Preferred Model Research Articles

Leptomeningeal metastases from solid tumors: A Society for Neuro-Oncology and American Society of Clinical Oncology consensus review on clinical management and future directions.

Leptomeningeal metastases (LM) are increasingly becoming recognized as a treatable, yet generally incurable, complication of advanced cancer. As modern cancer therapeutics have prolonged the lives of patients with metastatic cancer, specifically in patients with parenchymal brain metastases, treatment options, and clinical research protocols for patients with LM from solid tumors have similarly evolved to improve survival within specific populations. Recent expansions in clinical investigation, early diagnosis, and drug development have given rise to new unanswered questions. These include leptomeningeal metastasis biology and preferred animal modeling, epidemiology in the modern cancer population, ensuring validation and accessibility of newer leptomeningeal metastasis diagnostics, best clinical practices with multimodality treatment options, clinical trial design and standardization of response assessments, and avenues worthy of further research. An international group of multi-disciplinary experts in the research and management of LM, supported by the Society for Neuro-Oncology and American Society of Clinical Oncology, were assembled to reach a consensus opinion on these pressing topics and provide a roadmap for future directions. Our hope is that these recommendations will accelerate collaboration and progress in the field of LM and serve as a platform for further discussion and patient advocacy.

Mode selection for component mode synthesis with guaranteed assembly accuracy

In this work, a modular approach is introduced to select the most important eigenmodes for each component of a composed structural dynamics system to obtain the required accuracy of the reduced-order assembly model. To enable the use of models of complex (structural) dynamical systems in engineering practice, e.g., in a design, optimization and/or control context, the complexity of the models needs to be reduced. When the model consist of an assembly of multiple interconnected structural components, component mode synthesis is often the preferred model reduction method. The standard approach to component mode synthesis for such system is to select the eigenmodes of a component that are most important to accurately model the dynamic behaviour of this component in a certain frequency range of interest. However, often, a more relevant goal is to obtain, in this frequency range, an accurate model of the assembly. In the proposed approach, accuracy requirements on the level of the assembly are translated to accuracy requirements on component level, by employing techniques from the field of systems and control. With these component-level requirements, the eigenmodes that are most important to accurately model the dynamic behaviour of the assembly can be selected in a modular fashion. We demonstrate with two structural dynamics benchmark systems that this method based on assembly accuracy allows for a computationally efficient selection of eigenmodes that (1) guarantees satisfaction of the assembly accuracy requirements and (2) results in most cases in reduced-order models of significantly lower order with respect to the industrial standard approach in which component eigenmodes are selected using a frequency criterion.

Aspect-enhanced Explainable Recommendation with Multi-modal Contrastive Learning

Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, fostering a sense of confidence and reliability in the system’s outputs. Generally, the explanations are presented in a familiar and intuitive way, which is in the form of natural language, thus enhancing their accessibility to users. Recently, there has been an increasing focus on leveraging reviews as a valuable source of rich information in both modeling user-item preferences and generating textual interpretations, which can be performed simultaneously in a multi-task framework. Despite the progress made in these review-based recommendation systems, the integration of implicit feedback derived from user-item interactions and user-written text reviews has yet to be fully explored. To fill this gap, we propose a model named SERMON (A s pect-enhanced E xplainable R ecommendation with M ulti-modal C o ntrast Lear n ing). Our model explores the application of multimodal contrastive learning to facilitate reciprocal learning across two modalities, thereby enhancing the modeling of user preferences. Moreover, our model incorporates the aspect information extracted from the review, which provides two significant enhancements to our tasks. Firstly, the quality of the generated explanations is improved by incorporating the aspect characteristics into the explanations generated by a pre-trained model with controlled textual generation ability. Secondly, the commonly used user-item interactions are transformed into user-item-aspect interactions, which we refer to as interaction triple, resulting in a more nuanced representation of user preference. To validate the effectiveness of our model, we conduct extensive experiments on three real-world datasets. The experimental results show that our model outperforms state-of-the-art baselines, with a 2.0% improvement in prediction accuracy and a substantial 24.5% enhancement in explanation quality for the TripAdvisor dataset.

