Unwanted States Research Articles

Perceiving Social Pressure not to Feel Negative is Linked to a More Negative Self-concept

The current cultural standard in western societies expects people to be happy and not sad. While the pursuit of positive emotion is strongly encouraged in modern societal discourse, occasionally feeling negative is easily considered maladaptive or abnormal. It is in our human nature to comply with social expectations, and the extent to which we are able to do so has important consequences for how we view or present ourselves, generally referred to as our self-concept. Here, we investigate how the perception of the societal norm to avoid negative emotions relates to people’s self-concept. In an online survey (n = 98), we assessed people’s perceived social expectancies not to experience feelings such as anxiety or depression. We evaluated the emotional quality of participants’ self-concept via an open-response 15-statements task in which they freely described themselves, and from which we extracted the positive and negative emotion words using the Linguistic Inquiry and Word Count software. We found that people’s perceived social expectancies not to experience negative affect related to more negative and less positive self-descriptions. Furthermore, multiple linear regression revealed that this association was independent of people’s desire to socially conform, but a function of their actual emotion/symptom levels. Together, these findings further support the premise that today’s societal standard to avoid negative affect is unattainable, inevitably disclosing discrepancies between people’s actual occasional negative feelings and the desired emotion norm. Because this process is associated with negative self-evaluations, this may lead to an ironic amplification of these unwanted negative states.

Deep learning enabling quality improvement in rotogravure manufacturing

Advances in Computer Vision have helped the manufacturing industry achieve superior quality norms with a minimal inspection time due to optical quality surveillance systems. These inspections most often take place at the end of the value chain, insuring the quality standards of the manufactured pieces. The downside to this approach is that defective parts can still continue through the value chain. Wasting a lot of resources and increasing the lead time. To avoid this drain, the machines in the value stream should only produce error-free parts or at least detects them. An optical quality inspection system at every production step would add a high price cost. For this reason, existing sensors should detect unwanted states. With structured data, a person with specific domain knowledge could rate this. This is a tough task, as a lot of unknown factors can influence each step. Therefore, this paper proposes steps to improve quality in rotogravure manufacturing using deep learning. Further research will be conducted in the coming months to expand these results. The proposed procedures will be applied to live data of a rotogravure manufacturing site and the effectiveness of this approach will be analysed.

The face of memory: experiential avoidance and facial expressions during the retrieval of autobiographical memories

ABSTRACTExperiential avoidance refers to attempts to control or suppress unwanted thoughts, feelings and emotions. We investigated whether experiential avoidance is associated with fewer facial expressions during autobiographical retrieval (i.e. retrieval of memory for personal information). We invited participants to retrieve autobiographical memories, and recall was analysed by a facial analysis software that detects and classifies emotional expressions. Participants were divided into high vs. low experiential avoidance. Analysis showed fewer emotional facial expressions in participants with high experiential avoidance than in those with low experiential avoidance during autobiographical retrieval. This low emotional expression can be regarded as an attempt by individuals with high experiential avoidance to avoid communicating the emotional load to others. This low emotional expression can be also regarded as an attempt by individuals with high experiential avoidance to control or suppress the internal events that contribute to the appearance or persistence of unwanted emotional states during retrieval.

Development of Electronic Water Meter Based on Wireless Network and RF

The global need for water is increasing. Water must be used more economically in order to be sustainable. Water consumption per capita in Turkey is below the world regarding its geographical location. Manual reading water meters are inconvenient and time-consuming, they have low accuracy, they are very slow and resource wasting. Using wireless communication will mitigate these unwanted states. Consumers will be able to observe their water consumption in real time and be able to compare with the average water consumption in their district. With this system water pressure of whole pipes could be observed.

Analysis, modeling, control and operation of an interleaved three-port boost converter for DMPPT systems including PV and storage at module level

This paper presents the analysis, control and implementation of the interleaved three-port boost converter. The scope of this paper is the interfacing of photovoltaic systems that include storage. A new symmetrical PWM modulation strategy that prevents unwanted switching states without requiring external circuitry is presented. This modulation allows for proper sampling of the measurements, increasing thus their accuracy. Large- and small-signal models of the interleaved and non-interleaved three port boost converters are presented and transfer functions are derived for control design purposes. The different currents in the converter are controlled using control loops that govern the behavior of the converter. These loops are intuitively designed by treating them independently. With the proper loop bandwidth selection, the converter achieves fast response and good reference tracking and is suitable to interface photovoltaic and storage systems with different kinds of loads. The presented models, modulation and control loops are validated through simulation and with experimental results.

