Minimum Value Research Articles

Immuno-informatics study identifies conserved T cell epitopes in non-structural proteins of Bluetongue virus serotypes: formulation of a computationally optimized next-generation broad-spectrum multi-epitope vaccine.

Bluetongue (BT) poses a significant threat to the livestock industry, affecting various animal species and resulting in substantial economic losses. The existence of numerous BT virus (BTV) serotypes has hindered control efforts, highlighting the need for broad-spectrum vaccines. In this study, we evaluated the conserved amino acid sequences within key non-structural (NS) proteins of BTV and identified numerous highly conserved murine- and bovine-specific MHC class I-restricted (MHC-I) CD8+ and MHC-II-restricted CD4+ epitopes. We then screened these conserved epitopes for antigenicity, allergenicity, toxicity, and solubility. Using these epitopes, we developed in silico-based broad-spectrum multiepitope vaccines with Toll-like receptor (TLR-4) agonists. The predicted proinflammatory cytokine response was assessed in silico using the C-IMMSIM server. Structural modeling and refinement were achieved using Robetta and GalaxyWEB servers. Finally, we assessed the stability of the docking complexes through extensive 100-nanosecond molecular dynamics simulations before considering the vaccines for codon optimization and in silico cloning. We found many epitopes that meet these criteria within NS1 and NS2 proteins and developed in silico broad-spectrum vaccines. The immune simulation studies revealed that these vaccines induce high levels of IFN-γ and IL-2 in the vaccinated groups. Protein-protein docking analysis demonstrated promising epitopes with strong binding affinities to TLR-4. The docked complexes were stable, with minimal Root Mean Square Deviation and Root Mean Square Fluctuation values. Finally, the in silico-cloned plasmids have high % of GC content with > 0.8 codon adaptation index, suggesting they are suitable for expressing the protein vaccines in prokaryotic system. These next-generation vaccine designs are promising and warrant further investigation in wet lab experiments to assess their immunogenicity, safety, and efficacy for practical application in livestock. Our findings offer a robust framework for developing a comprehensive, broad-spectrum vaccine, potentially revolutionizing BT control and prevention strategies in the livestock industry.

Do improvements in motivational language predict alcohol use in motivational interviewing? Ambivalence matters.

Motivational Interviewing (MI) is described as a method for improving clinical outcomes by reducing client ambivalence. If this is true, MI's focus on improving clients' motivational language should be most useful for clients with ambivalence about change and less valuable for those who are ready to implement new behaviors or are opposed to change. To address this hypothesis and potentially add precision to MI delivery in clinical settings, we tested whether the relationship between clients' in-session motivational language and posttreatment alcohol use depended on their baseline motivation to change. Client speech from 149 sessions from Project MATCH were analyzed. A cluster analysis of the percent change talk during the first decile of the session identified three motivational groups: opposed, ambivalent, and ready. The change in percent change talk (C-PCT) across the session was calculated for each group. Zero-inflated negative binomial analysis was used to test whether the effect of C-PCT on end-of-treatment drinking varied between motivational groups. The count part of the model revealed a significant interaction between C-PCT and membership in the ambivalent group (b = -17.710, 95% CI [-25.775, -9.645], p < .001), only for those who received MI. Favorable C-PCT was associated with less drinking (b = -15.735, p = .004). Only baseline drinking was a significant predictor of abstinence at follow-up (b = .032, 95% CI [0.012, 0.051], p = .001). A putative MI mechanism-improved client motivational language-appears most important for clients who express ambivalence in the opening minutes of the session, with minimal value for those who do not. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Vibration-Induced Alteration in Trunk Extensor Muscle Proprioception as a Model for Impaired Trunk Control in Low Back Pain.