KGR: A Kernel-Mapping Based Group Recommender System Using Trust Relations

A massive amount of information explosion over the internet has caused a possible difficulty of information overload. To overcome this, Recommender systems are systematic tools that are rapidly being employed in several domains such as movies, travel, E-commerce, and music. In the existing research, several methods have been proposed for single-user modeling, however, the massive rise of social connections potentially increases the significance of group recommender systems (GRS). A GRS is one that jointly recommends a list of items to a collection of individuals based on their interests. Moreover, the single-user model poses several challenges to recommender systems such as data sparsity, cold start, and long tail problems. On the contrary hand, another hotspot for group-based recommendation is the modeling of user preferences and interests based on the groups to which they belong using effective aggregation strategies. To address such issues, a novel “KGR” group recommender system based on user-trust relations is proposed in this study using kernel mapping techniques. In the proposed model, user-trust networks or relations are exploited to generate trust-based groups of users which is one of the important behavioral and social aspects. More precisely, in KGR the group kernels and group residual matrices are exploited as well as seeking a multi-linear mapping between encoded vectors of group-item interactions and probability density function indicating how groups will rate the items. Moreover, to emphasize the relevance of individual preferences of users in a group to which they belong, a hybrid approach is also suggested in which group kernels and individual user kernels are merged as additive and multiplicative models. Furthermore, the proposed KGR is validated on two different trust-based datasets including Film Trust and CiaoDVD. In addition, KGR outperforms with an RMSE value of 0.3306 and 0.3013 on FilmTrust and CiaoDVD datasets which are lower than the 1.8176 and 1.1092 observed with the original KMR.

Does childhood adversity affect household portfolio decisions? Evidence from the Chinese Great Famine

We employ the 1959–1961 Chinese Great Famine as a quasi-natural experiment to examine the relationship between experiencing adversity in childhood and financial decisions in adulthood. Using data from the 2017 China Household Finance Survey and the intensity of excess deaths during the Great Famine, results from our preferred two-part fractional regression model suggest that, for an additional excess death per thousand people during the Famine, cohorts who were in their infancy and early childhood during the Famine are 0.2–0.3 percentage points less likely to hold risky financial assets than other cohorts. We also find that, conditional on the decision to hold risky assets, those who experienced an additional excess death per thousand people during the Famine in their middle to late childhood hold 1.1 percentage points higher share of risky assets in their household financial portfolios than those who did not experience the Famine. We explore several potential mechanisms and find that financial literacy, risk-taking preferences and locus of control are channels through which childhood adversity in the famine years affects household portfolio decisions. Our findings are robust to a series of sensitivity checks.

Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning.

Retrospective multi-site cohort study. To develop an accurate machine learning predictive model using predictor variables from the acute rehabilitation period to determine ambulatory status in spinal cord injury (SCI) one year post injury. Model SCI System (SCIMS) database between January 2000 and May 2019. Retrospective cohort study using data that were previously collected as part of the SCI Model System (SCIMS) database. A total of 4523 patients were analyzed comparing traditional models (van Middendorp and Hicks) compared to machine learning algorithms including Elastic Net Penalized Logistic Regression (ENPLR), Gradient Boosted Machine (GBM), and Artificial Neural Networks (ANN). Compared with GBM and ANN, ENPLR was determined to be the preferred model based on predictive accuracy metrics, calibration, and variable selection. The primary metric to judge discrimination was the area under the receiver operating characteristic curve (AUC). When compared to the van Middendorp all patients (0.916), ASIA A and D (0.951) and ASIA B and C (0.775) and Hicks all patients (0.89), ASIA A and D (0.934) and ASIA B and C (0.775), ENPLR demonstrated improved AUC for all patients (0.931), ASIA A and D (0.965) ASIA B and C (0.803). Utilizing artificial intelligence and machine learning methods are feasible for accurately classifying outcomes in SCI and may provide improved sensitivity in identifying which individuals are less likely to ambulate and may benefit from augmentative strategies, such as neuromodulation. Future directions should include the use of additional variables to further refine these models.

The mathematical model for colour preference in Chinese painting within museum lighting environments

The mathematical model for colour preference in Chinese painting within museum lighting environments

Improving query efficiency of black-box attacks via the preference of deep learning models

