Behavioral Analysis System Research Articles

A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

Modeling and control of dynamical biomechanical arm systems with elastic joints sensitive to the effect of an external load

In this study, a biomechanical model mimicking the human hand-arm system under heavy disk excitation is developed to define the stability threshold between the preload vibration and the stress of the human hand-arm system. The fully electromechanical system, consisting of a DC motor, a transmission system, and three discrete masses representing the upper arm, forearm, and hand lifting a heavy disk, is connected by shock absorbers and various springs. The challenge of mimicking the angular activity of the elbow joint in torsion and flexion was also included to assess its contribution to system stability and to study the absorption of mechanical energy in the human hand-arm system. Finally, the movements of the model were described by a differential matrix equation, and its analysis facilitates the understanding of the threshold of the chaotic behaviors observed in an oscillating arms system. Specifically, it explores how the motion of a DC motor can be regulated using a single controller parameter within the context of a multi-degree-of-freedom human hand-arm system. The study uses numerical integrations to analyze system behaviors, with an emphasis on the use of frequency spectra, Poincaré maps, and bifurcation diagrams for visualization and interpretation. The results indicate that the system exhibits chaotic behavior when subjected to certain conditions, particularly when the mass load exceeds 35 kg. Imposing motion on the mass block further intensifies the chaos, leading to manifestations such as strong oscillations and frequency-synchronization effects. These characteristics, exhibited by the idealized model, which imitates the human body through a mechanical model and computation of the motions of the body, play a vital role in various fields of ergonomics and sports biomechanics. Understanding the dynamic behaviors of such systems, especially in response to varying conditions, has significant implications for engineering applications.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

A behavioral analysis system MCFBM enables objective inference of songbirds’ attention during social interactions

Understanding animal behavior is crucial in behavioral neuroscience, aiming to unravel the mechanisms driving these behaviors. A significant milestone in this field is the analysis of behavioral reactions during social interactions. Despite their importance in social learning, the behavioral aspects of these interaction are not well understood in detail due to the lack of appropriate tools. We introduce a high-precision, marker-based motion-capture system for analyzing behavior in songbirds, accurately tracking body location and head direction in multiple freely moving finches during social interaction. Focusing on zebra finches, our analysis revealed variations in eye use based on individuals presented. We also observed behavioral changes during virtual and live presentations and a conditioned-learning paradigm. Additionally, the system effectively analyzed social interactions among mice. This system provides an efficient tool for advanced behavioral analysis in small animals and offers an objective method to infer their focus of attention.

Childhood Trauma Questionnaire-based child maltreatment profiles to predict efficacy of the Cognitive Behavioral Analysis System of Psychotherapy versus non-specific psychotherapy in adults with early-onset chronic depression: cluster analysis of data from a randomised controlled trial

Child maltreatment is a broadly confirmed risk factor for mental and physical illness. Some psychological treatments specifically target mental health conditions associated with child maltreatment. For example, the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) focuses on maladaptive interpersonal behaviours in chronic depression. However, how the assessment of child maltreatment could inform personalised treatment is unclear. We used data from a previously published clinical trial to investigate whether a pre-established child maltreatment clustering approach predicts differential outcomes after CBASP versus non-specific supportive psychotherapy in patients with early-onset chronic depression. We did a cluster analysis of data from a previous randomised controlled trial of unmedicated adult outpatients with early-onset chronic depression who were treated at eight university clinics and psychological institutes in Germany with 32 sessions of CBASP or non-specific supportive psychotherapy. Participants were eligible for the original trial if they were aged 18-65 years; had major depressive disorder (MDD) with an early onset and duration of at least 2 years, current MDD superimposed on a pre-existing dysthymic disorder, or recurrent MDD with incomplete remission between episodes as defined by DSM-IV; and had a score of at least 20 points on the 24-item Hamilton Rating Scale for Depression (HRSD-24). Participants were included in the current study if they had completed the short form of the Childhood Trauma Questionnaire (CTQ) at trial baseline. We used an agglomerative hierarchical clustering approach to derive child maltreatment clusters from individual patterns across the five domains of the CTQ. We used linear mixed models to investigate whether clustering could predict differential clinical outcomes (change in symptom severity on the HRSD-24) up to 2 years after treatment onset. People with lived experience were involved in the current study. 253 patients (129 [51%] treated with CBASP and 124 [49%] with supportive psychotherapy) had complete CTQ records and were included in the analysis. 169 (67%) participants were women, 84 (33%) were men, and the mean age was 45·9 years (SD 11·7). We identified seven child maltreatment clusters and found significant differences in treatment effects of CBASP and supportive psychotherapy between the clusters (F(6,948·76)=2·47; p=0·023); differences were maintained over the 2-year follow-up. CBASP was superior in distinct clusters of co-occurring child maltreatment: predominant emotional neglect (change in β -6·02 [95% CI -11·9 to -0·13]; Cohen's d=-0·98 [95% CI -1·94 to -0·02]; p=0·045), predominant emotional neglect and abuse (-6·39 [-10·22 to -2·56]; -1·04 [-1·67 to -0·42]; p=0·0011), and emotional neglect and emotional and physical abuse (-9·41 [-15·91 to -2·91]; -1·54 [-2·6 to -0·47]; p=0·0046). CTQ-based cluster analysis can facilitate identification of patients with early-onset chronic depression who would specifically benefit from CBASP. Child maltreatment clusters could be implemented in clinical assessments and serve to develop and personalise trauma-informed care in mental health. The German Research Foundation and the German Federal Ministry of Education and Research.