This study examined the impact of personalizing muscle vibration parameters on trunk control. We assessed how altered trunk extensor muscle (TEM) proprioception affects seated trunk control in healthy controls (HCs). To explore the link between altered TEM proprioception and impaired trunk control in chronic low back pain (cLBP), we performed equivalence testing between HCs undergoing TEM vibration and cLBP without vibration. Twenty HCs performed active joint reposition error (AJRE) testing to determine personalized vibration parameters. Each participant maintained balance on an unstable chair with eyes open and closed, with and without TEM vibration. We compared trunk control between HCs and twenty age- and sex-matched cLBP participants, using mean velocity and 95% confidence ellipse area of center-of-pressure changes to quantify trunk postural control. Equivalence was examined by comparing mean difference scores to minimal detectable change values and calculating between-group effect sizes. Personalized vibration parameters led to larger lumbopelvic repositioning errors (d = 0.89) than any single vibration frequency (d = 0.31-0.36). In healthy adults with no back pain, vision had large effects on postural control (ηp2 = 0.604-0.842), but TEM vibration had no significant effects (p > 0.105) or interactions with vision (p > 0.423). Between-group effect sizes (d = 0.32-0.51) exceeded our threshold for performance equivalence (d < 0.2). Muscle vibration altered position sense during AJRE testing, and personalizing parameters amplified this effect. However, TEM vibration had minimal impact on seated trunk postural control in adults with no back pain and did not lead to performance degradation comparable to that in cLBP.

Hyperreflective focuses as a biomarker of multiple sclerosis progression

Purpose: to identify the intraretinal hyperreflective foci (HRF), their specific characteristics, localization features and correlations with the duration of the disease, type of course and previous episodes of optic neuritis (ON) in multiple sclerosis (MS) patients using a high-resolution OCT scanning protocol. Materials and methods. The study included 160 patients (318 eyes) divided into 8 groups, i.e. Group 1: 44 eyes (13.84 %) — MS onset without a history of ON; Group 2: 30 eyes (9.43 %) — debut of MS with ON; Group 3: 56 eyes (17.61 %) — relapsing-remitting MS (RRMS) lasting ≤ 10 years without ON; Group 4: 38 eyes (11.95 %) — RRMS lasting ≤ 10 years with ON; Group 5: 49 eyes (15.41 %) — RRMS lasting ˃ 10 years without ON; Group 6: 37 eyes (11.63 %) — RRMS lasting ˃ 10 years with ON; Group 7: 34 eyes (10.69 %) — secondary progressive MS (SPMS) without ON; Group 8: 30 eyes (9.43 %) — SPMS with ON. Results. HRF were identified in all groups, at the levels of both the outer nuclear layer (ONL) and the inner nuclear layer (INL). Their number increased depending on the duration, type of course, and the presence of ON history. The thickness of the inner layers of the retina corresponded to the average normative indicators in the group with the onset of MS without ON. In the SPMS group after ON, a decrease in the total thickness of ganglions retinal cells layer (GCL) and the inner plexiform layer (IPL) was noted, showing minimal values of both avGCL + IPL (65.83 ± 8.27 μm) and avRNFL (76.37 ± 6.94 μm). High inverse correlations were revealed between avGCL + IPL and the amount of GRF at the levels of ONL and INL (-0.82 and -0.85, respectively, p = 0.01). Conclusions. The presence and quantity of HRF can be considered as a prognostic non-invasive biomarker associated with the duration of MS and the history of ON, allowing one to visualize the penetration of microglia activated by immune cells into the unmyelinated part of the central nervous system, helpful in elucidating their role in the pathogenesis and progression of the disease.

Synthesis of novel chitosan-Schiff bases nanoparticles for high efficiency Helicobacter pylori inhibition

