the nitrification of urea in soil amended with PNB and help reduce the leaching losses of NO3 −–N in agriculture.

Mushroom polyphenol oxidase inactivation kinetics and structural changes during radiofrequency heating

The effects of radiofrequency (RF) heating on the activity, structure, and surface hydrophobicity of mushroom polyphenol oxidase (PPO) were examined and four kinetic models of inactivation (First-order model, Weibull model, Logistic model and Bi-phasic model) were fitted. The results demonstrated that RF heating could effectively inactivate PPO, logistic and Bi-phasic model attained the best fit. The circular dichroism (CD) and fluorescence spectroscopy spectra revealed that RF heating at 65 °C for 180 s caused changes in the secondary and tertiary structures of PPO, reducing the α-helix content from 38.5% to 30.3% and red-shifting the emission peak wavelength of the maximum fluorescence intensity from 318 nm to 321 nm. RF heating also caused a significant enhancement of the surface hydrophobicity of the PPO (P < 0.05). The color test results showed that RF heating could inhibit the browning of mushrooms (P < 0.05). Thus, RF heating is an effective technique for the inactivation of enzymes and further research should be performed on RF heating applications to inactivate enzymes in the food industry.

Au/ZnO nanocomposites based on simple laser ablation method for water treatment

A binary heterostructure of Au/ZnO nanocomposite is prepared in a novel way based on successive pulsed laser ablation of the two targets in an aqueous solution with varied pulse time. The development of the method allows generating a noble metal (Au) and metal oxide (ZnO) one after one to produce a novel heterostructure with a tunable shell applicable as a catalytic degradable material. The nanocomposite samples are tested to study their optical, morphological, and structural properties. The photocatalytic degradation study demonstrates a significant decrease in the absorbance intensity of Rohdamin B (RhB) dye as the irradiation time increases. It indicates a reduction in dye concentration with a remarkable improvement in contact time. Besides, the degradation percentage against pH value is investigated. It reveals an increasing trend as the pH value rises with a maximum percentage at pH 10. The study is also concerned with the influence of the initial concentration of RhB dye on the degradation efficiency of the nanocomposite. Higher initial concentrations of the dye lead to a decrease in the degradation efficiency of the catalysts. Finally, the kinetic analysis of RhB photodegradation reveals that the Langmuir-Hinshelwood first-order kinetic model accurately describes RhB's adsorption onto the nanocomposites.

Two-Stage Distributed Robust Optimization Scheduling Considering Demand Response and Direct Purchase of Electricity by Large Consumers

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.

Isotopic labeling evidence shows faster carbon release from microbial residues than plant litter

Abstract Carbon (C) release from plant and microbial residues is a primary pathway of energy flow from photosynthetic and metabolic biomass to carbon dioxide (CO2) in terrestrial ecosystems. Traditional view show that microbial residue C is more resistant to decompose than plant litter because their smaller particle sizes could be preferentially occluded in microaggregates with less microbial accessibility. However, we still lack a quantitative assessment (i.e. isotopic C labeling) to isolate the progressive release of C fractions from both plant and microbial residues. Here we used a global data set of 117 decomposition experiments that traced the 13C or 14C release of isotopically labeled plant and microbial residues to estimate the C release rates and turnover times by using a first-order exponential kinetics model. The average C release rates of crop, grass and tree litter were 7.78, 3.79 and 2.11 yr−1, which were significantly lower than microbial residues (13.07 yr−1). Although C release rates of both plant and microbial residues were positively correlated with site temperature, the mean turnover time of microbial residues was 2–6 times lower than plant litter. We suggest that a constraint in microbial and plant residues leads to a predictable pattern of C release during terrestrial decomposition, which could be included in Earth system models.

Goodness-of-fit testing in bivariate count time series based on a bivariate dispersion index

AbstractA common choice for the marginal distribution of a bivariate count time series is the bivariate Poisson distribution. In practice, however, when the count data exhibit zero inflation, overdispersion or non-stationarity features, such that a marginal bivariate Poisson distribution is not suitable. To test the discrepancy between the actual count data and the bivariate Poisson distribution, we propose a new goodness-of-fit test based on a bivariate dispersion index. The asymptotic distribution of the test statistic under the null hypothesis of a first-order bivariate integer-valued autoregressive model with marginal bivariate Poisson distribution is derived, and the finite-sample performance of the goodness-of-fit test is analyzed by simulations. A real-data example illustrate the application and usefulness of the test in practice.

