Optimal Frequency Research Articles

Developing Individualized Follow-Up Strategies Based on High-Risk Recurrence Factors and Dynamic Risk Assessment for Locally Advanced Rectal Cancer.

Locally advanced rectal cancer (LARC) is one of the most common malignant tumors worldwide, and its incidence is increasing year by year. Despite multimodal treatment, the recurrence rate of LARC patients remains high, about 20%-50%. However, the follow-up strategy according to tumor stage has certain limitations. There is no consensus on the optimal frequency and duration of follow-up. This study aims to comprehensively analyze the high-risk factors for recurrence in LARC from clinical characteristics, nutritional indicators, and imaging indexes. It intends to utilize conditional survival (CS) evaluation to assess dynamic survival and recurrence risks after comprehensive treatment of LARC and to develop individualized follow-up strategies. Logistic regression was utilized to analyze the independent recurrence factors in LARC patients. Calibration curve, decision curve, and ROC curve were employed to evaluate the model's efficacy. Kaplan-Meier curve was used to calculate CS rate and compare survival differences among different risk groups. A total of 561 patients were analyzed in our study. Our multivariable logistic regression analysis revealed that the prognostic nutritional index (PNI), extramural vascular invasion (EMVI), vascular tumor thrombus, perineural invasion, and tumor size were independent factors for recurrence. Subsequently, a nomogram model was constructed and risk stratification was performed. Calibration curves and decision curves demonstrated that the model exhibited good clinical efficacy. The area under the ROC curve for the model was 0.763, indicating good sensitivity and specificity. Kaplan-Meier curves showed significant differences in survival among different risk groups. Furthermore, we observed that the CS without local recurrence and distant metastasis increased each year, while the cumulative recurrence risk decreased annually with prolonged survival time. Tailored follow-up intensities were developed for different risk groups and clinical stages based on the cumulative recurrence risk. The personalized follow-up strategy based on risk stratification can optimize resource allocation, early detection of recurrence or metastasis, and ultimately enhance the overall care and prognosis of LARC patients.

Machine Learning-based Optimal Nitrate Dynamics and Flux Monitoring Frequency in Karst Catchment

Machine Learning-based Optimal Nitrate Dynamics and Flux Monitoring Frequency in Karst Catchment

AC-assisted microbially induced carbonate precipitation for sand reinforcement: An experimental study

Microbially induced carbonate precipitation (MICP) is a promising method for transforming natural soils into a rock-like material, enhancing soil strength and creating an environmentally friendly engineered geomaterial for load-bearing purposes. Applying alternating current (AC) for enhancing precipitation including changing the crystalline form of the calcium carbonate precipitates appeals as a possible solution to break the upper limit of the unconfined compressive strength (UCS) of bio-treated specimens. To assess the viability of AC-assisted MICP, a series of experiments were designed and conducted under various combination of conditions. The UCS, calcium carbonate content and permeability of the bio-fabricated specimens were obtained to evaluate the treatment effectiveness of AC-assisted MICP. The results demonstrate that the UCS of the sand column exhibits a linear increase with the applied voltage from 10 V to 30 V (i.e., electric field strength from 0.91 V/cm to 2.73 V/cm). The UCS value of the bio-specimen reaches 9.4 MPa after 3 treatments at a concentration of 1.00 mol/L, a voltage of 30 V, and a frequency of 100 Hz. With the assistance of an AC electric field, the adverse impacts caused by high chemical concentrations in the MICP process can be mitigated. We report that a more uniform distribution of the calcium carbonate content of the treated specimen is obtained under an optimal AC frequency of approximately 100 Hz in the current series of experiments. The induced ion vibration under the action of AC results in a change in crystalline form and an increase in the amount and uniformity of crystals precipitated on the surface of the soil grains, supported by X-ray diffraction (XRD) patterns and scanning electron microscope (SEM) images. For reference, the energy consumption and the cost for increasing the UCS of the bio-treated specimen to 5 MPa is estimated at 375.86 kWh and 676.55 HK$ per cubic meter, respectively. The findings from our experimental investigation and analysis provide compelling evidence that utilising AC electric field holds great potential for achieving an enhanced treatment effect of MICP and hence a stronger bio-soil.