Two chitosan Schiff bases were synthesized by condensation of chitosan with 2-(4-formylphenoxy)-N-phenylacetamide and N-(4-bromophenyl)-2-(4-formylphenoxy) acetamide denoted as Cs-SBA and Cs-SBBr, respectively. The molecular structures of the resulting chitosan derivatives were characterized using FTIR and 1HNMR and their thermal properties were investigated by TGA. These derivatives were treated with sodium tripolyphosphate (TPP) to produce Cs Schiff base nanoparticles. The nanoparticles physicochemical properties were determined by FTIR, XRD, TEM, and zeta potential analysis. The antimicrobial action against Helicobacter pylori (H. pylori) was evaluated and the results indicated that the anti-H. pylori activity had minimal inhibitory concentration MIC values of 15.62 ± 0.05 and 3.9 ± 0.03 μg/mL for Cs-SBA and Cs-SBBr nanoparticles (Cs-SBA NPs and Cs-SBBr NPs), respectively. The better biologically active nanoparticles, Cs-SBBr NPs, were tested for their cyclooxygenases (COX-1 and COX-2) inhibitory potential. Cs-SBBr NPs demonstrated COX enzyme inhibition activity against COX-2 (IC50 4.5 ± 0.165 μg/mL) higher than the conventional Indomethacin (IC50 0.08 ± 0.003 μg/mL), and Celecoxib (IC50 0.79 ± 0.029 μg/mL). Additionally, the cytotoxicity test of Cs-SBBr NPs showed cytotoxic effect on Vero cells (CCL-81) with IC50 = 17.95 ± 0.12 μg/mL which is regarded as a safe compound. Therefore, Cs-SBBr NPs may become an alternative to cure H. pylori and prevent gastric cancer.

Validation and reproducibility of in vivo dosimetry for pencil beam scanned FLASH proton treatment in mice

PurposeTo investigate quality assurance (QA) techniques for in vivo dosimetry and establish its routine uses for proton FLASH small animal experiments with a saturated monitor chamber. Methods and Materials227 mice were irradiated at FLASH or conventional (CONV) dose rates with a 250 MeV FLASH-capable proton beamline using pencil beam scanning to characterize the proton FLASH effect on abdominal irradiation and examining various endpoints. A 2D strip ionization chamber array (SICA) detector was positioned upstream of collimation and used for in vivo dose monitoring during irradiation. Before each irradiation series, SICA signal was correlated with the isocenter dose at each delivered dose rate. Dose, dose rate, and 2D dose distribution for each mouse were monitored with the SICA detector. ResultsCalibration curves between the upstream SICA detector signal and the delivered dose at isocenter had good linearity with minimal R2 values of 0.991 (FLASH) and 0.985 (CONV), and slopes were consistent for each modality. After reassigning mice, standard deviations were less than 1.85 % (FLASH) and 0.83 % (CONV) for all dose levels, with no individual subject dose falling outside a ± 3.6 % range of the designated dose. FLASH fields had a field-averaged dose rate of 79.0 ± 0.8 Gy/s and mean local average dose rate of 160.6 ± 3.0 Gy/s. In vivo dosimetry allowed for the accurate detection of variation between the delivered and the planned dose. ConclusionIn vivo dosimetry benefits FLASH experiments through enabling real-time dose and dose rate monitoring allowing mouse cohort regrouping when beam fluctuation causes delivered dose to vary from planned dose.

Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study.

This study assessed predictors of stress, anxiety and depression during the COVID-19 pandemic using a large number of demographic, COVID-19 context and psychological variables. Data from 741 adults were drawn from the Boston College daily sleep and well-being survey. Baseline demographics, the long version of the daily surveys and the round one assessment of the survey were utilized for the present study. A Gaussian graphical model (GGM) was estimated as a feature selection technique on a subset of ordinal/continuous variables. An ensemble Random Forest (RF) machine learning algorithm was used for prediction. GGM was found to be an efficient feature selection method and supported the findings derived from the RF machine learning model. Psychological variables were significant predictors of stress, anxiety and depression, while demographic and COVID-19-related factors had minimal predictive value. The outcome variables were mutually predictive of each other, and negative affect and subjective sleep quality were the common predictors of these outcomes of stress, anxiety, and depression. The study identifies risk factors for adverse mental health outcomes during the pandemic and informs interventions to mitigate the impact on mental health.

