Frequency Variability Research Articles

Seizure quantification in sunflower syndrome by a wrist-worn device.

Sunflower syndrome is a rare photosensitive childhood-onset epilepsy, featuring repetitive handwaving events (HWE) triggered by light. Objective documentation of these HWE can be difficult due to the numerous events occurring daily and/or caregivers who document the seizures but are not always present. Hence, seizure diaries can be underreporting. We performed a feasibility study in three Belgian Sunflower syndrome individuals to assess the possibility to quantify the HWEby a wrist-worn wearable device (Axivity AX6). We conducted a structured exercise aiming to capture patterns of possible confounders in a controlled environment. Subsequently, patients wore the device for three to six consecutive days and nights at home. Spectral power analyses were performed to characterize the frequency signature of the different movements. The HWE of patient A and B showed homogeneity and narrow-band frequencies. Patient C did not experience any HWE at the start of the study due to proper seizure control. Regarding HWE, there was a higher spectral power for Gyroscope Z (Gz) compared to Gy. The inter-subject variability for HWE frequency peaks was in the 3-6 Hz range. Computer analysis by visual annotation, without checking the seizure diary, detected 71% of the HWE if the HWE lasted for longer than 5 s (sensitivity 64%). For shorter HWE duration, the detection rate was 50% but seemed to be higher if there was a concordant change of eye movement (63%) (sensitivity 36%). The most obvious confounder was toothbrushing (TB). However, TB showed a different pattern: that is, higher or comparable spectral power for Gy compared to Gz. There was also a higher or comparable spectral power for Gy compared to Gz for "waving hello". We show that the wearable movement sensor Axivity AX6 can detect HWE in Sunflower syndrome individuals and distinguish them from confounders in a real-world setting.

How Do Urban Environments Impact Walkability? An Analysis Using Multi-Source Data of Beijing

Purpose: Recognizing that walkability is a fundamental element of sustainable urban development, this study focuses on the variables affecting walkability in the living environment and aims to provide insights into land use planning strategies that can contribute to carbon emission reduction. The research specifically examines how to create a pedestrian-friendly street environment in existing urban areas, where employment and residential functions are mixed and co-exist. Methodology: By using multi-source data, this research evaluates streets based on two binary characteristics: the occurrence of walking activity and the walking experience. Findings: The results reveal a strong relationship between walking opportunities and street usage frequency, with the road network being identified as the most influential factor. Additionally, the impact of walking experience was found to be more significant than that of walking opportunity. Contributions: First, the study developed a matrix of dependent variables for street usage frequency and walking experience, and an independent variable matrix for street environment factors. Second, the research created an analytical framework to examine factors influencing street use and experience across different travel needs.

Space-Time Variability of Maximum Daily Rainfall in Piura River Basin in Peru Related to El Niño Occurrence

This study analyzes hydrometeorological data (1950–2023) to examine the signatures of El Niño and La Niña events and assess their impact on rainfall distribution in the Piura Region, Peru. Using data from 23 stations, high-resolution gridded rainfall datasets (PISCO), and oceanic–atmospheric indices we investigated the frequency, intensity, and spatial variability of these events in the Piura River Basin (PRB). Return periods for very strong El Niño and La Niña events are 25 and 19 years, respectively, compared to 2 years for neutral conditions. Over the past 30 years, the recurrence of Coastal El Niño has significantly increased. This increased frequency contributes to the global rise in El Niño events, reducing the return period for very strong events from 5.2 to 3.4 years. This rise correlates with an increase in maximum daily precipitation across the basin centered in the middle PRB during El Niño years. Future rainfall projections, based on 20 CMIP6 GCMs under SSP2-4.5 and SSP5-8.5 scenarios, suggest continued intensification of rainfall events. These findings highlight the necessity of incorporating El Niño variability into infrastructure design, water resource management, and climate adaptation strategies to mitigate the impacts of these increasingly frequent and severe events in the PRB.

Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery Data.