The Analysis of children's sports behavior analysis and health management system based on deep learning

The Analysis of children's sports behavior analysis and health management system based on deep learning

On robustification of digital event-based controllers for control-affine nonlinear systems

In this paper, the robust digital stabilization problem of nonlinear systems is investigated. In particular, a methodology for the design of robust quantized sampled-data stabilizers updated via an event-triggered mechanism is provided for time-varying control-affine nonlinear systems affected by actuation disturbances and measurement noises. The notion of time-varying steepest descent feedback (TSDF), continuous or not, and the Input-to-State Stability (ISS) redesign methodology are used for the development of the proposed robust event-based digital controller. Under the assumption that the actuation disturbances and measurement noises are bounded with a-priori known bounds and that the amplitude of the measurement noises satisfies a certain condition related to the new added robustification term, the following result is proved: there exist a suitably fast sampling and an accurate quantization of the input/output channels such that the digital implementation of robustified TSDF controllers, updated through a proposed event-triggered mechanism, ensures semi-global practical stability of the related closed-loop system regardless of the above uncertainties. In the methodology here proposed, time-varying sampling periods and the non-uniform quantization of the input/output channels are allowed. Moreover, the theory here developed includes the analysis of the intersampling system behaviour. Possible discontinuities in the function describing the TSDF at hand are also managed. The provided results are validated through a numerical example.

Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation

A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.

Optimization of Impedance Relay Placement in Medium-Voltage Electrical Distribution Systems through Clustering Algorithms and Metaheuristics

This study explores the feasibility of using impedance relays in electrical distribution systems, a context where their application is not as common as in transmission systems. Given the dynamic nature and complex topology of medium-voltage distribution systems, this work proposes an innovative methodology integrating clustering algorithms and metaheuristic techniques to optimize the placement of impedance relays and enhance system reliability and resilience. Using CYME simulation and the Ant Colony Optimization (ACO) method, case studies were designed to validate the effectiveness of the proposed methodology. The results demonstrated that strategic placement of impedance relays reduces failure frequency and significantly improves the system’s response to such failures. This approach allows for a more efficient configuration and quicker response, which is crucial for maintaining continuity and quality of power supply. A detailed analysis of system behavior under various fault scenarios illustrates the robustness and adaptability of the proposed solution, marking a significant advancement in the protection and optimization of electrical distribution systems.

A Virtual Testing Framework for Real-Time Validation of Automotive Software Systems Based on Hardware in the Loop and Fault Injection.

To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort.

Mechanism of Chaijin Jieyu Anshen Tablets in regulating abnormal ACC-vHPC glutaminergic neural circuit to alleviate synaptic remodeling of ventral hippocampal neurons in depressed rats

