Search Results (347)

Background: Latent myofascial trigger points (MTrPs) are clinically relevant because they lower local pressure pain thresholds (PPTs), can perturb motor control, and may sustain shoulder symptoms even when overt pain is absent. However, even if previous studies assessed stiffness and mechanosensitivity differences between MTrPs and asymptomatic regions, objective patient-level cut-offs and diagnostic-accuracy metrics to distinguish latent MTrPs from adjacent asymptomatic tissue are lacking. Objective: To quantify the diagnostic accuracy of pressure algometry (PPT) and shear-wave elastography (SWE) for distinguishing latent MTrPs from adjacent asymptomatic tissue. Methods: A single-center cross-sectional study was conducted including 76 volunteers with ≥1 latent infraspinatus MTrP (assessed by following the current Delphi consensus criteria). The most sensitive latent MTrP and a control site 2 cm cranial was measured on the dominant side infraspinatus muscle in each participant. PPT and SWE were acquired with a standardized protocol (long-axis imaging, anisotropy control, minimal probe pressure; three captures per site; 1 cm rectangular ROI; operator blinded to site type). ROC analyses estimated areas under the curve (AUCs), Youden-optimal cut-offs, sensitivity, specificity, and likelihood ratios (LR+/−). Results: Latent MTrPs showed lower PPTs than controls (p < 0.001) and higher stiffness (shear modulus: p = 0.009; shear-wave speed: p = 0.022). PPT yielded AUC = 0.704 with an optimal cut-off of 47.5 N (sensitivity 0.75; specificity 0.592; LR+ 1.84; LR− 0.42), outperforming SWE metrics (shear modulus AUC 0.611; cut-off 23.6 kPa; sensitivity 0.632; specificity 0.605; LR+ 1.60; LR− 0.61; shear-wave speed AUC 0.601; cut-off 2.55 m/s; sensitivity 0.592; specificity 0.632; LR+ 1.61; LR− 0.65). Conclusions: In the infraspinatus, PPT provides moderate discrimination between latent MTrPs and adjacent asymptomatic tissue, whereas resting SWE—despite small mean differences—exhibited lower accuracy. These findings support mechanosensitivity as a primary measurable signal and position SWE as an adjunct. External validation across devices and operators, and multivariable models integrating sensory, imaging, and clinical features, are warranted. Full article

Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, we propose a machine-learning (ML)-based tool condition monitoring system based on multi-sensor data to preempt excessive tool wear during drilling in robotic surgery. Real-time data is acquired from the six-component force sensor of a collaborative arm along with the data from the temperature and multi-axis vibration sensor mounted on the bone specimen being drilled upon. Raw data from the sensors may have noises and outliers. Signal processing in the time- and frequency-domain are used for denoising as well as to obtain additional features to be derived from the raw sensory data. This paper addresses the challenging problem of identification of the most suitable ML algorithm and the most suitable features to be used as inputs to the algorithm. While dozens of features and innumerable machine learning and deep learning models are available, this paper addresses the problem of selecting the most relevant features, the most relevant AI models, and the optimal hyperparameters to be used in the AI model to provide accurate prediction on the tool condition. A unique framework is proposed for classifying tool wear that combines machine learning-based modeling with multi-sensor data. From the raw sensory data that contains only a handful of features, a number of additional features are derived using frequency-domain techniques and statistical measures. Using feature engineering, we arrived at a total of 60 features from time-domain, frequency-domain, and interaction-based metrics. Such additional features help in improving its predictive capabilities but make the training and prediction complicated and time-consuming. Using a sequence of techniques such as variance thresholding, correlation filtering, ANOVA F-test, and SHAP analysis, the number of features was reduced from 60 to the 4 features that will be most effective in real-time tool condition prediction. In contrast to previous studies that only examine a small number of machine learning models, our approach systematically evaluates a wide range of machine learning and deep learning architectures. The performances of 47 classical ML models and 6 deep learning (DL) architectures were analyzed using the set of the four features identified as most suitable. The Extra Trees Classifier (an ML model) and the one-dimensional Convolutional Neural Network (1D CNN) exhibited the best prediction accuracy among the models studied. Using real-time data, these models monitored the drilling tool condition in real-time to classify the tool wear into three categories of slight, moderate, and severe. Full article

