Set-point Model Research Articles

Exploring a Language-Based Interest Assessment: Predicting Vocational Interests on Social Media Using Natural Language Processing

For more than a century, self-report inventories have been the traditional method for assessing vocational interests. Little research has examined the use of machine learning techniques, such as natural language processing (NLP), in interest assessment. This paper explores the extent to which natural language on social media can be used to predict individuals’ self-ratings on eight basic interests representing the SETPOINT model: Agriculture, Engineering, Human Resources, Life Science, Management/Administration, Mechanics/Electronics, Media, and Social Science. Leveraging closed- (Linguistic Inquiry and Word Count; LIWC) and open-vocabulary NLP approaches (Latent Dirichlet allocation (LDA) topic modeling), we analyzed 3.2 million Facebook posts from 2,834 participants who completed a 32-item basic interest measure. We found that the convergent validities of these NLP approaches for predicting vocational interest scores (LIWC: [Formula: see text] = 0.19; LDA topic modeling: [Formula: see text] = 0.24) are comparable to prior research on language-based personality assessments. Our study also revealed largely face-valid language markers that characterize different basic interests. Implications for developing language-based interest assessments for applied settings (e.g., career guidance and employee selection) and future research directions are discussed.

Affective foundations in AI-human interactions: Insights from evolutionary continuity and interspecies communications

The imminent arrival of Artificial General Intelligence (AGI) compels a reevaluation of AI-human interactions, particularly through affective communication. This research synthesizes insights from evolutionary biology, comparative psychology, and AI development, advocating for a paradigm shift beyond conventional human-like cognitive processes. It emphasizes the universal nature of affective pathways, as evidenced across various species. We introduce three affective interaction models — the Affective Threshold Model, the Dynamic Set-Point Model, and the Affective Schema Model — all of which stem from an in-depth analysis of interspecies communications. These models present a roadmap to craft AI interfaces attuned to human affective experiences, elucidating avenues of trust, intuition, and reciprocal recognition between machines and their human counterparts. By further crystallizing the concept of the “Large Affect Model”, we project a horizon where AI not only deciphers but also empathizes with human partners, paving the way for a revolutionary cooperative paradigm between AI and humanity.

Measuring SETPOINT vocational interest dimensions: The development and validation of three short scales

Vocational interest research has seen a resurgence in the applied psychology literature, given evidence showing its predictive validity for key work outcomes. There is a need for integrative, reliable, and valid measures to advance research in this space. While the RIASEC model of vocational interests (Holland, 1997) has been the most widely used and studied typology for the assessment of six broad interest types, more recent work with the SETPOINT model (Su et al., 2019) suggests that eight interest dimensions provide better fit to interest data and demonstrates stronger criterion-related validity evidence. However, to date, no short scales are available for measuring the broad SETPOINT dimensions. We developed three short scales with 8, 24, and 41 items, respectively, that capture the eight dimensions of the SETPOINT model in an integrative manner. Using a sample of 972 full-time working adults assessed across two-time points over five weeks, we validated these three short scales following robust psychometric analyses. These scales are shown to have good psychometric properties. The development and validation of the three short scales help close the operational gap for the SETPOINT model and further facilitate the study of interests and use of interest measures in academic and applied settings.

Models of body weight and fatness regulation.

Body weight and fatness appear to be regulated phenomena. Several different theoretical models are available to capture the essence of this idea. These include the set-point, dynamic equilibrium, adiposity force, control theory-settling point, Hall-Guo, operation point and dual intervention point (DIP) models. The set-point model posits a single reference point around which levels of fat are regulated. The dynamic equilibrium model suggests that the apparent regulation of body fat around a reference point is an illusion owing to the necessary impacts of weight change on energy expenditure. Control theory focuses on the importance of feedback gain and suggests set-point and dynamic equilibrium are ends of a continuum of feedback gain. Control theory models have also been called 'settling point' models. The Hall-Guo, operation point and DIP models also bring together the set-point and dynamic equilibrium ideas into a single framework. The DIP proposes a zone of indifference where dynamic equilibrium 'regulation' predominates, bounded by upper and lower intervention points beyond which physiological mechanisms are activated. The drifty gene hypothesis is an idea explaining where this individual variation in the upper intervention point might come from. We conclude that further experiments to test between the models are sorely required. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

We've come full circle: The universality of People-Things and Data-Ideas as core dimensions of vocational interests