Unveiling the anti-Candida albicans activity of bioactive fractions from mining soil-dwelling Streptomyces sp. DS104

Due to an increase in the incidence of Candida albicans infection and its growing resistance to currently used antifungal drugs, further development of more potent antimicrobials is needed. This study sought to assess the antifungal properties of Streptomyces sp. DS104, isolated from a mining soil, against C. albicans and identify the bioactive substances that are produced by this strain. Ethyl acetate supernatant's crude extract showed a high inhibition against C. albicans, with a minimal inhibitory concentration value as low as 4 µg/mL. Two bioactive fractions (F5 and F6) have been isolated using silica gel column chromatography, demonstrating zones of inhibition against C. albicans measuring 28.33 ± 01.52 to 29.00 ± 02.64 mm. The GC–MS analysis was carried out on the purified fractions and allowed the identification of 21 compounds predominantly consisting of carboxylic acids and pyrrolopyrazine derivatives. In Fraction F5, the major compounds detected are pentadecanoic and hexadecanoic acids, constituting the highest area percentages of 41.99 % and 33.77 %, respectively. As for fraction F6, the predominant compounds include pyrrolo[1,2-a]pyrazine-1,4‑dione, hexahydro-3-(2-methylpropyl) and pyrrolo[1,2-a]pyrazine-1,4‑dione, hexahydro-3-(methylphenyl), accounting for area percentages of 20.18 % and 12.16 %, respectively. Various other bioactive compounds, such as isocoumarins, and phenolic compounds, had previously been identified as components with antifungal activity, were also detected. It's worth highlighting that three compounds specifically, 2-hydrazino-8‑hydroxy-4-phenylquinoline, hydroxyphenylacetic acid, and 3-carboxaldehyde-1H-indole have been reported for the first time to be detected within the Streptomyces genus. These results suggested that Streptomyces sp. DS104 is a promising strain and thus appears to be a potential candidate for the development of antifungal drugs.

An Enhanced Fuzzy Hybrid of Fireworks and Grey Wolf Metaheuristic Algorithms

This research work envisages addressing fuzzy adjustment of parameters into a hybrid optimization algorithm for solving mathematical benchmark function problems. The problem of benchmark mathematical functions consists of finding the minimal values. In this study, we considered function optimization. We are presenting an enhanced Fuzzy Hybrid Algorithm, which is called Enhanced Fuzzy Hybrid Fireworks and Grey Wolf Metaheuristic Algorithm, and denoted as EF-FWA-GWO. The fuzzy adjustment of parameters is achieved using Fuzzy Inference Systems. For this work, we implemented two variants of the Fuzzy Systems. The first variant utilizes Triangular membership functions, and the second variant employs Gaussian membership functions. Both variants are of a Mamdani Fuzzy Inference Type. The proposed method was applied to 22 mathematical benchmark functions, divided into two parts: the first part consists of 13 functions that can be classified as unimodal and multimodal, and the second part consists of the 9 fixed-dimension multimodal benchmark functions. The proposed method presents better performance with 60 and 90 dimensions, averaging 51% and 58% improvement in the benchmark functions, respectively. And then, a statistical comparison between the conventional hybrid algorithm and the Fuzzy Enhanced Hybrid Algorithm is presented to complement the conclusions of this research. Finally, we also applied the Fuzzy Hybrid Algorithm in a control problem to test its performance in designing a Fuzzy controller for a mobile robot.

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.

Effects of a pulsed electric field on the ejection of an electric-neutral nanocapsule out of a water-filled CNT barrel

Accurate manipulation of a nanoparticle is of great interest in various fields. This study developed a nanoejector model to preciously control the exit velocity (vout) of a nanocapsule from a water-filled nanotube barrel triggered by a pulsed electric field (EF). When switching on the EF, the water cluster below the capsule within the barrel has a sudden expansion along the tube axis, which propels the capsule out of the barrel. This study investigated the effects of the turn-on duration (s), the intensity (E) and the direction of the EF on final position and vout of the capsule by molecular dynamic simulations. We discovered that the axial intensity of a pulsed EF with s > 10 ps has minimal and maximal critical values (ECMin and ECMax, respectively) that guarantee the nanocapsule escaping from the barrel. ECMin decreases with increasing s. ECMax is independent of s > 10 ps, and the maximal value of vout (∼700 m/s) remains unchanged with E (>ECMax) and s (>10 ps). When the EF direction deviates from the nanotube axis, vout depends on the water structure under the EF. The conclusions are helpful for the design of a nano-ejector with a controllable exit velocity of a nanoparticle.