Black-box query attacks are effective at compromising deep-learning models using only the model's output. These attacks typically face challenges with low attack success rates (ASRs) when limited to fewer than ten queries per example. Recent approaches have improved ASRs due to the transferability of initial perturbations, yet they still suffer from inefficient querying. Our study introduces the Gradient-Aligned Attack (GAA) to enhance ASRs with minimal perturbation by focusing on the model's preference. We define a preference property where the generated adversarial example prefers to be misclassified as the wrong category with a high initial confidence. This property is further elucidated by the gradient preference, suggesting a positive correlation between the magnitude of a coefficient in a partial derivative and the norm of the derivative itself. Utilizing this, we devise the gradient-aligned CE (GACE) loss to precisely estimate gradients by aligning these coefficients between the surrogate and victim models, with coefficients assessed by the victim model's outputs. GAA, based on the GACE loss, also aims to achieve the smallest perturbation. Our tests on ImageNet, CIFAR10, and Imagga API show that GAA can increase ASRs by 25.7% and 40.3% for untargeted and targeted attacks respectively, while only needing minimally disruptive perturbations. Furthermore, the GACE loss reduces the number of necessary queries by up to 2.5x and enhances the transferability of advanced attacks by up to 14.2%, especially when using an ensemble surrogate model. Code is available at https://github.com/HaloMoto/GradientAlignedAttack.

Using machine learning algorithms to identify farms on the 2022 Census of Agriculture

As is the case for many National Statistics Institutes, the United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) has observed dwindling survey response rates, and the requests for more information at finer temporal and spatial scales have led to increased response burdens. Non-survey data are becoming increasingly abundant and accessible. Consequently, NASS is exploring the potential to complete some or all of a survey record using non-survey data, which would reduce respondent burden and potentially lead to increased response rates. In this paper, the focus is on a large set of records associated with potential farms, which are operations with undetermined farm status (farm/non-farm) and are referred to here as operations with unknown status (OUS). Although they usually have some agriculture, most OUS records are eventually classified as non-farms. Those OUS that are classified as farms tend to have higher proportions of producers from under-represented groups compared to other records. Determining the probability that an OUS record is a farm is an important step in the imputation process. The OUS records that responded to the 2017 U.S. Census of Agriculture were used to develop models to predict farm status using multiple data sources. Evaluated models include bootstrap random forest (RF), logistic regression (LR), neural network (NN), and support vector machine (SVM). Although the SVM had the best outcomes for three of the five metrics, the sensitivity for identifying farms was the lowest (13.8%). The NN model had a sensitivity of 80.5%, which was substantially higher than the other models, and its specificity of 45.3% was the lowest of all models. Because sensitivity was the primary metric of interest and the NN performed reasonably well on the other metrics, the NN was selected as the preferred model.

The N-S direction strike-slip activities in the Pamir hinterland under oblique convergence: the 2015 and 2023 earthquakes

SUMMARY The prominent Pamir plateau holds considerable significance in comprehending the processes of Asian continental collisional orogeny. However, due to harsh natural conditions and low seismic activity within the Pamir hinterland, our understanding of this region remains deficient. Recent major events and the accumulation of geodetic observations present a rare opportunity for us to get insights into the tectonic activities and orogenic processes occurring in this region. First, employing Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Synthetic Aperture Radar (SAR) images, we acquire coseismic displacements associated with the most recent earthquakes in 2015 and 2023. Subsequently, we conduct the source models inversion with the constraints of surface displacements based on a finite-fault model. Our results reveal displacements ranging from −0.8 to 0.8 m for the 2015 Mw 7.2 Tajik earthquake and −0.25 to 0.25 m for the 2023 Mw 6.9 Murghob event, respectively. The optimal three-segment model for the 2015 event ruptured a fault length of 89 km with a surface rupture extending 59 km along the Sarez–Karakul fault (SKF), characterized predominantly by left-lateral strike-slip motion, with a maximum slip of 3.5 m. Meanwhile, our preferred uniform slip model suggests that the 2023 event ruptured an unmapped fault in the southern Pamir region with a strike angle of 31° and a dip angle of 76.8°. The distributed slip model indicates that the 2023 event ruptured a fault length of 32 km, resulting in an 8 km surface rupture. This event is characterized by left-lateral strike slip, with a peak slip of 2.2 m. Secondly, the Coulomb stress calculations demonstrate that the 2023 event was impeded by the 2015 event. Finally, interseismic Global Positioning System data revel a relative motion of 3.4–5.7 mm yr−1 in the N-S component and 3.2–3.8 mm yr−1 in the E-W component along the SKF in the Pamir hinterland, respectively. These N-S direction strike-slip activities and slip behaviours support an ongoing strong shear and extension in the Pamir regime, which is a response to the oblique convergence between the Indian and Eurasian plates.