Vocational interest research relies on interest taxonomies that partition the construct space of activity preferences into a small number of broad interest domains. To this day, the most widely used classification system is Holland's (1997) RIASEC taxonomy, which distinguishes between six overarching interest domains. A central feature of this model is that the six domains are connected via a circular similarity structure, the circumplex, which is often described with the help of two orthogonal core dimensions: People-Things and Data-Ideas. In recent years, alternative interest taxonomies have been proposed, which suggest different partitionings of the construct space that are said to better reflect today's world of work. Using the example of one such alternative, namely, the recently introduced SETPOINT model (Su et al., 2019), the current article argues that such taxonomies still strongly reflect the underlying core dimensions that define the interest circumplex. Using a mixed online sample from Germany (N = 560), it is shown that 1) the main and subdomains of the SETPOINT model reflect a circular similarity structure, 2) this circular similarity structure is conceptually identical to the ones identified in previous research, and 3) the discriminatory power of the SETPOINT scales for occupational group membership can largely be traced back to the core dimensions of the interest circumplex.

A detailed view of the Adaptive-Comfort-Control-Implementation Script (ACCIS): The capabilities of the automation system for adaptive setpoint temperatures in building energy models

Recent research has looked into the potential for energy savings from adopting setpoint temperatures based on adaptive comfort. The recently suggested Adaptive-Comfort-Control-Implementation Script (ACCIS), a computational method that expands the use of air conditioning to adaptive comfort, can be used to accomplish this. The user's setup and an Input Data File (IDF) are used by ACCIS to convert PMV-based EnergyPlus models into adaptive setpoint models. The Adaptive Comfort Control Implemented Model (“accim”) Python module has been used to nest ACCIS, which was originally an Energy Management System (EMS) script. This research focuses on the creation of ACCIS and offers a more in-depth understanding of how the computational method functions based on the connections between the objects that compose it. Since all weather file quantity constraints have been overcome and studies can now be generated based on millions of simulations, ACCIS opens up new opportunities for performing energy simulations on a worldwide scale rather than only on a national or continental level. This potential is increased considering that, until recently only international models ASHRAE Standard 55 and European standard EN16798-1 were considered in ACCIS, but now a recent update allows to select local comfort models developed for specific areas. As a result, this potential is firstly demonstrated by means of the study of the 3 arguments that have the most important impact on the setpoint temperatures: the thermal comfort model, the acceptability levels and the behaviour of setpoint temperatures when adaptive comfort models are not applicable; and secondly by means of an actual example of use of accim.

HD-RMPC: A Hierarchical Distributed and Robust Model Predictive Control Framework for Urban Traffic Signal Timing

Due to the nonlinearity and dynamics of transportation systems, traffic signal control (TSC) in urban traffic networks has always been an important challenge. In recent years, model predictive control (MPC) has shown extraordinary potential in TSC due to its outstanding ability to model dynamic systems. However, the relatively complex online computing, lack of reasonable setpoints for target solving, and uncertainty of traffic network hinder MPC from being further applied. To address these problems, we propose a hierarchical, distributed, and robust model predictive control (HD-RMPC) framework for urban TSC. At the slow-update layer, the road network is dynamically divided into several subareas according to regional attributes and real-time traffic demand. Meanwhile, the volume is coordinated in a robust way for the purpose of traffic equilibrium and overflow prevention. Then, the set-point matrix of each subarea is calculated to equalize the flow in the subarea. This distributed framework guarantees the real-time performance of MPC in urban traffic networks. At the fast-update layer, we adopt an improved prediction model by explicit modeling of the disturbance and reduce the prediction error. Finally, the objective function is reconstructed and solved at the control layer to obtain the optimal control law. Through continuous and asynchronous optimization of the set point and prediction model, the framework significantly improves the control effect. Simulation evaluation based on a real-world road network demonstrates that the proposed HD-RMPC method outperforms all baselines and maintains excellent real-time performance.

Methanol–Water Purification Control Using Multi-Loop PI Controllers Based on Linear Set Point and Disturbance Models

Dimethyl ether (DME), derived from methanol, has been issued as an alternative to diesel and LPG. This study will continue the research of an optimized process control design for DME purification plants, specifically the methanol purification process. This way, water can be separated, and the main product, methanol, can be recycled for DME synthesis. A setpoint-based linear model of a methanol–water purification system has been developed with four controlled variables (CV) with objectives to; maintain a separated liquid water stream in the bottom stage (Stage 30) of the distillation column for methanol–water separation at 11.22%, keep the purified liquid methanol at a condenser at 58.81 °C and 49.96% of level, and the last CV is the cooler in the distillate stream, to keep the purified methanol’s top product at 40.75 °C. To complete the model, a first order plus dead time (FOPDT) disturbance model is created against the inlet temperature and flow rate of the feed, the major cause of disturbances in the industry. Using a traditional proportional integral controller connected to each controlled variable, a multi-loop control system is formed with optimization and compared to the disturbance rejection of multivariable model predictive control (MMPC). The final improvement against the feed temperature and flow for CV1, CV2, CV3, and CV4 is shown by, respectively, Integral Absolute Error (IAE) values of 79.49%, 99.90%, 100%, and 99.99% and Integral Square Error (ISE) values of 97.1%, 100%, 99.99%, and 100%.

