A heuristic-based approach for assessing usability in electric powered wheelchairs: A preliminary investigation

Objective: An important issue related to electric powered wheelchair (EPW) is usability. Recent studies did not use heuristic evaluation and did not consider users’ and developers’ participation in the usability evaluation process of the EPW, especially when it has to be driven using alternative commands. Thus, this study investigates the use of heuristics to evaluate the usability of EPW driven by alternative commands, considering the opinion of users and assistive technology (AT) development professionals.MethodsThe study was carried out with 54 participants: 28 EPW users (Group I) and 26 AT developers (Group II). We built a set of 25 heuristics that affects EPW usability. Participants rated each of the 25 heuristics according to their importance for the usability of EPW using the five-point Likert scale. We used the R Software to perform the Wilcoxon Mann–Whitney test as a statistical comparisons test between Group I and II.ResultsThe results showed a statistically significant difference (p<0.05) between Group I and II in the evaluation of 16 heuristics. We identified an important set of heuristics that could help evaluate and improve the usability of EPW.ConclusionThe findings highlighted the importance of considering users’ and developers’ points of view in developing an EPW and its evaluation criteria. It could help the design of the device match the user's needs and expectations. The set of heuristics in this study can be adapted to evaluate other devices’ usability using the heuristic evaluation approach. (AU)

Objetivo: La usabilidad es una cuestión importante relacionada con las sillas de ruedas eléctricas (SRE). Los estudios recientes no han utilizado evaluaciones heurísticas ni han contemplado la participación de los usuarios y desarrolladores en el proceso de evaluación de dichas sillas, especialmente cuando deben conducirse utilizando comandos alternativos. Por ello, este estudio investiga el uso de la heurística para evaluar la usabilidad de las SRE impulsadas por comandos alternativos, considerando la opinión de los usuarios y los profesionales del desarrollo de la tecnología asistencial (TA).MétodosEl estudio se realizó con 54 participantes: 28 usuarios de SRE (Grupo I) y 26 desarrolladores de TA (Grupo II). Construimos un conjunto de 25 heurísticas que afectan a la usabilidad de las SRE. Los sujetos calificaron cada una de las 25 heurísticas de acuerdo con su importancia para la usabilidad de las SRE utilizando la escala de Likert de cinco puntos. Utilizamos el software R para realizar la prueba de Wilcoxon-Mann-Whitney como prueba de comparación estadística entre los Grupos I y II.ResultadosLos resultados reflejaron una diferencia estadística significativa (p < 0,05) entre los Grupos I y II en la evaluación de las 16 heurísticas. Identificamos un conjunto importante de heurísticas que podrían ayudar a evaluar y mejorar la usabilidad de las SRE.ConclusiónLos hallazgos subrayaron la importancia de estimar los puntos de vista de los usuarios y desarrolladores a la hora de desarrollar una SRE, así como sus criterios de evaluación. Estos podrían contribuir a que el diseño del dispositivo coincidiera con las necesidades y expectativas de los usuarios. El conjunto de heurísticas de este estudio puede adaptarse, para evaluar la usabilidad de otros dispositivos, utilizando el enfoque de evaluación heurística. (AU)

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.

Lung cancer is one of the most deadly diseases in the world. Lung cancer detection can save the patient's life. Despite being the best imaging tool in the medical sector, clinicians find it challenging to interpret and detect cancer from Computed Tomography (CT) scan data. One of the most effective ways for the diagnosis of certain malignancies like lung tumours is Positron Emission Tomography (PET) imaging. So many diagnosis models have been implemented nowadays to diagnose various diseases. Early lung cancer identification is very important for predicting the severity level of lung cancer in cancer patients. To explore the effective model, an image fusion-based detection model is proposed for lung cancer detection using an improved heuristic algorithm of the deep learning model. Firstly, the PET and CT images are gathered from the internet. Further, these two collected images are fused for further process by using the Adaptive Dilated Convolution Neural Network (AD-CNN), in which the hyperparameters are tuned by the Modified Initial Velocity-based Capuchin Search Algorithm (MIV-CapSA). Subsequently, the abnormal regions are segmented by influencing the TransUnet3+. Finally, the segmented images are fed into the Hybrid Attention-based Deep Networks (HADN) model, encompassed with Mobilenet and Shufflenet. Therefore, the effectiveness of the novel detection model is analyzed using various metrics compared with traditional approaches. At last, the outcome evinces that it aids in early basic detection to treat the patients effectively.

Evaluating the Usability of an mHealth App for Empowering Cancer Survivors With Disabilities: Heuristic Evaluation and Usability Testing.