Trichotillomania: a psychopathological perspective and the psychiatric comorbidity of hair pulling

Trichotillomania, or hair-pulling disorder, is classified as an obsessive-compulsive spectrum disorder and is seen predominantly in females. This is a non-systematic review article focusing on the psychopathological features of hair pulling. It is speculated that hair pulling may function to provide short-term relief from stress and other unwanted emotional states, thus serving as a method of emotion regulation. The prevalence of trichotillomania ranges from 1 to 3%. The most targeted site is the scalp, and other common areas include pubic hair and facial regions such as the eyebrows, eyelashes, and beard. Individuals suffering from this disorder tend to avoid social environments due to embarrassment regarding their appearance and fears of being judged by peers. Trichotillomania is associated with significant functional impairment and increased risks of comorbid psychiatric disorders such as other body-focused repetitive behaviors, depression, anxiety, and addictive disorders. This article reviews the epidemiology, clinical features, diagnostic criteria, and psychopathology of trichotillomania with an emphasis on psychopathology and psychiatric comorbidity.

A Proactive Decision Support System for Online Event Streams

In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision making model. A Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well for congestion control in transportation system.

A Computationally Efficient FCS-MPC Method Without Weighting Factors for NNPCs With Optimal Duty Cycle Control

In this paper, a computationally efficient finite control-set model predictive control (FCS-MPC) method without weighting factors is proposed for nested neutral point-clamped converters (NNPCs) with optimal duty cycle control. The aim of this paper is to design a modification of the FCS-MPC scheme that improves the steady-state performance, as well as to avoid the weighting factors selection while remaining computationally feasible. First, in order to improve the calculation efficiency, a simplified computational method is proposed by introducing the Lyapunov principle into the design of sector distribution method based on space vector modulation technique, and eliminates unwanted switching states. Second, aiming at improving the steady-state control performance, an optimal duty cycle control technique is incorporated into the proposed FCS-MPC strategy to determine the time duration of the selected voltage vector. In that sense, a new FCS of candidate voltage vectors with different duty cycles are synthesized without sacrificing the simplicity of the control structure. Besides, in order to avoid the selection of weighting factors, the proposed method replaces the single cost function with simplified multiobjective optimization strategy based on a fuzzy-decision-making method. Finally, the performance of the proposed strategy for NNPCs is evaluated by simulation studies in various operating conditions.

Not-quite-free shortcuts to adiabaticity

Given the increasing use of shotcuts to adiabaticity (STA) to optimize power and efficiency of quantum heat engines, it becomes a relevant question if there are any theoretical limits to their application. We argue that quantum fluctuations in the control device which implements the shortcut deflect the system from the adiabatic path. This not only induces transitions to unwanted final states but also changes the system energy, so that using the STA has a definite cost in terms of conventional work definitions. This may be the ultimate cost of an adiabatic shortcut, in the sense that it is present even for a frictionless, zero temperature driving. We estimate the effect, to lowest nontrivial order in the derivatives of the time-dependent frequency, on a parametric harmonic oscillator, thus providing a consistency condition for the validity of the classical approximation.

Autonomic Mobile Virtual Network Operators for Future Generation Networks

The effort of telecommunications operators in 5G design and implementation is oriented to effective and efficient verticals support. Full network softwarization will deploy tools such as software-defined networks and network functions virtualization to allow dynamic service provisioning on the same physical infrastructure. However, current 5G proposed architecture still requires further enhancement to guarantee full real-time on-demand services. Current virtualization technologies still miss complete automation and adaptation, while they do not have dynamic negotiation capabilities. These issues are bounding the desired complete automation and the significant reduction in OPEX and CAPEX. Thus, this article proposes a full architecture to realize autonomic mobile virtual network operators. These autonomic virtual operators can be deployed by Internet service providers to guarantee efficient and effective network adaptation to unexpected events and real-time resource requests. The objective of an AMVNO is to exploit softwarization of networks and experiential intelligence to play the role of a local operator by performing network management and service release without human intervention. Its autonomic character can also provide proactive actions against unwanted network states.

Accurate Mapping of Multilevel Rydberg Atoms on Interacting Spin- 1/2 Particles for the Quantum Simulation of Ising Models

We study a system of atoms that are laser driven to nD_{3/2} Rydberg states and assess how accurately they can be mapped onto spin-1/2 particles for the quantum simulation of anisotropic Ising magnets. Using nonperturbative calculations of the pair potentials between two atoms in the presence of electric and magnetic fields, we emphasize the importance of a careful selection of experimental parameters in order to maintain the Rydberg blockade and avoid excitation of unwanted Rydberg states. We benchmark these theoretical observations against experiments using two atoms. Finally, we show that in these conditions, the experimental dynamics observed after a quench is in good agreement with numerical simulations of spin-1/2 Ising models in systems with up to 49 spins, for which numerical simulations become intractable.