Enhancing conversational recommendation systems through the integration of KNN with ConLinUCB contextual bandits

In recommender system research, contextual multi-armed bandits have shown promise in delivering tailored recommendations by utilizing contextual data. However, their effectiveness is often curtailed by the cold start problem, arising from the lack of initial user data. This necessitates extensive exploration to ascertain user preferences, consequently impeding the speed of learning. The advent of conversational recommendation systems offers a solution. Through these systems, the conversational contextual bandit algorithm swiftly learns user preferences for specific key-terms via interactive dialogues, thereby enhancing the learning pace. Despite these advancements, there are limitations in current methodologies. A primary issue is the suboptimal integration of data from key-term-centric dialogues and arm-level recommendations, which could otherwise expedite the learning process. Another crucial aspect is the strategic suggestion of exploratory key phrases. These phrases are essential in quickly uncovering users potential interests in various domains, thus accelerating the convergence of accurate user preference models. Addressing these challenges, the ConLinUCB framework emerges as a groundbreaking solution. It ingeniously combines feedback from both arm-level and key-term-level interactions, significantly optimizing the learning trajectory. Building upon this, the framework integrates a K-nearest neighbour (KNN) approach to refine key-term selection and arm recommendations. This integration hinges on the similarity of user preferences, further hastening the convergence of the parameter vectors.

Embedding Public Involvement in a PhD Research Project With People Affected by Advanced Liver Disease.

Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members.

Patterns of Causative Faults of Normal Earthquakes in the Fluid‐Rich Outer Rise of Northeastern Japan, Constrained With 3D Teleseismic Waveform Modeling

AbstractAccurate earthquake source parameters are crucial for understanding plate tectonics, yet, it is difficult to determine these parameters precisely for offshore events, especially for outer‐rise earthquakes, as the limited availability of direct P or S wave data sets from land‐based seismic networks and the unsuitability of simplified 1D methods for the complex 3D structures of subducting systems. To overcome these challenges, we employ an efficient hybrid numerical simulation method to model these 3D structural effects on teleseismic P/SH and P‐coda waves and determine the reliable centroid locations and focal mechanisms of outer‐rise normal‐faulting earthquakes in northeastern Japan. Two M6+ events with reliable locations from ocean bottom seismic observations are utilized to calibrate the 3D velocity structure. Our findings indicate that 3D synthetic waveforms are sensitive to both event location, thanks to bathymetry and water reverberation effects, and the shallow portion of the lithospheric structure. With our preferred velocity model, which has Vs ∼16% lower than the global average, event locations are determined with uncertainties of <5 km for horizontal position and <1 km for depth. The refined event locations in a good match between one of the nodal strikes and the high‐resolution bathymetry, enabling the determination of the causative fault plane. Our results reveal that trench‐ward dipping normal faults are more active, with three parallel to the trench as expected, while five are associated with the abyssal hills. The significant velocity reduction in the uppermost lithosphere suggests abundant water migrating through active normal faults, enhancing both mineral alteration and pore density.

Paramedical staffs' perception of their role during the continuum of care from detection to treatment in nursing homes: Results of the ONCOPAD-Pre survey.

e13768 Background: Prevalence of cancer in nursing homes (NH) increases. ONCOPAD is a 4-step program dedicated to identifying and acting on educational needs, cultural and ethical barriers associated with cancer treatment in NH. The program included a scoping review (step 1), semi-directive interviews with care providers (step 2) designed to identify the major topics to be included in a tailored intervention and evaluated before and after it. Here we report the results of the survey before the intervention. Methods: Questions were mainly presented as 7-point Likert scales and classified into 6 sections: interviewee’s description, detection phase, treatment phase, relations and communication at work, ethical questioning, continuing education. Statistical analyses included descriptive statistics, exploratory factorial analyses, testing of model sphericity and sampling adequation. Results: From 06/2022 to 03/2023, 65 participants (median age 41 years; females: 84.6%) completed the questionnaire; 40.0% were nurses, 35.4% nurse assistants; 87.7% were full-time employees, and 73.8% were post holder. Interviewees considered their role as important at the detection stage (mean 4.82), to inform the physician (5.58) and their colleagues (5.45), their role during the decisional process (4.40) and to inform the patient on his diagnosis (4.05) were perceived as less important; organizational problems (lack of staff, time, facilities) could impede this stage. Factors perceived important for treatment decision were related to the patient (physical performance, cognition, age), his opinion (4.49), the opinion of his family (4.38), of his physician (5.17) and less to the paramedical team (4.02). The decision to pursue was mainly focused on care: pain relief (5.48), detection of clinical and psychological signs (5.18); the organizational barriers were perceived as major as well. Relations and communication at work, both internal and external to the institutions, were perceived as poor quality; 8 (12.3%) of the interviewees even skipped this section. The ethical questioning was permanent and homogeneous. Considering education, the interviewees felt insufficiently trained and the preferred model for training was short sequences integrated into daily work schedules; coordinating physicians appear as good mediators for such training courses. Conclusions: In this survey, paramedical staffs perceived their role as important during both detection and treatment of cancers, focusing more on care than cure; however, organizational constraints and poor quality of internal and external relations, and communication appear as major barriers in the completion of their role. Educational intervention should be tailored, favoring short sequences integrated into daily work schedules, coordinating physicians appear as good mediators for improvement in the future.