Type 1 diabetes (T1D) is a chronic condition in which the body produces too little insulin, a hormone needed to regulate blood glucose. Various factors such as carbohydrates, exercise, and hormones impact insulin needs. Beyond carbohydrates, most factors remain underexplored. Regulating insulin is a complex control task that can go wrong and cause blood glucose levels to fall outside a range that protects people from adverse health effects. Automated insulin delivery (AID) has been shown to maintain blood glucose levels within a narrow range. Beyond clinical outcomes, data from AID systems are little researched; such systems can provide data-driven insights to improve the understanding and treatment of T1D. The aim is to discover unexpected temporal patterns in insulin needs and to analyze how frequently these occur. Unexpected patterns are situations where increased insulin does not result in lower glucose or where increased carbohydrate intake does not raise glucose levels. Such situations suggest that factors beyond carbohydrates influence insulin needs. We analyzed time series data on insulin on board (IOB), carbohydrates on board (COB), and interstitial glucose (IG) from 29 participants using the OpenAPS AID system. Pattern frequency in hours, days (grouped via k-means clustering), weekdays, and months were determined by comparing the 95% CI of the mean differences between temporal units. Associations between pattern frequency and demographic variables were examined. Significant differences in IOB, COB, and IG across temporal dichotomies were assessed using Mann-Whitney U tests. Effect sizes and Euclidean distances between variables were calculated. Finally, the forecastability of IOB, COB, and IG for the clustered days was analyzed using Granger causality. On average, 13.5 participants had unexpected patterns and 9.9 had expected patterns. The patterns were more pronounced (d>0.94) when comparing hours of the day and similar days than when comparing days of the week or months (0.3<d<0.52). Notably, 11 participants exhibited a higher IG overnight despite concurrently higher IOB (10/11). Additionally, 17 participants experienced an increase in IG after COB decreased after meals. The significant associations between pattern frequency and demographics were moderate (0.31≤τ≤0.48). Between clusters, mean IOB (P=.03, d=0.7) and IG (P=.02, d=0.67) differed significantly, but COB did not (P=.08, d=0.55). IOB and IG were most similar (mean distance 5.08, SD 2.25), while COB and IG were most different (mean distance 11.43, SD 2.6), suggesting that AID attempts to counteract both observed and unobserved factors that impact IG. Our study shows that unexpected patterns in the insulin needs of people with T1D are as common as expected patterns. Unexpected patterns cannot be explained by carbohydrates alone. Our results highlight the complexity of glucose regulation and emphasize the need for personalized treatment approaches. Further research is needed to identify and quantify the factors that cause these patterns.

Type 1 Immune Responses Related to Viral Infection Influence Corticosteroid Response in Asthma.

Corticosteroid-responsive type 2 (T2) inflammation underlies the T2-high asthma endotype. However, we hypothesized that type 1 (T1) inflammation, possibly related to viral infection, may also influence corticosteroid response. To determine the frequency and within-patient variability of T1-high, T2-high, and T1/T2-high asthma endotypes and whether virally influenced T1-high disease influences corticosteroid response in asthma. Patients in the Severe Asthma Research Program (SARP)-3 had sputum collected at baseline, after intramuscular (triamcinolone acetonide) corticosteroid treatment, and at 1 and 3-year follow-ups. Sputum cell RNA was used for whole transcriptome gene network and viral metagenomic analyses. We then profiled patients as highly expressing T1 and/or T2 gene networks and established the influence of these endotypes on corticosteroid responsiveness and likelihood of viral transcript detection in the airways. We found that 22% and 35% of asthma patients highly expressed T1 and T2 network genes, respectively, and that 8.5% highly expressed both networks. Asthma severity outcomes were worse in T2-high compared to T1-high asthma and most severe in the T1-high/T2-high subgroup. Corticosteroid treatment strongly suppressed T2 but poorly suppressed T1 gene expression, and corticosteroid-associated improvements in FEV1 occurred only in patients with T1-low/T2-high disease and not in T1-high/T2-high patients. Viral metagenomic analyses uncovered that 24% of asthma sputum samples tested positive for a respiratory virus and high viral carriage was associated with 14-fold increased risk of T1-high disease. Airway type 1 immune responses are relatively common in asthma, are largely corticosteroid-resistant, and are associated with sub-clinical viral infection.