Optimal and Robust Load Frequency Control for Hybrid Power System Integrated with Energy Storage Device by Sine Cosine Algorithm

Optimal and Robust Load Frequency Control for Hybrid Power System Integrated with Energy Storage Device by Sine Cosine Algorithm

Counting the days: Exploring healthcare contact days during episodes of cancer care.

233 Background: Cancer diagnosis and treatment brings about not only emotional upheavals but also logistical hurdles of navigating a fragmented healthcare system. Little is known about the frequency of healthcare contact for cancer patients and its implications. Healthcare contact days, defined as the number of days a patient spends in direct contact with healthcare professionals outside of home, measures the patient’s interaction with the healthcare system. Methods: Using historical claims data (Part A institutional and Part B ambulatory claims) from the Enhancing Oncology Model (EOM) for 12 practices in The US Oncology Network covering the period between January 2019 to June 2022, we derived healthcare contact days per episode (6-month care periods initiating with the administration of chemotherapy), counting each unique date once. When patients encountered multiple care settings on a single date, the following hierarchy was used: inpatient (IP) > emergency room (ER) > skilled nursing facility (SNF)> hospice > ambulatory care. We further counted contact days for different services conducted during the ambulatory visits. Results: For the 75,246 episodes, an average of 31.9 days per episode (1 in every 6 days) were spent in healthcare contact. Cancer patients’ healthcare contact was predominantly within ambulatory care, and visits with oncology providers exceeded that with all other specialties. ER and IP visits, generally a representation of acute care events, accounted for <10% of the contact days. Conclusions: During cancer treatment, patients spend approximately one-sixth of their time in healthcare contact. The optimal frequency of contact, pattern of primary care support, care of comorbidities, and the interrelationship between acute care and clinician visits are apt for further exploration. Healthcare contact days can reflect different care delivery approaches between providers and may be a metric to assess both healthcare efficiency and accessibility. Understanding caregiver burden and social disparities associated with high contact days offers an opportunity to improve care coordination and streamline healthcare encounters while balancing patients’ care needs. CMMI Disclaimer: The statements contained in this document are solely those of the authors and do not necessarily reflect the views or policies of CMS. The authors assume responsibility for the accuracy and completeness of the information contained in this document. Healthcare contact days. Mean Percentiles 5 th -95 th Per Episode Healthcare Contact Days 31.9 10-65 Ambulatory Visits* 8-55 Chemotherapy 6.6 0-18 Imaging 2.7 0-7 Procedure 1.8 0-7 Radiation 2.4 0-23 Testing 11.8 3-25 Clinician Oncology 6 1-14 Other Specialties 3.4 0-10 Primary Care 2.8 0-8 Facility Visits ER 0.4 0-2 Hospice (Facility) 0.1 0-0 IP 2.7 0-16 SNF 0.8 0-0 *Ambulatory visits days are not mutually exclusive.

Model-Free Fast Frequency Support of Wind Farms for Tracking Optimal Frequency Trajectory

Model-Free Fast Frequency Support of Wind Farms for Tracking Optimal Frequency Trajectory

Defining the Optimal Frequency of Dialysis Adequacy Test for Patients on Peritoneal Dialysis

Defining the Optimal Frequency of Dialysis Adequacy Test for Patients on Peritoneal Dialysis

A MATLAB platform for production planning in batch processing firms producing items that are jointly replenished

Rising levels of poverty have prompted manufacturers of non-durable consumer goods to explore opportunities at the lower end of the wealth spectrum. To enhance affordability for low-income consumers, these goods are often packaged in various sizes, including single-serve sachets. While numerous models and algorithms have been developed to determine the optimal frequency of manufacturing runs and packaging setups for the simultaneous replenishment of different pack sizes, there is a notable lack of literature addressing their implementation by small and medium-sized enterprises (SMEs) in this sector. This gap may be attributed to the absence of accessible source codes, a shortage of model experts, and the complexity involved in manual calculations. To address this issue, we developed a user-friendly MATLAB software platform based on Goyal’s algorithm, tailored specifically for SMEs. The software prioritizes simplicity, interactivity, and flexible visual displays. It was validated against examples from existing literature and through randomly generated scenarios. Users can easily compute the optimal number of manufacturing setups, packaging frequencies for each pack size, and the associated costs by inputting relevant data such as manufacturing setup, inventory holding and packaging setup costs, and demand for each pack size. Importantly, users do not need expertise in modeling to operate the software. However, it is designed for single-product deterministic environments; future research should explore multi-product and stochastic modeling scenarios.