The modified placenta model for microsurgery training: Recommended methods for comprehensive microsurgery experience

PurposeCurrently, rats are the most preferred models for microsurgery training. However, living rats come with several limitations. The use of the placenta in this field is still restricted. Our aim was to increase the realism of the placenta model by introducing several improvements such as pulsation and thrombus models. Additionally, it was also aimed to provide a specific time schedule for the reliable use of the placenta. MethodsThe study included 30 placentas and 10 participants. Placentas were divided into three groups; Group A (ten placentas) were allocated for training. At the end of each training day, trainees were asked to complete a questionnaire. To obtain samples for histopathological evaluation another group (Group B) was formed. The placentas of both of the groups (A and B) were used as fresh at the first day and then stored in the refrigerator (under 4 ​°C) for the following 3 days. The remaining ten placentas (Group C) were kept at room temperature. This group was also subjected to histopathological analysis. Histopathological evaluation was performed to determine the necrosis ratio within the layers of Endothelium and Media (E/M). Vessels were divided into 6 levels and microscopically analysis was performed to determine the diameters of these vessels. ResultsThe average questionnaire scores throughout the first three days were high enough to indicate a better/significantly better microsurgery experience compared to that on rats. Subsequent necrosis ratios (E/M) were relatively low during the first three days which confirms the results of the questionnaire. This is mainly attributed to the cold ischemia precautions taken during the study. The vessel diameters (A/V) of the placenta from level 1 to level 6 were as follows; 2.95/6.64, 3.56/3.84, 2.65/2.97, 1.63/1.82, 1.19/1.48 and 0.56/0.49 ​mm respectively. ConclusionThe inclusion of pulsation and thrombus models in the modified placenta model enhances the realism of the microsurgery training experience. According to trainee feedback, the modified placenta model offered a superior training experience compared to living rats during the first three days. We recommend integrating this model with a living rat in a hybrid approach to obtain a comprehensive microsurgery training program.

On the Initial Spin Period Distribution of Neutron Stars

We derive the initial spin period distribution of neutron stars by studying the population of young pulsars associated with supernova remnants. Our hierarchical Bayesian approach accounts for the measurement uncertainties of individual observations and selection effects. Without correcting for selection effects, as done in previous studies, we find that pulsar initial spin periods follow a Weibull distribution, peaking at 40 ms, which is favored against a lognormal distribution with a Bayes factor of 200. The known selection effects in radio pulsar surveys, including pulse broadening and period-dependent beaming fraction, have been quantitatively investigated. We show that, based on measurements of pulsar luminosity and spin period from the ATNF Pulsar Catalogue, the impact of pulse broadening on the inference of the pulsar period distribution is likely to be insignificant. Correcting for the beaming selection effect, the Weibull distribution remains the preferred model, while its peak slightly shifts to longer periods at 50 ms. Our method will prove useful in constraining the birth properties of neutron stars in the Square Kilometre Array era.

Aqueous size exclusion chromatography applied to polymer analysis: experimental conditions and molecular weight calibration curves

