Individual and relational factors related to disclosure of epilepsy in the workplace.

BACKGROUND: The decision to disclose epilepsy in the workplace is complex, as it entails both advantages and disadvantages. In this study, we aimed to identify the factors associated with disclosure of epilepsy in the workplace based on the disclosure decision-making model for patients who required underwent comprehensive assessment in the Epilepsy Monitoring Unit (EMU). METHODS: This retrospective study included 193 patients with epilepsy (112 men, aged 18-66 years) who underwent comprehensive assessment, including long-term video-EEG monitoring, neuroimaging studies, and neuropsychological and psychosocial assessment in the Tohoku University Hospital EMU. Data were obtained from the medical records and self-reported questionnaires at our EMU. The outcome variable was disclosure of epilepsy. Predictive variables were selected based on the disclosure decision-making model: individual factors (i.e., age, sex, age at onset of epilepsy, seizure frequency, generalized tonic-clonic seizures or focal to bilateral tonic-clonic seizures in the last 2 years, experiences of viewing own seizure, and felt stigma), and relational factors (i.e., experiences of discrimination, enacted stigma, and social support). Data were analyzed using a logistic regression analysis model. RESULTS: Our results indicated that 43.5% of patients disclosed epilepsy to their employer. The factors that associated with disclosure of epilepsy were experiences of discrimination (odds ratio [OR], 7.78; 95% confidence interval [CI], 2.84-21.34, p < 0.01), experiences of viewing own seizure (OR, 3.51; 95% CI, 1.27-9.72, p < 0.05), and level of enacted stigma (OR, 0.69; 95% CI, 0.48-0.99, p < 0.05). CONCLUSION: This study indicated that the decision to disclose epilepsy was associated with both individual factors, such as experience of viewing own seizures, and relational factors, such as experience of discrimination and enacted stigma.

A self-reported symptom-based decision-making model helps to rule out outpatient cases at low risk for CRC before colonoscopy.

Objectives: Effective exclusion of low-risk symptomatic outpatient cases for colorectal cancer (CRC) remains diagnostic challenges. We aimed to develop a self-reported symptom-based decision-making model for application in outpatient scenarios. Methods: In total, 8233 symptomatic cases at risk for CRC, as judged by outpatient physicians, were involved in this study at seven medical centers. A decision-making model was constructed using 60 self-reported symptom parameters collected from the questionnaire. Further internal and external validation cohorts were built to evaluate the discriminatory power of the CRC model. The discriminatory power of the CRC model was assessed by the C-index and calibration plot. After that, the clinical utility and user experience of the CRC model were evaluated. Results: Nine symptom parameters were identified as valuable predictors used for modeling. Internal and external validation cohorts verified the adequate discriminatory power of the CRC model. In the clinical application step, all 17 physicians found the model easy to grasp, 99.9 % of the patients were satisfied with the survey form. Application of this model detected all CRC cases. The total consistency ratio of outpatient cases undergoing colonoscopy was 81.4 %. None of the low-risk patients defined by the CRC model had been diagnosed with CRC. Conclusion: This multicenter study developed and validated a simple and user-friendly decision-making model covering self-reported information. The CRC model has been demonstrated to perform well in terms of rapid outpatient decision-making scenarios and clinical utility, particularly because it can better rule out low-risk outpatient cases.

Optimizing material circularity pathways in industrial waste streams: A decision-making model.

The increasing pressures of environmental regulation and the introduction of new policy frameworks by various nations have accelerated the popularization of industrial solid waste management and recovery, underscoring the transition towards a circular economy. This paradigm shift emphasizes the importance of material recovery, reuse, and recycling of industrial waste to minimize environmental impact and enhance sustainability. Despite the availability of individual approaches for waste recovery, there exists a significant gap in the systematic selection of optimal recovery pathways that facilitate the reintegration of materials into the production cycle. Addressing this gap, our study introduces a novel optimization model designed to identify the most efficient material circularity routes that leverage both the technical and biological cycles of the circular economy framework. Utilizing the Genetic Algorithm optimization tool in MATLAB, our model prioritizes pathways that maximize material recovery and profit generation simultaneously. This dual-objective function serves as the cornerstone of our analysis, ensuring a balanced approach to environmental sustainability and economic viability. The model's efficacy was tested on pre-calculated quantities of fabric waste generated by the Biyagama Export Processing Zone, providing a practical case study for its application. Our findings reveal diverse scenarios under which the model can allocate varying weights to each objective, demonstrating its flexibility and utility as a decision-making tool for stakeholders in the waste management sector. The results indicate that the model is not only capable of optimizing waste circularity pathways for maximum material recovery and profit generation but also offers a customizable framework that can adapt to the specific priorities of different stakeholders. This research contributes to the existing body of knowledge by filling a critical gap in the selection of sustainable waste recovery pathways, offering a practical, optimized, and scalable solution that can significantly advance the goals of the circular economy in the industrial sector.•Decision-making model for stakeholders in the waste management sector.•Model selects the best material recovery pathways.•Textile industrial fabric waste stream used as a pilot to test the model's effectiveness.

Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer.

Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.

Design and experimental evaluation of a variable pesticide application control system for the air-assisted rubber tree powder sprayer.

BACKGROUND: In order to address the issues of uneven pesticide deposition and low pesticide utilization in rubber gardens caused by the traditional diffuse plant protection spraying method, this study focuses on the air-assisted powder sprayer and proposes a variable pesticide application control system. A variable pesticide application decision-making model integrating the leaf area index (LAI) was designed based on powdery mildew control standards and individual rubber tree information. According to the target powder spraying accuracy requirements, a control model of the air velocity adjustment device was established and a fuzzy proportional-integral-differential (PID) air velocity control system was developed. RESULTS: The simulation results indicate that the wind speed control system exhibits a maximum overshoot of 2.18% and an average response time of 1.48 s. The field experiment conducted in a rubber plantation revealed that when the air-assisted powder sprayer operates in the variable powder spraying mode, the average response time of the control system is 2.5 s. The control accuracy of each executive mechanism exceeded 95.9%. The deposition coefficient of variation (CV) at different canopy heights was relatively consistent, with values of 35.38%, 36.26% and 36.90%. In comparison to the quantitative mode, the variable mode showed a significant 20.03% increase in the effective utilization rate of sulfur powder. CONCLUSION: These research findings provide valuable technical support for the advancement of mechanized variable powder spraying equipment in rubber tree cultivation. © 2024 Society of Chemical Industry.

A neurocomputational model of decision and confidence in object recognition task.

How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confidence. In this circumstance, confidence is making a bridge between seeing and believing. Our study unveils how the brain processes visual information to make such decisions with an assessment of confidence, using a model inspired by the visual cortex. To computationally model the process, this study uses a spiking neural network inspired by the hierarchy of the visual cortex in mammals to investigate the dynamics of feedforward object recognition and decision-making in the brain. The model consists of two modules: a temporal dynamic object representation module and an attractor neural network-based decision-making module. Unlike traditional models, ours captures the evolution of evidence within the visual cortex, mimicking how confidence forms in the brain. This offers a more biologically plausible approach to decision-making when encountering real-world stimuli. We conducted experiments using natural stimuli and measured accuracy, reaction time, and confidence. The model's estimated confidence aligns remarkably well with human-reported confidence. Furthermore, the model can simulate the human change-of-mind phenomenon, reflecting the ongoing evaluation of evidence in the brain. Also, this finding offers decision-making and confidence encoding share the same neural circuit.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Listening efficiency in adult cochlear-implant users compared with normally-hearing controls at ecologically relevant signal-to-noise ratios.

