A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data.

Recently, due to the difficulty of collecting condition data covering all mechanical fault types in industrial scenarios, the fault diagnosis problem under incomplete data is receiving increasing attention where no target prior information can be available. The existing open-set or universal domain adaptation (DA) diagnosis methods typically treat private fault samples in the target as a generalized "unknown" fault class, neglecting their inherent structure. This oversight can lead to confusion in latent feature space representations and difficulties in separating unknown samples. Therefore, a universal DA method with unsupervised clustering is developed to explore the intrinsic structure of the target samples for mechanical fault diagnosis, where multi-source information on different working conditions is considered to transfer complementary knowledge. First, a composite clustering metric combining single-domain and cross-domain evaluation is constructed to recognize shared and unknown health classes on source-target domains. Second, to alleviate the intra-class shift while enlarging the inter-class gap, a class-wise DA algorithm is suggested which operates on the basis of maximum mean discrepancy. Finally, an entropy regularization criterion is utilized to facilitate clustering of different health classes. The efficacy of the presented approach in the fault diagnosis issues when monitoring data is inadequate has been verified through extensive experiments on three rotating machinery datasets.

Missed nursing care in emergency departments: a cross-sectional descriptive study.

BACKGROUND: Missed care refers to the omission or delay in performing any aspect of patient's care (either a part of the care or the entire care). Currently, missed care has become a growing concern at the international level, which threatens the quality and safety of care and cases many unwanted consequences. This study aims to investigate the frequency and types of missed nursing care in the emergency departments of selected hospitals affiliated to Tehran University of Medical Sciences. METHODS: This is a cross-sectional and descriptive- observational study that was conducted with the aim of determining the frequency and types of missed nursing care in the emergency departments of selected hospitals affiliated to Tehran University of Medical Sciences from January 2020 to June 2020. The research community included all nursing care offered in the designated areas, as well as all nurses working in the emergency departments of selected hospitals. Finally, 146 nurses were selected by census method. The information was collected by self-reporting method and the researcher's observation. Demographic information questionnaire, a researcher-made checklist were used to determine the frequency and types of missed nursing care. 384 observations were made for each item. Descriptive statistics methods were used to analyze the data. RESULTS: The area of checking equipment and emergency trolley(mean = 81.80) had the lowest and the area of patient communication(mean = 55.72) had the highest level of missed care. CONCLUSIONS: The level of missed nursing care in the emergency departments of selected hospitals affiliated to Tehran University of Medical Sciences was found to be high and the highest amount was related to the field of communication with the patient. Therefore, it is recommended that the details of missed nursing care in each area should be considered by nursing managers.

On the fatigue life of dental implants: Numerical and experimental investigation on configuration effect.

Dental implants have seen widespread and successful use in recent years. Given their long-term application and the critical role of geometry in determining fracture and fatigue characteristics, fatigue assessments are of utmost importance for implant systems. In this study, nine dental implant system samples were subjected to testing in accordance with ISO 14801 standards. The tests included static evaluations to assess ultimate loads and fatigue tests conducted under loads of 270 N and 230 N at a frequency of 15 Hz, aimed at identifying fatigue failure locations and fatigue life. Fatigue life predictions and related calculations were carried out using Fe-safe software. The initial model featured a 22° angle for both the fixture and abutment. Subsequently, variations in abutment angles at 21° and 23° were considered while keeping the fixture angle at 22°. In the next phase, the fixture and abutment angles were set as identical, at 21° and 23°. The results unveiled that when the angles of the abutment and fixture matched, stress values decreased, and fatigue life increased. Conversely, models featuring abutment angles of 21° and 23°, with a 22° angle for the fixture, led to a 49.1 % increase in stress and a 36.9 % decrease in fatigue life compared to the primary model. Notably, in the case of the implant with a 23° angle for both abutment and fixture, the fatigue life reached its highest value at 10 million cycles. Conversely, the worst-case scenario was observed in the implant with a 21° abutment angle and a 23° fixture angle, with a fatigue life of 5.49 million cycles.

The predictors of social capital in agricultural consultation, technical, and engineering service companies.

