Assessment Of Drivers Research Articles

A tool for the assessment of the risk drivers and public perception of WASH in South Africa.

Therefore, the study findings could be used to optimise the design and targeting of public awareness campaigns on WASH during the coronavirus pandemic or similar infectious disease burdens and related disaster risks.

Cross-Country Assessment of Socio-Ecological Drivers of COVID-19 Dynamics in Africa: A Spatial Modelling Approach

Understanding how countries’ socio-economic, environmental, health status, and climate factors have influenced the dynamics of COVID-19 is essential for public health, particularly in Africa. This study explored the relationships between African countries’ COVID-19 cases and deaths and their socio-economic, environmental, health, clinical, and climate variables. It compared the performance of Ordinary Least Square (OLS) regression, the spatial lag model (SLM), the spatial error model (SEM), and the conditional autoregressive model (CAR) using statistics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and coefficient of determination (R2). Results showed that the SEM with the 10-nearest neighbours matrix weights performed better for the number of cases, while the SEM with the maximum distance matrix weights performed better for the number of deaths. For the cases, the number of tests followed by the adjusted savings, Gross Domestic Product (GDP) per capita, dependence ratio, and annual temperature were the strongest covariates. For deaths, the number of tests followed by malaria prevalence, prevalence of communicable diseases, adjusted savings, GDP, dependence ratio, Human Immunodeficiency Virus (HIV) prevalence, and moisture index of the moistest quarter play a critical role in explaining disparities across countries. This study illustrates the importance of accounting for spatial autocorrelation in modelling the dynamics of the disease while highlighting the role of countries’ specific factors in driving its dynamics.

Assessment of Driver's Stress State Using Smart T-Shirt Textile Electrodes and Multimodal Cross-Attention Networks

Assessment of Driver's Stress State Using Smart T-Shirt Textile Electrodes and Multimodal Cross-Attention Networks

Uncovering the Paradox: Exploring the Departure of Exceptional Employees from Supposedly 'Exceptional Cultures'

While companies spend significantly on hiring top talent, retaining high performers remains a challenge even in strong workplace cultures. This article explores common reasons talented employees leave seemingly positive company cultures and provides recommendations for improvement. It discusses how misalignment of organizational and individual values and priorities can create cognitive dissonance over time. Lack of growth opportunities and autonomy are also cited as major factors, with high performers needing new challenges and independence. Issues like opaque decision-making, inconsistent policies, and lack of work-life balance further undermine engagement. The article analyzes real examples where misalignment of priorities, lack of development pathways, micromanagement, unfair processes, and inflexible work policies drove away satisfied employees. It concludes that ongoing assessment of dissatisfaction drivers is needed to refine cultures and meet the evolving needs of all employees, including top performers, for long-term retention success.

Development and Validation of a Novel Night-Time Hazard Visibility Test.

Night-time driving is dangerous, with increased crash rates, particularly involving vulnerable road users. A Night-Time Hazard Visibility Test (NHVT) was developed and validated by exploring the effects of refractive and cataract blur on performance. The NHVT comprised video clips of night-time roads from the driver's perspective, including different hazards (pedestrians, cyclists, and vehicles). Participants responded when they first recognized hazards requiring them to take evasive action to avoid a collision. In experiment 1, there were 16 young visually normal drivers (mean age = 22.3, standard deviation [SD] = 2.2 years) who completed 2 NHVT sets, viewed separately through best-correction and refractive blur (+1.00 diopter sphere [DS]). In experiment 2, a refined version of the NHVT was administered to an additional 16 young visually normal drivers (mean age = 21.1, SD = 1.2 years) with best-correction and cataract blur. The order of visual conditions and NHVT sets were counterbalanced. In experiment 1, refractive blur significantly reduced photopic visual acuity (VA) compared to best-corrected vision (+0.09 vs. -0.21 logMAR, P < 0.001) and delayed response times by 0.69 seconds (3.10 vs. 2.41 seconds, P < 0.001) compared to best-corrected vision. In experiment 2, cataract blur reduced VA compared to best-corrected vision (+0.03 vs. -0.17 logMAR, P < 0.001) and delayed response times by 0.63 seconds (2.92 vs. 2.29 seconds, P < 0.001). The NHVT was sensitive to refractive and cataract blur, providing preliminary support of its validity as a measure of night-time hazard visibility performance. The NHVT has potential as an off-road assessment of visibility for night driving and application for assessment of drivers with different refractive corrections and ocular diseases.