Amphibian responses in experimental thermal gradients: Concepts and limits for inference.

The interpretation of thermal-gradient data depends on the behavioral drives reported or assumed, and on the underlying behavioral models explaining how such drives operate. The best-known example is positive thermotaxis, a thermoregulatory behavioral drive frequently linked to a dual set-point model of thermoregulation around a target range. This behavioral drive is often assumed as dominant among 'ectotherms', including amphibians. However, we argue that, because amphibians are extremely diverse, they may exhibit alternative behavioral drives in thermal gradients, and tackle this idea from two perspectives. First, we provide a historical review of original definitions and proposed limits for inference. Second, although caveats apply, we propose that a cross-study analysis of data of temperature settings of gradients and the temperatures selected by amphibians would corroborate alternative behavioral drives, including negative thermotaxis. Therefore, we analyzed published data focusing on such relationships and show that gradient temperature settings influence the temperatures selected by amphibians, with further effects of phylogeny and ontogeny. We conclude that thermal gradient experiments are outstanding tools to investigate behavioral drives, but no given drive can be assumed a priori unless additional information about thermoregulation is available. Based on the historical debate, we propose using selected temperatures and preferred temperatures as different concepts, the former merely operational and the second explicitly linked to positive thermotaxis (and thus compatible with dual set-point thermoregulation). Under this view, thermal preferences would stand for a hypothesis of a behavioral drive (positive thermotaxis) requiring formal testing. These considerations impact the scope for inference based on thermal gradient experiments, particularly ecological modeling and emerging disease.

Population data provide evidence against the presence of a set point for hemoglobin levels or tissue oxygen delivery.

Hemoglobin levels are believed to be regulated as per a set point model of regulation. This model of regulation, by which specific levels of a parameter are targeted and defended by physiological systems, implies a particular population correlation between the parameter and its controlling hormone. Empirical population correlations of other parameters and their controlling hormones, have denied the presence of such set point‐based regulation. To assess if hemoglobin is regulated according to a set point model we performed a systematic search of PubMed/MEDLINE and Web of Science identifying relevant reports published up to November 2018. Population hemoglobin/erythropoietin level correlations were retrieved, and these empirically derived correlations were compared with the positive correlation implied by a set point model of regulation. Authors of papers containing potentially suitable data were contacted with requests for further analyses, and a meta‐analysis was performed. Twelve correlations between hemoglobin and erythropoietin levels from eleven papers were analyzed. None of these correlations were significantly positive, three, restricted to the normal range of hemoglobin, were significantly negative. All but one of the other correlations showed a negative trend. New analyses of previously published data sets resulted in similar findings. In particular a new analysis of large data sets of males (n = 2417) and females (n = 2592) with normal range hemoglobin levels, revealed significantly negative correlations. A meta‐analysis of our results indicated that the data overall are not consistent with a positive relationship between hemoglobin and erythropoietin (P < 0.0001). Population data indicate that individuals do not have set point levels of hemoglobin.

Dynamic set point model for driver alert state using digital image processing

The driver fatigue and lose of attention while driving are the most important causes of traffic accidents. Each year more than one million of deaths occur due to these facts. Thus, this problem has been converted into a serious social issue with high impact not only in economic terms, but also in the public health sector all around the world. Several approaches based on computer vision systems have been proposed to deal with this severe situation, but none of them have fully considered the non-fatigue state as a primary knowledge to detect an unusual event of a person while driving. In fact, typical approaches to deal with the problem of fatigue detection, are based on the analysis of behavioral features extracted with digital image processing such as frequency of blinking, yawning, among others. However, the huge limitation is the short interval of time between each analysis, that generally is few frames per second. Furthermore, all available methods are focus in modeling the fatigue, instead of representing the set point alert state of the driver, which is the main core of the proposed strategy. Hence, in this paper a dynamic set point model for alert state while driving using digital image processing and machine learning techniques is presented. The approach uses an embedded system build with a Raspberry prototyping board and a USB HD camera. Raspbian operative system controls OPEN CV libraries written in Python to detect face parts with an algorithm running Harr descriptors. The features extracted were the position and orientation of the head throw several minutes. Then, a mixture of Gaussians model with its learning and updating stages is used to represent the behaviour of features. Also, a dataset was built considering professional and non-professional drivers under two main scenarios: real and simulated conditions. Experimental results show the viability of the method for posterior analysis of unusual events while driving like fatigue detection, cellphone call or chat detection, or any other distraction not related to the driving process.

