Search Results (32)

Diversity and inclusion in organizational settings are still under-researched themes despite their societal relevance. In this preregistered systematic literature review, we examine how nudging as an agency-preserving intervention tool can create a more inclusive and diverse workplace. Nudging is rooted in behavioral economics and aims to influence decision-making processes without restricting freedom of choice. Inclusion refers to creating a work environment where everyone feels valued and encouraged to contribute. Diversity reaches beyond demographic factors, fostering more innovative and creative organizational practices, and better decisions. We searched for applications nudging towards diversity and inclusion initiatives at the workplace in four databases: PsycINFO, Scopus, EBSCOhost, and Web of Science. Peer-reviewed articles published in the last 15 years were included regardless of article type in organizational settings were included. Nine studies met our inclusion criteria. Based on their findings, we show a positive association between the use of nudging techniques to create more inclusive and diverse workplaces and advance a classification of nudge types in this domain. We discuss the importance of being aware of the potential drawbacks and negative consequences of using nudging interventions. Potential drawbacks that may arise include lack of autonomy and overload. Further research is needed to explore which nudging techniques are most effective in promoting diversity and inclusion. Full article

A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and Forecast (WRF) model. Its impact on the analysis and forecast of Typhoon Talim in 2023 at its initial developing stage is demonstrated. First, the conditional generative adversarial networks–bidirectional ensemble binned probability fusion (CGAN-BEBPF) model ) is applied to retrieve three-dimensional (3D) CloudSat CPR (cloud profiling radar) equivalent W-band (94 Ghz) radar reflectivity factor for the typhoons Talim and Chaba using the MODIS L2 data. Next, a W-band to S-band radar reflectivity factor mapping algorithm (W2S) is developed based on the collocated measurements of the retrieved W-band radar and ground-based S-band (4 Ghz) radar data for Typhoon Chaba at its landfall time. Then, W2S is utilized to project the MODIS-retrieved 3D W-band radar reflectivity factor of Typhoon Talim to equivalent ground-based S-band reflectivity factors. Finally, data assimilation and forecast experiments are conducted by using the WRF Hydrometeor and Latent Heat Nudging (HLHN) radar data assimilation technique. Verification of the simulation results shows that assimilating the MODIS L2 cloud products dramatically improves the initialization and forecast of the cloud and precipitation fields of Typhoon Talim. In comparison to the experiment without assimilation of the MODIS data, the Threat Score (TS) for general cloud areas and major precipitation areas is increased by 0.17 (from 0.46 to 0.63) and 0.28 (from 0.14 to 0.42), respectively. The fraction skill score (FSS) for the 5 mm precipitation threshold is increased by 0.43. This study provides an unprecedented data assimilation method to initialize 3D cloud and precipitation hydrometeor fields with the MODIS imagery payloads for numerical weather prediction models. Full article

Local learning algorithms, such as Equilibrium Propagation (EP), have emerged as alternatives to global learning methods like backpropagation for training neural networks. EP offers the potential for more energy-efficient hardware implementation by utilizing only local neuron information for weight updates. However, the practical implementation of EP using memristor-based circuits has significant challenges due to the immature fabrication processes of memristors, resulting in defects and variability issues. Previous implementations of EP with memristor crossbars use two separate circuits for the free and nudge phases. This approach can suffer differences in defects and variability between the two circuits, potentially leading to significant performance degradation. To overcome these limitations, in this paper, we propose a novel time-multiplexing technique that combines the free and nudge phases into a single memristor circuit. Our proposed scheme integrates the dynamic equations of the free and nudge phases into one circuit, allowing defects and variability compensation during the training. Simulations using the MNIST dataset demonstrate that our approach maintains a 92% recognition rate even with a 10% defect rate in memristors, compared to 33% for the previous scheme. Furthermore, the proposed circuit reduces area overhead for both the memristor circuit solving EP’s algorithm and the weight-update control circuit. Full article

In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (S_N2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method’s performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies. Full article