Synthesis of Poly(L-lactide)-poly(ε-caprolactone)-poly(ethylene glycol) Terpolymer Grafted onto Partially Oxidized Carbon Nanotube Nanocomposites for Drug Delivery.

Nanocomposites prepared with a terpolymer of poly(L-lactide) (PLLA)-poly(ε-caprolactone) (PCL)-poly(ethylene glycol) (PEG) and partially oxidized carbon nanotubes (CNTspo) were synthesized and characterized to evaluate their ability to act as an effective nanocarrier of the anticancer drug methotrexate. The homopolymers of PLLA and PCL were synthesized through ring-opening polymerization (ROP) and characterized through gel permeation chromatography (GPC). The PLLA-PCL-PEG terpolymers were synthesized through a four-step chemical route using oxalyl chloride as a linker agent and analyzed with 1H-NMR, 13C-NMR, and FTIR spectroscopies. Additionally, the nanocomposites were characterized through FTIR, and X-ray photoelectron spectroscopy (XPS), as well as the differential scanning calorimetry (DSC) technique. XPS analysis revealed that PLLA-PCL-PEG terpolymer chains are grafted onto CNTspo. Moreover, evaluations through FTIR and DSC strongly suggest that the PCL-rich domains are preferentially oriented toward CNTspo. The release tests exhibited a "burst effect" profile, which was more evident in the terpolymers than in the nanocomposites. Five models were used to assess methotrexate's in vitro release. For the nanocomposites, the best fit to the experimental data was obtained using the first-order model, whereas the results obtained from the Korsmeyer-Peppas model indicated that Fickian diffusion drives methotrexate's release.

Macrolide-loaded nanofibrous inserts with polycaprolactone and cellulose acetate base for sustained ocular delivery: Pharmacokinetic study in Rabbit’s eye

The present study aimed to prepare nanofibrous inserts for sustained ocular drug delivery of Azithromycin (AZM) toward conquering the obstacles of conventional topical drug delivery. Nanofibers were fabricated by electrospinning using polycaprolactone (PCL) and cellulose acetate (CA) which are biocompatible and biodegradable polymers. Prepared nanofibers were evaluated in terms of physicochemical, morphological properties, pharmacokinetic study and ocular irritation. SEM images revealed average diameters of about 160 nm and 190 nm for CA and PCL nanofibers, respectively. These ocular drug delivery systems were strong, flexible, and stable under humid and dry conditions. Quantification was performed using microbiological assay by M. luteus as a microorganism. While PCL-based nanofibrous inserts released AZM in a two-step manner initiated by a burst release via Peppas kinetical model, CA-based inserts showed a gradual release profile without any burst release which followed the first-order model. Results showed that these inserts were non-cytotoxic and non-irritating. The nanofibers showed antibacterial efficacy against Escherichia coli and Staphylococcus aureus. In addition, according to a pharmacokinetic study in Rabbit’s Eye, a higher Cmax and lower Tmax were achieved by PCL nanofibers compared to CA-based ones. The pharmacokinetic study of nanofibers in rabbit eyes showed that all formulations were able to maintain the effective concentration of AZM for about 6 days. In conclusion, the prepared nanofibers can be effectively utilized for prolonged ocular delivery of AZM in the treatment of conjunctival infections.

Synthesis, characterization, and kinetic approach of a polyvinyl chloride embedded-thiosemicarbazide sequestering agent for efficient uranium adsorption from aqueous solution

A new promising polyvinyl chloride embedded-thiosemicarbazide sequestering agent (PVC-TSC), was exploited to separate U(VI) from its water solution. Specifications for PVC-TSC were carried out efficaciously using respective performances such as BET, TGA, 1H NMR, SEM-EDX, 13C NMR, FT-IR, XPS, and GC–MS analyses, which assure a satisfactory synthesis. Variables like initial pH, agitation duration, temperature, starting U(VI) concentration, PVC-TSC dosage, co-existing ions, and eluting agents have all been fine-tuned by experimental optimization. In conditions of 298 K, pH 4.5, 30 min of agitation, and 0.63 × 10−3 mol/L uranium ions, the maximal sorption competence of PVC-TSC is 30 mg/g. Based on the adsorption-distribution isotherm estimation, the experimental value of 30.12 mg/g is similar to the theoretical value that Freundlich came up with. However, Langmuir's value is higher and more in line with the practical data. Modeling of the U(VI) kinetics adsorption by PVC-TSC yielded a theoretical retaining capacity of 29.66 mg/g for first-order (PFO) modeling and 32.051 mg/g for second-order (PSO) modeling, as suggested by a hybrid PFO and PSO kinetic model. By taking into account the thermodynamical factors ΔS° (−0.063 kJ/mol.K), ΔH° (−16.2 kJ/mol), and ΔG°, the adsorption evolution was anticipated to be exothermic, spontaneous, and best performed at down temperatures. From a cost perspective, eluting the loaded PVC-TSC composite with 0.5 M H2SO4 yields a 90 % efficiency rate for U(VI) ions.