Digital-twin-driven intelligent tracking error compensation of ultra-precision machining

In ultra-precision machining (UPM), linear axis tracking affects contour accuracy and final machining quality. Traditional error modeling is complicated by the identification of numerous unknown parameters linked to nonlinear characteristics in the linear feed axes. To fill this gap, this study proposed a digital-twin-driven framework integrating the developed G-code interpreter and the deep learning model to achieve real-time tracking error compensation for UPM. To enhance the prediction accuracy of the tracking error of each axis of UPM machines, Bayesian hyperparameter optimization and feature importance analysis were conducted in the proposed TCN-BiLSTM model using high-quality training datasets from well-designed experiments. Ultimately, validation of the proposed system on a three-axis ultra-precision milling machine demonstrated its excellent performance. The experimental results showed that the optimized TCN-BiLSTM model exhibited an excellent capacity to predict the tracking error of the X-axis and Y-axis with minimal mean absolute error values of 0.000009 and 0.000023, respectively. Implementing the customized application reduced X-axis and Y-axis tracking errors by approximately 45–75% and 40–70%, respectively. This study first validates the feasibility of deep learning to improve accuracy in the UPM field, which will provide significant insight into speeding up the digitalization and intellectualization of the UPM scenario.

Dynamic subsidence in the Colorado basin, offshore Argentina, South Atlantic

The Colorado basin in the SW Atlantic is one of the key exploratory frontier basins along the Argentine shelf. The stratigraphy shows a typical rift basin, divided into two major seismic successions, a Jurassic-Lower Cretaceous synrift followed by an Upper Cretaceous to Cenozoic postrift. They represent, respectively, the breakup of Gondwana and passive margin evolution. While the synrift basin formation was associated with the development of structural grabens, the preservation of thick (thousands of meters) postrift sequences are still debated because they cannot be accounted for only by thermal subsidence. In this work, we conducted a multimethodological subsidence analysis across the Colorado basin, including part of the continental slope. We performed a 1D backstripping analysis on 10 hydrocarbon exploration wells and 32 pseudo-wells, covering the whole basin area and a uniform stretching model, which were supported using dynamic topography data from the study region. From the backstripping analyses, considering the six major postrift sedimentary successions described in the literature, we could recognize three subsidence stages. An initial rapid subsidence stage during the post-rift (125–83 Ma), which is restricted to the main depocenters, followed by a stage of generalized and low subsidence focused on the main axis of the basin (83–33 Ma), with an increase in the last 33 my (33–0 Ma). The stretching models exhibit a typical asymptotic curve, depicting significant subsidence at the onset of the postrift stage, gradually diminishing to minimal values within a few tens of millions of years. Upon comparing the backstripping curves with the stretching subsidence models, particularly during the final flat segments, a residual negative value emerges over time (locally exceeding −500 m), indicating considerable residual subsidence during the Cenozoic postrift evolution along the Colorado Basin. This could be interpreted as dynamic subsidence. This residual sinking in the Argentina shelf matches with dynamic topography numerical models in the study region as well as the occurrence of accumulated slabs in the mantle detected by seismic tomography studies. We propose that the accumulation of slabs along this segment of the South American subduction zone might be the drivers of dynamic subsidence, which assisted to the preservation of a large thickness of post-rift strata along the SW Atlantic margin.

Vitamins Can Increase Antibiotic Effects Against Multidrug-Resistant Pseudomonas aeruginosa and Acinetobacter baumannii in an In Vitro Infection Model.

