Minimal Deviations Research Articles

EXPLORING OPEN STAR CLUSTER MEMBERSHIPS WITH N-BODY SIMULATIONS AND MACHINE LEARNING

This work explores the application of supervised machine learning algorithms on N-body simulations to analyze the membership of open star clusters. The simulations used in this study are based on the Plummer model, clusters formed with constant star-formation efficiency (SFE) per free-fall time. We use simulations with different SFE and initial random realization. The random forest model was trained using simulations based on a 15% SFE over a time period of 20-100 million years. Subsequently, the model was tested on other N-body simulations with SFEs ranging from 17% to 25%, demonstrating consistently high classification accuracy throughout the dynamic evolution of the tested simulations. The model successfully identified cluster members with minimal deviations despite variations in SFE. Additionally, the algorithm maintained robustness against noise and initial conditions. Most of the errors observed in the model were false positives (FP), often located within a 2 Jacobi radius, suggesting gravitational binding to the cluster's center. This framework and learning strategy exhibit effectiveness and hold promise for further application in analyzing mock observations obtained from N-body simulations. Future work will focus on extending this method to more realistic observational scenarios.

Beyond effectiveness in eHealth trials: Process evaluation of a stepped-care programme to support healthcare workers with psychological distress (RESPOND-HCWs).

This study presents the process evaluation of an effective stepped-care programme of eHealth interventions (Doing What Matters in Times of Stress [DWM] and Problem Management Plus [PM+]) for healthcare workers (HCWs) with psychological distress (RESPOND-HCWs trial) conducted in Spain. The aim is to analyse the context in which the programme was delivered, assess key implementation outcomes and explore mechanisms of action. We used mixed methods. Quantitative data came from routine randomised control trial monitoring and structured observation, and qualitative data were collected using semi-structured, in-depth interviews with trial participants (n = 12) and decision-makers (n = 7) and a focus group discussion with intervention providers (n = 7). We conducted a descriptive analysis of quantitative data using R software and a thematic analysis of qualitative data using NVivo. Context analysis revealed implementation barriers, including unrealistic expectations of participants about the programme and mental health-related stigma. The flexibility of interventions and the opportunity for mental health actions were enabling factors. Implementation outcomes showed that the trial was feasible, appropriate and timely, and that the intervention was delivered with minimal protocol deviations and good acceptance among participants. Mechanisms of action included confidence in the positive effect of the intervention, a good therapeutic relationship and specific intervention components. These results supplement the outcome evaluation and can help inform large-scale implementation in similar settings. Specific recommendations include increasing mental health awareness and reducing stigma in the implementation setting, including a short orientation session and ensuring flexibility in schedules and peer support. NCT04980326.

Experimental study of characteristics of a novel waste-modified phase change material with paraffin wax and raw fly ash

Thermal energy storage systems (TES) can fully utilize solar energy by storing excess and using it when required. Latent heat storage systems (LHS) have gained popularity due to their simplicity and high storage capacity. The present work deals with the development of a waste-modified phase change material (PCM) with raw fly ash (RFA) (a byproduct of the clay brick industry). The base or parent PCM is selected as paraffin wax due to its easy availability and nontoxic nature. Some characterization methods like XRD, FTIR, and DSC were applied to study the prepared composite PCM’s different surface, thermal, and chemical properties with 1 wt% of fly ash particles. Good compatibility among the constituents is evident from the XRD and FTIR spectra. There are minimal deviations of 1% and 2% in melting and freezing latent heat, respectively, compared to pristine paraffin wax, as evident from DSC analysis. The thermal reliability of the developed composite is apparent from the constant properties as shown in FTIR and DSC analyses after 100 repeated heating/cooling cycles. This waste-modified PCM with desirable characteristics like thermal reliability and stable temperature range can be an effective energy storage medium, ensuring proper reuse and recycling of wastes like fly ash particles.*

Analytical optimization of open hole effects on the tensile properties of SS400 sheet specimens using an integrated FFD-CRITIC-DFA method