Mixed variational formulation and finite-element implementation of second-order poro-elasticity

We present a variational formulation of second-order poro-elasticity that can be readily implemented into finite-element codes by using standard Lagrangian interpolation functions. Point of departure is a two-field minimization principle in terms of the displacement and the fluid flux as independent variables. That principle is taken as a basis for the derivation of continuous and incremental saddle-point formulations in terms of an extended set of independent variables. By static condensation this formulation is then reduced to a minimization principle in terms of the displacement and fluid flux as well as associated higher-order fields. Once implemented into a finite-element code, the resulting formulation can be applied to the numerical simulation of porous media in consideration of second-order effects. Here, we analyze the model response by means of several example problems including two standard tests in poro-elasticity, namely the consolidation problems of Terzaghi and Mandel, and compare the results with those of a corresponding first-order model. As becomes clear, the second-order formulation can unleash its full potential when applied to the study of porous media having spatial dimensions comparable to the size of their microstructure. In particular, it is capable to regularize steep field gradients at external as well as internal surfaces and to describe material dilatation effects known from experiments.

Synthesis and characterization of Bi2S3@NH2-MIL125(Ti) and survey the sono-catalytic ozonation process’s efficiency in the degradation of tocilizumab from aqueous

A Bi2S3@NH2-MIL125(Ti) core–shell nanocomposite was synthesized using a solvothermal method for the degradation of tocilizumab through a sono-catalytic ozonation process. The physicochemical properties of the catalyst were characterized using FESEM, EDS, TEM, XRD, FT-IR, DRS, BET, and XPS techniques. The optimum conditions were found to be as 0.75 g/L nanocatalyst, 10 mg/L tocilizumab, 5 L/hr ozone flow, 240 W ultrasonic power, and pH 9.0 over 15 min. Under these conditions, the process achieved approximately 100 % removal of tocilizumab, 86 % reduction in COD, and 76 % reduction in TOC, with only slight decreases in efficiency after five consecutive cycles. The degradation of tocilizumab was attributed to 38 % ozonation, 20 % sorption, and 15 % sonolysis. Methanol had the most significant impact on the process, while sodium azide had the least. The primary reactive species were identified as α-Hydrogen/OH radicals, with e−-h+ pairs playing a minor role. PO43− had the greatest impact on process efficiency, while SO42− had the least. The reaction followed a first-order kinetic model with an R2 value of 0.94. The synergistic effect coefficient of 1.88 indicated a significant synergistic impact from the combined ozonation and sonolysis processes. The energy efficiency (EE/O) of the sono-catalytic ozonation process was calculated to be 66.67 kW/h-m3-order.

Z-scheme flower-like Ag/AgCl/NiZnAl-LDH for high efficiency removal of Methylene Blue: Performance and mechanism insights

3D flower-like NiZnAl-LDH was prepared, and then the Z-scheme Ag/AgCl/NiZnAl-LDH heterojunctions were further constructed by loading different amounts of AgCl on NiZnAl-LDH surface. The structure, morphology and photoelectric properties were characterized by a variety of advanced techniques, such as XRD, XPS, SEM, HRTEM and EIS. Furthermore, methylene blue, abbreviated MB, was used as the target pollutant, and the photocatalytic degradation activity of Ag/AgCl/NiZnAl-LDH on it was explored. Results displayed that the Ag/AgCl/NiZnAl-LDH photocatalysts displayed excellent photocatalytic degradation activity for MB dye compared with AgCl and NiZnAl-LDH, and the loading of different AgCl on the surface of NiZnAl-LDH has a considerable influence on the photocatalytic performance. The alkaline condition and low concentration of MB are more conducive to the photocatalytic degradation of MB by Ag/AgCl/NiZnAl-LDH. The photocatalytic process conforms to the first-order kinetic model with a rate constant of 0.0082 min−1, which is about 3.4 times that of AgCl and NiZnAl-LDH. Compared with AgCl and NiZnAl-LDH, it is found that Ag/AgCl/NiZnAl-LDH increases the separation rate of photogenerated electrons and holes, the photogenerated carrier recombination is inhibited, and the electrochemically active area is increased. The outstanding photocatalytic activity and stability make Ag/AgCl/NiZnAl-LDH a perfect material to ease the environmental crisis. Besides, combined with free radical trapping experiments, a possible photocatalytic mechanism was raised.