BACKGROUND: More than 18 million cancer survivors are living in the United States. The effects of cancer and its treatments can have cognitive, psychological, physical, and social consequences that many survivors find incredibly disabling. Posttreatment support is often unavailable or underused, especially for survivors living with disabilities. This leaves them to deal with new obstacles and struggles on their own, oftentimes feeling lost during this transition. Mobile health (mHealth) interventions have been shown to effectively aid cancer survivors in dealing with many of the aftereffects of cancer and its treatments; these interventions hold immense potential for survivors living with disabilities. We developed a prototype for WeCanManage, an mHealth-delivered self-management intervention to empower cancer survivors living with disabilities through problem-solving, mindfulness, and self-advocacy training. OBJECTIVE: Our study conducted a heuristic evaluation of the WeCanManage high-fidelity prototype and assessed its usability among cancer survivors with known disabilities. METHODS: We evaluated the prototype using Nielsen's 10 principles of heuristic evaluation with 22 human-computer interaction university students. On the basis of the heuristic evaluation findings, we modified the prototype and conducted usability testing on 10 cancer survivors with a variety of known disabilities, examining effectiveness, efficiency, usability, and satisfaction, including a completion of the modified System Usability Scale (SUS). RESULTS: The findings from the heuristic evaluation were mostly favorable, highlighting the need for a help guide, addressing accessibility concerns, and enhancing the navigation experience. After usability testing, the average SUS score was 81, indicating a good-excellent design. The participants in the usability testing sample expressed positive reactions toward the app's design, educational content and videos, and the available means of connecting with others. They identified areas for improvement, such as improving accessibility, simplifying navigation within the community forums, and providing a more convenient method to access the help guide. CONCLUSIONS: Overall, usability testing showed positive results for the design of WeCanManage. The course content and features helped participants feel heard, understood, and less alone.

La labor del Protomedicato en la salud pública en el virreinato del Perú / O trabalho do Protomedicato na saúde pública no vice-reinado do Peru / The work of the Protomedicato in public health in the viceroyalty of Peru

El objetivo de la presente investigación es conocer el papelque cumplió el Protomedicato en la salud publica en elvirreinato del Perú. La metodología utilizada fue la narraciónhistórica siguiendo el método heurístico y hermenéutico,utilizando fuentes primarias documentales de archivoshistóricos peruanos y españoles. El resultado obtenido fueconfirmar que el Protomedicato veló por la salud de losvecinos y que tenía carácter administrativo judicial y susfunciones principales fueron regularizar la labor de los queejercieran cualquier tipo de atención sanitaria, otorgandolicencias, fijando aranceles y vigilando el trabajo realizadoen las boticas y la calidad de los medicamentos, a través devisitas periódicas. La conclusión determina que hubo mejorassanitarias como consecuencia de la gestión del Protomedicatoen el virreinato del Perú, proponiendo medidas salubrese, incluso, disponía la movilización de personal médico através del virreinato y participaba ante cualquier consultade otras entidades coloniales, como el cabildo, referentes asalud pública, como eliminación de acequias, declaraciónde pestes, y otros.(AU)

The objective of this research is to know the role that theProtomedicato played in public health in the viceroyaltyof Peru. The methodology used was historical narrationfollowing the heuristic and hermeneutic method, usingprimary documentary sources from Peruvian and Spanishhistorical archives. The result obtained was to confirm thatthe Protomedicato looked after the health of the neighborsand that it had a judicial administrative nature and its main functions were to regularize the work of those whocarried out any type of health care, granting licenses, settingtariffs and monitoring the work carried out in pharmaciesand the quality of medicines, through periodic visits. Theconclusion determines that there were health improvementsas a consequence of the management of the Protomedicato inthe viceroyalty of Peru, proposing health measures and evenordered the mobilization of medical personnel throughoutthe viceroyalty and participated in any consultation fromother colonial entities, such as the council, referring to publichealth, such as elimination of ditches, declaration of pests,and others.(AU)

O objetivo desta pesquisa é conhecer o papel que o Protomedicatodesempenhou na saúde pública no vice-reinado do Peru. Ametodologia utilizada foi a narração histórica seguindo o métodoheurístico e hermenêutico, utilizando fontes documentaisprimárias de arquivos históricos peruanos e espanhóis. Oresultado obtido foi confirmar que o Protomedicato zelava pelasaúde dos vizinhos e que tinha caráter administrativo judiciale suas principais funções eram regularizar o trabalho de quemrealizava qualquer tipo de assistência à saúde, concedendolicenças, fixando tarifas e acompanhamento do trabalhorealizado nas farmácias e da qualidade dos medicamentos,através de visitas periódicas. A conclusão determina quehouve melhorias na saúde como consequência da gestão doProtomedicato no vice-reinado do Peru, propondo medidassanitárias e ainda ordenou a mobilização de pessoal médicoem todo o vice-reinado e participou de qualquer consultade outras entidades coloniais, como o conselho. , referentesà saúde pública, como eliminação de valas, declaração depragas, entre outros.(AU)

Heuristic strategy of intuitive statistical inferences in 7- to 10-year-old children.