Exploring the critical drivers of blockchain technology adoption in Indian industries using the best-worst method

PurposeThe purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.Design/methodology/approachThis study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.FindingsThe authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.Research limitations/implicationsWhile this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.Originality/valueBlockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

Assessment of grassland carrying capacity drivers and evaluation of pasture-livestock balance: A case study of Xinjiang, China

Evaluating the spatiotemporal variations and driving mechanisms of carrying capacity (CC) is critical for optimizing grassland resource utilization and promoting sustainable development in grassland animal husbandry. This study focuses on Xinjiang, an arid and semi-arid region significantly impacted by environmental changes and human activities. To capture the dynamics of grassland CC, we employed Theil-Sen trend analysis and the Mann-Kendall test to assess spatiotemporal trends. The innovative use of the Geodetector method allowed for a detailed quantification of the influence of various environmental and human-related factors on grassland CC. Additionally, we incorporated livestock data to evaluate the overgrazing conditions of the grassland ecosystem. Our findings demonstrate that: (1) Grassland CC has shown a pronounced overall upward trajectory over the last twenty years, with notable inter-annual fluctuations and significant spatial variations, particularly between the northern and southern regions. (2) The spatial distribution of grassland CC is primarily influenced by precipitation patterns and population density, with key determining factors varying across different types of grasslands. Except for alpine steppe and alpine desert, the spatial distribution of grassland CC was primarily influenced by two-factor interactions, surpassing the impacts of single-factor effects. (3) The overgrazing rate has generally declined, peaking in 2014 and showing the mildest condition in 2017. In comparison, the overgrazing situation in Northern Xinjiang is relatively more favorable, whereas the southern and eastern regions necessitates more immediate and comprehensive ecological restoration and management measures. In summary, this study provides important scientific evidence for prioritizing grassland protection and planning for the sustainable animal husbandry development in arid and semi-arid regions.

An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa

Abstract The largest tropical peatland complex in the Cuvette Centrale, is marked by persistent knowledge gaps. We assessed recent peat forest disturbances and their direct drivers from 2019 to 2021 in Cuvette Centrale, spanning the Republic of Congo (ROC) and the Democratic Republic of Congo (DRC). Utilizing peatland extent and Radar for Detecting Deforestation (RADD) alert data, we analyzed spatial and temporal patterns of disturbances. Further, we examined 2,267 randomly sampled peat forest disturbance events through visual interpretation of monthly Planet and Sentinel 2A data to identify direct drivers. Our findings revealed that between 2019 and 2021, about 91% of disturbances occurred in DRC, with hotspots concentrated in the northwest Sud-Ubangi district. Disturbances predominantly followed a sharp seasonal pattern, recurring during the first half of each year with temporal hotspots emerging between February and May, closely associated with smallholder agriculture activities. Smallholder agriculture accounted for over 88% of disturbances in Cuvette Centrale, representing a leading role both in ROC (~77%) and DRC (~89%). While, small-scale logging contributed 7% to disturbances in the region, it constituted an important driver (18 %) in the ROC. Other drivers included flood, roads and settlements. Approximately 77% of disturbances occurred outside managed forest concessions in Cuvette Centrale, with 40% extending into protected areas. About 90% of disturbances were concentrated within 1 km of peat forest edges and ~76% of the disturbances occurred within 5 km of road or river networks. The insights underscore the crucial need for effective peat forest conservation strategies in Cuvette Centrale and can inform national policies targeting peatland protection, aligning with commitments in the Brazzaville Declaration and Paris Agreement. Further, our findings on direct driver assessment could serve as a reference dataset for machine learning models to automate the visual interpretation and upscale the assessment across the entire region.

