Worst Case Research Articles

Assessing antiviral treatment efficacy and risk factors for severe COVID-19 in kidney transplant recipients during the Omicron subvariant-dominant period: a retrospective study

BackgroundKidney transplant recipients (KTRs) are at risk of severe coronavirus disease 2019 (COVID-19), and even now that Omicron subvariants have become dominant, cases of severe disease are certain to occur. The aims of this retrospective study were to evaluate the efficacy of antiviral treatment for COVID-19 and to identify risk factors for severe disease in KTRs during Omicron subvariant-dominant periods.MethodsA total of 65 KTRs diagnosed with COVID-19 who received antiviral treatment between July 2022 and September 2023 were analyzed. Mild cases received oral molnupiravir (MP) as outpatient therapy, while moderate or worse cases received intravenous remdesivir (RDV) as inpatient therapy. In principle, mycophenolate mofetil was withdrawn and switched to everolimus. We investigated the efficacy of antiviral treatment and compared the clinical parameters of mild/moderate and severe/critical cases to identify risk factors for severe COVID-19.ResultsAmong 65 cases, 49 were mild, 6 were moderate, 9 were severe, and 1 was of critical severity. MP was administered to 57 cases; 49 (86%) improved and 8 (14%) progressed. RDV was administered to 16 cases; 14 (87%) improved and 2 (13%) progressed. Seventeen (26%) cases required hospitalization, and none died. Comparisons of the severe/critical group (n = 10) with the mild/moderate group (n = 55) demonstrated that the severe/critical group had a significantly higher median age (64 vs. 53 years, respectively; p = 0.0252), prevalence of diabetes (70% vs. 22%, respectively; p = 0.0047) and overweight/obesity (40% vs. 11%, respectively; p = 0.0393), as well as a significantly longer median time from symptom onset to initial antiviral therapy (3 days vs. 1 day, respectively; p = 0.0026). Multivariate analysis showed that a longer time from symptom onset to initial antiviral treatment was an independent risk factor for severe COVID-19 (p = 0.0196, odds ratio 1.625, 95% confidence interval 1.081–2.441).ConclusionThese findings suggest that a longer time from symptom onset to initial antiviral treatment is associated with a higher risk of severe COVID-19 in KTRs. Initiating antiviral treatment as early as possible is crucial for preventing severe outcomes; this represents a valuable insight into COVID-19 management in KTRs.

Implementing policies and predictive stochastic models to restrict borderline personality disorder’s access to restricted medications: comorbidity with factitious disorder, functional neurological disorder and medically unexplained symptoms

IntroductionWe are facing increased access to hospital beds and increased use of restricted medications by people with borderline personality disorder (BPD). Our former research shows BPD comorbidity with factitious conditions, functional neurological disorder and medically unexplained symptoms. We also registered that persons with BPD might craft or exaggerate symptoms to access restricted medications. In the worst cases, they might share these medications (benzodiazepines, hypnotics, and anxiolytics) with street values for profit or other recreational purposes.ObjectivesTo generate forecasting models and preventive policies to deal with BPD factitious disorders and improve the effectiveness of the UK National Healthcare Service (NHS) in reducing unnecessary admissions to general and psychiatric hospitals. More selective policies will capture and discourage BPD’s feigning and exaggerating symptoms for accessing restricted medications.MethodsThe underlying analysis framework is stochastic forecasting. We used current knowledge and data to complete systematic future predictions extracted from recent trends. A logical-mathematical model generated the required expressions. We identify four major model components to be introduced in the model: BPD (A), factious disorders (B), prescribing restricted medications (C), antisocial behaviours (D), and access to hospital beds (E).ResultsThe Boolean expression becomes [A then B then C then D then E], or [A ⇒ (B ⇒ (C ⇒ (D ⇒ E))] with a truth density of 96.875% (Figure 1).ConclusionsBPD should alert healthcare of the risks of symptom exaggeration and factitious mental diseases. These conditions are used to access often restricted medications, such as benzodiazepines, sleep tablets, and anxiolytics, for personal and communal use. Street sharing of these last increases local criminality. In worst cases, a hospital bed is granted without preventive triage. The risk is the indoor access to these medications. We advocate policies for the discontinuation of community prescription of these drugs.Disclosure of InterestNone Declared

Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust.