This research paper unveils an all-encompassing literature exploration into “nudging” in digital platforms and its profound impact on the user experience. This study delved into various sources spanning academic research papers, corporate reports, books, and online publications, acquired through a thorough four-step approach. The methodology entailed unearthing pertinent sources via diverse academic databases and industry networks, and a diligent review process to estimate their relevance and calibre. Data extraction from each selected source focused on the employed nudge techniques, underlying behavioural principles, and their repercussions on the user experience. The findings were subsequently synthesised to unearth the existing literature’s prevalent themes, disparities, and prospective gaps. The paper underscores the importance of nudging as a potent driver of user actions while safeguarding their autonomy. We employed a comprehensive approach to explore nudging application and influences on digital platforms, including academic database searches, corporate reports, and web blogs. We thoroughly extracted data on platform types, nudging strategies, behavioural theories, and user experience influences and impacts. Our study deliberates on potential future research trajectories, encompassing ethical considerations and personalised nudging methodologies. Ultimately, this study underscores how applying nudge techniques in the architecture of digital platforms can elevate user experiences and confer value upon both users and providers. However, the findings acknowledge the inherent limitations that accompany any literature review and may not encapsulate every facet of the subject matter. Full article

Equilibrium propagation (EP) has been proposed recently as a new neural network training algorithm based on a local learning concept, where only local information is used to calculate the weight update of the neural network. Despite the advantages of local learning, numerical iteration for solving the EP dynamic equations makes the EP algorithm less practical for realizing edge intelligence hardware. Some analog circuits have been suggested to solve the EP dynamic equations physically, not numerically, using the original EP algorithm. However, there are still a few problems in terms of circuit implementation: for example, the need for storing the free-phase solution and the lack of essential peripheral circuits for calculating and updating synaptic weights. Therefore, in this paper, a new analog circuit technique is proposed to realize the EP algorithm in practical and implementable hardware. This work has two major contributions in achieving this objective. First, the free-phase and nudge-phase solutions are calculated by the proposed analog circuits simultaneously, not at different times. With this process, analog voltage memories or digital memories with converting circuits between digital and analog domains for storing the free-phase solution temporarily can be eliminated in the proposed EP circuit. Second, a simple EP learning rule relying on a fixed amount of conductance change per programming pulse is newly proposed and implemented in peripheral circuits. The modified EP learning rule can make the weight update circuit practical and implementable without requiring the use of a complicated program verification scheme. The proposed memristor conductance update circuit is simulated and verified for training synaptic weights on memristor crossbars. The simulation results showed that the proposed EP circuit could be used for realizing on-device learning in edge intelligence hardware. Full article

This study attempts to improve the accuracy of wind field simulations in the Weather Research and Forecasting (WRF) model by incorporating Doppler lidar-based wind observations over the Osaka region of Japan. To achieve this, a Doppler lidar was deployed in Osaka city, and multi-layer wind measurements were obtained for one month (August 2022). These measurements were then assimilated into the WRF model using the observation nudging technique. Two simulations were conducted: one with nudging disabled, and the other with nudging enabled with data assimilation, while keeping all other configurations constant. The results were evaluated by comparing the simulations with the lidar observation at the lidar location where the wind data were nudged during the simulation, as well as with the AMeDAS station observations at other locations far from the lidar. The results indicated that not only the wind field, but other weather variables such as temperature, were better captured in the simulation using lidar-based nudging compared to the simulation without nudging. Full article

People are the weakest link in the cybersecurity chain when viewed in the context of technological advancement. People become vulnerable to trickery through contemporary technical developments such as social media platforms. Information accessibility and flow have increased rapidly and effectively; however, due to this increase, new electronic risks, or so-called cybercrime, such as phishing, scams, and hacking, lead to privacy breaches and hardware sabotage. Therefore, ensuring data privacy is vital, particularly in an educational institute where students constitute the large majority of users. Students or trainees violate cybersecurity policies due to their lack of awareness about the cybersecurity environment and the consequences of cybercrime. This paper aims to assess the level of awareness of cybersecurity, users’ activities, and user responses to cybersecurity issues. This paper collected data based on a distributed questionnaire among trainees in the Technical and Vocational Training Corporation (TVTC) to demonstrate the necessity of increasing user awareness and training. In this study, quantitative research techniques were utilized to analyze the responses from trainees using tests such as the Chi-Squared test. Proof of the reliability of the survey was provided using Cronbach’s alpha test. This research identifies the deficiencies in cybersecurity awareness among TVTC trainees. After analyzing the gathered data, recommendations for tackling these shortcomings were offered, with the aim of enhancing trainees’ decision-making skills regarding privacy and security using the Nudge model. Full article