During the transition from urban expansion to renewal, optimizing environmental comfort under resource constraints presents critical challenges. While existing research confirms that multisensory interactions critically shape environmental comfort, these insights are rarely operationalized into protocols for resource-constrained contexts. Focusing on historic urban quarters that need to balance modification and preservation, this study quantifies multisensory (acoustic, visual, thermal) interactions and integrations to establish operational resource-optimization strategies. Through laboratory reproduction of 144 field-based experimental conditions (4 sound sources × 3 sound pressure levels × 4 green view indexes × 3 air temperatures), systematic subjective evaluations of acoustic, visual, thermal, and overall comfort were obtained. Key findings demonstrate: (1) Eliminating extreme comfort evaluations (e.g., “very uncomfortable”) within any single sensory domain stabilizes cross-sensory contributions to overall comfort, ensuring predictable cross-domain compensations and safeguarding resource efficacy; (2) Accumulating modest improvements across ≥2 sensory domains reduces per-domain performance threshold for satisfactory overall comfort, enabling constraint resolution (e.g., visual modification limits in historic districts); (3) Cross-domain optimization of environmental factors (e.g., sound source and air temperature) generates mutual enhancement effects, maximizing resource economy, whereas intra-domain optimization (e.g., sound source and sound pressure level) induces competitive inefficiencies. Collectively, these principles establish operational strategies for resource-constrained environmental improvements, advancing sustainable design and governance through evidence-based multisensory approaches. Full article

Background/Objectives: Electrical threshold testing (ETT) offers a promising method for assessing somatosensory function. Despite its growing use, fundamental aspects such as the physiological recovery time required between repeated threshold measurements remain poorly understood. This gap is critical when evaluating sensory, motor, or pain thresholds (EST, EMT, EPT) in pre–post designs or rapid intra-session protocols. The aim is to investigate the short-term recovery dynamics of electrical thresholds following electrical threshold testing, and to determine the minimum interval required for values to return to a stable baseline. Methods: In this pilot, repeated-measures study, 10 healthy adults (20 upper limbs) underwent three progressive stimulation trials (sensory, motor, and pain). Electrical thresholds were assessed at fixed recovery intervals (0–120 s), with duplicate measurements at each time point. Stability was defined as the absence of significant differences between repeated measures. Results: EST stabilized rapidly after sensory or motor stimulation, showing no significant differences beyond 0 and 15 s, respectively. Within pain stimulation, EST recovered at 60 s. EMT showed immediate recovery with motor stimulation and required longer recovery with pain stimulation, with stabilization observed at 90 s. EPT exhibited the highest variability, with the smallest time-dependent differences observed immediately after the first assessment. Conclusion: Recovery time after electrical stimulation varies by threshold type and intensity of the stimuli. EST and EMT can be reliably reassessed immediately after sensory and motor stimulation, respectively. However, when stimulation reaches EPT level, EST requires 60 s to recover and EMT needs 90 s. EPT demonstrates higher variability, indicating the need for further investigation. These findings support the implementation of standardized recovery intervals in ETT and underscore the importance of interpreting EPT results with caution during rapid assessments. Full article