The study delves into the antimicrobial properties of vitamins P, K, and E against A. baumannii and P. aeruginosa, employing the minimum inhibitory concentration method and scrutinizing biofilm formation within the A549 cell culture. The combined application of vitamins and antibiotics exhibited a statistically significant effect on both the minimal inhibitory concentration values and biofilm formation (P < .05). Within the realm of fat-soluble vitamins, vitamins K and E, when integrated with antibiotics, revealed varying degrees of influence, with vitamin K displaying the most substantial fractional inhibitory concentration value. Vitamin E, on the other hand, demonstrated a comparatively weaker fractional inhibitory concentration than the other constituents. Nevertheless, it exhibited robust optical density and a pronounced capacity to inhibit biofilm formation. Moreover, among the vitamin groups, it is evident that vitamin E surpasses the efficacy of others (antibacterials affecting K < P <E). The collective application of all vitamins manifested the most potent fractional inhibitory concentration. The synergistic effects of vitamins with antibiotics, as evidenced in this study, may offer a promising alternative for treating multidrug-resistant A. baumannii and P. aeruginosa, subject to further investigation through molecular studies.

A new intervention to prevent social isolation in people with complex communication needs

While implementing communication interventions, practitioners follow diverse theoretical models. Different conceptual orientations influence the way professionals embrace the subject of communication and its disorders. This research project explores the co-creation and validation of a new model and intervention program to analyze and improve communication between persons with Complex Communication Needs and their caregivers. The methodology incorporated a comprehensive narrative review, as foundation for the new model and intervention proposal. Succeeding this stage, the team implemented an online Delphi Panel to improve and validate these results, involving 17 international renowned experts. Following the Appropriateness Method, 25 indications were subject to scrutiny and rated as appropriate with minimal values of disagreement among the evaluators. Qualitative feedback was used to improve the research products. Quality assurance measures were taken to ensure quality and transparency of the results. A new conceptual framework of atypical interpersonal communication and intervention program result from the investigation. The new model is inspired by the Transactional model and principles of Dialogism. The intervention consists of consultations with caregivers, using video analysis and a dialogical methodology to enhance communication. The next research phase is to pilot-test the intervention program with clinicians supporting persons with disability at risk of social isolation.

Supportive psychotherapeutic intervention and video directing observed therapy to increase family adherence

To determine the effect of supportive psychotherapy and video-directing observed therapy interventions on family compliance in assisting patients with mental disorders in the working area of the Kabila Health Center, Bone Bolango District, Gorontalo City. Research design quasi-experimental design with a one group pre- and post-test design, where the variables to be measured are family adherence in providing treatment assistance, which are included in the dependent variable. The independent variables in this study were supportive psychotherapy and video-directed observation therapy. The sampling technique used was a total sampling technique with a sample size of 39 respondents. Data analysis was performed using univariate analysis, namely descriptive analysis (frequency, percentage, median, minimum value, and maximum value) and bivariate analysis (Wilcoxon test), with a significance level of &amp;lt;0.05, which was performed using SPSS version 25 software. Supportive psychotherapy and video-directing observed therapy increased family compliance accompanying the treatment of patients with mental disorders (p-value of x1=0.020, x2=0.000, p&amp;lt;0.05). Supportive psychotherapy interventions and video-directed observed therapy have been proven to increase family compliance in treatment assistance, which can improve the quality of life of patients with mental disorders.

Instrumented Static and Reactive Balance in Collegiate Athletes: Normative Values and Minimal Detectable Change.