Examination of the psychometric properties of the Ethnic Identity Scale (EIS) and Multicultural Identity Integration Scale (MULTIIS) in a multiracial population in the United States.

Ethnic identity is theorized to be a critical aspect of human development and is shown to be associated with health and well-being. The Ethnic Identity Scale is a widely used measure that assesses key aspects of ethnic identity development (Umaña-Taylor et al., 2004). The Multicultural Identity Integration Scale (MULTIIS) is a measure that has been more recently developed to assess key aspects of identity integration for individuals with multicultural identities (Yampolsky et al., 2016). Despite the ongoing utilization of these instruments, a comprehensive psychometric evaluation within Multiracial populations has yet to be established in extant literature. Addressing this gap, the present study aims to examine the internal consistency, factor structure, and other psychometric characteristics of the Ethnic Identity Scale and MULTIIS within a sample of 1,012 Multiracial adults in the United States. The majority of the sample identified as female (67.5%, n = 683), straight (80.1%, n = 798), having attained less than a college degree (62.3%, n = 627), and having a household income less than $60,000 (57.4%, n = 552). The majority of participants (55%, n = 557) were classified as having White and non-White racial/ethnic backgrounds, 45.0% (n = 455) as non-White. Findings suggest the Ethnic Identity Scale fits the data poorly by all measures, despite supporting the three-factor structure recommended in the original study; the MULTIIS fits the data acceptably by all measures and supports both a three-factor first-order and eight-factor second-order structure recommended in the original study. Analyses of the MULTIIS three-factor first-order model's measurement invariance across race, gender, educational attainment, and household income identified variance for specific latent factors. Overall, the MULTIIS performed acceptably; however, studies relying on the MULTIIS should account for differential measurement. Implications for clinical, scientific, and public health practice are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring the usefulness of the INLA model in predicting levels of crime in the City of Johannesburg, South Africa

Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to identify high-crime areas, and optimise resource allocation and personnel management for crime prevention. Traditionally, techniques such as the Poisson model and regression analysis have been widely used for crime prediction. However, recent statistical advancements have introduced Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focuses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction. Secondly, it develops a model using no demographic information other than clustering attributes as an exogenous variable. Thirdly, it quantifies prediction uncertainty. Finally, it addresses data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model ranked areas by crime levels, obtaining a 29.3% Mean Absolute Percentage Error (MAPE) and 0.8 R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} value for crime predictions. These findings and contributions presents the potential of INLA in advancing evidence-based decision-making for crime prevention.

Machine learning based damage identification in SiC/SiC composites from acoustic emissions using autoencoders

Developing the ability to leverage machine learning (ML) to identify damage mechanisms in heterogeneous materials from their acoustic emissions (AE) has wide-reaching ramifications for multi-modal experimentation. It would allow researchers to augment damage triangulation, lifetime prediction, and high-resolution optical studies with complementary mechanism-informed data streams. However, developing this capability hinges on the collection of ground truth acoustic libraries from damage in realistic geometries. Due to time and monetary considerations, there is a dearth of ground truth libraries which can be used to robustly characterize ML mechanism identification frameworks. Addressing this gap, we present a multi-modal acoustic emission and x-ray computed tomography study where AE is gathered, and subsequently labeled, from SiC/SiC unidirectional composites under monotonic tension. This library is used to demonstrate that acoustic signals from early fiber breaks are obscured by matrix cracking. A first-order micromechanical model is used to explain the origin of this obscuring effect, and identify fundamental limitations of unsupervised frameworks. An autoencoder-based anomaly detector approach is used for the first time to overcome these limitations, additionally demonstrating that the frequency distribution of fiber break acoustic signals is narrow. Implications of these findings for enhanced multi-modal testing and online health monitoring are discussed, and strategies for implementation of supervised damage mechanism identification frameworks are proposed.