Background: Childhood obesity is a growing public health challenge, with altered taste perception potentially influencing food choices and contributing to weight gain. Objective: To determine detection thresholds for linoleic acid (fat taste) and sucrose (sweet taste) in children aged 6–12 years, and to explore associations with obesity, dietary intake, and food preferences. Methods: In this cross-sectional study, 100 Tunisian children (mean age: 8.05 ± 1.44 years; 54% girls; 45 obese, 55 non-obese) were recruited from an educational support center in Nabeul. Taste sensitivity was evaluated using the 3-alternative forced choice (3-AFC) method with ascending concentrations of linoleic acid (0.018–12.0 mM) for fat taste and sucrose (0.00125–0.32 mol/L) for sweet taste. Participants were categorized as tasters or non-tasters based on detection thresholds. Anthropometric measurements, 24 h dietary recalls, food frequency questionnaires, and food preference assessments were also conducted. Results: Low taste sensitivity was common (93% for sweet, 49% for fat). Girls were more often fat tasters than boys (68.6% vs. 31.4%, p = 0.003). Children with obesity had higher fat taste thresholds (median 3.00 mM, range 0.37–12.0) than non-obese peers (median 1.50 mM, range 0.018–6.0; p = 0.012), indicating reduced fat taste sensitivity. Linear regression showed a significant positive association between fat taste threshold and BMI (p = 0.001), meaning higher detection thresholds corresponded to higher BMI. Sweet taste thresholds did not differ significantly between children with and without obesity (p = 0.731). Sweet non-tasters consumed more sucrose (85.9 ± 64.9 g/d vs. 70.3 ± 62.3 g/d; p = 0.033) and reported more frequent table sugar use (p = 0.047). Fat non-tasters consumed more magnesium (425 ± 414 mg/d vs. 287 ± 60.8 mg/d; p = 0.026) and fiber (22.9 ± 7.51 g/d vs. 20.3 ± 5.32 g/d; p = 0.048) and reported higher intake frequencies of cheese (p = 0.039), sour cream (p = 0.004), and fast food (p = 0.012). Food preferences reflected similar patterns, with non-tasters generally rating high-fat or high-sugar foods more favorably. While most children demonstrated high detection thresholds, girls showed significantly higher fat taste sensitivity compared to boys (p = 0.03). Children with obesity exhibited significantly higher fat taste detection thresholds compared to non-obese children (p = 0.012), with thresholds ranging from 0.37 to 12.0 mM versus 0.018 to 6.0 mM, respectively. No significant difference was observed for sweet taste perception between weight groups (p = 0.731). Conclusions: Nearly half of the children exhibited reduced fat taste sensitivity, which was moderately associated with obesity and positively linked to BMI. Full article

This paper presents a methodology for determining manufacturing tolerances of feature lines on automotive outer panels using visual sensory tests. Feature lines—narrow and long curved surfaces on automotive panels—play a critical role in the visual appeal of vehicles. However, achieving precise feature lines in mass production is challenging due to material spring-back during the stamping process. Conventional tolerance determination methods are unsuitable for these esthetic elements. To address this, we employed psychophysical sensory tests to find the visual difference thresholds for feature lines. By creating geometric models and conducting controlled sensory tests, we identified the minimum radius variations perceptible to the human eye. Thirty-four participants were tested using the method of constant stimuli, resulting in psychometric functions for feature lines with radii of 8, 10, and 12 mm. The findings suggest manufacturing tolerances of ±1.2 mm, ±1.3 mm, and ±1.5 mm, respectively. This approach provides a quantitative foundation for setting tolerances that balance visual quality with production feasibility. Full article

Background/Objectives: Hearing loss is a prevalent sensory impairment in older adults, linked to reduced quality of life, cognitive decline, and social isolation. While it usually begins in the high-frequency range, some individuals present with isolated low-frequency hearing loss (LFHL). The long-term prognostic implications of such frequency-specific patterns remain unclear. This study aimed to evaluate the risk of long-term hearing deterioration by initial hearing loss type: LFHL, high-frequency hearing loss (HFHL), and combined-frequency hearing loss (CFHL). Methods: We retrospectively analyzed pure-tone audiometry (PTA) data from 10,261 patients who underwent at least two pure-tone audiometry assessments between 2011 and 2022 at a tertiary hospital. Each ear was treated as an independent observation. Hearing loss was defined as a threshold > 20 dB HL at 250, 500, 4000, or 8000 Hz. Participants were classified into normal hearing (NH), LFHL, HFHL, and CFHL groups. The outcome was a final four-frequency pure-tone average (4PTA) ≥ 40 dB HL. Logistic regression adjusted for age and sex was used, with subgroup analyses by follow-up duration. Results: HFHL (OR = 1.66, 95% CI: 1.47–1.89) and CFHL (OR = 2.23, 95% CI: 1.97–2.53) showed significantly higher risks of hearing loss compared with NH. LFHL did not show a significant increase (OR = 0.94, 95% CI: 0.76–1.16). These results were consistent across follow-up durations, with CFHL showing the most extensive deterioration. Conclusion: HFHL is a strong predictor of long-term auditory decline, and risk is further elevated with CFHL. In contrast, isolated LFHL was not associated with increased risk, suggesting relatively favorable outcomes. Frequency-specific classification may aid risk stratification and long-term monitoring strategies. Full article