The first step toward investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average response of the treatment group receiving the treatment to the control group receiving the placebo. To ensure that the difference between the two groups is caused only by the treatment, it is crucial that the control and the treatment groups have similar statistics. Indeed, the validity and reliability of a trial are determined by the similarity of two groups' statistics. Covariate balancing methods increase the similarity between the distributions of the two groups' covariates. However, often in practice, there are not enough samples to accurately estimate the groups' covariate distributions. In this article, we empirically show that covariate balancing with the standardized means difference (SMD) covariate balancing measure, as well as Pocock and Simon's sequential treatment assignment method, are susceptible to worst case treatment assignments. Worst case treatment assignments are those admitted by the covariate balance measure, but result in highest possible ATE estimation errors. We developed an adversarial attack to find adversarial treatment assignment for any given trial. Then, we provide an index to measure how close the given trial is to the worst case. To this end, we provide an optimization-based algorithm, namely adversarial treatment assignment in treatment effect trials (ATASTREET), to find the adversarial treatment assignments.

Optimal Design of LCC Resonant Converter With Phase Shift Control for Wide Input/Output Voltage Ranges in Fuel Cell System

This paper proposes an optimal design of an LCC resonant converter with a phase shift control for a fuel cell system which has wide input and output voltage ranges. The proposed optimal design focuses on the circulating energy minimization by adjusting the transformer turns ratio according to the maximum gain of resonant tank. This makes the root mean square (RMS) value of resonant current and turn-off current stationary even though the resonant tank change. Thus, designers can select resonant tank optimized for the converter specifications without considering the conduction and switching losses. By the proposed optimal design, the efficiency degradation at worst case, which is a significant issue of the fuel cell system with wide output voltage range, can be minimized. In addition, for the fuel cell converter with wide input voltage range, a phase shift control which operates the full-bridge inverter with a constant effective duty cycle according to the fuel cell voltage is proposed. This control scheme improves the light-load efficiency without deteriorating the rated-load efficiency by reducing the circulating current and switching frequency. The validity of the proposed works is confirmed by experimental results of the 30kW LCC resonant converter.

A Cost Model for Ocean Iron Fertilization as a Means of Carbon Dioxide Removal That Compares Ship‐ and Aerial‐Based Delivery, and Estimates Verification Costs

AbstractWe present a cost model for implementing a deployment scale effort for conducting ocean iron fertilization (OIF) for marine‐based carbon dioxide removal (CDR). The model incorporates basic oceanographic parameters critical for estimating the effective export of newly fixed CO2 into biomass that is stimulated by Fe addition to an Fe‐limited region of the Southern Ocean. Estimated costs can vary by nearly 100‐fold between best‐case and worst‐case scenarios, with best‐case values of $7/net tonne C captured versus worst‐case $1,500/net tonne C captured, without accounting for verification costs. Primary oceanographic factors that influence cost are the net primary productivity increases achieved via OIF, the amount of C exported into the deep ocean, and the amount of CO2 ventilated back to the atmosphere. The model compares ship‐based versus aerial delivery of Fe to the ocean, and estimates aerial delivery can be 30%–40% more cost effective; however, the specific requirements for aerial delivery require additional research and development. The model also estimates costs associated with verification and environmental monitoring of OIF. These costs increase $/net tonne C captured by 3–4‐fold. Best, intermediate, and worst cases for aerial delivery and ship delivery are $21, $83, $2,033, and $24, $94, $4,691, respectively, inclusive of verification costs. The primary goal of this model is to demonstrate the variability in cost of OIF as a CDR method, and to better understand where additional research is needed to determine the major factors that may make OIF a tractable, nature‐based CDR method.

Worst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis

We study worst-case VCG redistribution mechanism design for the public project problem. The mechanism design task comes down to designing a payment function that maximizes the worst-case allocative efficiency ratio. We use a multilayer perceptron (MLP) with ReLU activation to model the payment function and use mixed integer programming (MIP) to solve for the worst-case type profiles that maximally violate the mechanism design constraints. We collect these worst-case type profiles and use them as training samples to train toward better worst-case mechanisms. In practice, we require a tiny neural network structure for the above approach to scale. The Lottery Ticket Hypothesis states that a large network is likely to contain a "winning ticket" -- a much smaller subnetwork that "won the initialization lottery", which makes its training particularly effective. Motivated by this hypothesis, we train a large network and prune it into a tiny subnetwork. We run MIP-based worst-case training on the drawn subnetwork and evaluate the resulting mechanism's worst-case performance. If the subnetwork does not achieve good worst-case performance, then we record the type profiles that cause the current draw to be bad. To draw again, we restore the large network to its initial weights and prune using recorded type profiles from earlier draws, therefore avoiding drawing the same ticket twice. We expect to eventually encounter a tiny subnetwork that leads to effective training for our worst-case mechanism design task. Lastly, a by-product of multiple ticket draws is an ensemble of mechanisms with different worst cases, which improves the worst-case performance further. Using our approach, we find previously unknown optimal mechanisms for up to 5 agents. Our results confirm the tightness of existing theoretical upper bounds. For up to 20 agents, we derive significantly improved worst-case mechanisms, surpassing a long list of existing manual results.