Wearable sensors are increasingly popular in concussion research because of their objective quantification of subtle balance deficits. However, normative data and minimal detectable change (MDC) values are necessary to serve as references for diagnostic use and tracking longitudinal recovery. To identify normative and MDC values for instrumented static- and reactive-balance tests, an instrumented static mediolateral (ML) root mean square (RMS) sway standing balance assessment and the instrumented, modified push and release (I-mP&R), respectively. Cross-sectional study. Clinical setting. Normative static ML RMS sway and I-mP&R data were collected on 377 (n = 184 female) healthy National Collegiate Athletic Association Division I athletes at the beginning of their competitive seasons. Test-retest data were collected in 36 healthy control athletes based on standard recovery timelines after concussion. Descriptive statistics, intraclass correlation coefficients (ICCs), and MDC values were calculated for primary outcomes of ML RMS sway in a static double-limb stance on firm ground and a foam block, and time to stability and latency from the I-mP&R in single- and dual-task conditions. Normative outcomes across static ML RMS sway and I-mP&R were sensitive to sex and type of footwear. Mediolateral RMS sway demonstrated moderate reliability in the firm condition (ICC = 0.73; MDC = 2.7 cm/s2) but poor reliability in the foam condition (ICC = 0.43; MDC = 11.1 cm/s2). Single- and dual-task times to stability from the I-mP&R exhibited good reliability (ICC = 0.84 and 0.80, respectively; MDC = 0.25 and 0.29 seconds, respectively). Latency from the I-mP&R had poor to moderate reliability (ICC = 0.38 and 0.55; MDC = 107 and 105 milliseconds). Sex-matched references should be used for instrumented static- and reactive-balance assessments. Footwear may explain variability in static ML RMS sway and time to stability of the I-mP&R. Moderate-to-good reliability suggests time to stability from the I-mP&R and ML RMS static sway on firm ground can be used for longitudinal assessments.

Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell

Proton Exchange Membrane Fuel Cells (PEMFCs) provide a reliable, pollution-free, sustainable, and stable power generating alternative to non-renewable resources, and they do not self-discharge. Proton exchange membrane fuel cells (PEMFCs) necessitate correct parameter estimates for effective investigation, modelling and designing effective fuel cells, highlighting the importance of exact modelling for successful use in many industries. The present research aims to determine the approximate parameters estimation of PEMFC using a modified algorithm derived from the Ant Colony Optimization (ACO) meta-heuristic algorithm. In order to provide justification for the algorithm, it is initially benchmarked against 10 functions. The study compares the outcomes of PEMFC parameter estimation through the Dynamic Ant Colony Optimisation (DACO) algorithm including some additional metaheuristic algorithms such as Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), Differential Evolution (DE) algorithm, and an algorithm known as Grey Wolf Optimisation - Cuckoo Search (GWOCS) which is hybrid in nature. The suggested algorithm’s performance evaluation is based on minimising the Square Error (SSE). The modified proposed optimization algorithm exhibits superior performance compared to other alternative meta-heuristic algorithms due to its minimal SSE value. The effectiveness and efficiency of the modified method based on the Ballard Mark V datasheet were evaluated using statistical error analysis and non-parametric testing. The convergence curves of DACO demonstrate a faster convergence compared to the other optimization algorithms.

Geoelectric Modelling Based on Bessel Functions Integral and Damped Least-Square Inversion for Layered Earth Models

Apparent resistivity formulations of a geoelectric method for layered Earth have been obtained analytically in the Bessel functions integral. This formulation is obtained through the application of two boundary conditions. First, the current density is zero at the Earth's surface. Second, current density and electric potential are continuous at the boundary between the layers. Geoelectric modelling can be done using several model parameters through this formulation. The modelling results show that apparent resistivity formulations based on Bessel function integrals can simulate apparent resistivity curves for layered earth models. The inversion process uses the apparent resistivity formulation based on Bessel function integrals. In this study, the inversion process uses a dumped Least-squared inversion method. The first step begins by testing the inversion program using synthetic data. After that, the inversion program is used in field data. The inversion results using field data from geoelectric data in Mount Pandan, East Java, show that the program was well done, with a minimal error value of 1.24%.

Quality Assessment of Developed Cumin Cookies

Abstract: Cookies are good carrier of nutrients like carbohydrate and fat which can be enriched with protein. Refined Flour is the finest type of wheat flour containing high calorie content but minimal nutritional value that is used to make pizza, bread, samosa, kachoris, puri, cakes and biscuits.by partially replacing refined wheat flour with wheat flour up to an acceptable level. It is a fast and quick recipe for cookies made with Refined wheat flour, wheat flour and cumin seeds. The special characteristic of these cookies is that, as opposed to other cookie recipes, they do not contain plain flour, sugar or even jaggery. It is an excellent snack or coffee snack for munching and is enjoyed by all age groups, including children and adults because of its rich nutrients.