Breaking bread: Assessment of household bread waste incidence and behavioural drivers

Bread, the most consumed floury product in the world, is one of the most wasted food products across the food supply chain. Yet, there is a paucity of research examining the magnitude of bread waste and behavioural drivers associated with it at the household level. This study analyses the incidence, behavioural drivers and disposal pathways of bread waste using a sample of 1462 households in Australia and a 7-day electronic diary method. Behavioural drivers related to bread waste were modelled using logistic regression models. The findings suggest that bread rolls, sliced bread and flat bread are the three most wasted bread products in Australian households. Higher grocery shopping frequency, ‘just-in-case’ buying, family households with young children and takeaway meal ordering are associated with higher bread waste. Bread disposal pathway analysis revealed that 55% of bread and bakery products are discarded through general waste bins, highlighting the immense potential of utilising bread waste in industrial processes such as biofuel production. Addressing bread and bakery waste can significantly contribute to achieving the SDG12.3 goal of halving the per capita food waste by 2030.

Leveraging Flow-Assisted Electrochemistry Toward Decarbonization of Cement Manufacturing

In an effort to decarbonize the global economy by 50% in 2030 and achieve net-zero carbon emissions by 2050, there is a dire need to electrify industrial processes [1]. Approximately 8% of global carbon emissions comes from cement production [2]. Considering the rising demand for cement due to infrastructure expansion, it is critical to develop new concepts for the decarbonization of cement production. With this goal in mind, our work explores the effectiveness of flow-assisted cement electrolysis as a replacement to conventional fossil fuel based high-temperature kilns in cement manufacturing plants [3].In this study, we employ a scalable 3-channel flow-reactor with acidic anolyte, basic catholyte, and a neutral chemical flush in between to generate calcium hydroxide, Ca(OH)2 [4, 5], which is the key ingredient in cement manufacturing. The central channel is separated from either half cells by a cation exchange and an anion exchange membrane. The electrochemical performance of the proposed cement electrolyzer under a variety of operating conditions is investigated by considering the key variables in reactor design, including flow rate, temperature, catalyst choice and feed concentrations, to shed light into the practical challenges of running a 3-channel flow reactor. Our preliminary study indicates that membranes are possibly responsible for a significant fraction of the limited yields of Ca(OH)2. Specifically, the anion exchange membranes tested are susceptible to degradation in alkaline conditions displaying poor ionic conductance, whereas the cation exchange membranes allow significant water crossover. The findings from this study pave the way in understanding key components for cement electrolysis and other electrochemical systems employing 3-channel flow reactors. References DeAngelo, J., et al., Energy systems in scenarios at net-zero CO2 emissions. Nature Communications, 2021. 12(1): p. 6096. Ellis, L.D., et al., Toward electrochemical synthesis of cement—An electrolyzer-based process for decarbonating CaCO3 while producing useful gas streams. PNAS, 2019. 117(23): p.12584. Rissman, J., et al., Technologies and policies to decarbonize global industry: Review and assessment of mitigation drivers through 2070. Applied Energy, 2020. 266: p. 114848. Perego, A., et al., Electrochemical Flow Reactor for Cement Clinker Production. ECS Meeting s, 2021. MA2021-02(27): p. 840. Olsson, J.A., S.A. Miller, and M.G. Alexander, Near-term pathways for decarbonizing global concrete production.Nature Communications, 2023. 14(1): p. 4574.

Export as a driver of the development of the agro-industrial complex of the Russian Federation

Relevance. The implementation of the export potential of the agro-industrial complex of the Russian Federation is largely related to the concentration on priority supply areas, peculiar drivers of growth in the export of agroindustrial complex products. The relevance of the article is based on the identification and assessment of product drivers of export growth (exporting firms) in 2023.The purpose of the study is to analyze the export of agricultural products of the Russian Federation.Objectives. This goal was achieved by solving the following tasks: analyzing the volume of exports of products of the agro-industrial complex of the Russian Federation within the framework of the National Project "International Cooperation and Export" and the annual minimum permissible effectiveness of export support measures in the agro-industrial complex of the Russian Federation under the FP "Export of agro-industrial complex" in 2021-2023; evaluation of food drivers of export growth (exporting firms) in 2023 wheat, barley, corn, peas, lamb, pork milk powder.Methodology. The work used well-known methods of economic research: systemic analysis, comparison, generalization, interpretation of results and other general scientific methods. In the process of research, the systematization of statistical data, scientific publications of scientists on the topic of the presented work was carried out.Results. The analysis of the export of agricultural products of the Russian Federation was carried out and the assessment of the product drivers of export growth (exporting firms) in 2023 was given: wheat, barley, corn, peas, lamb, pork, milk powder.Conclusions. The main idea of the article is that it is exports that are the key driver of growth in the production of agricultural products of the Russian Federation. An important condition for the development of exports in our country is the opening of new markets, which is happening by 2024, access was obtained to more than 90 countries in 236 types of agricultural products. In addition, the number of enterprises certified for export is growing every year.