ANALYSIS OF DISTRIBUTED SYSTEMS DYNAMICS WITH ERLANG PERFORMANCE LAB

Modern, highly concurrent and large-scale systems require new methods for design, testing and monitoring. Their dynamics and scale require real-time tools, providing a holistic view of the whole system and the ability of showing a more detailed view when needed. Such tools can help identifying the causes of unwanted states, which is hardly possible with static analysis or metrics-based approach. In this paper a new tool for analysis of distributed systems in Erlang is presented. It provides real-time monitoring of system dynamics on different levels of abstraction. The tool has been used for analyzing a large-scale urban traffic simulation system running on a cluster of 20 computing nodes.

Electrical characterization of two analogous Schottky contacts produced from N-substituted 1,8-naphthalimide.

The aim of this study was to analyze the interface states (Nss) in pure Al//p-Si/Al, Al/N-F Nft/p-Si/Al and Al/N-T Nft/p-Si/Al Schottky barrier diodes (SBDs). N-Substituted 1,8-naphthalimide thin films were deposited on a p-Si substrate by spin coating and annealed at ∼200 °C for 60 s under an air atmosphere. Al contacts were obtained via reactive magnetron sputtering. The current voltage (I-V) characteristics of the SBDs were measured at room temperature. From the I-V characteristics, the SBDs ideality factor (n) and zero-bias barrier height values (Φb) of 1.27, 1.00, and 1.05 and 0.66 eV, 0.70 eV, and 0.64 eV were observed for the Al//p-Si/Al, Al/N-F Nft/p-Si/Al and Al/N-T Nft/p-Si/Al Schottky barrier diodes, respectively. The interface state density distribution profile (Nss) as a function of (Ess-Ev) was extracted from the forward-bias I-V measurements by considering the effective barrier height and (Φe) and series resistance (Rs) of the Schottky diode. The obtained Nss plot tendency showed that the existence of interface states has no significant effect on the rectifying and capacitance characteristics. The Nss values with the 1,8-naphthalimide layer were lower than that without it. This shows that naphthalimide exhibits a strong contribution by blocking the unwanted states and some traps in the conduction mechanism, which may cause possible cracks or deep paths for carriers to travel along the junction.

Hopfield networks as a model of prototype-based category learning: A method to distinguish trained, spurious, and prototypical attractors

We present an investigation of the potential use of Hopfield networks to learn neurally plausible, distributed representations of category prototypes. Hopfield networks are dynamical models of autoassociative memory which learn to recreate a set of input states from any given starting state. These networks, however, will almost always learn states which were not presented during training, so called spurious states. Historically, spurious states have been an undesirable side-effect of training a Hopfield network and there has been much research into detecting and discarding these unwanted states. However, we suggest that some of these states may represent useful information, namely states which represent prototypes of the categories instantiated in the network’s training data. It would be desirable for a memory system trained on multiple instance tokens of a category to extract a representation of the category prototype. We present an investigation showing that Hopfield networks are in fact capable of learning category prototypes as strong, stable, attractors without being explicitly trained on them. We also expand on previous research into the detection of spurious states in order to show that it is possible to distinguish between trained, spurious, and prototypical attractors.

Schema Therapy for Emotional Dysregulation: Theoretical Implication and Clinical Applications.

The term emotional dysregulation refers to an impaired ability to regulate unwanted emotional states. Scientific evidence supports the idea that emotional dysregulation underlies several psychological disorders as, for example: personality disorders, bipolar disorder type II, interpersonal trauma, anxiety disorders, mood disorders and post-traumatic stress disorder. Emotional dysregulation may derive from early interpersonal traumas in childhood. These early traumatic events create a persistent sensitization of the central nervous system in relation to early life stressing events. For this reason, some authors suggest a common endophenotypical origin across psychopathologies. In the last 20 years, cognitive behavioral therapy has increasingly adopted an interactive-ontogenetic view to explain the development of disorders associated to emotional dysregulation. Unfortunately, standard Cognitive Behavior Therapy (CBT) methods are not useful in treating emotional dysregulation. A CBT-derived new approach called Schema Therapy (ST), that integrates theory and techniques from psychodynamic and emotion focused therapy, holds the promise to fill this gap in cognitive literature. In this model, psychopathology is viewed as the interaction between the innate temperament of the child and the early experiences of deprivation or frustration of the subject’s basic needs. This deprivation may lead to develop early maladaptive schemas (EMS), and maladaptive Modes. In the present paper we point out that EMSs and Modes are associated with either dysregulated emotions or with dysregulatory strategies that produce and maintain problematic emotional responses. Thanks to a special focus on the therapeutic relationship and emotion focused-experiential techniques, this approach successfully treats severe emotional dysregulation. In this paper, we make several comparisons between the main ideas of ST and the science of emotion regulation, and we present how to conceptualize pathological phenomena in terms of failed regulation and some of the ST strategies and techniques to foster successful regulation in patients.