This study aims to reveal the molecular mechanism of Chaijin Jieyu Anshen Tablets(CJJYAS) in regulating the abnormal anterior cingulate cortex(ACC)-ventral hippocampus(vHPC) glutaminergic neural circuit to alleviate synaptic remodeling of ventral hippocampal neurons in depressed rats. Firstly, the study used chemogenetics to localize glutaminergic adeno-associated virus(AAV) into the ACC brain region of rats. The model of depressed rats was established by chronic unpredictable mild stress(CUMS) combined with independent feeding. The rats were randomly divided into control group, model group, AAV empty group, AAV group, AAV+ glucocorticoid receptors(GR) blocker group, AAV+chemokine receptor 1(CX3CR1) blocker group, and AAV+CJJYAS group. Depressive-like behaviors of rats were evaluated by open-field, forced-swimming, and Morris water maze tests, combined with an animal behavior analysis system. The morphological and structural changes of ACC and vHPC neurons in rats were observed by hematoxylin-eosin(HE) staining. Immunofluorescence and nuclear phosphoprotein(c-Fos) were used to detect glutaminergic neural circuit activation of ACC-vHPC in rats. The changes in dendrites, synaptic spines, and synaptic submicrostructure of vHPC neurons were observed by Golgi staining and transmission electron microscopy, respectively. The expressions of synaptic remodeling-related proteins N-methyl-D-asprtate receptor 2A(GRIN2A), N-methyl-D-asprtate receptor 2B(GRIN2B), Ca~(2+)/calmodulin-dependent protein kinase Ⅱ(CaMKⅡ), mitogen-activated protein kinase-activated protein kinase 2(MK2), and a ubiquitous actin-binding protein(cofilin) in vHPC glutaminergic neurons of rats were detected by immunofluorescence and Western blot, respectively. The results indicated that the activated glutaminergic AAV aggravated the depressive-like behaviors phenotype of rats in the model group and deteriorated the damage of morphology and structure of ACC and vHPC neurons and synaptic ultrastructure. However, both GR and CX3CR1 bloc-kers could reverse the abnormal changes to varying degrees, suggesting that the abnormal activation of ACC-vHPC glutaminergic neural circuit mediated by GR/CX3CR1 signals in gliocytes in the ACC brain region may be closely related to the occurrence and development of depression. Interestingly, CJJYAS significantly inhibited the activation of the ACC-vHPC glutaminergic neural circuit induced by AAV and the elevated Glu level. Furthermore, CJJYAS could also effectively reverse the aggravation of depressive-like behaviors and synaptic remodeling of vHPC neurons of rats in the model group induced by the activated AAV. Additionally, the findings suggested that the molecular mechanism of CJJYAS in improving synaptic damage of vHPC neurons might be related to the regulation of synaptic remodeling-related signals such as NR/CaMKⅡ and MK2/cofilin. In conclusion, this research confirms that CJJYAS effectively regulates the abnormal ACC-vHPC glutaminergic neural circuit and alleviates the synaptic remodeling of vHPC glutaminergic neurons in depressed rats, and the molecular mechanism might be associated with the regulation of synapse-related NR/CaMKⅡ and MK2/cofilin signaling pathways, which may be the crucial mechanism of its antidepressant effect.

YOLO-based anomaly activity detection system for human behavior analysis and crime mitigation

YOLO-based anomaly activity detection system for human behavior analysis and crime mitigation

Spatio-Temporal Network-based Bank Transactional Behavior Analysis to Detect Suspicious Activities

Money laundering refers to the act of disguising the proceeds of illegal activities as legitimate funds. This is a significant problem as it enables criminals to profit from illegal activities and finance further criminal endeavors. Money laundering is also linked to other crimes, such as drug trafficking, terrorism financing, and corruption. To combat money laundering, governments and financial institutions have implemented various measures, such as Know Your Customer (KYC) regulations, Anti-Money Laundering (AML) laws, and the use of financial intelligence units. However, the existing money laundering system is complex, making it difficult to detect and prevent money laundering activities. Many current systems rely on outdated technology and manual processes, which can be time-consuming and prone to error. Therefore, there is a need for effective detection and prevention systems that can identify suspicious transactions and patterns of behavior. This project aims to prevent and detect money laundering activities by identifying suspicious transactions and monitoring the movement of funds through the financial system. The proposed system uses Long Short-Term Memory (LSTM) to detect and prevent money laundering activities. By analyzing the data in a time-series format, LSTM can identify unusual patterns of transactions and flag them for further investigation. LSTM can also predict future trends in financial data, allowing for the detection and prevention of potential money laundering activities before they occur. The proposed transactional network and behavior analysis system provides a more efficient and accurate method for identifying potential money laundering activities. This system can ultimately lead to a more effective and efficient anti-money laundering system. Keywords: Anti-Money laundering, Long short-term memory, Legitimate funds, Transaction network, behavior analysis, Time series format

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare.

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. This study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 key points using an open-source tool called DeepLabCut, which accurately helped mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the long short-term memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior exceeded 90%.

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

AbstractThe escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Psychological interventions for depression in children and adolescents: A bibliometric analysis.