Aqueous mode size exclusion chromatography (SEC) was employed for the analysis and construction of molecular weight (MW) calibration curves of three water-soluble polymers, namely, polyethylene glycol, polyethylene oxide, and polyacrylic acid sodium salt. Several calibration curves were obtained, varying chromatographic conditions such as columns arrangement, ionic strength, temperature and pH, in addition trends in polymeric chromatographic behavior were examined. The variation in SEC distribution coefficients at different temperatures was found to be below 10 %, indicating that the studied polymers follow an ideal SEC mechanism under the tested conditions. Thus, differences in chromatographic behavior were ascribed to changes in polymer configuration induced by media and/or temperature. These variations in morphology were consistent with the observed SEC behavior.Regarding MW calibration, polynomial regression models ranging from first to fifth order were applied, and the most adequate ones were selected based on their fit and prediction capabilities. Third order polynomials were the preferred models for polyethylene glycol and polyacrylic acid sodium salt, independently of chromatographic conditions. Meanwhile for polyethylene oxide, either third or fifth-order polynomial models were optimal depending on the chromatographic conditions. All the selected regression models presented coefficients of multiple determination (R2) above 0.990, while achieving relative errors of prediction (REP%) in MW ranging from 0.3 to 4 % for cross-validation.

Spatial Distribution of Earthquake Occurrence for the New Zealand National Seismic Hazard Model 2022

ABSTRACT We develop candidate hybrid models representing the spatial distribution of earthquake occurrence in New Zealand over the next 100 yr. These models are used within the onshore/near-shore, shallow component of the distributed seismicity model within the New Zealand National Seismic Hazard Model 2022. They combine a variety of spatially gridded covariates based on smoothed seismicity, strain rates, and proximity to mapped faults and plate boundaries in both multiplicative and additive hybrids. They were optimized against a standardized catalog of New Zealand earthquakes with magnitude M ≥ 4.95 and hypocentral depth ≤40 km from 1951 to 2020. We extract smoothed seismicity covariates using three different methods. The additive models are linear combinations of earthquake likelihood models derived from individual covariates. We choose three preferred hybrid models based on the information gain statistics, consideration of the ongoing Canterbury sequence and regions of low seismicity, and inclusion of the most informative covariates. Since the hazard model is designed for the next 100 yr, the preferred hybrid models are also combined with 20-year earthquake forecasts from the “Every Earthquake a Precursor According to Scale” model. Thus, in total, six hybrid spatial distribution candidates are advanced for sensitivity analyses and expert elicitation for inclusion in the final logic tree for the New Zealand National Seismic Hazard Model.

The The impact of credit risk on the profitability of banks in South Africa

The purpose of this study is to determine the impact of credit risk on the profitability of commercial banks in South Africa. This study purposefully chose five banks that control more than 70 % of the market in South Africa. The secondary balanced panel data was gathered from each bank's audited annual financial statements over a ten years (2013-2022). This study was conducted using an explanatory study as our research design. The return on equity was used to measure profitability, while the credit loss ratio, the ratio of loss allowances to total loans and advances, and the ratio of total loans and advances to total assets were used to assess credit risk for each bank. The Variance Inflation Factor was used to test for multicollinearity, while Hausman model specification test was used to determine the preferred model for the study. The data were analysed using a balanced panel data regression with the random effects model. The study's findings show that the credit loss ratio has a positive effect on profitability, whereas the ratio of loss allowances to total loans and advances has a negative effect. The ratio of total loans to total assets was discovered to have a positive and significant relationship with bank profitability. As a result, this study concluded that banks should be aggressive in deploying deposits to extend credit to customers while maintaining effective credit management practices.

Application of growth models to South African Boer goat castrates and does under feedlot conditions

Mathematical models may aid researchers in describing biological processes, like growth, in animals. This study aimed to collect the body weight data of 18 Boer goat castrates and 20 Boer goat does, from birth until maturity, to model growth and determine growth trends. This is a novel investigation as sufficient information on an age-weight database for these two Boer goat sexes from birth to maturity, is lacking. Using age-weight data, four nonlinear models, namely the Brody, Gompertz, Logistic and Von Bertalanffy growth models, were plotted and evaluated. The model parameters of each growth model were compared for differences between the two sexes. The statistical effectiveness of fit was determined for each model using AIC and RMSE, with R2 also being considered. All models except the Brody model, predicted significantly heavier mature weights for castrates. The Brody model was deemed unfit to describe Boer goat growth as the function severely over-predict weights from birth until maturity for both sexes. The Von Bertalanffy (R2 = 91.3) and Gompertz functions (R2 = 91.3) showed the best fit for Boer goat castrates, while the Gompertz model (R2 = 95.1) showed the best fit for Boer goat does. The Gompertz function is the preferred model to depict Boer goat growth overall, as it accurately characterized growth of both sexes. According to the Gompertz model the age at which the inflection point of the growth curve was reached, did not differ significantly between castrates and does (141.80 days versus 136.31 days). There was also no significant difference in maturation rate between the two sexes.