Sika deer trajectory prediction considering environmental factors by timeseries transformer-based architecture

Recently, the damage to agriculture, forestry, and fishery by wildlife has become a serious social problem. The damage caused by a conflict between wildlife and human society often causes less motivation for farming and is expected to significantly inhibit the development of local agriculture and forestry. However, the current management of wildlife is limited to primitive methods such as stabilizing the population by culling reliant on experience. To prevent this problem, the trajectory prediction of wildlife is one of the solutions for efficient wildlife management.In this study, we propose a machine learning architecture for predicting wildlife trajectories, considering surrounding environmental factors. Particularly, we propose a machine learning architecture that more accurately predicts trajectories using a timeseries transformer model that can learn long-term dependencies and add a submodel that can consider surrounding environmental factors. The proposed architecture allows interpreting which past trajectory areas should be focused on when predicting a trajectory by visualizing the weight of the focus mechanism, one of the elements comprising the transformer model. Further, the proposed architecture can decide the priority of elements of a multivariate timeseries input by considering a variable selection network (VSN), which considers the relationship of timeseries between a given time point and those before and after using casual convolutions. The main advantage of our methods is to understand the potential environmental factors to predict animal trajectories by analyzing the importance of each input feature representation in VSN.We experimented to evaluate the proposed method using one of the largest biologging datasets of sika deer (Cervus nippon) in Kanagawa Prefecture, Japan. The proposed method outperforms existing methods on various evaluation metrics and can effectively consider environmental factors in predicting trajectories. In addition, we analyze the results and show that the proposed architecture can pay attention to the times that individuals moved significantly within the observation period by visualizing the trajectories for the best and worst cases and the weight of each environmental factor.

The Constrained 2-Maxian Problem on Cycles

This paper deals with p-maxian problem on cycles with an upper bound on the distances of all facilities. We consider the case of p=2 and show that, in the worst case, the optimal solution contains at least one vertex of the underlying cycle, which helps to develop an efficient algorithm to solve the constrained 2-maxian problem. Based on this property, we develop a linear time algorithm for the constrained 2-maxian problem on a cycle. We also discuss the relations between the constrained and unconstrained 2-maxian problems on which the underlying graphs are cycles.

DeepMedFeature: An Accurate Feature Extraction and Drug-Drug Interaction Model for Clinical Text in Medical Informatics

Drug-drug interactions (DDIs) are an important biological phenomenon which can result in medical errors from medical practitioners. Drug interactions can change the molecular structure of interacting agents which may prove to be fatal in the worst case. Finding drug interactions early in diagnosis can be pivotal in side-effect prevention. The growth of big data provides a rich source of information for clinical studies to investigate DDIs. We propose a hierarchical classification model which is double-pass in nature. The first pass predicts the occurrence of an interaction and then the second pass further predicts the type of interaction such as effect, advice, mechanism, and int. We applied different deep learning algorithms with Convolutional Bi-LSTM (ConvBLSTM) proving to be the best. The results show that pre-trained vector embeddings prove to be the most appropriate features. The F1-score of the ConvBLSTM algorithm turned out to be 96.39% and 98.37% in Russian and English language respectively which is greater than the state-of-the-art systems. According to the results, it can be concluded that adding a convolution layer before the bi-directional pass improves model performance in the automatic classification and extraction of drug interactions, using pre-trained vector embeddings such as Fasttext and Bio-Bert.

Millimeter wave-based patient setup verification and motion tracking during radiotherapy.