Development and performance evaluation of an infinite horizon LQ optimal tracker

The paper presents an infinite horizon LQ optimal tracking control solution (LQ tracker) for discrete time linear time invariant systems. The reference preview need is reduced to only two steps irrespective of the type of reference signal making real-time implementation an achievable goal. A rigorous proof of optimality is provided for a set of infinite horizon reference commands which includes the linear combination of constant and exponentially bounded signals. Dissipativity, finite gain and l1 performance of the controlled system are also evaluated. The behaviour of the proposed LQ tracker and its previously published sub-optimal version with one-step preview is demonstrated in conjunction with an application example. Their performances are compared to those of alternative solutions including set point control and model predictive control considering aspects of practical application. Finally, it is concluded that the proposed rigorous solution of the infinite horizon tracking problem and its sub-optimal version are real-time realizable and perform advantageously in some aspects compared to other solutions.

Obesity Energetics: Body Weight Regulation and the Effects of Diet Composition

Weight changes are accompanied by imbalances between calorie intake and expenditure. This fact is often misinterpreted to suggest that obesity is caused by gluttony and sloth and can be treated by simply advising people to eat less and move more. Rather various components of energy balance are dynamically interrelated and weight loss is resisted by counterbalancing physiological processes. While low-carbohydrate diets have been suggested to partially subvert these processes by increasing energy expenditure and promoting fat loss, our meta-analysis of 32 controlled feeding studies with isocaloric substitution of carbohydrate for fat found that both energy expenditure (26 kcal/d; P <.0001) and fat loss (16 g/d; P <.0001) were greater with lower fat diets. We review the components of energy balance and the mechanisms acting to resist weight loss in the context of static, settling point, and set-point models of body weight regulation, with the set-point model being most commensurate with current data.

Central cholinergic synaptic vesicle loading obeys the set-point model in Drosophila.

Experimental evidence shows that neurotransmitter release, from presynaptic terminals, can be regulated by altering transmitter load per synaptic vesicle (SV) and/or through change in the probability of vesicle release. The vesicular acetylcholine transporter (VAChT) loads acetylcholine into SVs at cholinergic synapses. We investigated how the VAChT affects SV content and release frequency at central synapses in Drosophila melanogaster by using an insecticidal compound, 5Cl-CASPP, to block VAChT and by transgenic overexpression of VAChT in cholinergic interneurons. Decreasing VAChT activity produces a decrease in spontaneous SV release with no change to quantal size and no decrease in the number of vesicles at the active zone. This suggests that many vesicles are lacking in neurotransmitter. Overexpression of VAChT leads to increased frequency of SV release, but again with no change in quantal size or vesicle number. This indicates that loading of central cholinergic SVs obeys the "set-point" model, rather than the "steady-state" model that better describes loading at the vertebrate neuromuscular junction. However, we show that expression of a VAChT polymorphism lacking one glutamine residue in a COOH-terminal polyQ domain leads to increased spontaneous SV release and increased quantal size. This effect spotlights the poly-glutamine domain as potentially being important for sensing the level of neurotransmitter in cholinergic SVs.

Emotion and the Joint Structure of Personality and Psychopathology

There are numerous well-documented problems with the DSM’s polythetic-categorical approach to the delineation of mental disorders. However, the DSM-5 introduces an empirically based dimensional model of personality traits. These traits form a hierarchical structure that represents the organization of dispositions to common mental disorders. We connect emotions to this joint hierarchical structure using a modified set point model, which accommodates major theories linking personality and psychopathology—continuum, risk, scar, and pathoplasty—as well as more dynamic multivariate models. We argue that these traits represent typical causal pathways that can be extracted from a complex web of equifinality and multifinality. Ultimately, research on mood disorders provides a stronger account of their underling traits than the variably endorsed symptoms of their polythetic criteria.