How Much Does the Dynamic F0 Curve Affect the Expression of Emotion in Utterances?

The modulation of vocal elements, such as pitch, loudness, and duration, plays a crucial role in conveying both linguistic information and the speaker’s emotional state. While acoustic features like fundamental frequency (F0) variability have been widely studied in emotional speech analysis, accurately classifying emotion remains challenging due to the complex and dynamic nature of vocal expressions. Traditional analytical methods often oversimplify these dynamics, potentially overlooking intricate patterns indicative of specific emotions. This study examines the influences of emotion and temporal variation on dynamic F0 contours in the analytical framework, utilizing a dataset valuable for its diverse emotional expressions. However, the analysis is constrained by the limited variety of sentences employed, which may affect the generalizability of the findings to broader linguistic contexts. We utilized the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), focusing on eight distinct emotional states performed by 24 professional actors. Sonorant segments were extracted, and F0 measurements were converted into semitones relative to a 100 Hz baseline to standardize pitch variations. By employing Generalized Additive Mixed Models (GAMMs), we modeled non-linear trajectories of F0 contours over time, accounting for fixed effects (emotions) and random effects (individual speaker variability). Our analysis revealed that incorporating emotion-specific, non-linear time effects and individual speaker differences significantly improved the model’s explanatory power, ultimately explaining up to 66.5% of the variance in the F0. The inclusion of random smooths for time within speakers captured individual temporal modulation patterns, providing a more accurate representation of emotional speech dynamics. The results demonstrate that dynamic modeling of F0 contours using GAMMs enhances the accuracy of emotion classification in speech. This approach captures the nuanced pitch patterns associated with different emotions and accounts for individual variability among speakers. The findings contribute to a deeper understanding of the vocal expression of emotions and offer valuable insights for advancing speech emotion recognition systems.

Pain Assessment in Older Adults After Traumatic Injury

This cross-sectional study analyzes how pain assessment frequency and pain score magnitude and variability differ by age among trauma patients admitted in hospitals.

Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk

Operational risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures, or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technical failures and failures in the execution and administration of processes. However, the low frequency with which a loss event occurs creates a technological challenge for insurers in estimating the operational value at risk (OpVar) for the protection derived from an organization’s business activities. Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). For the estimation of OpVar, the DFCS model incorporates a novel activation function based on the generalized log-logistic function to model random variables of frequency and severity that define a loss event (linguistic random variables), as well as a credibility surface to integrate the magnitude and heterogeneity of losses in a single structure as a result of the integration of databases. The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. These features make the DFCS model a reference for estimating the OpVar to protect the risk arising from an organization’s business operations by integrating internal and external loss event databases.

Uncertainty quantification in the modal analysis of aircraft stiffeners: A Perturbation Technique approach in SFEM

Structural members, such as stiffeners, are crucial in aeronautical structures, providing essential dynamic stiffness. However, variability introduced by manufacturing and assembly processes, as well as material inconsistencies, can lead to uncertainties in structural performance. It is crucial to incorporate these uncertainties into structural analysis to ensure reliable design. This paper utilizes the Stochastic Finite Element Method (SFEM) to address uncertainties in typical structural members used in aeronautics. The Perturbation Technique, based on Taylor series expansions, was employed to model uncertainties in aircraft stiffeners. The study focuses on natural frequencies and modal analysis to evaluate the impact of uncertainties on beams with hat and Z sections, commonly used as stiffeners in aircraft panels. These stiffeners were modeled using the Timoshenko beam theory, and sensitivity analysis was performed to identify key contributors to variability. The perturbation parameter was validated through Monte Carlo simulations. Sensitivity analysis, employing gradient-based methods, identified significant factors affecting variability in natural frequencies. A different perturbation parameter was necessary based on the stiffener's geometry: thickness variations required a perturbation parameter on the order of 10−3, whereas dimensions changes in the flange and height required parameters on the order of 10−2. These results underscore the importance of choosing appropriate perturbation magnitudes to avoid inaccuracies in the deterministic frequency response. Once a perturbation parameter is established, it can be applied to similar regions, ensuring the robustness of the SFEM methodology in analyzing the dynamic response of aeronautical structural reinforcements.