Occurrence and predictors of laboratory abnormalities during outpatient parenteral antimicrobial therapy – A multicenter cohort study to inform laboratory test monitoring

ObjectivesEvidence on the optimal frequency of laboratory testing during outpatient parenteral antimicrobial therapy (OPAT) is lacking. Therefore, we investigated how often and when laboratory abnormalities occur during OPAT and which factors are associated with these abnormalities. MethodsWe performed a multicenter cohort study in four Dutch hospitals among adult patients receiving OPAT and collected routinely obtained laboratory test results. Incidence and incidence rates were calculated for various laboratory abnormalities. Survival analysis was performed to visualize the time to the first occurrence of laboratory abnormalities and Poisson regression analysis to compare the number of abnormalities in the first and second 30 OPAT days among patients receiving OPAT for ≥60 days. Predictors were identified using a multivariable Cox proportional hazard regression model. Results45.1% of 1152 included patients developed laboratory abnormalities, but only 2% led to OPAT discontinuation. Hepatotoxicity was most common (33.9 events/1000 OPAT days), with a time-dependent decrease in the occurrence of the first hepatotoxic event, while hypokalemia was rare (1.7 events/1000 OPAT days). In the subgroup of patients receiving ≥60 days of OPAT, nephrotoxicity was more common in days 31-60. We observed partly toxicity-specific associations between antibiotic type, concomitant medication, baseline laboratory values, patient characteristics, and the occurrence of laboratory abnormalities. ConclusionsWhile laboratory abnormalities are frequently observed during OPAT, they rarely lead to discontinuation of OPAT. Specific patient, treatment and laboratory characteristics were associated with the occurrence of laboratory abnormalities. Based on our results, we recommend a more personalized laboratory monitoring policy with less blood sampling.

Factors Influencing Radiation Sound Fields in Logging While Drilling Using an Acoustic Dipole Source

With the increasing number of complex well types in the development stage of oil and gas fields, it is becoming increasingly urgent to use remote detection logging while drilling (LWD) to explore the geological structures in a formation. In this paper, the feasibility and reliability of the dipole remote detection of logging while drilling are demonstrated theoretically. For this purpose, we use an asymptotic solution of elastic wave far-field displacement to derive the calculation formula for the radiation pattern and energy flux of an LWD dipole source. The effects of influencing factors, including the source frequency, formation property, drill collar size, and mud parameter, on the radiation pattern and energy flux are analyzed. The results show that the horizontally polarized shear wave (SH-wave) has a greater advantage in imaging the reflector compared with the cases of the compressive wave (P-wave) and vertically polarized shear wave (SV-wave), which indicates the dominance of the SH-wave in dipole remote detection while drilling. The optimal source excitation frequency of 2.5 kHz and inner and outer radii of the drill collar of 0.02 and 0.1 m, respectively, should be considered in the design of an LWD dipole shear wave reflection tool. However, the heavy drilling mud is not conducive to remote detection during logging while drilling. In addition, the reflection of the SH-wave for the LWD condition is simulated. Under the conditions of optimal source frequency, drill collar size, and mud parameters, the reflection of the SH-wave signal is still detected under the fast formation.