Assessment of Spatial Patterns and Drivers of Respiratory Health Risks Based on Air Pollution Spatiotemporal Data in Shenyang City, China

Assessment of Spatial Patterns and Drivers of Respiratory Health Risks Based on Air Pollution Spatiotemporal Data in Shenyang City, China

Analyzing the impact of Social Interaction on Stock Market Participation: A Qualitative Study Using NVivo

Background: Stock market participation plays a crucial role in wealth creation and economic welfare. Prior studies show social interaction factors like parental guidance, partner opinions and online networks can influence such financial decisions. However, little is known about their combined impact in developing markets. Methods: This study analyzed the influence of social interaction on stock market participation in Kathmandu Valley, Nepal. Qualitative data was collected through interviews of 10 investors and analyzed using NVivo software. Tools included word clouds, cluster analysis, sentiment analysis and thematic coding. Results: Results indicated that while parents generally lacked knowledge, some were influenced by participating relatives. Partners motivated research through information sharing and encouragement. Community reputation had a positive effect. Internet emerged as the dominant information source compared to offline relationships. Moderately positive sentiments were observed for community, partner and internet influences. Conclusion: Social interactions, particularly through the internet, collectively contributed to stock market participation. However, parental influence was less direct. Partners influenced primary market involvement through discussions. This provides novel insights into socio-cultural determinants of equity investing in an emerging South Asian context. Novelty: The study makes an original qualitative assessment of multiple social drivers on capital market engagement in Kathmandu Valley, adding new dimensions for understanding household finance decisions and developing inclusive capital markets.

On-road driving remediation following acquired brain injury: a randomized controlled trial

ABSTRACT Objective To investigate the relationship between on-road driving remediation and achieving fitness to drive following acquired brain injury. Design Randomized controlled trial. Setting Tertiary hospital outpatient driver assessment and rehabilitation service, Australia. Participants Thirty-five participants (54.3% male), aged 18–65 years, 41 days-20 years post-acquired brain injury (including stroke, aneurysm, traumatic brain injury) recommended for on-road driving remediation following occupational therapy driver assessment were randomly assigned to intervention (n = 18) and waitlist control (n = 17) groups. Intervention Intervention group received on-road driving remediation delivered by a qualified driving instructor in a dual-control vehicle. The waitlist control group completed a 6 week period of no driving-related remediation. Main measure Fitness to drive rated following the conduct of an on-road occupational therapy driver assessment with a qualified driving instructor where outcome assessors were blinded to group allocation. Results The intervention group were significantly more likely to achieve a fit to drive recommendation than no driving specific intervention (p = 0.003). Conclusion Following comprehensive assessment, individualized on-road driving remediation programs devised by an occupational therapist with advanced training in driver assessment and rehabilitation and delivered by a qualified driving instructor are significantly associated with achieving fitness to drive after acquired brain injury.

Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications.

Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.

The Convergence of COSYSMO Parametric Cost Estimation with Model‐Based Systems Engineering

AbstractThe promise of Model‐Based Systems Engineering (MBSE) and its advertised benefits hinge on the ability of our profession to integrate engineering disciplines and project management across the system life cycle. In particular, connecting system architecture and design decisions to the economics of the system's development is a critically important topic, but has often been overlooked by the system engineering community. Achieving such integration requires two key elements: (1) the standardization of multidisciplinary terms and functions, and (2) the establishment of rules governing relationships between cross‐functional models and modeling environments. This paper contributes to these areas by (1) establishing common terminology describing systems engineering and cost estimating, and (2) proposing specific cost drivers and counting rules within an MBSE environment that can be applied to estimating systems engineering effort using the COSYSMO cost model. COSYSMO is a systems engineering cost model, originally developed in 2005, and was grounded on document‐based systems engineering methods. This paper facilitates the convergence of COSYSMO and MBSE by updating the COSYSMO counting rules to specifically address size driver selection and assessment in a SysML model. It illustrates how advanced modeling tool features streamline and automate cost estimation activities, while queries and crosscutting views offered by modern MBSE tools enhance the completeness, quality, and consistency of parametric cost estimation process. Additionally, it provides guidelines for identifying system model content and level of detail for cost estimating, and proposes an approach to link attributes and properties in a system model to the variables in the COSYSMO cost estimating relationship.