Beyond the family's cooking frequency: The influence of cooking techniques on vegetable and fruit consumption among the U.S. population

BackgroundDespite the well-established health benefits of fruits and vegetables, U.S. consumption remains far below the WHO's recommended 400 g per day. Understanding how culinary techniques influence intake could elucidate strategies for improving dietary habits. ObjectiveTo explore whether the use of cooking techniques for fruits and vegetables is associated with increased consumption of these foods and the corresponding variation in total fiber intake among the U.S. population. MethodsData from the National Health and Nutrition Examination Survey (NHANES) 2009–2010, participants (n = 9754) were categorized into groups based on their self-reported cooking frequency. Cooking techniques were classified into types. The mean consumption of fruits and vegetables was segmented into quartiles. Cooking frequency was evaluated, and differences between groups were assessed using chi-square tests and mean comparison tests. The study also assessed the prevalence of cooking techniques for different food groups. Additionally, linear regression analyses were conducted to adjust the mean daily per capita consumption of fruits, vegetables, and dietary fiber, considering cooking frequency and other socioeconomic variables as predictors. ResultsThe studied population's mean consumption of fruits and vegetables was 302.9 g, with half of this amount consisting of fruits. Individuals from households with frequent cooking practices (≥5x a week) consumed, on average, 48.2 g more fruits and vegetables daily than those from households with less frequent cooking (<5x a week). The use of diverse cooking techniques significantly improved vegetable intake, showing a fivefold increase in consumption among those employing multiple techniques. Moreover, frequent home cooking was associated with a 1.0-g increase in daily fiber intake. ConclusionPublic policies aimed at improving fruit and vegetable intake among the U.S. population should consider, in addition to cooking frequency, the role of cooking techniques in influencing the consumption of these food groups.

Direct Observations of Coastally Generated Near-Inertial Waves During a Wind Event.

Wind over the ocean generates near-inertial velocities. In the open ocean, horizontal variability in the inertial frequency and mesoscale vorticity generate internal waves that transport energy laterally and drive diapcynal mixing in remote locations. In the coastal ocean, horizontal variability is produced by the coastline. This study analyzes observations along a straight coastline in Lake Superior, which acts as a "natural laboratory" for the coastal ocean. Depth-profiles of velocity, temperature, and turbulent miscrostructure were collected during a 96hr repeat survey from 3 to 20km offshore in Aug 2018. Wind work was 2mW and generated 0.2m near-inertial velocities that were inhibited within two internal Rossby radii (6km) of the coast. The velocities are interpreted as a superposition of a "forced flow", which is horizontally uniform, and a "wave flow", associated with offshore propagating near-inertial waves. A 1D momentum equation skillfully predicts the horizontally averaged near-inertial velocities and the TKE shear production, which matches the 1mW observed TKE dissipation rate. The offshore propagating wave has an energy flux of 10W (m-coastline)-1 and a downward energy flux of 1mW . These results suggest that most near-inertial wind work is lost directly to TKE shear production, but some energy is transferred to offshore propagating waves that may help catalyze shear instability away from the coast.

A Prospective Observational Study on Analyzing Lung Cancer Gene Mutation Variant Allele Frequency (VAF) and Its Correlation with Treatment Efficacy.