Effectiveness of interventions for informal caregivers of people with end-stage chronic illness: a systematic review

BackgroundPeople living with advanced, non-malignant chronic conditions often have extensive and complex care needs. Informal or family caregivers often provide the care and support needed by those with advanced chronic conditions at home. These informal caregivers experience many challenges associated with their caring role, which can impact their own wellbeing. Whilst there is growing evidence around the impact on carers, guidance on support for informal caregivers of patients with advanced, non-malignant, chronic conditions is lacking, with little evidence available on effective psychosocial carer interventions. This systematic review explored existing interventions for caregivers of those with advanced, non-malignant, chronic illness, in order to assess the effectiveness of these interventions in improving psychosocial outcomes.MethodsElectronic databases, Medline, CINAHL, EMBASE, and PsycINFO, were searched up to the end of March 2023. Studies meeting the inclusion criteria, focusing on interventions to improve psychosocial outcomes, such as depression, anxiety, quality of life, and caregiver burden, in this cohort of caregivers were included. Data were extracted regarding study setting, design, methods, intervention components, and outcomes. Risk of bias and quality assessment were conducted.ResultsA total of 5281 articles were screened, ultimately identifying 12 studies for inclusion, reported in 13 publications. A narrative synthesis revealed mixed results. Psychosocial interventions resulted in more significant improvements in psychosocial outcomes than psychoeducational or support interventions, with interventions for carer-patient dyads also reflecting more positive outcomes for caregivers. Evidence-based interventions, guided by an appropriate theoretical model, were reportedly more effective in improving caregiver outcomes. Differences in outcomes were related to intervention development, design, delivery, and outcome assessment.ConclusionsThis review, to our knowledge, is the first to explore the effectiveness of interventions in improving psychosocial outcomes for caregivers of those with advanced, non-malignant, chronic conditions. The review highlights the need for more robust, sufficiently powered, high-quality trials of evidence-based interventions for caregivers of people with advanced chronic illness. Optimal intervention duration and frequency of sessions are unclear and need further exploration.

High-Resolution Dipole Remote Detection Logging Based on Optimal Nonlinear Frequency Modulation Excitation.

In order to improve the detection range and imaging resolution of dipole remote detection logging, an optimal nonlinear frequency modulation (ONLFM) excitation method is proposed in this paper. In this method, the optimal waveform model of the sound source is designed with objective functions of SNR and resolution to obtain the highest resolution under the condition of the required SNR. This optimal model is a multi-constraint optimization problem, and the simulated annealing method has been used to solve it. Solving the optimal model can obtain the optimal spectrum, and the ONLFM waveform can be designed by using the stationary phase principle. Compared with the electric pulse sound source used in traditional technology, this ONLFM sound source can improve the energy and extend the frequency band range of the reflected wave, which can provide the higher resolution and SNR. The simulation results show that the ONLFM sound source can effectively improve the SNR and resolution of the reflected wave, and the detection range and imaging resolution of dipole shear wave remote detection will be improved.

The Use of Intralesional Corticosteroids in Idiopathic Granulomatous Mastitis: A Systematic Review.

Idiopathic granulomatous mastitis (IGM) is a debilitating, chronic, inflammatory condition of the breast. Several studies have emerged evaluating intralesional steroid (ILS) injection and topical steroid administration as a treatment for IGM. However, there is a dearth of international consensuses with regards to the management of IGM. Therefore, we have systematically reviewed the effectiveness of ILS in the management of IGM. A systematic search was conducted in PubMed and Cochrane Library databases, the Google Scholar website and by citation searching up to June 15th, 2023. Eight articles were selected and analyzed. A total of 397 IGM patients were included in the review. The mean patient age was 35.7 years, ranging from 23-62 years. The mean pre-treatment diameter of lesions was 27.5 mm. A total of 184 patients were treated with ILS. The mean complete clinical response time was 2.6 months. The overall complete response rate was 92.8%. Complications following ILS were minor, with hematoma, skin atrophy and hyperemia being commonly described, while avoiding the systemic side effects of oral steroid use, such as weight gain and hirsutism, which were the most commonly reported side effects with oral steroids. The recurrence rates in the ILS group (6.6%) appear to be lower than in the oral steroid group (25.8%) and surgery group (26.3%). ILS seem to show a favorable outcome in terms of complete response rate, complete clinical response time and has a lower recurrence rate and complication rate when compared to other intervention strategies. However, more comparative studies with standardized protocols are necessary to ascertain the optimum type, dosage and frequency of ILS regimens.