Switching and Frequency Response Assessment of Photovoltaic Drivers and Their Potential for Different Applications.

Newly introduced Photovoltaic (PV) devices, featuring a built-in chip with an illuminating Light Emitting Diode (LED), have emerged in the commercial market. These devices are touted for their utility as both low- and high-side power switch drivers and for data acquisition coupling. However, comprehensive knowledge and experimentation regarding the limitations of these Photovoltaic Drivers in both switching and signal processing applications remain underexplored. This paper presents a detailed characterization of a Photovoltaic Driver, focusing on its performance under resistive and capacitive loads. Additionally, it delineates the device's constraints when employed in signal processing. Through the analysis of switching losses across various power switches (Silicon and Silicon Carbide) in both series and parallel driver configurations, this study assesses the driver's efficacy in operating Junction Field-Effect Transistors (JFETs). Findings suggest that Photovoltaic Drivers offer a low-cost, compact solution for specific applications, such as high-voltage, low-bandwidth measurements, and low-speed turn-on with fast turn-off power switching scenarios, including solid-state switches and hot-swap circuits. Moreover, they present a straightforward, cost-effective method for driving JFETs, simplifying the circuit design and eliminating the need for an additional negative power source.

Assessment of Driver Understanding of Traffic signs: A Case of Lere Local Government, Kaduna State

Assessment of Driver Understanding of Traffic signs: A Case of Lere Local Government, Kaduna State

Ecological drivers of ultraviolet colour evolution in snakes

Ultraviolet (UV) colour patterns invisible to humans are widespread in nature. However, research bias favouring species with conspicuous colours under sexual selection can limit our assessment of other ecological drivers of UV colour, like interactions between predators and prey. Here we demonstrate widespread UV colouration across Western Hemisphere snakes and find stronger support for a predator defence function than for reproduction. We find that UV colouration has evolved repeatedly in species with ecologies most sensitive to bird predation, with no sexual dichromatism at any life stage. By modelling visual systems of potential predators, we find that snake conspicuousness correlates with UV colouration and predator cone number, providing a plausible mechanism for selection. Our results suggest that UV reflectance should not be assumed absent in “cryptically coloured” animals, as signalling beyond human visual capacities may be a key outcome of species interactions in many taxa for which UV colour is likely underreported.

Historic water quality patterns in endemic schistosomiasis regions of southern Africa: Implications for disease vector distribution

Alterations in atmospheric temperature and rainfall have a major influence on the distribution patterns of vector-borne diseases, including schistosomiasis, by changing the optimum habitat of the intermediate hosts (vectors) that transmit these diseases. Water quality degradation and the creation of artificial freshwater habitats may widen or restrict the distribution of disease vectors. Understanding the influence of water quality on ecosystems is essential in determining the environments that vectors thrive in and where potential outbreaks of the disease they carry will occur. This study aims to analyse the changes in various water quality variables between 1977 and 2009 in the Mbombela river systems and how this may have altered the presence of schistosomiasis snail vectors in these areas. Historical water quality data for the Sabie, Kaap, Komati and Crocodile rivers were obtained from the Department of Water and Sanitation monitoring stations and separated into three decades (1977–1987; 1988–1998 and 1999–2009). The Physicochemical Driver Assessment Index (PAI) was used to determine the ecological category of the rivers in each decade. A Principal Component Analysis was also created for the different river systems to identify possible changes in water quality over the 3 decades. According to the PAI, the Sabie River remained in a relatively good state while the Crocodile, Komati and Kaap rivers had a low water quality to begin with and became further degraded over the three decades. The PCAs indicated the Sabie and Crocodile rivers had increased levels of salts, nutrients and pH between 1988-1998 and 1999–2009 while the Kaap River had high salt content and pH between 1977 and 1998 and high nutrients during the decade of 1999–2009. These results indicate that there has been a change in the water quality of Mbombela river systems with notable increases in salt, pH and nutrient levels. As a result, the aquatic biota have likely been negatively affected and this poses severe impacts on the presence and abundance of schistosomiasis vectors as the snails are sensitive to changes in salinity, nutrients and pH.