Key-rate enhancement using qutrit states for quantum key distribution with askew aligned sources

It is known that measurement-device-independent quantum key distribution (MDI-QKD) provides ultimate security from all types of side-channel attack against detectors at the expense of low key generation rate. Here, we propose MDI-QKD using 3-dimensional quantum states and show that the protocol improves the secret key rate under the analysis of mismatched-basis statistics. Specifically, we analyze security of the 3d-MDI-QKD protocol with uncharacterized sources, meaning that the original sources contain unwanted states instead of expected one. We simulate secret key rate of the protocol and identify the regime where the key rate is higher than the protocol with the qubit MDI-QKD.

Doping of Large Ionization Potential Indenopyrazine Polymers via Lewis Acid Complexation with Tris(pentafluorophenyl)borane: A Simple Method for Improving the Performance of Organic Thin-Film Transistors

Molecular doping, under certain circumstances, can be used to improve the charge transport in organic semiconductors through the introduction of excess charge carriers which can in turn negate unwanted trap states often present in organic semiconductors. Here, two Lewis basic indenopyrazine copolymers with large ionization potential (5.78 and 5.82 eV) are prepared to investigate the p-doping efficiency with the Lewis acid dopant, tris(pentafluorophenyl)borane, using organic thin-film transistors (OTFTs). The formation of Lewis acid–base complex between the polymer and dopant molecules is confirmed via optical spectroscopy and electrical field-effect measurements, with the latter revealing a dopant-concentration-dependent device performance. By adjusting the amount of p-dopant, the hole mobility can be increased up to 11-fold while the OTFTs’ threshold voltages are reduced. The work demonstrates an alternative doping mechanism other than the traditional charge transfer model, where the energy level matchin...

The Role of Positive Expectancies in Risk Behavior.

Alcohol use and nonsuicidal self-injury (NSSI) appear to share a conceptual overlap in functions (e.g., tension reduction). Alcohol use has been identified as a risk factor for NSSI, and higher rates of alcohol use have been documented among those with NSSI history. This study examined whether NSSI-related alcohol expectancies affect relations between NSSI and alcohol use. Participants were 367 college students (73% female) asked to complete an online survey about their drinking behavior and lifetime NSSI. NSSI and alcohol use were highly prevalent in this sample: 56% endorsed lifetime NSSI and 74% endorsed current alcohol use. Of note, 43% (n = 147) endorsed both behaviors. Positive NSSI-related alcohol expectancies showed a significant association with lifetime NSSI. In addition, positive NSSI-related alcohol expectancies were associated with more frequent drinking behavior for individuals with a history of NSSI, particularly those who had engaged in two or more methods of lifetime NSSI. Alcohol use and NSSI represent high-risk behaviors commonly employed to regulate unwanted affective states. Interventions targeting substance use and/or NSSI may consider assessing positive NSSI and substance use expectancies, as the presence of these beliefs suggests a higher risk profile.

Counterdiabatic driving in spin squeezing and Dicke-state preparation

A method is presented to transfer a system of two-level atoms from a spin coherent state to a maximally spin squeezed Dicke state, relevant for quantum metrology and quantum information processing. The initial state is the ground state of an initial linear Hamiltonian which is gradually turned into a final quadratic Hamiltonian whose ground state is the selected Dicke state. We use compensating operators to suppress diabatic transitions to unwanted states that would occur if the change were not slow. We discuss the possibilities of constructing the compensating operators by sequential application of quadratic Hamiltonians available in experiments.

Intelligent Transportation Control based on Proactive Complex Event Processing

Complex Event Processing (CEP) has become the key part of Internet of Things (IoT). Proactive CEP can predict future system states and execute some actions to avoid unwanted states which brings new hope to intelligent transportation control. In this paper, we propose a proactive CEP architecture and method for intelligent transportation control. Based on basic CEP technology and predictive analytic technology, a networked distributed Markov decision processes model with predicting states is proposed as sequential decision model. A Q-learning method is proposed for this model. The experimental evaluations show that this method works well when used to control congestion in in intelligent transportation systems.