Structural components are generally composed of material discontinuities, including open holes, which are considered stress concentrators in engineering components. In view of this, assessing the influence of open holes on the tensile properties is crucial to determine the sensitivity and tensile strength of a particular material. Nevertheless, investigation of the impact of open holes on the tensile properties of SS400 steel sheets is very limited and yet to be explored. Therefore, this study was performed to optimize the effects of open holes on the tensile properties of SS400 sheet specimens based on a Full Factorial Design (FFD) experiment. Four input parameters that represent various hole configurations, which include the hole diameter, location of the hole, number of holes, and hole shape, were considered in this study to develop the experimental-based prediction models to optimize the output performance, namely yield strength, ultimate tensile strength, and ultimate elongation, commonly denoted as YS, UTS, and UE respectively. A total of 10 additional experimental trials were then utilized to verify the constructed models. In addition, the weight fractions for YS, UTS, and UE were identified using the Criteria Importance Through Inter-Criteria Correlation (CRITIC) method. Subsequently, the Desirability Function Analysis (DFA) is utilized to pinpoint the optimal parameter conditions for maximizing the tensile properties. Based on the results, all four parameters showed significant effects on the response variables, except the number of holes for UTS and hole location for UE. The diameter also recorded the highest contribution toward UTS and UE, followed by the hole shape. Regarding YS, hole diameter takes precedence, with the number of holes as the second most influential factor. Furthermore, the average absolute percent deviation for the prediction responses of 10 experimental cases were 1.06 %, 0.90 %, and 0.85 % for YS, UTS, and UE, respectively, confirming the validity of the constructed models. Meanwhile, the CRITIC method estimated the weight fractions for YS, UTS and UE from the experimental data, which were 0.3825, 0.2559, and 0.3616, respectively. The DFA-derived composite desirability, rated at 0.9820, suggests optimal conditions: a 1 mm hole diameter, centered hole location, three holes, and a hexagonal shape. The minimal deviations between predicted and experimental values affirm the robustness of the models. Overall, this investigation yields important insights for optimizing open holes and elevating the tensile performance of SS400 sheet specimens.

Surviving and thriving: How changes in teaching modalities influenced student satisfaction before, during and after COVID-19

This paper leverages analytics methods to investigate the impact of changes in teaching modalities shaped by the COVID-19 pandemic on undergraduate students’ satisfaction within a Spanish brick-and-mortar higher education institution. Unlike research that has focused on faculty- or programme-level data, this study offers a comprehensive institutional perspective by analysing large-scale data (N = 83,532) gathered from satisfaction surveys across all undergraduate courses in eight faculties from 2018 to 2021. The longitudinal analysis revealed significant changes (p < 0.05) in satisfaction indicators, particularly overall satisfaction and perceived workload. During the emergency remote teaching period, there was a significant decrease in satisfaction and high levels of variability across courses. However, a year after emergency remote teaching, with increased implementations of technology-supported online and mixed teaching modalities, satisfaction measures not only recovered but exceeded pre-COVID levels in the aforementioned indicators when the teaching modality was fully co-located. The variability of answers also reached historical lows, reflecting more uniform student experiences. These findings highlight the resilience of educators and the current higher education system and suggest a capacity to learn and improve from disruptive pedagogical changes. The study also provides insights into how data analytics can help monitor and inform the evolution of teaching practices. Implications for practice or policy Higher education institution administrators should improve the understanding of the effects derived from changes in their teaching and learning models, for example, in teaching modalities and related technology support. Student satisfaction data analytics offer useful indicators to study the impact of those effects. Higher education institutions should provide support for educators to ensure minimal deviations from expected averages of educational quality indicators regardless of the educators’ capacity to adapt to changes in the teaching models.

Long-term effects of multiple concussions on prefrontal cortex oxygenation during a hypercapnic challenge in retired contact sport athletes