South Africa is blessed with vast plant resources and unique vegetation types. Indigenous South African medicinal plants have been well-harnessed to generate income in rural communities. Many of these plants have been processed into natural products to heal a variety of diseases, making them valuable export commodities. South Africa has one of the most effective bio-conservation policies in Africa, which has protected the South African indigenous medicinal vegetation. However, there is a strong link between government policies for biodiversity conservation, the propagation of medicinal plants as a source of livelihood, and the development of propagation techniques by research scientists. Tertiary institutions nationwide have played a crucial role in the development of effective propagation protocols for valuable South African medicinal plants. The government-restricted harvest policies have also helped to nudge natural product companies and medicinal plant marketers to embrace the cultivated plants for their medicinal uses, and thus have helped support the South African economy and biodiversity conservation. Propagation methods used for the cultivation of the relevant medicinal plants vary according to plant family and vegetation type, among others. Plants from the Cape areas, such as the Karoo, are often resuscitated after bushfires, and propagation protocols mimicking these events have been established through seed propagation protocols with controlled temperatures and other conditions, to establish seedlings of such plants. Thus, this review highlights the role of the propagation of highly utilized and traded medicinal plants in the South African traditional medicinal system. Some valuable medicinal plants that sustain livelihoods and are highly sought-after as export raw materials are discussed. The effect of South African bio-conservation registration on the propagation of these plants and the roles of the communities and other stakeholders in the development of propagation protocols for highly utilized and endangered medicinal plants are also covered. The role of various propagation methods on the bioactive compounds’ composition of medicinal plants and issues of quality assurance are addressed. The available literature, media online news, newspapers, and other resources, such as published books and manuals, were scrutinized for information. Full article

Background: The World Health Organization identified alcohol and tobacco consumption as the risk factors with a greater attributable burden and number of deaths related to non-communicable diseases. A promising technique aimed to modify behavioral risk factors by redesigning the elements influencing the choice of people is nudging. Methodology: A scoping review of the literature was performed to map the literature evidence investigating the use of nudging for tobacco and alcohol consumption prevention and/or control in adults. Results: A total of 20 studies were included. The identified nudging categories were increasing salience of information or incentives (IS), default choices (DF), and providing feedback (PF). Almost three-quarters of the studies implementing IS and half of those implementing PF reported a success. Three-quarters of the studies using IS in conjunction with other interventions reported a success whereas more than half of the those with IS alone reported a success. The PF strategy performed better in multi-component interventions targeting alcohol consumption. Only one DF mono-component study addressing alcohol consumption reported a success. Conclusions: To achieve a higher impact, nudging should be integrated into comprehensive prevention policy frameworks, with dedicated education sessions for health professionals. In conclusion, nudge strategies for tobacco and alcohol consumption prevention in adults show promising results. Further research is needed to investigate the use of nudge strategies in socio-economically diverse groups and in young populations. Full article

Numerous approaches address over-fitting in neural networks: by imposing a penalty on the parameters of the network (L1, L2, etc.); by changing the network stochastically (drop-out, Gaussian noise, etc.); or by transforming the input data (batch normalization, etc.). In contrast, we aim to ensure that a minimum amount of supporting evidence is present when fitting the model parameters to the training data. This, at the single neuron level, is equivalent to ensuring that both sides of the separating hyperplane (for a standard artificial neuron) have a minimum number of data points, noting that these points need not belong to the same class for the inner layers. We firstly benchmark the results of this approach on the standard Fashion-MINST dataset, comparing it to various regularization techniques. Interestingly, we note that by nudging each neuron to divide, at least in part, its input data, the resulting networks make use of each neuron, avoiding a hyperplane completely on one side of its input data (which is equivalent to a constant into the next layers). To illustrate this point, we study the prevalence of saturated nodes throughout training, showing that neurons are activated more frequently and earlier in training when using this regularization approach. A direct consequence of the improved neuron activation is that deep networks are now easier to train. This is crucially important when the network topology is not known a priori and fitting often remains stuck in a suboptimal local minima. We demonstrate this property by training a network of increasing depth (and constant width); most regularization approaches will result in increasingly frequent training failures (over different random seeds), whilst the proposed evidence-based regularization significantly outperforms in its ability to train deep networks. Full article