Introduction: Due to having to work with an impoverished auditory signal, cochlear-implant (CI) users may experience reduced speech intelligibility and/or increased listening effort in real-world listening situations, compared to their normally-hearing (NH) peers. These two challenges to perception may be usefully integrated in a measure of listening efficiency: conceptually, the amount of accuracy achieved for a certain amount of effort expended. Methods: We describe a novel approach to quantifying listening efficiency based on the rate of evidence accumulation toward a correct response in a linear ballistic accumulator (LBA) model of choice decision-making. Estimation of this objective measure within a hierarchical Bayesian framework confers further benefits, including full quantification of uncertainty in parameter estimates. We applied this approach to examine the speech-in-noise performance of a group of 24 CI users (M age: 60.3, range: 20-84 years) and a group of 25 approximately age-matched NH controls (M age: 55.8, range: 20-79 years). In a laboratory experiment, participants listened to reverberant target sentences in cafeteria noise at ecologically relevant signal-to-noise ratios (SNRs) of +20, +10, and +4 dB SNR. Individual differences in cognition and self-reported listening experiences were also characterised by means of cognitive tests and hearing questionnaires. Results: At the group level, the CI group showed much lower listening efficiency than the NH group, even in favourable acoustic conditions. At the individual level, within the CI group (but not the NH group), higher listening efficiency was associated with better cognition (i.e., working-memory and linguistic-closure) and with more positive self-reported listening experiences, both in the laboratory and in daily life. Discussion: We argue that listening efficiency, measured using the approach described here, is: (i) conceptually well-motivated, in that it is theoretically impervious to differences in how individuals approach the speed-accuracy trade-off that is inherent to all perceptual decision making; and (ii) of practical utility, in that it is sensitive to differences in task demand, and to differences between groups, even when speech intelligibility remains at or near ceiling level. Further research is needed to explore the sensitivity and practical utility of this metric across diverse listening situations.

Preserving the Role of the Audiologist in a Clinical Technology, Consumer Channel, Clinical Service Model of Hearing Healthcare.

The past decade has been characterized by significant changes in the distribution and sale of hearing aids. Alternatives to the clinical technology, clinical channel, clinical service (i.e., traditional) hearing healthcare delivery model have been driven by growth in hearing aid dispensaries housed in large retail establishments and direct-to-consumer hearing aid sales by internet-based companies unaffiliated with major hearing aid manufacturers (e.g., Eargo). These developments have been accompanied by acceleration in the growth of teleaudiology services as a direct result of the COVID-19 pandemic. The resulting development of nontraditional hearing aid distribution and sales models can be categorized into distinct archetypes as reviewed earlier in this publication. This article will review the Clinical Technology-Consumer Channel-Clinical Service model as exemplified by Jabra Enhance. We will describe a completely digital model of hearing aid distribution and sales that maintains the professional service component throughout the client journey to include an online tone test, the use of a risk mitigation questionnaire, virtual consultations, remote hearing aid adjustments, and the establishment and monitoring of client-centered treatment goals. Furthermore, this article will review the Jabra Enhance model within the context of consumer healthcare decision-making theory with a focus on the Consumer Decision-Making Model.

A novel collision warning system based on the visual road environment schema: An examination from vehicle and driver characteristics.

Drivers pay unequal attention to different road environmental elements and visual fields, which greatly influences their driving behavior. However, existing collision warning systems ignore these visual characteristics of drivers, which limits the performance of collision warning systems. Therefore, this study proposes a novel collision warning system based on the visual road environment schema, in order to enhance the support for avoiding potential dangers in objects and areas that are easily overlooked by the drivers' vision. To capture the above visual characteristics of drivers, the visual road environment schema that consists of the semantic layer, the scene depth layer, the sensitive layer, and the visual field layer is established by using several different deep neural networks, which realizes the recognition, quantization, and analysis of the road environment from the drivers' visual perspective. The effectiveness of the novel collision warning system is verified by the driving simulation experiment from six indicators, including warning distance, maximum lateral acceleration, maximum longitudinal deceleration, minimum collision time, reaction time, and heart rate. Additionally, a grey target decision-making model is built to comprehensively evaluate the system. The results show that compared with the traditional collision warning system, the novel collision warning system proposed in this study performs significantly better and can discover potential dangers earlier, give timely warnings, enhance the vehicles' lateral stability and driving comfort, shorten reaction time, and relieve the drivers' nervousness. By integrating the drivers' visual characteristics into the collision warning system, this study could help to optimize the existing collision warning system and promote the mutual understanding between intelligent vehicles and human drivers.

The Role of Shame, Stigma, and Family Communication Patterns in the Decision to Disclose STIs to Parents in Order to Seek Support.

Emerging adulthood is identified as a time of personal growth wherein emerging adults engage in sexual exploration and risky behaviors, potentially resulting in the contraction of a sexually transmitted infection (STI). Due to the continued reliance on parents for support during this developmental period, emerging adults (EAs) may need to disclose their STI status to their parents. This study applies the health disclosure decision-making model (DD-MM) to extend our understanding of EA disclosures of sensitive health information such as STIs to parents. Data were collected from 204 college students. The results of mediational analyses provided some support for the mediating effects of family communication patterns on the relationship between relational quality and illness assessment (i.e., stigma) and willingness to disclose in a given scenario. The theoretical and practical implications of this are discussed.

Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model.

This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multi-criteria decision-making (MCDM) model integrating Interval Rough Numbers (IRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) was used to investigate the interrelationships between critical hydrogeologic factors (and determine their relative weights) via a novel vulnerability index based on the DRASTIC model. The flexibility of GIS in handling spatial data was employed to delineate thematic map layers of the hydrogeologic factors and to improve the DRASTIC model. The hybrid MCDM model results show that net recharge (a key hydrogeologic factor) had the highest priority with a weight of 0.1986. In contrast, the topography factor had the least priority, with a weight of 0.0497. A case study validated the hybrid model using Anambra State, Nigeria. The resultant vulnerability map shows that 12.98% of the study area falls into a very high vulnerability class, 31.90% falls into a high vulnerability, 23.52% falls into the average vulnerability, 21.75% falls into a low vulnerability, and 9.85% falls into very low vulnerability classes, respectively. In addition, nitrate concentration was used to evaluate the degree of groundwater pollution. Based on observed nitrate concentration, the modified DRASTIC model was validated and compared to the traditional DRASTIC model; interestingly, the spatial model of the modified DRASTIC model performed better. This study is thus critical for environmental monitoring and implementing appropriate management interventions to protect groundwater resources against indiscriminate sources of pollution.

A fuzzy rough hybrid decision making technique for identifying the infected population of COVID-19.

Decision theoretic rough set model have been used over many years in most of the application areas. It provides a novel way for knowledge acquisition, especially when dealing with vagueness and uncertainty. Many mathematical modelings have been presented recently to control the pandemic nature of COVID-19 and along with its control model as well. Decision-based treatment recommendation has not yet been found so far in any of the articles. In this paper, we have proposed a novel approach of three-way decision based on linguistic information of a COVID-19 susceptible person. To present this, we have discussed the probabilistic rough fuzzy hybrid model with linguistic information. This model helps us to guess the infected person and decide whom to send for self-isolation, home quarantine and medical treatment in an emergency situation. The significance of the proposed hybrid model has been discussed by presenting a comparative study and reported along with justifications too.

Development of a Novel Scoring System to Quantify the Severity of Incident Reports: An Exploratory Research Study.

Incident reporting systems have been widely adopted to collect information about patient safety incidents. Much of the value of incident reports lies in the free-text section. Computer processing of semantic information may be helpful to analyze this. We developed a novel scoring system for decision making to assess the severity of incidents using the semantic characteristics of the text in incident reports, and compared its results with experts' opinions. We retrospectively analyzed free-text data from incident reports from January 2012 to September 2021 at Nagoya University Hospital, Aichi, Japan. The sample was allocated to training and validation datasets using the hold-out method. Morphological analysis was used to segment terms in the training dataset. We calculated a severity term score, a severity report score and severity group score, by report volume size, and compared these with conventional severity classifications by patient safety experts and reporters. We allocated 96,082 incident reports into two groups. We calculated 1,802 severity term scores from the 48,041 reports in the training dataset. There was a significant difference in severity report score between reports categorized as severe and not severe by experts (95% confidence interval [CI] -0.83 to -0.80, p < 0.001, d = 0.81). Severity group scores were positively associated with severity ratings from experts and reporters (correlation coefficients 0.73 [95% CI 0.63-0.80, p < 0.001] and 0.79 [95% CI 0.71-0.85, p < 0.001]) for all departments. Our severity scoring system could therefore contribute to better organizational patient safety.

Vigilance in the Decision-Making Process Regarding Termination of Pregnancy Following Prenatal Diagnosis of Congenital Heart Disease-Application of the 'Conflict Decision-Making Model'.