This exploratory study aimed to investigate the long-term effects of multiple concussions on prefrontal cortex oxygenation during a five-minute hypercapnic challenge using Near Infrared Spectroscopy (NIRS). 55 physically active retired contact sport male athletes with three or more previous concussions (mTBI) were recruited along with 29 physically active males with no concussions history (CTRL). Participants completed five minutes of seated rest prior to the five-minute hypercapnic challenge (20-second breath-hold, 40-second recovery breathing; five times). NIRS measured right and left side oxygenated (O2Hb), deoxygenated (HHb), total (tHb) haemoglobin, and haemoglobin difference (HbDiff) with all parameters analysed through changes in average maximal and minimal values (ΔMAX), Z-scores, and standard deviations. Right prefrontal cortex HbDiff ΔMAX was significantly higher in the mTBI compared to CTRL (p = 0.045) group. Left prefrontal cortex O2Hb ΔMAX (p = 0.040), HHb Z-Scores (p = 0.008), and HbDiff ΔMAX(p = 0.014) were significantly higher in the mTBI group. Within-group right vs left analyses demonstrated significantly lower left HbDiff ΔMAX (p = 0.048) and HbDiff Z-scores (p = 0.002) in the mTBI group, while the CTRL group had significantly lower left HHb Z-scores (p = 0.003) and left tHb Z-scores (p = 0.042). This study provides preliminary evidence that athletes with a history of three or more concussions may have impaired prefrontal cortex oxygenation parameters during a hypercapnic challenge.

Determination of synephrine in herbal plants using an isotopically labeled internal standard HPLC–MS/MS method

We developed a rapid, precise, and sensitive method to quantify synephrine in herbal plants, using methanol–water solution (8:2, v/v) as the extracting agent and high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) for analysis. To mitigate matrix effects, we employed synephrine-13C2, 15N as an internal standard and separated synephrine using a reverse-phase column. We established a highly accurate standard curve (R2 ≥ 0.9997). The method demonstrated excellent intra-day recoveries (96.35 %–103.19 %) at three spiked concentrations (0.5, 10, 100 μg/g), with minimal standard deviations (<4.84 %). The limits of detection and quantification of this method were 0.1 μg/g and 0.5 μg/g, respectively. Using this approach, we analyzed synephrine content in 24 herbal plants. Substantial concentrations of synephrine were found in Citri Reticulatae Pericarpium Viride (13.204 mg/g) and Aurantii Fructus Immaturus (7.077 mg/g), whereas the remaining species either exhibited lower concentrations or were devoid of synephrine. This approach fulfills the requirements for synephrine detection.

In silico approach reveals N-(5-phenoxythiophen-2-yl)-2-(arylthio)acetamides as promising selective SIRT2 inhibitors: the case of structural optimization of virtual screening-derived hits

Epigenetic modifications play an essential role in tumor suppression and promotion. Among the diverse range of epigenetic regulators, SIRT2, a member of NAD+-dependent protein deacetylates, has emerged as a crucial regulator of cellular processes, including cell cycle progression, DNA repair, and metabolism, impacting tumor growth and survival. In the present work, a series of N-(5-phenoxythiophen-2-yl)-2-(arylthio)acetamide derivatives were identified following a structural optimization of previously reported virtual screening hits, accompanied by enhanced SIRT2 inhibitory potency. Among the compounds, ST44 and ST45 selectively inhibited SIRT2 with IC50 values of 6.50 and 7.24 μM, respectively. The predicted binding modes of the two compounds revealed the success of the optimization run. Moreover, ST44 displayed antiproliferative effects on the MCF-7 human breast cancer cell line. Further, the contribution of SIRT2 inhibition in this effect of ST44 was supported by western blotting, affording an increased α-tubulin acetylation. Furthermore, molecular dynamics (MD) simulations and binding free energy calculations using molecular mechanics/generalized born surface area (MM-GBSA) method evaluated the accuracy of predicted binding poses and ligand affinities. The results revealed that ST44 exhibited a remarkable level of stability, with minimal deviations from its initial docking conformation. These findings represented a significant improvement over the virtual screening hits and may contribute substantially to our knowledge for further selective SIRT2 drug discovery. Communicated by Ramaswamy H. Sarma

Prioritizing the Precursors of Patients’ Experience in Indian Corporate Hospitals: Application of hybrid RIDIT-GRA approach

