Acceptable Level Of Performance Research Articles

A Systematic Review of Traffic Incident Detection Algorithms

Traffic incidents have negative impacts on traffic flow and the gross domestic product of most countries. In addition, they may result in fatalities and injuries. Thus, efficient incident detection systems have a vital role in restoring normal traffic conditions on the roads and saving lives and properties. Researchers have realized the importance of Automatic Incident Detection (AID) systems and conducted several studies to develop AID systems to quickly detect traffic incidents with an acceptable performance level. An incident detection system mainly consists of two modules: a data collection module and a data processing module. The performance of AID systems is assessed using three performance measures; Detection Rate (DR), False Alarm Rate (FAR) and Mean Time to Detect (MTTD). Based on data processing and incident detection algorithms, AID can be categorized into four categories: comparative, statistical, artificial intelligence-based and video–image processing algorithms. The aim of this paper is to investigate and summarize the existing AID systems by assessing their performance, strengths, limitations and their corresponding data collection and data processing techniques. This is useful in highlighting the shortcomings of these systems and providing potential solutions that future research should focus on. The literature is sought through an extensive review of the existing refereed publications using the Google Scholar search engine and Scopus database. The methodology adopted for this research is a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This study can serve as a reference for researchers who are interested in developing new AID systems.

Situational Leadership Role in Enhancing Organizational Performance during Covid 19 Pandemic among a Sample of Kuwaiti SMEs

COVID 19 hit the world during the end of 2019 and beginnings of 2020, the virus was deadly and fast to spread which had caused a lot of disturbance among country in terms how to avoid such crisis and make sure to get out of it with the least losses possible. The current research sought to examine the impact of situational leadership theory and its dimensions (directive, supportive and delegative) in enhancing organizational performance during COVID 19 pandemic, and how this type of leadership managed to preserve the best performance of many organizations. Depending on quantitative approach and utilizing an electronic questionnaire, researcher adopted a sample of (344) managers and leaders within Kuwaiti SMEs during the fiscal year 2021-2022. SPSS was used to analyze collected primary data, and outcomes of analysis indicated that situational leadership helped a lot during COVID 19 breakout as it saved and preserved an acceptable level of performance among SMEs, in addition to that, it was seen that the directive leadership style of situational leadership theory was the most influential in running a good and acceptable level of performance during the pandemic. Based on such results, current research recommended the need to increase attention to the fact that one of the defects of situational leadership is its failure to identify the leadership skills required for certain situations, and thus it is necessary to identify these skills and work on developing them among individuals.

High-Energy and Long-Lasting Lithium Metal Batteries Employing Garnet-Type Solid Electrolytes with Tailored Lithium Metal Compatibility

One of the viable options to significantly augment the energy density of lithium-ion batteries (LIBs) is to adopt lithium metal anode in the cell, which offers the highest theoretical capacity (3,860 mAh g-1) among anode materials.1 However, the practical obstacles such as dendrite growth, low Coulombic efficiency, and safety issues still remain unresolved, despite the extensive efforts to employ the lithium metal anode in LIBs.2 Recent progress in solid-state electrolyte development has granted a new promising opportunity for the utilization of lithium metal anodes, whose mechanical rigidity and non-flammable nature are supposed to effectively suppress lithium dendrite short-circuiting, thereby securing battery safety. Nevertheless, there has been no report thus far that demonstrates acceptable levels of electrochemical performance of solid-state lithium metal batteries for real-world applications.3,4 In our research, a lithium-metal-battery employing tailored garnet-type Li7-xLa3-aZr2-bO12 (LLZO) solid-electrolytes that can meet the lifespan requirements of both electric vehicles and stationary applications has been achieved, affording remarkable stability and energy density over 2,000 cycles. It is demonstrated that the compatibility between LLZO and lithium metal is crucial for the long-term stability, which can be accomplished by regulating bulk dopants and the corresponding dopant-specific interfacial treatment using protonation/etching. The appropriate selection of dopant and dopant-specific protonation/etching agent for LLZO leads to (i) the formation of a stable passivation layer at the interface with lithium metal, (ii) effective release of residual stress in LLZO, and (iii) intact contact at the interface.The lithium-metal cell with 2 mAh cm-2 cathode (12 mg cm-2) delivers a cumulative capacity of over 4,000 mAh cm-2 at 3 mA cm-2, which to the best of our knowledge, is the highest long-term cycle value reported for lithium metal batteries with LLZO electrolytes. Moreover, even with the thin 110-µm-LLZO electrolyte and 20-µm lithium metal, a high-loading-capacity cell (3.2 mAh cm-2) exhibits a superior cycle life (>600 cycles) at 2 mA cm-2 without short-circuiting, affording a basis for the high-energy-cell design with ultra-thin electrolyte. In addition, an all-solid-state-battery, excluding the ionic liquid electrolyte, was successfully demonstrated using the composite cathode, which could cycle over 1,000 times at a high current density of 3 mA cm-2 without short-circuiting. To the best of our knowledge, this is the first all-solid-state battery that can operate over 1,000 cycles, enabled by the garnet-type electrolytes and cathode with a commercially acceptable capacity.These findings are expected to open a new avenue for developing long-lasting solid-state lithium metal batteries by highlighting the efficacy of the coupled bulk and interface doping of solid electrolytes.