Control of milk pasteurization process using model predictive approach

A milk pasteurization process, a nonlinear process and multivariable interacting system, is difficult to control by the conventional on–off controllers since the on–off controller can handled the temperature profiles for milk and water oscillating over the plant requirements. The multi-variable control approach with model predictive control (MPC) is proposed in this study. The proposed algorithm was tested for control of a milk pasteurization process in four cases of simulation such as set point tracking, model mismatch, difference control and prediction horizons, and time sample. The results for the proposed algorithm show the well performance in keeping both the milk and water temperatures at the desired set points without any oscillation and overshoot and giving less drastic control action compared to the cascade generic model control (GMC) strategy.

Essential Hypertension: An Approach to Its Etiology and Neurogenic Pathophysiology

Essential hypertension, a rise in blood pressure of undetermined cause, includes 90% of all hypertensive cases and is a highly important public health challenge that remains, however, a major modifiable cause of morbidity and mortality. This review emphasizes that, from an evolutionary point of view, we are adapted to ingest and excrete <1 g of sodium (2.5 g of salt) per day and that essential hypertension develops when the kidneys become unable to excrete the amount of sodium ingested, unless blood pressure is increased. The renal-mean arterial pressure set-point model is briefly described to explain that a shift of the pressure natriuresis relationship toward abnormally high pressure levels is a pathophysiological characteristic of essential hypertension. Evidence indicating that this anomaly in the pressure natriuresis relationship arises from a sympathetic nervous system dysfunction is briefly formulated, and the most widely accepted pathophysiologic proposal to explain the development of this sympathetic dysfunction is described, with commentaries about novel action mechanisms of some drugs currently used in essential hypertension treatment.

Integrated Modeling and Intelligent Control Methods of Grinding Process

The grinding process is a typical complex nonlinear multivariable process with strongly coupling and large time delays. Based on the data-driven modeling theory, the integrated modeling and intelligent control method of grinding process is carried out in the paper, which includes the soft-sensor model of economic and technique indexes, the optimized set-point model utilizing case-based reasoning, and the self-tuning PID decoupling controller. For forecasting the key technology indicators (grinding granularity and mill discharge rate of grinding process), an adaptive soft-sensor modeling method based on wavelet neural network optimized by the improved shuffled frog leaping algorithm (ISFLA) is proposed. Then, a set point optimization control strategy of grinding process based on case-based reasoning (CBR) method is adopted to obtain the optimized velocity set-point of ore feed and pump water feed in the grinding process controlled loops. Finally, a self-tuning PID decoupling controller optimized is used to control the grinding process. Simulation results and industrial application experiments clearly show the feasibility and effectiveness of control methods and satisfy the real-time control requirements of the grinding process.

Optimal Model for HVAC Based on System Energy Input Ratio

According to the problem that the components of HVAC system have the strong coupling and the low energy efficiency, a kind of optimal model for HVAC based on system energy input ratio (SEIR) is proposed. Firstly, according to each equipment characteristic, the performance curve model of each equipment power is built. Based on these models, the performance curve model of system energy consumption is built too. Then, basing on energy conservation constraint, each equipment performance constraint and coupling constraint, the constraint equation is built. Finally, simplifying the model parameter, taking the least energy input ratio as an objective function, the optimal set point model of various equipments is built. The experimental results indicate that compared with the least energy consumption optimal method, the model has better energy saving effect.

Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity.

The close correspondence between energy intake and expenditure over prolonged time periods, coupled with an apparent protection of the level of body adiposity in the face of perturbations of energy balance, has led to the idea that body fatness is regulated via mechanisms that control intake and energy expenditure. Two models have dominated the discussion of how this regulation might take place. The set point model is rooted in physiology, genetics and molecular biology, and suggests that there is an active feedback mechanism linking adipose tissue (stored energy) to intake and expenditure via a set point, presumably encoded in the brain. This model is consistent with many of the biological aspects of energy balance, but struggles to explain the many significant environmental and social influences on obesity, food intake and physical activity. More importantly, the set point model does not effectively explain the ‘obesity epidemic’ – the large increase in body weight and adiposity of a large proportion of individuals in many countries since the 1980s. An alternative model, called the settling point model, is based on the idea that there is passive feedback between the size of the body stores and aspects of expenditure. This model accommodates many of the social and environmental characteristics of energy balance, but struggles to explain some of the biological and genetic aspects. The shortcomings of these two models reflect their failure to address the gene-by-environment interactions that dominate the regulation of body weight. We discuss two additional models – the general intake model and the dual intervention point model – that address this issue and might offer better ways to understand how body fatness is controlled.