Social capital is an essential type of capital that influences the growth and development of societies. The present descriptive-survey research aimed to capture CEOs' social capital predictors in the agricultural consultation, technical, and engineering service companies in Fars province, Iran. The CEOs, who amounted to 107 people, all participated in the research. The data collection instrument was a questionnaire whose content and face validity were confirmed by a panel of experts and whose reliability was calculated by Cronbach's alpha at 0.82. Data were analyzed in the SPSS22 software package. Based on data analysis, eight social capital items were derived and prioritized. They included social participation, social proactivity, social trust, neighborhood connections, friends and family connections, capacity to accept differences, appreciation of life, and work connections. Based on the ranking of these elements, social proactivity, work connections, and friends and family connections were ranked first to third, respectively. Also, step-by-step multiple regression analysis revealed that the three variables of the feeling of job security, investment, and media were the independent variables that accounted for the CEO's social capital. Programs provided by the media should focus on promoting people's social solidarity. Some investment must be made by these companies in social activities and encouragement of the target community's participation and trust. The success of the agricultural consultation, technical, and engineering service companies is based on the principles of specialty, trust, participation, and social solidarity, showing the existence of social capital in these companies. Therefore, social capital and factors that predict it influence the productivity and efficiency of the companies.

Stuttering on Instagram: What is the focus of stuttering-related Instagram posts and how do users engage with them?

PURPOSE: Instagram has become a popular platform for sharing and seeking health-related information, including stuttering. However, concerns have been raised about the accuracy, confidentiality, and potential negative impact of such information. This study aims to examine how stuttering is defined and understood on Instagram, and how users engage with related content. METHODS: We analyzed highly engaged Instagram posts with the hashtag "#Stuttering" published within a year and their corresponding comments using thematic analysis. RESULTS: The results revealed four main themes and nine sub-themes that highlighted different understandings of stuttering on Instagram, including the need for intervention, emotional impact on people who stutter, positive meanings, and mental health implications. User engagement varied based on the nature of the post, with users showing appreciation, objections, seeking advice, celebrating success stories, mocking, or advocating for people who stutter. CONCLUSION: Although Instagram can serve as a means of normalizing stuttering and highlighting success stories, it raises concerns about the promotion of non-evidence-based treatments and the use of stuttering for political or entertainment purposes. This study emphasizes the need to critically evaluate health-related information presented on social media platforms. To provide reliable information to PWS and their families who seek information on social media, it is recommended to promote evidence-based information on stuttering through trustworthy organizations such as the National Stuttering Association or the Stuttering Foundation, particularly on special occasions like International Stuttering Awareness Day.

Strategic sustainability practices in intercropping-based family farming systems: study on rural communities of Iran.

This paper reports a realistic analysis of a region using Grounded Theory (GT) to provide a sustainable model for family farming systems based on the intercropping system in rural communities of Iran. Furthermore, the fuzzy analytic hierarchy process (FAHP) was applied to assign weights to the criteria and sub-criteria of intercropping and monocropping systems. According to the model, the main phenomenon was "sustainability in the family farming system based on intercropping". In this model, the causal factors were found to include behavioral and attitudinal motivators. Micro- and macro-factors were identified as the interfering factors in family farming systems based on intercropping. Social factors, economic components, and environmental potentials were the contextual factors of this system. Finally, the consequences included the conceptual development and evolution of sustainability, socioeconomic transformation, and ecological-environmental transformation. The results of FAHP showed that the environmental criterion was ranked the first among all criteria underpinning the sustainability of the intercropping system.

Fixed-time periodic stabilization of discontinuous reaction-diffusion Cohen-Grossberg neural networks.

This paper aims to study the fixed-time stabilization of a class of delayed discontinuous reaction-diffusion Cohen-Grossberg neural networks. Firstly, by providing some relaxed conditions containing indefinite functions and based on inequality techniques, a new fixed-time stability lemma is given, which can improve the traditional ones. Secondly, based on state-dependent switching laws, the periodic wave solution of the formulated networks is transformed into the periodic solution of ordinary differential system. By utilizing differential inclusions theory and coincidence theorem, the existence of periodic solutions is obtained. Thirdly, based on the new fixed-time stability lemma, the periodic solutions are stabilized at zero in a fixed-time, which is a new topic on reaction-diffusion networks. Moreover, the established criteria are all delay-dependent, which are less conservative than the previous delay-independent ones for ensuring the stabilization of delayed reaction-diffusion networks. Finally, two examples give numerical explanations of the proposed results and highlight the influence of delays.