A few studies have reported on the variability of the variant allele frequency (VAF) of gene mutations and the relationship between VAF and the therapeutic effect of molecular-targeted drugs. This joint study was conducted from May 2020 to January 2022 between St. Marianna University School of Medicine and the DNA Chip Research Institute. Cytology samples were used to verify the usefulness of a lung cancer compact panel test, which is a high-sensitivity next-generation sequencing (NGS) gene panel test. We analyzed the distribution of VAF and the duration of the initial molecular-targeted drug administration in patients with advanced-stage epidermal growth factor receptor (EGFR) mutations. Of the 196 patients diagnosed with non-small cell carcinoma (NSCLC), gene mutations were detected in 114 (58.2%) cases and in 68.7% of patients with adenocarcinomas (an increased detection rate). A VAF of 30% or more for DNA mutations was confirmed in 35 patients (33.7%), 10% to <30% in 38 (36.5%), and <10% in 31 (29.8%), including 18 patients (17.3%) with <6%, which is considered the lower limit of the LOD for other companion diagnostic kits. EGFR mutations were detected in 59 patients (30%), of whom 35 were in an advanced stage and received molecular-targeted drugs as their initial treatment. Groups A and B comprised patients with a VAF of ≥10% and <10%, respectively, with 27 patients (median VAF 30.6%, range 11.4-77%) in group A and 8 (median VAF 5.4%, range 0.1-8.5%) in group B. The duration of the administration of molecular-targeted drugs was 17 months (range 3-42 months) in group A and 14 months (range 1-45 months) in group B, with no statistically significant difference between the two groups (p-value = 0.7). Twenty-seven percent of patients had a VAF < 10%. Even in patients with a low VAF for gene mutations, sufficient therapeutic effects were observed with molecular-targeted drugs, suggesting the importance of detecting mutations using a highly sensitive method at initial diagnosis. Further prospective validation studies of a larger number of cases are warranted.

Tremor after solid organ transplantation: Results from the TransplantLines Biobank and Cohort Study.

Tremor is a frequent complaint of solid organ transplant recipients. We report on the largest population investigated with clinical neurophysiological methods. Our aim is to objectively establish the tremor prevalence and syndrome in the largest population of solid organ transplant recipients. Tremor was measured in heart, kidney, liver, and lung recipients, using accelerometers during rest, postural, and weight-loaded conditions. The 95th percentile of healthy kidney donors' tremor amplitude was used as the cutoff to determine the presence of tremor in transplant recipients. Tremor frequency, frequency variability, and effect of loading were used to investigate enhanced physiological tremor as the likely tremor syndrome. Impact on activities of daily life was assessed, and correlations with tacrolimus blood levels were investigated. Tremor was present in 52% of 246 transplant recipients, typically in postural positions. Mean tremor frequency was 6.1 (±2.0) Hz; mean tremor variability was 2.6 (±1.8) Hz. A frequency decrease upon loading was found in 83% of patients with tremor. Sixty-five percent of patients met formal clinical neurophysiological criteria for enhanced physiological tremor. Tremor-related impairment was present in 55% and correlated with tremor amplitude (ρ = 0.23, p ≤ 0.001). In a binominal regression analysis, tacrolimus blood levels were independently associated with tremor prevalence (p = 0.009). More than half of solid organ transplant recipients experience a tremor that best fits the syndrome of enhanced physiological tremor. This is the first objective study on tremor that has established a better understanding of the neurophysiological mechanisms of tremor in a large population of solid organ transplant recipients.

Teacher gestures bridge meaning: Unpacking teacher gesture in storybook read alouds to support vocabulary

Teacher gesturing is an effective strategy for promoting vocabulary learning during book reading in early childhood settings. While studies find significant variation in teachers’ use of gesture, existing studies have not explored within-teacher and across-book differences in gesturing, reducing the ability to better understand how teachers and books drive gesturing to support language learning. To address this gap, this study investigated the gestures of nine teachers across 108 observations of the same 12 books to understand factors that explain teachers’ frequency of gesturing and the ways in which teachers gesture. Using mixed methods, we estimated multilevel models and descriptive statisics to examine whether books and teacher characteristics explained teachers’ frequency of engaging in representational and iconic gestures and employed multimodal content analysis and a grounded theory approach to better understand how and why certain books elicit more of these gestures across teachers. Study findings show greater within teacher variability in frequency of gesturing as a function of book, a positive association between teacher expertise and frequency of representational gesturing, and that teachers gestured to create emotive, conceptual, and animation bridges to connect the text and illustration to support student understanding of the language in picture books. Results showed that the content of the text also explained teacher gesturing, and thus that gesturing is not solely a teacher trait, but also reflects qualities of the text. Gesture research and practice should continue to consider text selection as critical to promoting this pedagogical strategy.