Multiparametric assay of screening for the diagnosis of mycoses of interest in Public Health: standardization of methodology

The standardization and validation of a multiplex assay requires the combination of important parameters such as sensitivity and specificity, acceptable levels of performance, robustness, and reproducibility. We standardized a multiparametric Dot-blot aimed at the serological screening of paracoccidioidomycosis, histoplasmosis, and aspergillosis. A total of 148 serum were evaluated: 10 from healthy subjects, 36 from patients with paracoccidioidomycosis, 62 from patients with histoplasmosis, and 40 from patients with aspergillosis. It was found that the multiparametric Dot-blot showed a high percentage of cross-reactivity. However, when evaluated individually, in the serological screening of histoplasmosis, a good performance was observed when compared to the double immunodiffusion assay, considered the gold standard test, with 100% co-positivity and 83.3% co-negativity. The performance of serological screening for aspergillosis was not satisfactory when compared to double immunodiffusion, showing 71.4% co-positivity and 100% co-negativity. The evaluation of the stability of nitrocellulose membranes showed that membranes sensitized with H. capsulatum antigen remained stable for 90 days and those sensitized with A. fumigatus antigen for 30 days. We conclude that the use of crude antigens was not suitable for the standardization of the multiparametric Dot-blot assay, due to the high cross-reactivity, and that further tests should be performed with purified proteins.

Occupational Stress and Employee Performance: How Covid-19 Revolutionized All We Thought We Knew

Despite the ongoing spread of the new COVID-19 pandemic, only a few studies have been conducted on the stress induced by this epidemic. Many businesses find it difficult to maintain acceptable levels of staff performance in the face of uncertain situations like the COVID-19. This study provides important insights into the impact of epidemics on employee performance by examining the characteristics of reported epidemic fears. The investigation can identify the issues from a broad perspective and more clearly outline the issues of concern after acquiring basic data. For business, job stress has become a major concern since it may lead to lower productivity, higher absences from the workplace, and other issues for workers. COVID-19's global proliferation has resulted in a number of severe and unwelcome consequences for human health. Through a review of existing literature and employing qualitative methodologies, we want to shed light on how employees who work from their offices or work remotely in risky environments, such as those affected by the COVID-19 virus, might better deal with the stress that comes from these intense situations. Employees and employers alike face new challenges and opportunities as a result of companies being forced to make difficult decisions about whether or not to keep, send home, or allow remote work. We decided to conduct this research since it is a novel and complicated problem, and because stress at work may occur anywhere which, among others, were the motivator factors. Occupational health has risen in importance over the last few decades, and this research aimed to demonstrate just how essential it has become. Stress is a constant and inescapable threat to organizational and personnel productivity in today's world. Employers and workers alike may benefit from stress-reduction strategies, which include methods for preventing, combating, and managing excessive and unnecessary stress.

Seismic vulnerability assessment of reinforced concrete buildings having nonlinear fluid viscous dampers

Seismic resilience is the residual capacity of a damaged system for recovering and sustaining an acceptable level of performance as a consequence of a seismic event. Firstly, this paper summarizes the current calculation framework of seismic resilience using fragility analyses. After that, a new metric based on vulnerability analyses is developed to calculate seismic resilience. Finally, a typical hospital building is adopted as a single building and then retrofitted with supplemental Energy Dissipation Systems (EDS) to show the applicability of the new metric and compare it with the current calculation model of the seismic resilience. The considered supplemental EDS is the Nonlinear Fluid Viscous Damper (NFVD) located as the diagonal damper-brace system. The seismic performance of the supplemental EDSs is to enhance structural performance by reducing induced lateral displacements and as a result the level of physical damages and losses. To this end, Incremental Dynamic Analysis (IDA) is implemented to achieve the damage fragility and vulnerability curves. A comparison of the fragility, vulnerability, and functionality curves indicates that NFVDs are potentially useful for attenuating seismic damages to structural and non-structural components.