Intuitive statistical inferences refer to making inferences about uncertain events based on limited probabilistic information, which is crucial for both human and non-human species' survival and reproduction. Previous research found that 7- and 8-year-old children failed in intuitive statistical inference tasks after heuristic strategies had been controlled. However, few studies systematically explored children's heuristic strategies of intuitive statistical inferences and their potential numerical underpinnings. In the current research, Experiment 1 (N = 81) examined 7- to 10-year-olds' use of different types of heuristic strategies; results revealed that children relied more on focusing on the absolute number strategy. Experiment 2 (N = 99) and Experiment 3 (N = 94) added continuous-format stimuli to examine whether 7- and 8-year-olds could make genuine intuitive statistical inferences instead of heuristics. Results revealed that both 7- and 8-year-olds and 9- and 10-year-olds performed better in intuitive statistical inference tasks with continuous-format stimuli, even after focusing on the absolute number strategy had been controlled. The results across the three experiments preliminarily hinted that the ratio processing system might rely on the approximate number system. Future research could clarify what specific numerical processing mechanism may be used and how it might support children's statistical intuitions.

Choosing the best way: how wild common marmosets travel to efficiently exploit resources.

While foraging, animals have to find potential food sites, remember these sites, and plan the best navigation route. To deal with problems associated with foraging for multiple and patchy resources, primates may employ heuristic strategies to improve foraging success. Until now, no study has attempted to investigate experimentally the use of such strategies by a primate in a context involving foraging in large-scale space. Thus, we carried out an experimental field study that aimed to test if wild common marmosets (Callithrix jacchus) employ heuristic strategies to efficiently navigate through multiple feeding sites distributed in a large-scale space. In our experiment, we arranged four feeding platforms in a trapezoid configuration with up to 60 possible routes and observe marmosets' decisions under two experimental conditions. In experimental condition I, all platforms contained the same amount of food; in experimental condition II, the platforms had different amounts of food. According to the number and arrangement of the platforms, we tested two heuristic strategies: the Nearest Neighbor Rule and the Gravity Rule. Our results revealed that wild common marmosets prefer to use routes consistent with a heuristic strategy more than expected by chance, regardless of food distribution. The findings also demonstrate that common marmosets seem to integrate different factors such as distance and quantity of food across multiple sites distributed over a large-scale space, employing a combination of heuristic strategies to select the most efficient routes available. In summary, our findings confirm our expectations and provide important insights into the spatial cognition of these small neotropical primates.

The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses.

Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.

Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China.

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

Do large language models show decision heuristics similar to humans? A case study using GPT-3.5.

A Large Language Model (LLM) is an artificial intelligence system trained on vast amounts of natural language data, enabling it to generate human-like responses to written or spoken language input. Generative Pre-Trained Transformer (GPT)-3.5 is an example of an LLM that supports a conversational agent called ChatGPT. In this work, we used a series of novel prompts to determine whether ChatGPT shows heuristics and other context-sensitive responses. We also tested the same prompts on human participants. Across four studies, we found that ChatGPT was influenced by random anchors in making estimates (anchoring, Study 1); it judged the likelihood of two events occurring together to be higher than the likelihood of either event occurring alone, and it was influenced by anecdotal information (representativeness and availability heuristic, Study 2); it found an item to be more efficacious when its features were presented positively rather than negatively-even though both presentations contained statistically equivalent information (framing effect, Study 3); and it valued an owned item more than a newly found item even though the two items were objectively identical (endowment effect, Study 4). In each study, human participants showed similar effects. Heuristics and context-sensitive responses in humans are thought to be driven by cognitive and affective processes such as loss aversion and effort reduction. The fact that an LLM-which lacks these processes-also shows such responses invites consideration of the possibility that language is sufficiently rich to carry these effects and may play a role in generating these effects in humans. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing.

BACKGROUND: Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF. OBJECTIVES: This study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal. METHODS: We collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set. RESULTS: The results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed). CONCLUSIONS: DNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability.

Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, we report a new method for predicting synthesizability using a simple yet accurate thermochemical descriptor. We introduce Emin, the energy difference between a molecule and its lowest energy constitutional isomer, as a synthesizability predictor that is accurate, physically meaningful, and first-principles based. We apply Emin to 134,000 molecules in the QM9 data set and find that Emin is accurate when used alone and reduces incorrect predictions of "synthesizable" by up to 52% when used to augment commonly used prediction methods. Our work illustrates how first-principles thermochemistry and heuristic approximations for molecular stability are complementary, opening a new direction for synthesizability prediction methods.

Extraction of time-related expressions using text mining with application to Hebrew.

In this research, we extract time-related expressions from a rabbinic text in a semi-automatic manner. These expressions usually appear next to rabbinic references (name / nickname / acronym / book-name). The first step toward our goal is to find all the expressions near references in the corpus. However, not all of the phrases around the references are time-related expressions. Therefore, these phrases are initially considered to be potential time-related expressions. To extract the time-related expressions, we formulate two new statistical functions, and we use screening and heuristic methods. We tested these statistical functions, grammatical screenings, and heuristic methods on a corpus containing responsa documents. In this corpus, many rabbinic citations are known and marked. The statistical functions and the screening methods filtered the potential time-related expressions and reduced 99.88% of the initial expressions (from 484,681 to 575).

Consumers' perception of novel foods and the impact of heuristics and biases: A systematic review.

According to the definition adopted in the European Union, novel foods are foods that were not consumed to a significant degree within the Union before May 15, 1997. This includes cultivated meat and insects. Novel foods are meant to play a critical role in the transition towards sustainable food systems. However, their success depends on whether and to what extent they will be incorporated into the diets at the population level. This review investigates consumers' perception of novel food products by narratively synthesising results on the influence of heuristics and biases triggered by emotions, personality traits, and socio-cultural factors. Empirical studies conducted in Western countries and published in English after 1997 were eligible, which led to 182 studies being included. Notably, most included studies focused on insects and cultivated meat. Disgust and fear are shown to be the main emotions driving rejection of novel foods, together with food neophobia and specific cultural norms common across countries included in the scope of the review. Familiarity with novel foods and curiosity both led to higher acceptance. Despite being investigated directly in a minority of studies, heuristics and related biases mostly fell under the "affect," the "natural-is-better," and the "trust" heuristics. The review also discusses to what extent consumers' perception reflects in the regulatory framework applicable to novel foods in the European Union, how it influences the regulation of insects and cultivated meat and which lessons can be drawn for the future of the regulatory framework.

The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer.

BACKGROUND: Human activity Recognition (HAR) using smartphone sensors suffers from two major problems: sensor orientation and placement. Sensor orientation and sensor placement problems refer to the variation in sensor signal for a particular activity due to sensors' altering orientation and placement. Extracting orientation and position invariant features from raw sensor signals is a simple solution for tackling these problems. Using few heuristic features rather than numerous time-domain and frequency-domain features offers more simplicity in this approach. The heuristic features are features which have very minimal effects of sensor orientation and placement. In this study, we evaluated the effectiveness of four simple heuristic features in solving the sensor orientation and placement problems using a 1D-CNN-LSTM model for a data set consisting of over 12 million samples. METHODS: We accumulated data from 42 participants for six common daily activities: Lying, Sitting, Walking, and Running at 3-Metabolic Equivalent of Tasks (METs), 5-METs and 7-METs from a single accelerometer sensor of a smartphone. We conducted our study for three smartphone positions: Pocket, Backpack and Hand. We extracted simple heuristic features from the accelerometer data and used them to train and test a 1D-CNN-LSTM model to evaluate their effectiveness in solving sensor orientation and placement problems. RESULTS: We performed intra-position and inter-position evaluations. In intra-position evaluation, we trained and tested the model using data from the same smartphone position, whereas, in inter-position evaluation, the training and test data was from different smartphone positions. For intra-position evaluation, we acquired 70-73% accuracy; for inter-position cases, the accuracies ranged between 59 and 69%. Moreover, we performed participant-specific and activity-specific analyses. CONCLUSIONS: We found that the simple heuristic features are considerably effective in solving orientation problems. With further development, such as fusing the heuristic features with other methods that eliminate placement issues, we can also achieve a better result than the outcome we achieved using the heuristic features for the sensor placement problem. In addition, we found the heuristic features to be more effective in recognizing high-intensity activities.

Exact global alignment using A* with chaining seed heuristic and match pruning.