It is a common practice to use a buffer zone to damp out spurious wave growth due to computational error along the lateral boundary of limited-area weather and climate models. Although it is an effective technique to maintain model stability, an unintended side effect of using such buffer zones is the distortion of the data passing through the buffer zone. Various techniques are introduced to enhance the communication between the limited-area model’s inner domain and the outer domain, which provides lateral boundary values for the inner domain. Among them, scale-selective data assimilation (SSDA) and the spectral nudging (SPNU) techniques share similar philosophy, i.e., directly injecting the large-scale components of the atmospheric circulation from the outer model domain into the interior grids of the inner model domain by-passing the lateral boundary and the buffer zone, but the two methods are taking different implementation approaches. SSDA utilizes a 3-dimensional variational data assimilation procedure to accomplish the data injection objective, whereas SPNU uses a nudging process. In the present study, the two approaches are evaluated comparatively for simulating hurricane track and intensity in a pair of cases: Jeanne (2004) and Irma (2017) using the Weather Research and Forecasting (WRF) model. The results indicate that both techniques are effective in improving tropical cyclone intensity and track simulations by reducing the errors of the large-scale circulation in the inner model domain. The SSDA runs produced better simulations of temperature and humidity fields which are not directly nudged. The SSDA runs also produced more accurate storm intensities in both cases and more realistic structure in Hurricane Jeanne’s case than those produced by the SPNU runs. It should be noted, however, that extending these case study results to more general situations requires additional studies covering a large number of additional cases. Full article

Background: Child obesity rates are increasing worldwide. In Korea, the proportion of overweight students has steadily increased from 21.8% in 2015 to 25.8% in 2019. Childhood obesity causes mental problems, such as depression and social phobia, due to mental stress, feelings of inferiority, and low self-esteem. Methods: This experimental study aimed to verify the effect of the child obesity management program on body changes (height, weight, obesity degree, body mass index [BMI], body fat percentage), eating habits, exercise habits, obesity knowledge, and social support. This child obesity management program applies the nudge technique based on an ecological model and induces autonomous weight management through environmental control. Results: As results of this study, the child obesity management program using the nudge technique developed in this study improved the height (t = −5.19, p < 0.001), obesity degree (z = −3.28, p = 0.001), BMI (z = −3.22, p = 0.001), exercise habits (t = −2.09, p = 0.040), and obesity knowledge of obese children (z = −2.99, p = 0.003). Conclusions: This multidimensional intervention improved obesity by inducing and sustaining behavioral changes in obese children. Therefore, applying the nudge techniques and multidimensional intervention methods based on ecological model are proposed to increase the effectiveness of the health promotion programs. Full article

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories. Full article

This study introduces a new operational tool based on the AEIOU observational framework to support the design of adaptive human machine interfaces (HMIs) that aim to modify people’s behavior and support people’s choices, to improve safety using emotional regulation techniques, through the management of environmental characteristics (e.g., temperature and illumination), according to an approach based on the nudging concept within a design thinking process. The proposed approach focuses on research in the field of behavioral psychology that has studied the correlations between human emotions and driving behavior, pushing towards the elicitation of those emotions judged to be most suitable for safe driving. The main objective is to support the ideation of scenarios and/or design features for adaptive HMIs to implement a nudging strategy to increase driving safety. At the end, the results from a collaborative workshop, organized as a case study to collect concept ideas in the context of sports cars, will be shown and evaluated to highlight the validity of the proposed methodology, but also the limitations due to the requirement of prototypes to evaluate the actual effectiveness of the presented nudging strategies. Full article