Objective: This study aims to identify, understand, and prioritize the influence of distinct patient experience (PX) antecedents that have substantial evidence in shaping the patients’ preferences and experiences in Indian corporate hospitals. The study is expected to assist healthcare managers in the personalization and alignment of clinical services with consumer expectations and demands. Design/ Methodology: A cross-sectional study was conducted across eight corporate hospitals to collect 220 patient data samples. A recent measurement model, titled ‘Patient Experience Questionnaire’, was adopted to harness pre-validated PX factors and related items. Further, two independent prioritization techniques, Relative to an Identified distribution (RIDIT) analysis and Grey Relational Analysis (GRA), were executed to render item precedence of the precursors of PX. Results: Through RIDIT, items belonging to factors 'doctor services' and 'nursing services' secured favorable performance ratings, whereas items under 'information' and 'next-of-kin' obtained comparatively less favorable responses. There was evidence of minimal deviations when the results were verified through GRA, but the ranks obtained in both the independent techniques (RIDIT &amp; GRA) revealed a robust correlation of 99.5%. Moreover, the applicability of two independent prioritization techniques enhances the rigor and reliability of findings. Conclusions: Although the respondents were mostly satisfied with their care providers, an effective provider-patient communication was not evident in the care system. Patients seemed to be overly dependent on their physicians and showed limited intention to participate in a collaborative process. Lack of patient-centric culture, deficit infrastructure, excessive workload on healthcare providers, and restricted translation of patient-centric concepts into practice deterred organizations from fully benefiting from patients' involvement in clinical facets.

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model’s accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.

Enhanced frequency stabilization in a restructured power system leveraging Demand response strategy for source and Load Intermittency

ABSTRACT This article addresses the complexities brought about by the increased integration of renewable energy sources into power networks, a trend that can lead to system degradation and instability. In response, this study advocates for the adoption of a Demand Response (DR) approach as a pivotal component in ensuring the future reliability of the electric power system. To facilitate frequency regulation in an interconnected hybrid power system, a modified two-degree-of-freedom (fractional order proportional integral)-tilt derivative [TDOF (FOPI)-TD] controller has been developed. The controller’s parameters are finely tuned through the application of Quasi Opposition-based Harris Hawks Optimization (QOHHO), a method proven to outperform other optimization algorithms. The findings demonstrate a significant enhancement in system frequency stability with the implementation of the QOHHO-based controller, even when factoring in uncertainties, physical constraints, and high penetration of renewable energy sources. Additionally, an evaluation of variations in system frequencies and tie-line power adjustments reveals minimal deviations with DR, measuring at −3.59E–05, −1.89E–04, and 9.26E–05, respectively. In stark contrast, in the absence of DR implementation, the deviations are notably higher, recorded at −1.31E–04, −2.73E–04, and 1.73E–04, respectively. Furthermore, a real-time assessment conducted on the OPAL-RT OP4510 platform validates the proposed strategy’s applicability under source and load intermittency conditions. This substantiates its effectiveness in real-world scenarios.

Camellia oleifera derived carbon dots modified TiO2 as sensor for the rapid detection of 4-nitrophenol in wastewater

This paper presents an innovative electrochemical sensor utilizing carbon dots modified with titanium dioxide (TiO2-CDs) for the rapid and accurate detection of 4-nitrophenol (4-NP) in wastewater. This sensor offers a cost-effective and efficient approach to on-site monitoring of 4-NP, a highly toxic and persistent environmental pollutant. The modification of the electrode material with TiO2-CDs significantly enhances the sensor's performance by improving sensitivity, selectivity, and stability. The synthesis of TiO2-CDs involves the integration of carbon dots derived from biomass with TiO2, resulting in a composite material with an enlarged surface area and enhanced photocatalytic properties. The unique hollow spherical structure of the TiO2-CDs further amplifies their photon absorption capacity. Electrochemical characterization validates the successful modification of the electrode surface and demonstrates the electrocatalytic activity of the TiO2-CDs modified electrode towards 4-NP. The sensor exhibits a linear correlation between the peak oxidation current and the concentration of 4-NP, enabling precise and reliable quantification. Notably, the sensor's detection range spans from 10 μM to 350 μM, with an impressively low detection limit of 0.12 μM. Real sample analysis of wastewater underscores the sensor's practical utility, revealing high recovery rates and minimal relative standard deviations. The development of this electrochemical sensor, based on TiO2-CDs, presents a promising solution for the swift and accurate detection of 4-NP in wastewater, offering potential advancements in pollution control, environmental monitoring, and wastewater treatment strategies.

Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures

The assessment of rocking response is a challenging task due to its high nonlinearity. The present study investigates two methodologies to evaluate finite rocking rotations and overturn of three typical rocking systems. In particular, fuzzy linear regression (FLR) with triangular fuzzy numbers and a hybrid model combining logistic regression and fuzzy logic were adopted. To this end, three typical rocking structures were considered, and nonlinear time history analyses were performed to obtain their maximum response. Eighteen seismic intensity measures (IMs) extracted from recorded seismic accelerograms were considered to predict the responses. In the absence of rocking overturn, the finite rocking rotations and similarity ratios were calculated by adopting the FLR method. Moreover, extensive analysis was performed to evaluate the influence of each IM on the model’s predictions. On the other hand, rocking overturn was evaluated by logistic regression to compute the probability of collapse, followed by the FLR method to estimate the similarity between the different rocking-based structural systems. The root mean square error (RMSE) parameter and the log loss function were determined for every model to assess the predictions that emerged from the two fuzzy methods. As indicated, both methods demonstrated satisfactory results, presenting minimal deviations from the observed values. Finally, in the case of finite rocking rotation predictive models, remarkably high similarity ratios were observed among the various structures, with a median value of 0.96.

Cumulative Sum Chart as Complement to Objective Assessment of Graduating Surgical Resident Competency: An Exploratory Study.

Rater-based assessment and objective assessment play an important role in evaluating residents' clinical competencies. We hypothesize that a cumulative sum (CUSUM) chart of operative time is a complement to the assessment of chief general surgery residents' competencies with ACGME Milestones, aiding residency programs' determination of graduating residents' practice readiness. We extracted ACGME Milestone evaluations of performance of operations and procedures (POP) and 3 objective metrics (operative time, case type, and case complexity) from 3 procedures (cholecystectomy, colectomy, and inguinal hernia) performed by 3 cohorts of residents (N = 15) during their PGY4-5. CUSUM charts were computed for each resident on each procedure type. A learning plateau was defined as at least 4 cases consistently locating around the centerline (target performance) at the end of a CUSUM chart with minimal deviations (range 0 to 1). All residents reached the ACGME graduation targets for the overall POP by the end of chief year. A total of 2,446 cases were included (cholecystectomy N = 1234, colectomy N = 507, and inguinal hernia N = 705), and 3 CUSUM chart patterns emerged: skewed distribution, bimodal distribution, and peaks and valleys distribution. Analysis of CUSUM charts revealed surgery residents' development processes in the operating room towards a learning plateau vary, and only 46.7% residents reach a learning plateau in all 3 procedures upon graduation. CUSUM charts of operative time complement the ACGME Milestones evaluations. The use of both may enable residency programs to holistically determine graduating residents' practice readiness and provide recommendations for their upcoming career/practice transition.

Towards pre-emptive resilience in military supply chains: A compromise decision support model-based approach

ABSTRACT The complex and dynamic nature of military supply chains (MSC) requires constant vigilance to sense potential vulnerabilities. Several studies have employed decision support models for the optimization of their operations. These models are often limited to a best single-point solution unsuitable for complex MSC constellations. In this article, the authors present a novel approach based on decision support models to explore a range of satisficing solutions against disruptions in MSCs using a compromise Decision Support Problem (cDSP) construct and Decision Support in the Design of Engineered Systems (DSIDES). Two cases were evaluated: (1) a baseline scenario with no disruption and (2) with disruption to achieve target values of three goals: (1) minimizing lead time, (2) maximizing demand fulfilment and (3) maximizing vehicle utilization. The results obtained in Case 1 identified a more stable solution space with minimal deviations from the target value, while in Case 2 the solution space was unstable with deviations from the target values

Monitoring and Evaluation of School Finances to Increase Transparency and Accountability

This research aims to (1) find out how monitoring and evaluation of school finances is carried out; (2) identifying how schools apply the principles of transparency and accountability in managing school finances; (3) Identifying how monitoring and evaluating school finances has an impact on improving school financial management. For the successful implementation of education, one element of school financial management is managing and making the best use of financial management. Schools need to carry out good school financial management by planning and developing all the resources they have effectively and efficiently. This research approach uses qualitative methods and data collection techniques are carried out through interviews, observation and document research. The target of this research is school treasurers who have an important role in managing school finances. The research results show that the implementation of financial monitoring and evaluation was successful and was carried out directly by the school principal and school authorities. When monitoring and evaluating school finances, minimal deviations were found in the RKAS that had been prepared. Monitoring and evaluation practices aim to increase transparency and accountability in school financial management and prevent undesirable things from happening