Position verification and motion monitoring are critical for safe and precise radiotherapy (RT). Existing approaches to these tasks based on visible light or x-ray are suboptimal either because they cannot penetrate obstructions to the patient's skin or introduce additional radiation exposure. The low-cost mmWave radar is an ideal solution for these tasks as it can monitor patient position and motion continuously throughout the treatment delivery. To develop and validate frequency-modulated continuous wave (FMCW) mmWave radars for position verification and motion tracking during RT delivery. A 77GHz FMCW mmWave module was used in this study. Chirp Z Transform-based (CZT) algorithm was developed to process the intermediate frequency (IF) signals. Absolute distances to flat Solid Water slabs and human shape phantoms were measured. The accuracy of absolute distance and relative displacement were evaluated. Without obstruction, mmWave based on the CZT algorithm was able to detect absolute distance within 1mm for a Solid Water slab that simulated the reflectivity of the human body. Through obstructive materials, the mmWave device was able to detect absolute distance within 5mm in the worst case and within 3.5mm in most cases. The CZT algorithm significantly improved the accuracy of absolute distance measurement compared with Fast Fourier Transform (FFT) algorithm and was able to achieve submillimeter displacement accuracy with and without obstructions. The surface-to-skin distance (SSD) measurement accuracy was within 8mm in the anterior of the phantom. With the CZT signal processing algorithm, the mmWave radar is able to measure the absolute distance to a flat surface within 1mm. But the absolute distance measurement to a human shape phantom is as large as 8mm at some angles. Further improvement is necessary to improve the accuracy of SSD measurement to uneven surfaces by the mmWave radar.

Live update of the battery thermal parameters for reliable short-term temperature model

Battery Thermal Management systems are a key component for modern electric vehicles. Many systems use advanced models for temperature prediction and for the optimal cooling or heating strategies. Thermo-electric characterizations of cells and battery packs are performed inside the research labs to properly tune the models before uploading them to the on-board control units. However, these data depict the storage system under brand new conditions. Throughout the vehicle life, the battery pack behavior can change because of several factors related to the surrounding environment and operating conditions. Moreover, in the case of battery swap strategies, that consist in the pack replacement, the temperature model would be totally unsuitable for the new installed one. Therefore, an on-board, online procedure for the evaluation and update of the battery thermal behavior could be needed. This work presents a method for the evaluation of the battery thermal parameters on-board, during real driving cycles using data that are available in the vehicle control unit. The main novelty of this work consists in the solution to combine and assist the temperature model in the vehicle control units with an optimization algorithm which does not increase the computational load and provides reliable thermal parameters estimations. To demonstrate the potential of this methodology, the evaluated parameters are used in a short-term temperature model suitable for control strategies for battery thermal management systems. Concerning the first part, an optimization procedure is run for different driving cycles, recorded using a GPS system on a real vehicle. Finite-difference method is used to identify the convective heat transfer coefficient and the specific heat capacity of a single cell that composes the battery pack in laboratory tests. Then, the reliability of the estimated thermal parameters is analyzed reducing the number of source records and using the remaining cycles for the validation. Four worst cases have been identified and used to check the performance of the model prediction. Considering a temperature measure tolerance of 6 %, up to 93.75 % of the estimated values is reliable. Finally, the thermal parameters are used in a short-term temperature model for control strategies. The results highlight the good performance of the model in the estimation of the on-board battery temperature during a real driving cycle, simulating the future heat generation on the basis of the current load demand of the previous time step. The temperature predictions of the short-term model have been also tested in the worst cases denoting a good reliability; the maximum error is + 5 % in overestimation and 3 % in underestimation. Temperature predictions would help in the feed-forward control of battery thermal management systems for a smooth and safe operation of future electric vehicles. Moreover, this solution can be adopted for even more complex cooling methods, enhancing the update of the battery pack characteristics also in case of deterioration or battery swap.

Impact of setup errors on the robustness of linac-based single-isocenter coplanar and non-coplanar VMAT plans for multiple brain metastases.

Patient setup errors have been a primary concern impacting the dose delivery accuracy in radiation therapy. A robust treatment plan might mitigate the effects of patient setup errors. In this reported study, we aimed to evaluate the impact of translational and rotational errors on the robustness of linac-based, single-isocenter, coplanar, and non-coplanar volumetric modulated arc therapy treatment plans for multiple brain metastases. Fifteen patients were retrospectively selected for this study with a combined total of 49 gross tumor volumes (GTVs). Single-isocenter coplanar and non-coplanar plans were generated first with a prescribed dose of 40Gy in 5 fractions or 42Gy in 7 fractions to cover 95% of planning target volume (PTV). Next, four setup errors (+1 and +2mm translation, and +1° and +2° rotation) were applied individually to generate modified plans. Different plan quality evaluation metrics were compared between coplanar and non-coplanar plans. 3D gamma analysis (3%/2mm) was performed to compare the modified plans (+2mm and +2° only) and the original plans. Paired t-test was conducted for statistical analysis. After applying setup errors, variations of all plan evaluation metrics were similar (p>0.05). The worst case for V100% to GTV was 92.07%±6.13% in the case of +2mm translational error. 3D gamma pass rates were>90% for both coplanar (+2mm and +2°) and the +2mm non-coplanar groups but was 87.40%±6.89% for the +2° non-coplanar group. Translational errors have a greater impact on PTV and GTV dose coverage for both planning methods. Rotational errors have a greater negative impact on gamma pass rates of non-coplanar plans. Plan evaluation metrics after applying setup errors showed that both coplanar and non-coplanar plans were robust and clinically acceptable.