Adaptive neural-network-based sliding mode control of switching distributed delay systems with Markov jump parameters.

This paper is devoted to the issue of observer-based adaptive sliding mode control of distributed delay systems with deterministic switching rules and stochastic jumping process, simultaneously, through a neural network approach. Firstly, relying on the designed Lebesgue observer, a sliding mode hyperplane in the integral form is put forward, on which a desired sliding mode dynamic system is derived. Secondly, in consideration of complexity of real transition rates information, a novel adaptive dynamic controller that fits to universal mode information is designed to ensure the existence of sliding motion in finite-time, especially for the case that the mode information is totally unknown. In addition, an observer-based neural compensator is developed to attenuate the effectiveness of unknown system nonlinearity. Thirdly, an average dwell-time approach is utilized to check the mean-square exponential stability of the obtained sliding mode dynamics, particularly, the proposed criteria conditions are successfully unified with the designed controller in the type of mode information. Finally, a practical example is provided to verify the validity of the proposed method.

An adaptive reinforcement learning-based multimodal data fusion framework for human-robot confrontation gaming.

Playing games between humans and robots have become a widespread human-robot confrontation (HRC) application. Although many approaches were proposed to enhance the tracking accuracy by combining different information, the problems of the intelligence degree of the robot and the anti-interference ability of the motion capture system still need to be solved. In this paper, we present an adaptive reinforcement learning (RL) based multimodal data fusion (AdaRL-MDF) framework teaching the robot hand to play Rock-Paper-Scissors (RPS) game with humans. It includes an adaptive learning mechanism to update the ensemble classifier, an RL model providing intellectual wisdom to the robot, and a multimodal data fusion structure offering resistance to interference. The corresponding experiments prove the mentioned functions of the AdaRL-MDF model. The comparison accuracy and computational time show the high performance of the ensemble model by combining k-nearest neighbor (k-NN) and deep convolutional neural network (DCNN). In addition, the depth vision-based k-NN classifier obtains a 100% identification accuracy so that the predicted gestures can be regarded as the real value. The demonstration illustrates the real possibility of HRC application. The theory involved in this model provides the possibility of developing HRC intelligence.

Behavior toward on-farm food safety: Commercial and exporter pistachio growers.

Consumers' demand for high-degree food safety obliges the producers to respect health principles and enhance their product quality in the manufacturing process. Food safety refers to the conditions and practices that preserve food quality in order to prevent contamination and foodborne illnesses. This study aimed to investigate farmers' behavior toward on-farm food safety in Iran. So a survey study was conducted on the research population composed of commercial and exporter pistachio growers in Iran of whom 120 were selected. This paper reports the results of this exploratory study to conceptualize the measurement of pistachio growers' farm food safety using the theory of planned behavior. Structural equation modeling (partial least squares) was used to draw the research models and the relationships between latent variables and indicators. The findings revealed a statistically significant relationship between intention and self-efficacy. The intention is one of the most important variables in determining the planned behavior that has the greatest impact on behavior. Future research on this topic is recommended to use more variables that affect farmers' decision-making processes to form a strong opinion in predicting their behavior. It is crucial to consider some effective interventions such as providing large-scale training and community awareness programs for pistachio growers, particularly with the help of mass media, adopting suitable policy-making for on-farm food safety, and specifically supporting pistachio growers for the implementation of GAP-related practices.

Fe3O4@Gum Arabic modified polyvinyl chloride membranes to improve antifouling performance and separation efficiency of organic pollutants.