The decision-making process regarding termination of pregnancy following prenatal diagnosis of congenital heart disease is a stressful experience for future parents. Janis and Mann's conflict decision-making model describes seven ideal stages that comprise vigilant information-gathering as an expression of the qualitative decision-making process. In our study, we attempted to determine whether parents who face the decision regarding termination of pregnancy undertake a qualitative decision-making process. Data were collected over 2-year period using structural questionnaires. The sample consisted of two hundred forty participants; sixty-nine (28.75%) declared that their decision was to terminate the pregnancy. A significant difference in the quality of the decision-making score was noted between parents who decided to continue with the pregnancy vs. parents who opted for termination (mean score of 10.15 (5.6) vs. 18.51 (3.9), respectively, p < 0.001). Sixty-two (90%) participants within the termination of pregnancy group went through all seven stages of vigilant decision-making process and utilized additional sources for information and consultation. Parents who decided to continue with the pregnancy made swift decisions, often without considering the negative and positive outcomes; this decision-making pattern is considered non-vigilant and ineffective. Identification of future parents at risk of going through an ineffective decision-making process may help health professionals to determine the best way to provide them with information and support.

Developing a Decision-Making Model for Construction Safety Behavior Supervision: An Evolutionary Game Theory-Based Analysis.

Without the active participation of enterprises and front-line workers, it is difficult for the government to perform effective supervision to ensure behavioral safety among front-line workers. To overcome inadequate government supervision and information attenuation caused by vertical management mode and limited resources, and to change passive supervision into active control with the proactive participation of enterprises and workers, this paper combines the entity responsibility mechanism and the third-party participation mechanism based on government supervision to analyze the decision-making process of government and enterprises on safety behavior supervision. An evolutionary game model was established to describe the decision-making interactions between the government and construction enterprises under the two mechanisms, and a simulation was performed to illustrate the factors influencing the implementation of the mechanisms. The results show that both mechanisms have a positive effect on government supervision, and the third-party participation mechanism was found to be working better. The implementation of the two mechanisms is influenced by punishment, subsidy, and cost, and it has different sensitivities to the three influencing factors. This study provides a theoretical framework for enhancing the government supervision mechanism, and the decision-making between the government and construction enterprises enhances the management form and guides their actual supervision practices.

A hybrid statistical decision-making optimization approach for groundwater vulnerability considering uncertainty.

Recognizing the vulnerable areas for contamination is a feasible way to protect groundwater resources. The main contribution of the paper is developing a hybrid statistical decision-making model for evaluating the vulnerability of Shiraz aquifer, southern Iran, with modified DRASTIC (depth to the water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, and hydraulic conductivity) by using the genetic algorithm (GA), the analytical hierarchy process (AHP) method, and factorial analysis (FA). First, considering the variation of the uncertain parameters, 32 scenarios were defined to perform factorial analysis. Then using the AHP method and GA, DRASTIC parameters were rated and weighted in all scenarios. To achieve the optimal weights for parameters, the objective function in GA was maximizing the correlation coefficient between the vulnerability index and the nitrate concentration. The single and interactive effects of parameters on groundwater vulnerability were analyzed by factorial analysis. The results revealed that the net recharge had the highest single effect, and the resulted effect between net recharge and hydraulic conductivity was the most significant interactive effect on the objective function. Besides, the variation of aquifer media does not change the objective function. The application of the proposed method leads to a precise groundwater vulnerability map. This research provides valuable knowledge for assessing groundwater vulnerability and enables decision-makers to apply groundwater vulnerability information in future water resources management plans.

The Voice Problem Impact Scales (VPIS).