Развитие бизнес-планирования сбыта российских драгоценных металлов в условиях макроэкономической и геополитической нестабильности

Introduction. Under the influence of external limitations, Russian precious metals market faced new threats and challenges. Metallurgical companies have been forced to re-engineer affected business processes. One of the main tasks is to reassess their approach to sales planning. Theoretical analysis. High market volatility triggered by increased macroeconomic and geopolitical risks causes price bubbles. Bubbles have a negative impact on the market because they produce serious deviations from fundamental levels. There is a definite connection between negative news and bubbles growth. Empirical analysis. Precious metal prices showed high volatility in 2022. The analysis revealed at least one palladium price bubble formed during March. In the face of increased uncertainty, the main sales objective was to sell as much of the production as possible, which most metallurgical companies have coped with. Results. In order to develop sales planning under constraints, a combined approach is proposed that gives minimal deviations from average prices and reduces bubble probability. It is worth using econometric models for the early identification of price deviations from its fundamentals. It is necessary to encourage the regional trading hubs of precious metals.

Minimization of Peak Effect in the Free Motion of Linear Systems with Restricted Control

A peak effect minimization problem in the free motion of linear systems is considered in the paper. The paper proposes an iterative procedure for the peak effect minimization using a combination of the recently proposed gramian-based approach and the theory of using the condition number of an eigenvectors matrix for the upper bound estimations of the system state processes. Minimization of control costs is based on the analysis of the singular value decomposition of a gramian of control costs, followed by the formation of major and minor estimations of the gramian. Minimization of peak effect in the trajectories of free movement of systems is carried out by minimizing the condition number of the eigenvectors matrix of the matrix of a stable closed-loop system, while the state matrix with the desired eigenvalues and eigenvectors is designed with the generalized modal control. The development of an iterative algorithm for the peak effect minimization in the trajectories of linear systems under any non-zero initial conditions with restricted control is based on an aggregated index. The index takes into account both the estimate of the gramian of control costs and the condition number of the eigenvectors matrix of the stable closed-loop system. Minimization of the aggregated index makes it possible to ensure minimal deviations in the trajectories of free movement of systems of the considered class. The procedure is applied to a system of two satellites with restricted control, where peak effects in satellites relative trajectories are minimized. Two cases of peak affect minimization are considered. In the first case, the peak effect minimization in the trajectories of free movement of satellites is carried out only by minimizing the gramian of control costs. In the second case, the peak effect minimization is realized using the developed algorithm. The results illustrate the efficiency of the procedure and indicate the decrease of the peak effect for the satellites relative trajectories.

Clinical neurophysiological interrogation of motor slowing: A critical step towards tuning adaptive deep brain stimulation

ObjectiveSubthalamic nucleus (STN) beta activity (13–30 Hz) is the most accepted biomarker for adaptive deep brain stimulation (aDBS) for Parkinson’s disease (PD). We hypothesize that different frequencies within the beta range may exhibit distinct temporal dynamics and, as a consequence, different relationships to motor slowing and adaptive stimulation patterns. We aim to highlight the need for an objective method to determine the aDBS feedback signal. MethodsSTN LFPs were recorded in 15 PD patients at rest and while performing a cued motor task. The impact of beta bursts on motor performance was assessed for different beta candidate frequencies: the individual frequency strongest associated with motor slowing, the individual beta peak frequency, the frequency most modulated by movement execution, as well as the entire-, low- and high beta band. How these candidate frequencies differed in their bursting dynamics and theoretical aDBS stimulation patterns was further investigated. ResultsThe individual motor slowing frequency often differs from the individual beta peak or beta-related movement-modulation frequency. Minimal deviations from a selected target frequency as feedback signal for aDBS leads to a substantial drop in the burst overlapping and in the alignment of the theoretical onset of stimulation triggers (to ∼ 75% for 1 Hz, to ∼ 40% for 3 Hz deviation). ConclusionsClinical-temporal dynamics within the beta frequency range are highly diverse and deviating from a reference biomarker frequency can result in altered adaptive stimulation patterns. SignificanceA clinical-neurophysiological interrogation could be helpful to determine the patient-specific feedback signal for aDBS.