Nanofiltration (NF) membranes are promising media for water and wastewater treatment; however, they suffer from their hydrophobic nature and low permeability. For this reason, the polyvinyl chloride (PVC) NF membrane was modified by iron (III) oxide@Gum Arabic (Fe3O4@GA) nanocomposite. First, Fe3O4@GA nanocomposite was synthesized by the co-precipitation approach and then its morphology, elemental composition, thermal stability, and functional groups were characterized by various analyses. Next, the prepared nanocomposite was added to the casting solution of the PVC membrane. The bare and modified membranes were fabricated by a nonsolvent-induced phase separation (NIPS) method. The characteristics of fabricated membranes were assessed by mechanical strength, water contact angle, pore size, and porosity measurements. The optimum Fe3O4@GA/PVC membrane had a 52 L m-2. h-1. bar-1 water flux with a high flux recovery ratio (FRR) value (82%). Also, the filtration experiment exhibited that the Fe3O4@GA/PVC membrane could remarkably remove organic contaminants, achieving high rejection rates of 98% Reactive Red-195, 95% Reactive Blue-19, and 96% Rifampicin antibiotic by 0.25 wt% of Fe3O4@GA/PVC membrane. According to the results, adding Fe3O4@GA green nanocomposite to the membrane casting solution is a suitable and efficient procedure for modifying NF membranes.

Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis.

Intelligent fault diagnosis aims to build robust mechanical condition recognition models with limited dataset. At this stage, fault diagnosis faces two practical challenges: (1) the variability of mechanical working conditions makes the collected data distribution inconsistent, which brings about the domain shift; (2) some unpredictable unknown fault modes that do not observe in the training dataset may occur in the testing scenario, leading to a category gap. In order to cope with these two entangled challenges, an open set multi-source domain adaptation approach is developed in this study. Specifically, a complementary transferability metric defined on multiple classifiers is introduced to quantify the similarity of each target sample to known classes to weight the adversarial mechanism. By applying an unknown mode detector, unknown faults can be automatically identified. Moreover, a multi-source mutual-supervised strategy is further adopted to mine relevant information between different sources to enhance the model performance. Extensive experiments are conducted on three rotating machinery datasets, and the results show that the proposed method is superior to traditional domain adaptation approaches in the mechanical diagnosis issues that new fault modes occur.

Robot path planning based on artificial potential field with deterministic annealing.

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot's obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination. The initial path is optimised using an annealing algorithm to enhance the overall performance. The time, length and success rate of the planned path measures the quality of the solution. Simulation results and comparative experiments demonstrate that the proposed algorithm can solve path planning in different environments. The proposed algorithm is suitable for complex environments with convex or non-convex polygon obstacles.

Measurement outlier-resistant mobile robot localization using multiple Doppler-azimuth radars under round-robin protocol.

This paper is concerned with the measurement outlier-resistant mobile robot localization problem by using multiple Doppler-azimuth radars under round-robin protocol (R-RP). In the considered robot localization system, multiple Doppler-azimuth radars are equipped on the robot platform to produce the measurement including the Doppler frequency shift and the azimuth. In order to assuage communication link congestion, the R-RP is used. For mitigating the influence of outliers, a time-varying state estimator is constructed which contains a saturation function with variable saturation levels. This paper aims at seeking out a practicable yet effective solution to the addressed robot localization problem by devising the constructed estimator which can assure that, over a finite horizon, the localization error satisfies the given H∞ performance index. By constructing an appropriate Lyapunov function, the sufficient condition, which can guarantee the localization error to fulfill the given H∞ performance, is established. Then, by resorting to the solution to a set of linear matrix inequalities, the constructed estimator can be devised. In the light of the estimator design strategy proposed in this paper, the corresponding robot localization algorithm is developed. At last, some simulations are conducted to testify the usefulness of the developed robot localization algorithm.

An event-triggered mechanism to observer-based sliding mode control of fractional-order uncertain switched systems.

This paper is dealing with the problem of observer-based event-triggered sliding mode control for fractional-order uncertain switched systems with a positive order less than one. Firstly, a fractional-order state observer is designed, based on which a fractional-order integral sliding surface function is proposed. Then, utilizing the estimated observer error and sliding mode error vectors, an event-triggered condition is constructed to decide whether the current control signal should be updated or not. Besides, sufficient conditions are derived in the forms of linear matrix inequalities (LMIs) to ensure finite-time stability of the augmented closed-loop system by adopting an average dwell time approach. Thereafter, to avoid the occurrence of infinite triggers within finite time, this paper also discusses the Zeno behavior and refines the results in the previous literature. Finally, to illustrate the effectiveness and superiority of the proposed method, three numerical simulations are provided.

Hearing protection behavior of farmers in Iran: Application of the protection motivation theory.