Closed‐form optimal solution of two‐degree‐of‐freedom system with Inerter based on equal modal damping with potential application in non‐structural elevator for seismic control

AbstractTargeting the great demand for adding non‐structural elevators to old residential buildings, this article proposes an updated configuration of tuned mass damper inerter (U‐TMDI) applied in external non‐structural elevators. An analog 2‐DOF system is established to describe the residential building controlled by an inerter elevator. Then, three closed‐form optimal solutions for designing the U‐TMDI are derived via the fixed‐point method, equal modal damping criterion, and the infinity damping assumption. Subsequently, these optimal solutions are compared and discussed involving the expressions of tuning frequency ratio and the optimal damping parameters, root locus diagram and supplemental damping ratios, transfer function, and robustness to frequency variation, respectively. Finally, a residential building example is adopted to validate the feasibility of the proposed retrofitting strategy and the closed‐form optimal design solutions. It is demonstrated that the optimal tuning frequency ratios derived by the fixed‐point method and equal modal damping criterion are different for U‐TMDI due to the influence of elevator stiffness ratio , while its degradation forms for tuned mass damper (TMD) are identical, recognizing the importance of the elevator stiffness. Moreover, the proposed retrofitting strategy of using an inerter elevator can significantly mitigate the main structure displacement by about 18%∼23% for both far‐field and near‐fault earthquakes.

STAKgram: a method for optimal demodulation band selection in bearing fault diagnosis under complex interference

Abstract Within rotating machinery, bearings constitute critical and vulnerable components. When they operate in a compromised state, they generate periodic shock pulses. While spectral kurtosis (SK) has been demonstrated as an effective tool for pulse detection, in complex operating environments, the vibration signals of damaged bearings acquired through sensors often contain a variety of interfering pulses, such as ambient background noise, episodic single pulses, which cause SK to produce poor computational results. In this paper, an innovative bearing fault diagnostic method named subband trimmed average kurtogram (STAKgram) is proposed, aiming to minimize the effect of interfering signals on signal processing results, thus ensuring reliable diagnostic results even in complex interference environments. Firstly, the signal is segmented into N sub-signals by employing a sliding window method. Following, the subband kurtosis is calculated for every sub-signal. Ultimately, the trimmed average kurtosis of the respective sub-band is computed to construct the STAKgram, indicating the optimal demodulation frequency band. It is used for squared envelope demodulation analysis. By simulation analysis and the application of test signals obtained under conditions closely resembling authentic bearing operating conditions, the STAKgram method is proven to provide more accurate results for diagnosing bearing faults in complex interference.

Velocity-Based-Training Frequency Impacts Changes in Muscle Morphology, Neuromuscular Performance, and Functional Capability in Persons With Parkinson's Disease.

Calaway, C, Walls, K, Levitt, H, Caplan, J, Mann, B, Martinez, K, Gastaldo, R, Haq, I, and Signorile, JF. Velocity-based-training frequency impacts changes in muscle morphology, neuromuscular performance, and functional capability in persons with Parkinson's disease. J Strength Cond Res XX(X): 000-000, 2024-Velocity-based training (VBT) positively impacts muscle morphology and performance in persons with Parkinson's disease (PD); however, optimal training frequencies for VBT in patients with PD remain undetermined. Changes in ultrasound-determined muscle thickness (MT) and echo intensity (EI)-derived muscle quality of the rectus femoris (RF) and vastus lateralis (VL), neuromuscular performance, and functional capacity were examined following 2 VBT frequencies (2-3 d·wk -1 ) using 30% velocity loss thresholds for 12 weeks. Neuromuscular performance was assessed using computerized pneumatic resistance machines. For each variable, 2 (time) × 2 (group) repeated-measures analyses of variance (ANOVA) were used to determine significant main effects and interactions. Significant time effects were seen for MT and EI of all muscles ( p < 0.05). Muscle thickness improvements included right VL (RVL) (0.171 ± 0.065 cm; p = 0.019), left VL (LVL) (0.214 ± 0.101 cm; p = 0.049), right RF (RRF) (0.194 ± 0.077 cm; p = 0.023), and left RF (LRF) (0.318 ± 0.109 cm; p = 0.010). For EI, improvements occurred in RVL (-18.688 ± 3.600; p = <0.001), LVL (-10.959 ± 4.894; p = 0.040), RRF (-9.516 ± 3.537; p = 0.016), and LRF (-9.018 ± 3.444; p = 0.019). Time effects were seen for leg-press 1-repetition maximum and peak power ( p < 0.01) and habitual walking speed ( p = 0.022), with a group by time interaction for maximal gait speed favoring the 3 d·wk -1 condition (∆0.15 m·s -1 , p = 0.002). The results indicate that VBT at 2 or 3 d·wk -1 can significantly improve muscle morphology, neuromuscular performance, and functional capability in patients with PD; however, improvements in maximal gait speed require 3 d·wk -1 . These findings provide flexibility when developing exercise prescriptions for patients with PD.