MOTIVATION: Sequence alignment has been at the core of computational biology for half a century. Still, it is an open problem to design a practical algorithm for exact alignment of a pair of related sequences in linear-like time. RESULTS: We solve exact global pairwise alignment with respect to edit distance by using the A* shortest path algorithm. In order to efficiently align long sequences with high divergence, we extend the recently proposed seed heuristic with match chaining, gap costs, and inexact matches. We additionally integrate the novel match pruning technique and diagonal transition to improve the A* search. We prove the correctness of our algorithm, implement it in the A*PA aligner, and justify our extensions intuitively and empirically.On random sequences of divergence d=4% and length n, the empirical runtime of A*PA scales near-linearly with length (best fit n1.06, n≤107 bp). A similar scaling remains up to d=12% (best fit n1.24, n≤107 bp). For n=107 bp and d=4%, A*PA reaches >500× speedup compared to the leading exact aligners Edlib and BiWFA. The performance of A*PA is highly influenced by long gaps. On long (n>500kb) ONT reads of a human sample it efficiently aligns sequences with d<10%, leading to 3× median speedup compared to Edlib and BiWFA. When the sequences come from different human samples, A*PA performs 1.7× faster than Edlib and BiWFA. AVAILABILITY AND IMPLEMENTATION: github.com/RagnarGrootKoerkamp/astar-pairwise-aligner.

Acoustical and behavioral heuristics for fast interactive sound design.

During their creative process, designers routinely seek the feedback of end users. Yet, the collection of perceptual judgments is costly and time-consuming, since it involves repeated exposure to the designed object under elementary variations. Thus, considering the practical limits of working with human subjects, randomized protocols in interactive sound design face the risk of inefficiency, in the sense of collecting mostly uninformative judgments. This risk is all the more severe that the initial search space of design variations is vast. In this paper, we propose heuristics for reducing the design space considered during an interactive optimization process. These heuristics operate by using an approximation model, called surrogate model, of the perceptual quantity of interest. As an application, we investigate the design of pleasant and detectable electric vehicle sounds using an interactive genetic algorithm. We compare two types of surrogate models for this task, one based on acoustical descriptors gathered from the literature and the other based on behavioral data. We find that reducing by a factor of up to 64 an original design space of 4096 possible settings with the proposed heuristics reduces the number of iterations of the design process by up to 2 to reach the same performance. The behavioral approach leads to the best improvement of the explored designs overall, while the acoustical approach requires an appropriate choice of acoustical descriptor to be effective. Our approach accelerates the convergence of interactive design. As such, it is particularly suitable to tasks in which exhaustive search is prohibitively slow or expensive.

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company.

Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.

Heuristics in sport: A scoping review.

Judgement and decision-making under uncertainty often rely on simplistic" rules of thumb", known as "heuristics". The purpose of this scoping review is to explore the extant literature focussed on heuristics and sport. This study employed a five-stage scoping review methodology. The databases searched were Scopus, Web of Science, SPORTDiscus, and PsycInfo. The search terms were sport*, heuristic* (and its synonyms: cognitive shortcut, shortcut, rule of thumb, mental rule, cognitive rule) plus cognitive bias. The search identified 2019 studies, of which 38 were included in the analysis. Studies based in USA and Germany were most common. The use of heuristics by players were most common, while football (soccer) and basketball were the most frequently researched sport contexts. Both males and females were commonly included in most studies, but there were no studies with an explicit focus on females. The research was contextualized within several academic disciplines (e.g., psychology, forecasting, JDM, organization behavior, sports marketing and sponsorship, coaching science, risk analysis and sociology). Approximately 80 % of the studies were quantitative. Sixteen studies examined the fast and frugal heuristics approach (i.e., take-the-first heuristic (n = 8), recognition heuristic (n = 7), or gut instinct (n = 1), whereas eleven articles embraced the heuristics and biases approach. Future research should pursue a greater variety of heuristics, investigate the use of heuristics by selectors and boards of directors, and how best to design, implement, and evaluate heuristic education programs.

A High-Fidelity Combined ATC-Rxnorm Drug Hierarchy for Large-Scale Observational Research.

Observational research utilizes patient information from many disparate databases worldwide. To be able to systematically analyze data and compare the results of such research studies, information about exposure to drugs or classes of drugs needs to be harmonized across these data. The NLM's RxNorm drug terminology and WHO's ATC classification serve these needs but are currently not satisfactorily combined into a common system. Creating such system is hampered by a number of challenges, resulting from different approaches to representing attributes of drugs and ontological rules. Here, we present a combined ATC-RxNorm drug hierarchy, allowing to use ATC classes for retrieval of drug information in large scale observational data. We present the heuristic for maintaining this resource and evaluate it in a real world database containing drug and drug classification information.