Cluster Analysis of Furniture for Export Customers Position at UD HK Jepara

A furniture production company needs to categorize its customers, especially those who are involved in export. The company has analyzed export shipment records from June 6, 2015, to April 23, 2022. The analysis used the Recency, Frequency, and Monetary (RFM) variables, which have been widely used in the field of marketing. The k-means clustering algorithm was employed for the analysis, resulting in the division of customers into three clusters. Cluster 1 comprises customers with the highest and most valuable purchases, including those with the codes CL, FR, NC, and RE. Cluster 2 includes customers who excel in one of the RFM variables, with codes AN, AR, BN, IN, IT, KE, KR, LK, MU, MY, SA, SC, SM, TW, and UN. Customers in Cluster 3 have the fewest and least valuable purchases, including those with the codes GP, KN, NL, OM, PT, and TZ.

CYP3A4*1B but Not CYP3A5*3 as Determinant of Long-Term Tacrolimus Dose Requirements in Spanish Solid Organ Transplant Patients.

Tacrolimus (TAC) is a commonly used immunosuppressive drug in solid organ transplantation. Pharmacogenetics has been demonstrated before to be decisive in TAC pharmacotherapy. The CYP3A5*3 variant has been reported to be the main determinant of TAC dose requirements; however, other polymorphisms have also proven to be influential, especially in CYP3A5 non-expressor patients. The aim of this study is to evaluate the influence of genetic polymorphisms in TAC therapy in a cohort of Spanish transplant recipients. Genetic analysis including ten polymorphic variants was performed, and demographic and clinical data and pharmacotherapy of 26 patients were analyzed. No significant differences were found in weight-adjusted dose between CYP3A5 expressors and non-expressors (0.047 mg/kg vs. 0.044 mg/kg), while they were found for carriers of the CYP3A4*1B allele (0.101 mg/kg; p < 0.05). The results showed that patients with at least one CYP3A4*1B allele had a higher TAC dose and lower blood concentration. Dose-adjusted TAC blood levels were also lower in CYP3A4*1B carriers compared to non-carriers (0.72 ng/mL/mg vs. 2.88 ng/mL/mg). These results support the independence of CYP3A5*3 and CYP3A4*1B variants as determinants of dose requirements despite the linkage disequilibrium present between the two. The variability in genotype frequency between ethnicities may be responsible for the discrepancy found between studies.

Investigation of mothers' elicited infant-directed speech and singing for preterm infants.

Mother's voice is a salient auditory stimulus commonly experienced during early development; after birth, characteristic acoustic modulations of mothers' infant-directed speech (IDSpeech) and singing (IDSinging) contribute to neurodevelopment. For preterm infants, early separation leads to decreased exposure to mother's voice; the impact on maternal ability to produce IDSpeech/IDSinging and infant perception of mother's voice is unknown. Fifty mother/preterm-infant dyads were enrolled in this prospective cohort study. Forty-four mothers recorded Twinkle, Twinkle Little Star in coached adult-directed speech (ADSpeech), IDSpeech, and IDSinging. Between 34.0-36.9w CGA, infants underwent high-density EEG during exposure to their mother's voice recordings. Acoustic features of mothers' voice and infant cortical response were analyzed and correlated. Acoustic features of recorded maternal ADSpeech, IDSpeech, and IDSinging were significantly different. In 33 infants with EEG, mean fundamental frequency and speech production rate (SPR) variability correlated with infant responses to ADSpeech; SPR and SPR variability correlated with IDSpeech; SPR correlated with IDSinging. Correlations were found at differing scalp locations for speech versus singing. Mothers of hospitalized preterm infants differentially modulate their voice during coached recorded language; features can then be differentiated by their preterm infants thus presenting opportunities for targeted interventions when parents cannot be present. Mothers of preterm infants can record their voice with acoustically quantifiable characteristics of infant-directed singing and speech, even when not at their infant's bedside. Recorded adult- and infant-directed speech stimuli are differentially processed in the brains of hospitalized preterm infants. The ability for mothers to create acoustically-distinct infant-directed speech in the absence of their infant may be driven by coaching to achieve an approximated sense of connection with their infant.