INTRODUCTION: Patient-reported outcome measures (PROMs) are important for systematically assessing a person's perspectives and experiences with disease to inform clinical decision-making. However, PROMs can occasionally fail to capture subtle differences amongst subgroups. In response to this problem, the aim of the current study was to examine the convergent validity of four patient-reported voice activity and participation scales to better reflect and describe the impact of a voice problem in a patient's work, home, social and overall life. It was hypothesized that augmenting the validated PROM with a directed situational short instrument may enhance patient and clinician communication. This would allow for further description of individual areas of activity limitations or participation restrictions that are relevant to the patient, potentially informing therapeutic goals. METHODS: The Voice Problem Impact Scales (VPIS) were developed following the criteria outlined by Francis et al (2016). A retrospective chart review was completed for voice therapy treatment seeking patients at the USC Voice Center. Results from the Voice Handicap Index-10 (VHI-10) and VPIS scores were recorded at the time of the evaluation. Consensus Auditory Perceptual Evaluation of Voice (CAPE-V) assessment was performed by an SLP with fellowship training in voice. RESULTS: Three hundred four charts were reviewed, and 198 met inclusion criteria. When considering all patients, VHI-10 scores were significantly correlated with each domain of the VPIS, including overall (R = 0.635, P < 0.001), work (R = 0.436, P < 0.001), social (R = 0.714, P < 0.001), and home (R = 0.637, P < 0.001). For females aged 18-39 and aged ≥60, the VHI-10 was correlated with all domains except work. CAPE-V score was significantly correlated with the social domain (R = 0.236, P = 0.001). Using the corrected significance level, it was not correlated with the overall (R = 0.165, P = 0.022), home (R = 0.197, P = 0.006), or work domains (R = 0.042, P = 0.567). The VHI-10 was not correlated with any of the VPIS domains for males aged 18-39, was correlated with all domains for males aged 40-59, and was correlated with all domains except work for males aged ≥60. Age was the only significant predictor of the work domain (ß = -4.631 P < 0.001), with a model fit of R2 = 0.101. CONCLUSIONS: Scores from each domain of the VPIS are significantly correlated with VHI-10 scores thus confirming the instrument's convergent validity. There are certain groups for which currently used questionnaires may underrepresent the impact of dysphonia on the patient's life. The VPIS represents a broad tool that might allow the patient to interpret each scale within their individual context and cultural background. The VPIS emphasizes the significance of the dysphonia on quality of life in four common environments. Using this instrument can augment questionnaires and initiate conversations between the provider and patient to determine the area(s) where voice impairment is most important enhancing shared decision-making on therapeutic goals for plan of care.

Evaluation of the performance of a collaborative proposal of multiple access in cognitive radio networks.

Cognitive radio networks (CRN) allow for an increase in spectral efficiency and performance of today's wireless networks. Currently, multiple proposals exist in the area of spectral decision-making and mobility; however, very few evaluate the impact of collaboration between secondary users and the performance of spectrum access by many secondary users. Unlike existing works, this article provides a comprehensive quantitative analysis of the performance of CRN taking into account access to the spectrum simultaneously by multiple users and decision making based on collaboration through the exchange of information between nearby secondary users. This proposal is developed through the implementation of four modules: Input Module, Multi-user Module, Collaborative module and Decision-making module, where the results are evaluated comparatively through the handoff rate generated with two multicriteria techniques: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Criteria Optimization and Compromise Solution (VIKOR). The evaluation is carried out taking into account three levels of collaboration, three multi-user access scenarios, and two multi-criteria techniques for a total of 18 simulation scenarios. The results obtained show the importance of implementing collaboration strategies, as for multi-user access, the number of handoffs increases as the number of serial users increases. TOPSIS presented the best results in 76 % of the analyzed cases where VIKOR generated a smaller number of handoffs; TOPSIS maintained good performance with differences not exceeding 90 handoffs.

A Novel Vaccine Selection Decision-Making Model (VSDMM) for COVID-19.

Selecting a vaccine for fighting a pandemic is one of the serious issues in healthcare. Novel decision models for vaccine selection need to be developed. In this study, a novel vaccine selection decision-making model (VSDMM) was proposed and developed, based on the analytic hierarchy process (AHP) technique, which assesses many alternatives (vaccines) using multi-criteria to support decision making. To feed data to the VSDMM, six coronavirus disease-19 (COVID-19) vaccines were selected in a case study to highlight the applicability of the proposed model. Each vaccine was compared to the others with respect to six criteria and all criteria were compared to calculate the relative weights. The proposed criteria include (1) vaccine availability; (2) vaccine formula; (3) vaccine efficacy; (4) vaccine-related side effects; (5) cost savings, and (6) host-related factors. Using the selected criteria, experts responded to questions and currently available COVID-19 vaccines were ranked according to their weight in the model. A sensitivity analysis was introduced to assess the model robustness and the impacts of changing criteria weights on the results. The VSDMM is flexible in terms of its ability to accept more vaccine alternatives and/or more criteria. It could also be applied to other current or future pandemics/epidemics in the world. In conclusion, this is the first report to propose a VSDMM for selecting the most suitable vaccines in pandemic/epidemic situations or any other situations in which vaccine selection and usage may be deemed necessary.