Reference Interval for the Axis-Shield Clinical Chemistry Heparin-Binding Protein Assay.

The newly developed Axis-Shield clinical chemistry heparin-binding protein (HBP) assay (Axis-Shield Diagnostics Ltd., Dundee, Scotland) can be applied to fully automated platforms. We aimed to establish a reference interval (RI) of HBP using the Axis-Shield HBP assay, and to evaluate the analytical performance of this assay. An RI was established in 212 sodium citrated plasma samples using the non-parametric method (2.5th and 97.5th percentiles). Precision, linearity, and carry-over were evaluated according to the Clinical and Laboratory Standards Institute guidelines. The RI of HBP was between 5.3 ng/mL and 171.0 ng/mL, which could be applied regardless of gender and age. Percentage coefficients of variations (%CVs) of repeatability and within-laboratory precision were 4.9% and 6.3%, respectively, for low-concentration control and 1.6% and 3.0%, respectively, for high-concentration control. The linearity was excellent (coefficient of determination (R2) = 0.99), and the carry-over rate was negligible (0.05%). This is the first study to establish an RI of HBP using the newly developed and fully automated Axis-Shield HBP assay. The Axis-Shield HBP assay showed an acceptable level of analytical performance and could be used to measure HBP concentrations effectively in routine clinical practice. Further studies are awaited to evaluate the clinical utility of HBP using this automated assay.

Squeezing More Life From Old Bridges

Much of the U.S. civil infrastructure is now operating beyond its design life, but given the cost to replace these resources, we must continue using it as long as it's safe to do so. This condition begs the question, how can we determine the length of time a structure will remain safe to use? One approach that's used is structural health monitoring. SHM measures how a structure is performing under in-service conditions, and then compares the measured performance with some acceptable level of performance. The results can indicate how much service life might remain, and help in making decisions about upgrades to extend the service life of the structure. Extending service life can save money, maintain mobility, and minimize impacts to the community compared to remove and replace options. This article presents a case history where SHM helped extend the useful life of a major highway structure, and suggests ways in which the same approach might be useful for assessing the future performance of geotechnical assets.

Strengthening of fire damaged reinforced beams by using ferro cement

Now-a-days repair and rehabilitation of the existing structures in developing countries has become solitary of the most thought-provoking tasks in Civil Engineering sector. Imperfections, fiasco and general distress in the structures can be the outcome of fundamental deficiency produced by inaccurate design, poor workmanship or overloading of the construction. It can also be instigated by corrosion, fire and natural calamities. A spoiled or distrait structure can be modernized to an acceptable level of performance at a reasonable cost by different approaches is called retrofitting. One of these techniques consists of strengthening fire damaged concrete beams by applying Ferro cement with wire mesh. Two types of Ferro cement with wire mesh are used with single layer and double layer in beam. One third point load test according to ASTM C78-02 is performed to measure the flexure performance of four specimens. The ultimate load carrying capacity for using the single layer Ferro cement strengthening, double layer Ferro cement strengthening showed an improvement of 46% and 72% respectively over the fire damaged specimens.

Hydrogen Fuel for Future Mobility: Challenges and Future Aspects

Nowadays, the combustion of fossil fuels for transportation has a major negative impact on the environment. All nations are concerned with environmental safety and the regulation of pollution, motivating researchers across the world to find an alternate transportation fuel. The transition of the transportation sector towards sustainability for environmental safety can be achieved by the manifestation and commercialization of clean hydrogen fuel. Hydrogen fuel for sustainable mobility has its own effectiveness in terms of its generation and refueling processes. As the fuel requirement of vehicles cannot be anticipated because it depends on its utilization, choosing hydrogen refueling and onboard generation can be a point of major concern. This review article describes the present status of hydrogen fuel utilization with a particular focus on the transportation industry. The advantages of onboard hydrogen generation and refueling hydrogen for internal combustion are discussed. In terms of performance, affordability, and lifetime, onboard hydrogen-generating subsystems must compete with what automobile manufacturers and consumers have seen in modern vehicles to date. In internal combustion engines, hydrogen has various benefits in terms of combustive properties, but it needs a careful engine design to avoid anomalous combustion, which is a major difficulty with hydrogen engines. Automobile makers and buyers will not invest in fuel cell technology until the technologies that make up the various components of a fuel cell automobile have advanced to acceptable levels of cost, performance, reliability, durability, and safety. Above all, a substantial advancement in the fuel cell stack is required.

Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks

Most networks strive to provide good security and an acceptable level of performance. Quality of service (QoS) plays an important role in the performance of a network. Mobile ad hoc networks (MANETs) are a decentralized and self-configuring type of wireless network. MANETs are generally challenging and the provision of security and QoS becomes a huge challenge. Many researchers in literature have proposed parallel mechanisms that investigate either security or QoS. This paper presents a security framework that is QoS-aware in MANETs using a network protocol called optimized link state routing protocol (OLSR). Security and QoS targets may not necessarily be similar but this framework seeks to bridge the gap for the provision of an optimal functioning MANET. The framework is evaluated for throughput, jitter, and delay against a sinkhole attack presented in the network. The contributions of this paper are (a) implementation of a sinkhole attack using OLSR, (b) the design and implementation of a lightweight-intrusion detection system using OLSR, and (c) a framework that removes fake routes and bandwidth optimization. The simulation results revealed that the QoS-aware framework increased the performance of the network by more than 70% efficiency in terms of network throughput. Delay and jitter levels were reduced by close to 85% as compared to when the network was under attack.

A comparison of paediatric hypertension clinical practice guidelines and their ability to predict adult hypertension in an African birth cohort

It remains unclear which paediatric hypertension clinical practice guideline (CPG) should be applied in an African population. We, therefore, aimed to compare commonly used CPG (2017 AAP, 2016 ESH, 2004 Fourth Report) developed in high-income countries for use in South African children at four paediatric ages (children: 5 years, 8 years; adolescents: 13 years, 17 years) to determine which best predicts elevated blood pressure (BP) in adulthood (22 years, 28 years). Moreover, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each specific paediatric CPG was calculated across the age points. The 2017 AAP definition identified more children and adolescents with hypertension when compared to the 2004 Fourth Report and 2016 ESH guidelines. In computed hazards ratios, ages 8 years to 17 years, all three paediatric CPG significantly predicted the risk of elevated BP in young adulthood (p ≤ 0.032). However, sensitivity to predict elevated BP at age 22 years for all CPG was generally low (17.0%–33.0%) with higher specificity (87.4%–93.1%). Sensitivity increased at age 28 years (51.4%–70.1%), while specificity decreased (52.8%–65.1%). Both PPV and NPV at both adult age points varied widely (17.9%–79.9% and 29.3%–92.5% respectively). The performance of these paediatric CPG in terms of AUC were not optimal at both adult age points, however, the 2017 AAP definition at age 17 years met an acceptable level of performance (AUC = 0.71). Our results, therefore, highlight the need for more research to examine if an African-specific CPG would better identify high-risk children to minimise their trajectory towards adult hypertension.

Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization

The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is still a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large dispersion-induced memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while we have to keep an acceptable performance level of the simplified NN model. In this work, we address the complexity reduction problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), to mitigate the impairments for 30 GBd 1000 km transmission over a standard single-mode fiber, and demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 78.34%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense. Further, we examine the impact of using hardware with different CPU and GPU features on the power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, by implementing the reduced NN equalizer on two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano, which are used to process the data generated via simulating the signal’s propagation down the optical-fiber system.

Comparison of Two Automated Targeted Metabolomics Programs to Manual Profiling by an Experienced Spectroscopist for 1H-NMR Spectra.

Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student’s t-tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7–86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.

Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals

Seizure detection is a particularly difficult task for neurologists to correctly identify the Electroencephalography (EEG)-based neonatal seizures in a visual manner. There is a strong demand to recognize the seizures in more automatic manner. Developing an expert seizure detection system with an acceptable performance level can partly fill this research gap. This paper proposes a new framework for the automated detection of neonatal seizures based on the Morse Wavelet approach that is coupled with a local binary pattern algorithm, and a graph-based community detection algorithm. An ensemble classifier method is designed to detect neonatal seizures prevalent in EEG signals. Our findings show that only 59 of the texture features can exhibit the abnormal increase in an EEG amplitude and the spikes notable during a seizure. The present results demonstrate that the proposed seizure detection model is more accurate for the detection of seizures compared with some of the traditional approaches.