BACKGROUND: Noise in work environments is regarded as a serious issue. Hearing loss leads to socio-economic problems and huge costs to families and communities. Agriculture is one of the professions in which individuals face occupational noise. Farmers are the second leading group in suffering from hearing loss in the world. OBJECTIVE: This study aims to predict the hearing protection behavior of farmers by using the Protection Motivation Theory (PMT). METHOD: This descriptive study was conducted through a survey. The questionnaire was the main tool for data collection. The population of the study consisted of wheat farmers in Kermanshah province (N = 126,900). By using Krejcie and Morgan's table and stratified random sampling method, 382 farmers were taken as the research sample. The validity of the questionnaire was confirmed by the experts of this field, and the reliability was proved through a pilot study to calculate Cronbach's alpha. RESULTS: The findings showed that perceived self-efficacy, perceived response efficacy, perceived vulnerability, and perceived response costs had the strongest effects on farmers' motivation to protect their hearing, respectively. Furthermore, protection motivation had a significant effect on farmers' protection behavior. CONCLUSION: Threat and coping appraisals as cognitive mediating processes determined farmers' behavior for hearing protection. The results illustrated that the components of PMT were appropriate predictors of farmers' hearing protection behavior.

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health.

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

Socio-cognitive analysis of farmers' water conservation behaviour: The case of the Kavar plain, Iran.

Agricultural activities have a lot of effects on the environment so that water conservation measures help produce crops and consume water resources sustainably. The present research mainly aimed to explore farmers' water conservation behaviour using the comprehensive action determination model (CADM). The research was conducted on the farmers in the Kavar plain in Fars province, Iran (N = 4000). The sample including 351 farmers was taken by the stratified random sampling technique. The measurement instrument was a questionnaire whose face and content validity was confirmed by a panel of experts. To check its reliability, a pilot study was conducted to calculate Cronbach's alpha and composite reliability (CR). The results showed that the indicators used to measure the research variables properly fitted the factor structure and the theoretical framework of the study. Based on the results, social norms (ß = 0.26), personal norms (ß = 0.22), attitude (ß = 0.33), objective constraints (ß = 0.38), and subjective constraints (ß = 0.29) affect farmers' water conservation intention positively and significantly. Also, farmers' behaviour is influenced by their intention (ß = 0.6), as well as their subjective (ß = 0.73) and objective constraints (ß = 0.58). It can be concluded that farmers' water conservation behavior is shaped by a process that involves their normative process, habitual process, situation, and attitudes.

Neural network-based event-triggered data-driven control of disturbed nonlinear systems with quantized input.

This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example.

Validity and reliability of safety behaviors questionnaire for Persian adults who stutter: A cultural perspective.

INTRODUCTION: Adults who stutter (AWS) are reported to have higher social anxiety compared to those who do not stutter. Previous studies have suggested that safety behaviors, which are cognitive or behavioral strategies used by people with anxiety to prevent negative consequences, are important factors in maintaining anxiety. However, the frequency and types of such behaviors might vary in different cultures. Therefore, the aim of the present study was to develop a transcultural adaptation of Safety Behaviors Questionnaire (Helgadottir et al., 2014) for Persian-speaking AWS and assess its validity and reliability. METHODS: In the first step, the original English questionnaire was translated into Persian using the International Quality of Life Assessment (IQOLA) method. Then, 17 speech and language pathologists (SLPs) and 5 AWS evaluated all questionnaire items using Likert scales to determine face validity. Finally, 167 Persian-speaking AWS completed the questionnaire to assess its construct validity and reliability. Their responses were analyzed using factor analysis and Cronbach's Alpha. RESULTS: Some items of the original questionnaire were modified or combined with other similar items after assessing the face validity of the Persian-translated questionnaire. Construct validity analysis categorized the remaining 29 questionnaire items into four factors: general avoidance, practice and control, rehearsal, and choosing safe and easy people. The high Cronbach's Alpha of 0.89 for all items confirmed the internal reliability of the Persian-translated questionnaire. DISCUSSION/CONCLUSIONS: The results confirmed the psychometric characteristics of the Persian- translated version of the Safety Behaviors Questionnaire (Helgadottir et al., 2014); therefore, speech pathologists can apply it in future clinical and research settings. Similar to Australian AWS, Persian participants of this study self-reported use of various types of safety behaviors frequently.