This study investigates the effects of species, geographical origin, and processing on the proximate composition of frog leg meat, with a focus on developing predictive models for processing status. Data were systematically compiled from 18 published studies, yielding 32 entries across 10 edible frog species and multiple processing methods. Proximate composition parameters (moisture, protein, fat, ash) were compared between processed and unprocessed samples, and classification models were trained using moisture content as the primary predictor. Logistic regression and several machine learning algorithms, including Stochastic Gradient Descent, Support Vector Machine, Random Forest, and Decision Tree, were benchmarked under a Leave-One-Study-Out (LOSO) cross-validation framework. Results demonstrated that moisture content alone was sufficient to accurately distinguish processing status, with a critical threshold of ~73% separating processed from unprocessed frog legs. Logistic regression achieved perfect specificity and precision (100%) with an overall accuracy of 96.8%, while other classifiers also performed strongly (>90% accuracy). These findings confirm moisture as a species- and origin-independent marker of processing, offering a simple, rapid, and cost-effective tool for authenticity verification and quality control in frog meat and potentially other niche protein products. Future work should expand sample coverage, validate thresholds across processing types, and integrate biochemical and sensory quality assessments. Full article

The fortification of bakery products with alternative protein sources, including edible insects, offers a promising approach to improving nutritional quality while addressing sustainability challenges. This study evaluated graded replacement of type 750 wheat flour with Acheta domesticus (house cricket) powder—together with an extreme 100% cricket-powder formulation—on the nutritional composition, color, particle size distribution, fermentative properties, baking loss, crumb hardness, and sensory quality of bread. Fifteen baked variants were prepared: a 100% wheat flour control; thirteen wheat–cricket blends containing 5–90% cricket powder; and an extreme formulation with 100% cricket powder. Increasing cricket-powder levels significantly increased protein, fat, fiber, zinc, and riboflavin contents while decreasing carbohydrate and starch levels. Technologically, higher substitution levels resulted in darker crumb color, a shift toward coarser particle size distribution, reduced gas retention during proofing, and increased baking loss. Sensory analysis indicated that up to 15% inclusion maintained full consumer acceptability, while 20–25% was at the acceptance threshold. Above 35%, acceptability declined sharply due to intensified earthy flavors and textural changes. The findings highlight 15% inclusion as the optimal balance between enhanced nutritional value and sensory quality, with potential for higher incorporation if appropriate technological modifications are applied. Full article

This study investigates the impact of epigenetic modification on Saccharomyces cerevisiae using sodium butyrate (SB), a histone deacetylase inhibitor (HDACi), to enhance sensory characteristics in beer fermentation. Epigenetics offers a non-GMO approach to modifying gene expression, with potential for cost-effective strain development in brewing. A commercial ale yeast was cultured under different SB exposure regimes and used to ferment wort. Sensory evaluation was conducted with untrained participants, alongside GC-MS and enzymatic assays for ethanol, glycerol, and residual sugars. While no significant differences were found in ethanol production or smoothness and creaminess—likely due to uniform wort composition—flavor and taste scores varied between treatments. Notably, yeast pre-treated with SB but fermented without additional SB (1G W/O) received the highest flavor acceptability. Treatments involving SB during fermentation showed reduced sensory scores, likely due to butyric off-notes. Higher alcohol levels remained within acceptable thresholds and were more likely influenced by wort amino acid content than epigenetic modification. Though SB had a limited impact on metabolic pathways, this study highlights the feasibility of using dietary epigenetic modifiers to develop novel yeast strains with improved sensory profiles in beer or other fermented beverages and warrants further investigation with alternative compounds. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