Modeling Performance of Microservices Systems with Growth Theory

ContextThe microservices architectural style is gaining momentum in the IT industry. This style does not guarantee that a target system can continuously meet acceptable performance levels. The ability to study the violations of performance requirements and eventually predict them would help practitioners to tune techniques like dynamic load balancing or horizontal scaling to achieve the resilience property.ObjectiveThe goal of this work is to study the violations of performance requirements of microservices through time series analysis and provide practical instruments that can detect resilient and non-resilient microservices and possibly predict their performance behavior.MethodWe introduce a new method based on growth theory to model the occurrences of violations of performance requirements as a stochastic process. We applied our method to an in-vitro e-commerce benchmark and an in-production real-world telecommunication system. We interpreted the resulting growth models to characterize the microservices in terms of their transient performance behavior.ResultsOur empirical evaluation shows that, in most of the cases, the non-linear S-shaped growth models capture the occurrences of performance violations of resilient microservices with high accuracy. The bounded nature associated with this models tell that the performance degradation is limited and thus the microservice is able to come back to an acceptable performance level even under changes in the nominal number of concurrent users. We also detect cases where linear models represent a better description. These microservices are not resilient and exhibit constant growth and unbounded performance violations over time. The application of our methodology to a real in-production system identified additional resilience profiles that were not observed in the in-vitro experiments. These profiles show the ability of services to react differently to the same solicitation. We found that when a service is resilient it can either decrease the rate of the violations occurrences in a continuous manner or with repeated attempts (periodical or not).ConclusionsWe showed that growth theory can be successfully applied to study the occurences of performance violations of in-vitro and in-production real-world systems. Furthermore, the cost of our model calibration heuristics, based on the mathematical expression of the selected non-linear growth models, is limited. We discussed how the resulting models can shed some light on the trend of performance violations and help engineers to spot problematic microservice operations that exhibit performance issues. Thus, meaningful insights from the application of growth theory have been derived to characterize the behavior of (non) resilient microservices operations.

Perceived spiritual care competence and the related factors in nursing students during Covid-19 pandemic

BackgroundSpirituality is defined as the way people experience, express, and seek meanings. It is a major concept in the field of nursing care. Nursing students who are trained and exposed to patients' spiritual care will be better able to take care of their patients' spiritual needs. ObjectivesThe study aimed to assess perceived spiritual care competence and the related factors in nursing students during the Covid-19 pandemic. MethodsThis was a cross-sectional study. The participants were 191 undergraduate nursing students at Guilan University of Medical Sciences (GUMS) that were entered into the study from July 29 to December 21, 2021. The spiritual care competence scale was used to measure spiritual care competence in the participants. It is a 27-item questionnaire with six dimensions including assessing and implementing spiritual care, professionalism and improving the quality of spiritual care, personal support and patient counseling, referral to professionals, attitude towards patient’s spirituality, and communication. Data was collected using the convenience sampling method. Data analysis was done by SPSS software version 16.0 using descriptive, bivariate, and multivariate methods. ResultsThe mean total score of spiritual care competency was 106.8 (SD = 13.4). The mean total score of spiritual care competency was significantly higher in the native students (P = 0.031) and the students with experience of jobs in hospitals (P = 0.037). ConclusionThe findings indicated an acceptable level of performance in nursing students in spiritual care during the Covid-19 pandemic.

Evaluation of Operational Resilience in Cyber-Physical Production Systems: literature review

In Cyber-Physical Production Systems, smart production resources (e.g., augmented operators, machines, products) can collaborate according to innovative, adaptive, and autonomous ways. This context offers new methods and tools to deal with disturbances and operational risks effectively and efficiently. In such environments, resilience engineering aims to improve the systems’ abilities and capabilities to face disturbances and operational risks, and to be restored to safe behaviors, acceptable levels of performance and quality of service. Despite an increasing interest in operational resilience, and despite the importance of this topic in both research and industrial practice, there is no literature review and analysis to synthesize works and to identify the main issues encountered in this field. This article fills in this gap and shows that there are six main research streams in operational resilience.

Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes

Optimization is a process of decision-making in which some iterative procedures are conducted to maximize or minimize a predefined objective function representing the overall behavior of a considered system problem. Most of the time, one specific function cannot represent the overall behavior of a system with particular levels of complexity, so the multiple objective functions should be determined for this purpose which requires an algorithm with adaptability to this situation. Multi-objective optimization is a process of decision making in which maximization or minimization of multiple objective functions is considered for reaching the acceptable levels of performance for the considered system problem. In this paper, the multi-objective version of the Material Generation Algorithm (MGA) is proposed as MOMGA, one of the recently developed metaheuristic algorithms for single-objective optimization. To evaluate the overall performance of the MOMGA, the benchmark multi-objective optimization problems of the Competitions on Evolutionary Computation (CEC) are considered alongside the real-world engineering problems. Based on the results, the MOMGA is capable of providing very acceptable results in dealing with multi-objective optimization problems.

Predicting politicians’ misconduct: Evidence from Colombia

Abstract Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine-learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the receiver-operating characteristic curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important in predicting corruption.