HAFİF BETON PANEL VE METALİK ELEMAN KULLANILARAK BETONARME ÇERÇEVE DAVRANIŞININ İYİLEŞTİRİLMESİ

In this study, the effect of two different wall systems which are composed of the structural lightweight concrete panel and metallic elements between the wall and frame on the behaviour of the RC frame is investigated numerically by using the Abaqus software. In the second type of wall system, a polyurethane-based binder was used between the wall and the RC beam. A quasi-static lateral loading was applied to 5 different full scale RC frames with and without walls, and frames were pushed 80mm laterally. In the worst case, the wall system carried 1.52 times the lateral load bare frame carried, whereas in the best case, the frame with the wall system carried 2.12 times the load bare frame carried. When thinner metallic elements were used, the elements yielded. This is promising for increasing damping in structures also. The second type of wall which interacts with the beam increased the initial lateral stiffness of the frame by 156% when compared with the bare frame. All frames conducted a ductile behavior and bare frame results are validated by comparing with previous study.

Study of SVOM/ECLAIRs inhomogeneities in the detection plane below 8 keV and their mitigation for the trigger performances

Context. The Space-based multi-band astronomical Variable Objects Monitor is a Chinese-French mission dedicated to the study of the transient sky. It is scheduled to start operations in 2024. ECLAIRs is a coded-mask telescope with a large field of view. It is designed to detect and localize gamma-ray bursts in the energy range from 4 keV up to 120 keV. In 2021, the ECLAIRs telescope underwent various calibration campaigns in vacuum test-chambers to evaluate its performance. Between 4 and 8 keV, the counting response of the detection plane shows inhomogeneities between pixels from different production batches. The efficiency inhomogeneity is caused by low-efficiency pixels (LEPs) from one of the two batches, together with high-threshold pixels (HTPs) whose threshold was raised to avoid cross-talk effects. In addition, some unexpected noise was found in the detection plane regions close to the heat pipes. Aims. We study the impact of these inhomogeneities and of the heat-pipe noise at low energies on the ECLAIRs onboard triggers. We propose different strategies in order to mitigate these impacts and to improve the onboard trigger performance. Methods. We analyzed the data from the calibration campaigns and performed simulations with the ground model of the ECLAIRs trigger software in order to design and evaluate the different strategies. Most of the impact of HTPs can be corrected for by excluding HTPs from the trigger processing. To correct for the impact of LEPs, an efficiency correction in the shadowgram seems to be a good solution. An effective solution for the heat-pipe noise is selecting the noisy pixels and ignoring their data in the 4–8 keV band during the data analysis. Results. The trigger threshold is the minimum value of the signal-to-noise ratio that is required to claim that ECLAIRs has detected a candidate event that is not related to a background fluctuation. After introducing the efficiency inhomogeneity in the imaging simulation, the trigger threshold in the 4–8 keV band increased by a factor of 5.75 times and 1.43 times due to the impact of HTPs and LEPs, respectively, in the worst case (on a timescale of about 20 min). The trigger threshold value was restored to its normal value after we applied an efficiency-correction method. Introducing the heat-pipe noise in our simulations in the worst case (timescale of about 20 min) resulted in an increase in the trigger threshold of approximately 100% in the 4–8 keV band compared to observations without heat-pipe noise. Moreover, even with this increased threshold, we estimated a false-trigger rate of 99.26% in the 4–8 keV band and 4.44% in the 4-120 keV band. By accepting a loss of 2.5-5% noisy pixels in the 4–8 keV energy band, we can prevent false triggers caused by heat-pipe noise and reduce the threshold increment to about 20% for the longest timescale (about 20 min) of the ECLAIRs trigger in the 4–8 keV range.

Theoretical Exploration of Flexible Transmitter Model.