Depression has gradually become a common psychological disorder among children and adolescents. Depression in children and adolescents affects their physical and mental development. Psychotherapy is considered to be one of the main treatment options for depressed children and adolescents. However, our understanding of the global performance and progress of psychological interventions for depression in children and adolescents (PIDCA) research is limited. To identify collaborative research networks in this field and explore the current research status and hotspots through bibliometrics. Articles and reviews related to PIDCA from January 2010 to April 2023 were identified from the Web of Science Core Collection database. The Charticulator website, CiteSpace and VOSviewer software were used to visualize the trends in publications and citations, the collaborative research networks (countries, institutions, and authors), and the current research status and hotspots. Until April 16, 2023, 1482 publications were identified. The number of documents published each year and citations had increased rapidly in this field. The United States had the highest productivity in this field. The most prolific institution was the University of London. Pim Cuijpers was the most prolific author. In the context of research related to PIDCA, both reference co-citation analysis and keywords co-occurrence analysis identified 10 research hotspots, including third-wave cognitive behavior therapy, short-term psychoanalytic psychotherapy, cognitive behavioral analysis system of psychotherapy, family element in psychotherapy, modular treatment, mobile-health, emotion-regulation-based transdiagnostic intervention program, dementia risk in later life, predictors of the efficacy of psychological intervention, and risks of psychological intervention. This bibliometric study provides a comprehensive overview of PIDCA from 2010 to present. Psychological intervention characterized as psychological-process-focused, short, family-involved, modular, internet-based, emotion-regulation-based, and personalized may benefit more young people.

Symptom-specific improvement across therapies and their putative mediators: A mediation network intervention analysis

Objective We evaluated differential treatment effects on specific symptoms and their mediators for Cognitive Behavioral Analysis System of Psychotherapy (CBASP) and Supportive Psychotherapy (SP) in persistently depressed patients. Method We conducted a Bayesian mediation network intervention analysis with data from a randomized controlled trial comparing CBASP and SP. Three networks were calculated to investigate (1) differential treatment effects on specific symptoms, (2) differential treatment effects on the potential mediators interpersonal problems and social functioning, and (3) associations between change in symptoms and change in the potential mediators. Results First, we found no evidence that CBASP more strongly improves most depressive symptoms specifically, except minimal evidence of symptom-specific effects on sleeping problems and self-esteem. Second, no and minimal evidence for differential treatment effects on interpersonal problems and social functioning was shown, respectively. Third, interpersonal problems and social functioning were strongly related to depressive symptoms. Conclusion While CBASP showed superior treatment effects for overall symptom severity, this treatment might not be superior in improving specific symptoms and the potential mediators interpersonal problems and social functioning. Still, interpersonal problems and social functioning seem to play an important role for depression symptoms. Future research needs to further investigate potential working mechanisms of CBASP. Trial registration: ClinicalTrials.gov identifier: NCT00970437

In situ self-referenced intracellular two-electrode system for enhanced accuracy in single-cell analysis

Since the emergence of single-cell electroanalysis, the two-electrode system has become the predominant electrochemical system for real-time behavioral analysis of single-cell and multicellular populations. However, due to the transmembrane placement of the two electrodes, cellular activities can be interrupted by the transmembrane potentials, and the test results are susceptible to influences from factors such as intracellular solution, membrane, and bulk solution. These limitations impede the advancement of single-cell analysis. Here, we propose a highly miniaturized and integrated in situ self-referenced intracellular two-electrode system (IS-SRITES), wherein both the working and reference electrodes are positioned inside the cell. Additionally, we demonstrated the stability (0.28 mV/h) of the solid-contact in situ Ag/AgCl reference electrode and the ability of the system to conduct standard electrochemical testing in a wide pH range (pH 6.0–8.0). Cell experiments confirmed the non-destructive performance of the electrode system towards cells and its capacity for real-time monitoring of intra- and extracellular pH values. Moreover, through equivalent circuits, finite element simulations, and drug delivery experiments, we illustrated that the IS-SRITES can yield more accurate test results and exhibit enhanced resistance to interference from the extracellular environment. Our proposed system holds the potential to enable the precise detection of intracellular substances and optimize the existing model of the electrode system for intracellular signal detection, thereby spearheading advancements in single-cell analysis.

Pilot Study Dismantling the Cognitive Behavioral Analysis System of Psychotherapy: Identifying the Active Ingredients

BackgroundCBASP is a learning acquisition model of psychotherapy and until this present study, has not been systematically dismantled to identify the active ingredient.MethodsThe present investigation is the first formal dismantling pilot-study conducted to date on the Cognitive Behavioral Analysis System of Psychotherapy (CBASP). The two major treatment components of CBASP, Situational Analysis (SA) and the Interpersonal Discrimination Exercise (IDE), were administered alone and with CBASP treatment-as-usual (the combination of both) to determine if there were active ingredients operative in the model. Secondly, we questioned if the dismantling of CBASP should continue in a larger study in an effort to answer the greater dismantling question of whether to administer both SA and IDE separately based on the active ingredient outcomes.ResultsThe study did achieve visually inspected differential data patterns across the three groups of the dependent measures. Based on these outcomes, a larger dismantling study seems warranted and is recommended.ConclusionsWe strongly urge that a larger dismantling study using a similar design be conducted in an effort to further the dismantling process of the CBASP model.