Design, modeling and experiments of bistable wave energy harvester with directional self-adaptive characteristics

Wave energy is a abundant renewable resource, but the direction variability and ultra-low frequency limit its widespread application. Therefore, a novel bistable wave energy harvester with directional self-adaptive characteristics is proposed in this paper, which utilizes the collision between the driving ball and the generator beam to transform the multi-directional ultra-low frequency wave vibration into localized high-frequency vibration. Moreover, the theoretical analytical model of harvester when subjected to wave motions was established, including the dynamic analysis of driving ball before the collision, the collision response analysis during the collision, and the bistable properties of generator beam after the collision. Finally, the key parameters affecting the output performance, such as the mass of the driving ball, the maximum inclination angle of the wave and the external load resistance, were analyzed using experiments. The maximum open-circuit voltage RMS value is 1815.28 mV, and the maximum power is 2.06 μW. The proposed harvester can effectively harvest ultra-low frequency energy vibration energy in multi-directions.

Variability in Morphometric and Agronomic Traits of Native Bean Seeds (Phaseolus vulgaris L.) and Edaphoclimatic Characterization

Background: Farmers conserve bean germplasm due to its importance in nutrition despite recent socioeconomic changes in their communities. The objective of the study was to analyze the edaphoclimatic conditions, morphometric and agronomic characters in seeds of bean varieties. Methods: Seeds of native varieties were collected in the Las Montañas region, Veracruz, Mexico, with data on temperature, precipitation and soil properties were extracted. The native materials were grown together with the Negro INIFAP variety and the commercial grains Michigan and Pinto. Qualitative variables (frequency and correspondence analysis) and quantitative variables were evaluated, the latter related to the plant, as well as dimensions and weight of the seed, following a complete and completely randomized block design, respectively. Result: Native varieties develop in ranges of 18.4 to 25.5°C and between 1 827 to 2 610 mm of precipitation and soils with average values of organic matter and pH of 5.89-7.04. Differences were present in the qualitative characters and in the number of seeds per pod (NSE), weight of one hundred seeds (WHSE) and seed dimensions (p≤0.01). The Amarillo milpa variety reached the highest NSE with 7.33 and Pinto stood out over the WHSE (33.41 g). The native varieties are classified as small ( less than 25 g 100 seeds), characteristic of the Mesoamerican race. In conclusion, the diversity in qualitative and quantitative characters in bean varieties reflects adaptations to contrasting edaphoclimatic conditions, which has implications for genetic improvement, food security and sustainability.

Whistle or squeal? Distinguish and evaluate two typical abnormal sounds of collaborative robots

The whistle and squeal are two typical abnormal sounds among the radiated noise of running collaborative robots, indicating potential faults and causing discomfort through auditory perception. This study investigated whether psychoacoustic parameters for sound quality could distinguish the whistle and squeal and evaluate their discomfort. A total of 20 abnormal sound stimuli, including 6 whistles and 14 squeals, were obtained from various collaborative robots. The cepstrum characteristics and sound quality metrics (i.e., roughness, sharpness, fluctuation strength and tonality) of all stimuli were analyzed by controlling the equal loudness. Twenty-four participants rated the 'discomfort of abnormal sound in the stimuli' on an 11-point scale. The results show that the modulation frequencies of the fundamental frequency of the whistles were 2.9 Hz to 5.5 Hz, whereas the squeals had no obvious modulation characteristics. The modulation frequencies of the fundamental frequency were highly correlated (with a correlation coefficient of 0.89) to the discomfort responses for whistles, whereas tonality was significantly correlated (with a correlation coefficient of 0.82) to the discomfort responses for squeals. The linear models using single dependent variables of modulation frequency in cepstrum and tonality had determination coefficients of 0.77 and 0.66, respectively, in evaluating the discomfort of whistles and squeals.