Odorous emissions from hot-mix asphalt (HMA) plants are a growing environmental concern, particularly due to airborne aldehydes and ketones, which have low odor thresholds and a strong sensory impact. This study presents a field-ready analytical method for monitoring odor-active volatile compounds. The system uses dynamic solid-phase microextraction (SPME and SPME Arrow) with on-fiber derivatization via O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) and is coupled to gas chromatography–mass spectrometry (GC–MS) for direct detection. A flow-cell sampling unit enables the real-time capture of aliphatic aldehydes and ketones under transient emission conditions. Calibration using permeation tubes demonstrated sensitivity (limits of detection (LODs) below 0.13 μg/m³), recovery above 85% and consistent reproducibility. Compound identity was confirmed using retention indices and fragmentation patterns. Uncertainty assessment followed ISO GUM (Guide to the Expression of Uncertainty in Measurement) standards, thereby validating the method’s environmental applicability. Field deployment 200 m from an HMA facility identified measurable concentrations that aligned with CALPUFF model predictions. The method’s dual-isomer resolution and 10 min runtime make it ideal for responding to time-sensitive odor complaints. Overall, this approach supports regulatory efforts by enabling high-throughput on-site chemical monitoring and improving source attribution in cases of odor nuisance. Full article

Background/Objectives: Fibromyalgia syndrome is commonly associated with reduced intraepidermal nerve fiber density (IENFD), as assessed by skin biopsy, a finding referred to as small fiber pathology (SFP-FMG). The clinical significance of this abnormality, and how it relates to symptoms in fibromyalgia, remains uncertain. Reduced IENFD also represents the defining feature of small fiber neuropathy (SFN). While previous observations suggest that IENFD reduction is generally less severe in SFP-FMG than in SFN, no study has directly confirmed this finding in a large cohort. This retrospective study aimed to compare the severity of IENFD reduction in patients with SFP-FMG and those with SFN. Methods: To account for age and sex differences, we used the age-and sex-adjusted axonal loss density (ALD), defined as the percentage reduction from normative IENFD values. We retrospectively analyzed skin biopsy data from 73 patients with SFP-FMG and 134 patients diagnosed with SFN. Results: We found that the reduction in IENFD was significantly milder in patients with SFP-FMG than in those with SFN both at distal and proximal sites. Receiver operating characteristic analysis indicated that an ALD threshold of 37.6% provided good specificity for distinguishing SFN from SFP-FMG. Conclusions: These findings indicate that small fiber damage in fibromyalgia syndrome is quantitatively mild compared to patients with SFN. This may explain the absence of detectable sensory deficits on clinical examination and suggests a limited contribution of peripheral nerve damage to the pathophysiology of fibromyalgia syndrome. Full article

In this study, we investigated the impact of two native strains of Starmerella bacillaris, used both in pure culture and in a co-inoculation with Saccharomyces cerevisiae, on the volatile profile of a chemically defined fermented model must. The focus of this study was the production of monoterpenes and sesquiterpenes and their potential sensory contributions. Geraniol and linalool were detected in all fermentations with Starmerella bacillaris, in ranges of 26.7–43.9 µg/L and 34.3–41.3 µg/L, respectively, independent of the inoculation strategy used. Both strains produced concentrations above their respective odour thresholds of 20 µg/L and 25.5 µg/L. Odour activity value (OAV) analysis confirmed that fermentations with Starmerella bacillaris, particularly under co-inoculation conditions, generated the highest OAVs for these monoterpenes. Citronellol was only detected in mixed fermentations, while nerolidol and farnesol isomers were produced in variable amounts, depending on the strain and inoculation strategy, at concentrations below the odour threshold. These findings demonstrate the ability of Starmerella bacillaris to facilitate de novo biosynthesis of linalool, geraniol, and sesquiterpenes during alcoholic fermentation—in the case of linalool and geraniol, at concentrations exceeding their respective odour thresholds—highlighting the biotechnological potential of these native strains to enhance aroma in wines, particularly those made from neutral grape varieties. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article