Removal of dichloromethane from gas streams by droplet triggered gas discharge

The removal of dichloromethane (DCM) from gas streams by a novel gas discharge reactor triggered by solution droplets was experimentally investigated. The influences of operation parameters such as input energy, gas flow rate, oxygen concentration, droplet dropping frequency, and the solution composition of droplets on the effectiveness of reactor operation and DCM removal efficiency were examined. Experimental results showed that DCM in the gas stream can be effectively degraded, and the removal of DCM decreased with the increase of the initial DCM concentration in the gas stream. With the increase of droplet dropping frequency, the removal of DCM experiences a process from increase to decrease, and the optimal droplet dropping frequency is around 0.5–1 drop·s−1. It was also found that the oxygen concentration in the range of 5–10 % in the gas stream is better for DCM decomposition, and the use of alkaline solution as droplets can effectively improve the removal of DCM. The final gas phase products of DCM degradation were mainly CO2 and O3 with the presence of O2 in the gas stream, and the liquid phase products were chloride (Cl-) and hypochlorite (ClO-) ions.

Validation of Administrative Data and Timing of Point Prevalence Surveys for Antibiotic Monitoring

Point prevalence surveys (PPSs) are used globally to collect data on antibiotic prescriptions. However, the optimal frequency for data collection to ensure comprehensive understanding of antibiotic use and to target and monitor stewardship interventions remains unknown. To identify the optimal frequency for collecting data on antibiotic use among the pediatric population through PPSs leveraging administrative data. This prognostic study used a cross-sectional validation approach and was conducted in pediatric outpatient and inpatient settings in the Veneto region of Italy. Antibiotics were classified according to the World Health Organization Access, Watch and Reserve criteria. Prescribing rates of access antibiotics were analyzed for pediatric inpatients with records dated between October 1, 2014, and December 31, 2022, and outpatients with records dated between January 1, 2010, and December 31, 2022. The study included children younger than 15 years with an antibiotic prescription who were admitted to the pediatric acute care unit or evaluated by a primary care pediatrician. Data analysis was performed from October 2023 to January 2024. An algorithm was developed to identify optimal time frames for conducting PPSs. This approach sought to minimize the discrepancy between quarterly and yearly PPS results, aiming to accurately estimate annual antibiotic prescribing rates in both inpatient and outpatient settings (primary outcome). External validity of the optimal PPS time frames derived from outpatient data when applied to the inpatient setting was also investigated. Validation involved assessing the effectiveness of administrative data in identifying strategic PPS periods for capturing inpatient antibiotic use patterns (secondary outcome). This analysis included 106 309 children: 3124 were inpatients (1773 males [56.8%]) and 103 185 were outpatients (53 651 males [52.0%]). A total of 5099 and 474 867 antibiotic prescriptions from inpatients and outpatients were analyzed, respectively. Outpatients tended to be older than inpatients, with a median age of 3.2 (IQR, 1.3-6.3) years vs 2.6 (IQR, 0.6-6.6) years, respectively, and with a lower burden of clinical comorbidities (≥1 comorbidity: 6618 [6.4%] vs 1141 [36.5%], respectively). The algorithm successfully identified distinct time frames within the calendar year from inpatient and outpatient records optimized for PPS data collection. Rates obtained from the quarterly PPS during these identified periods exhibited greater agreement with annual antibiotic prescribing rates (inpatient: r = 0.17, P < .001; and outpatient: r = 0.42, P < .001) than those derived from the yearly PPS (inpatient: r = 0.04, P = .58; and outpatient: r = 0.05, P = .34), with a Δ reduction of up to 89.8% (where Δ represents the percentage point change in antibiotic prescribing rates). Furthermore, the optimal PPS time frames gleaned from the outpatient data demonstrated robust applicability to the inpatient setting, yielding comparable results in both scenarios. This study evaluated the potential of administrative data in determining the optimal timing of PPS implementation. The quarterly PPS balanced precision and sustainability, especially when implemented during strategically selected periods across different seasons. Further studies are needed to validate the algorithm used in this study, especially in post-COVID-19 pandemic years and different settings.