Neural network models generally involve two important components, i.e., network architecture and neuron model. Although there are abundant studies about network architectures, only a few neuron models have been developed, such as the MP neuron model developed in 1943 and the spiking neuron model developed in the 1950s. Recently, a new bio-plausible neuron model, flexible transmitter (FT) model (Zhang and Zhou, 2021), has been proposed. It exhibits promising behaviors, particularly on temporal-spatial signals, even when simply embedded into the common feedforward network architecture. This article attempts to understand the properties of the FT network (FTNet) theoretically. Under mild assumptions, we show that: 1) FTNet is a universal approximator; 2) the approximation complexity of FTNet can be exponentially smaller than those of commonly used real-valued neural networks with feedforward/recurrent architectures and is of the same order in the worst case; and 3) any local minimum of FTNet is the global minimum, implying that it is possible to identify global minima by local search algorithms.

A symmetric 8T2R NVSRAM with autosave function

AbstractThis letter presented a symmetric 8T2R NVSRAM with autosave function. By efficiently multiplexing the node voltages, the control of the nonvolatile memristor and the isolation of the data node from the memristor are achieved, which ensures an instantaneous transition to the memristor state. Store and restore can be achieved within 66.1ps and 2.97 ps in case of VDD is 1 V for the worst case. In addition, the 8T2R structure performs well at lower supply voltages as 243.3 mV for STORE and 411.8 mV for RESTORE. When a 10%‐dimensional error of the devices and 10% memristance mismatch are taken into account, the 8T2R still shows high stability.

Enhancing Seismic Performance of Semi-Commercial Apartment having Telecommunication Tower at Roof

Abstract: When studying the impact of seismic activities on a structure, we focused on the efficient case and didn't consider the worst-case scenario. If a telecommunication tower is planned on a multistoried building in the future, and the tower is in the worst possible location, certain measures need to be taken to stabilize it and adjust the parametric values. In this research, we analyzed a total of 5 semi-commercial apartment building cases with G+19 floors, each having different telecommunication tower locations. After the analysis, we identified the worst case and implemented an outrigger system, as discussed in the graphical representations in the discussion section. In conclusion, we compared parametric results and observed that Case TTD is very efficient, while Case TTA is the worst among all the cases. We also improved the stability of the worst-case TTA and transformed it into Case TTO through the implementation of the outrigger system, as indicated by our results and discussions.

Parameters identification associated with artichoke pâté thermal diffusivity via finite volume element method

The sterilization operation traditionally used in the food industry has been known for a long time and is still widely used in the conservation of canned food products. Knowledge of the value of the heat transport property thermal diffusivity is very important to define the required heat treatment time and to allow better control of the process in the face of variations in the temperature of the autoclave. This work presents the mathematical model that was developed and tested in the matlab/simulink™ application to estimate the value of thermal diffusivity. The model was used to simulate the temperature behavior of the studied food product based on a value identified for the thermal diffusivity. The results obtained were validated by comparison with those available in the literature and showed good adherence to the latter, with deviations of less than 5ºC in the worst case.

Horizontal Learning Approach to Discover Association Rules

Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from O(2m) to O2m2 in the worst case).

The impact of education on income inequality: A comparative analysis of Nigeria and South Africa

Nigeria is plagued with the problem of poverty, and this is exacerbated by the problem of income inequality. Inequality has also limited the opportunities of individuals in Nigeria as Valentine (1968) said that “the essence of poverty is inequality; in slightly different words the meaning of poverty can be said to be deprivation”. The Gini coefficient of Nigeria lies between 40 and 60% (Bakare A.S, 2012). Also, South Africa has one of the worst cases of income inequality in the World, As South Africa’s Gini index increased from 0.64 in 1995 to 0.69 in the year 2005 which is one of the highest in the world. Studies will be carried out on both countries to further understanding on the relationship between income inequality and Education. With the aid of descriptive and empirical analysis, this study aims at identifying factors that are responsible for the high levels of income inequality, with a focus on Education growth in Nigeria and South Africa. Both countries will be compared using time series data spanning from 1981 to 2016. The study examines the effect of education on income inequality in Nigeria and South Africa and examines if a long-run relationship exists between education and income inequality in Nigeria and South Africa. The Johansen Cointegration and Vector Error Correction Model were applied. The result of the Johansen Cointegration shows that there is a long-run relationship between the independent variables –Inflation, Real Gross Domestic Product, Foreign Direct Investment and Primary School Enrollment rate in Nigeria and South Africa. Findings also attest to the impact of education on income inequality in Nigeria but not in South Africa as further research showed that the income inequality situation in South Africa was connected to racial segregation. It was concluded that the government should devote more attention to this dwindling sector so as to reduce the income gap in the Country.