Study of the effect of technological clearances on electrophysical parameters of heated polymer materials

Among the reasons for the rejection of products made of polymer materials manufactured using HF electrothermy, an important place is occupied by the lack of available reference data on the modes of their processing. The inaccuracy of some electrical parameters, e.g., the dielectric loss tangent (tan δ) and the dielectric constant (ε) are attributed to neglecting the effect of gaps in granular multicomponent polymer materials or technological system, as well as subjective factors of the control process. We present the results of studying the impact of gaps of a technological system on the electrophysical parameters of polymer and composite materials. We studied samples of granular materials «Bimodal polypropylene» and «Basco» stabilizing additive which are used in production of UV-resistant films for packaging. It is shown that a resonance method with the possibility of using two- and three-electrode system is most appropriate when determining the electrophysical parameters of polymer materials, their multicomponent mixtures and the optimal frequency of exposure. Refined (determined with allowance for gaps) electrophysical data for the materials under study obtained at a frequency of 27.12 MHz and heating to 120°C are presented. Comparison of the results obtained with and without taking into account the impact of gaps revealed that the difference reaches up to 12 (for tan δ) and 9% (for ε) of their absolute value. Note that the parameter tan δ has the greatest effect on the heating efficiency. The optimal temperature of polymer heating at which tan δ reaches the maximum value is 50°C. The results obtained can be used to improve the quality of products produced from a wide range of multicomponent polymer and composite materials by optimizing the modes of their electrothermal processing.

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability

Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instruments, montage of recording, subject type, and scoring manual factors. This study aimed to develop a deep learning model that addresses generalization problems by integrating enzyme-inspired specificity and employing separating training approaches. Subject type and scoring manual factors were controlled, while the focus was on instruments and montage of recording factors. The proposed model consists of three sets of signal-specific models including EEG-, EOG-, and EMG-specific model. The EEG-specific models further include three sets of channel-specific models. All signal-specific and channel-specific models were established with data manipulation and weighted loss strategies, resulting in three sets of data manipulation models and class-specific models, respectively. These models were CNNs. Additionally, BiLSTM models were applied to EEG- and EOG-specific models to obtain temporal information. Finally, classification task for sleep stage was handled by ‘the-last-dense’ layer. The optimal sampling frequency for each physiological signal was identified and used during the training process. The proposed model was trained on MGH dataset and evaluated using both within dataset and cross-dataset. For MGH dataset, overall accuracy of 81.05 %, MF1 of 79.05 %, Kappa of 0.7408, and per-class F1-scores: W (84.98 %), N1 (58.06 %), N2 (84.82 %), N3 (79.20 %), and REM (88.17 %) can be achieved. Performances on cross-datasets are as follows: SHHS1 200 records reached 79.54 %, 70.56 %, and 0.7078; SHHS2 200 records achieved 76.77 %, 66.30 %, and 0.6632; Sleep-EDF 153 records gained 78.52 %, 72.13 %, and 0.7031; and BCI-MU (local dataset) 94 records achieved 83.57 %, 82.17 %, and 0.7769 for overall accuracy, MF1, and Kappa respectively. Additionally, the proposed model has approximately 9.3 M trainable parameters and takes around 26 s to process one PSG record. The results indicate that the proposed model demonstrates generalizability in sleep stage classification and shows potential as a feasibility tool for real-world applications. Additionally, enzyme-inspired specificity effectively addresses the challenges posed by varying montage of recording, while the identified optimal frequencies mitigate instrument-related issues.