Preferred Characteristics Research Articles

Thermal Enhanced Electrokinetic Bacterial Transport in Porous Media.

Soil bacterial communities are crucial to various ecosystem services, with significant implications for environmental processes and human health. Delivering functional bacterial strains to target locations enhances the preferred ecological features. However, the delivery process is often constrained by limited bacterial transport through low-permeability soil. Although electrokinetics breaks the bottleneck of bacterial transport in thin porous media, its efficiency remains limited. Here, we tested the hypothesis that thermal effects enhance electrokinetic transport by shifting the net force acting on the bacterium. We found that heating significantly increased electrokinetic transport by 2.75-fold at 1 V cm-1 through porous media. Thermal enhancement mechanisms were interpreted by the heating shift of net force integrating matrix attractive and electrokinetic forces and verified by the Quartz Crystal Microbalance with Dissipation Monitoring (QCMD) observed adhesion rigidity shift. Thermal-dependent parameters liquid viscosity and dielectric constant were the primary contributors to the net force shift. Their variations reduce the attractive force and augment the electrokinetic forces, resulting in lower adhesion rigidity and enhanced bacterial transport. A mechanism-based approach interlinking electric field strength, thermal effect, and collision efficiency was established to facilitate the application of thermally enhanced electrokinetic bacterial transport. These findings provide new prospects for improving bacterial transport, hence optimizing soil ecosystem functions.

"We Need Gentleness": Isolation, Loneliness, and Implications for Psychosocial Counseling Among People with HIV who are on Methadone Maintenance Treatment and Experience Common Mental Disorders in Vietnam.

The psychological experiences and needs of people with human immunodeficiency virus (HIV) who use methadone maintenance treatment and have common mental disorders in Hanoi, Vietnam remain unknown. Due to limited services for common mental disorders, optimal delivery methods for psychotherapy are also unknown. Accordingly, this qualitive study explored both symptoms of common mental disorders among this population and the preferred qualifications and characteristics of a mental health counselor. We interviewed patients, their family members and caretakers, clinic providers, and clinic directors to answer these questions. Feelings of loneliness, isolation, sadness, hopelessness, and a general lack of motivation were common among patient participants. Patient participants had no preference among counselor professional qualifications. Three key qualities‒gentleness, enthusiasm, and understanding ‒ emerged as preferred counselor characteristics. The results from this work will help inform the future tailoring of mental health interventions among a population with unique care needs in Hanoi as well as other populations of people with HIV with common mental disorders.

Consumer preference study for the interface design of traditional Chinese medicine applications using conjoint analysis method

The widespread use of mobile applications (apps) offers a new platform for sustaining traditional culture, yet insufficient focus on interface design has hindered user experience. This paper focuses on traditional Chinese medicine (TCM) apps, examining user preferences for interface design elements and their combinations across four dimensions: visual effects, functional attributes, layout, and interaction modes. Utilizing Conjoint Analysis Method (CAM), this study quantitatively explores user preferences for the combination schemes of 18 orthogonal designs. The study collected 464 users’ data through questionnaires and processed the data using CAM and Multivariate Analysis of Variance (MANOVA). The findings indicate that users have a high demand for theme colors (35.09%), text styles (27.32%), and icon styles (14.78%) in medical consultation apps. For health education apps, users exhibit preferences for text styles (24.95%), theme colors (24.58%), and functional architecture (16.30%). In professional learning apps, the preferred elements are theme colors (24.07%), text styles (18.54%), and icon styles (18.03%). Based on these results, the paper proposes corresponding design strategies for each type of TCM app. This study reveals the characteristics of user preferences for different categories of TCM apps, enriching the theories and methods of interaction design for similar mobile applications. Ultimately, this facilitates the integration of traditional Chinese medicine culture with modern digital technology, promoting the digital and sustainable transmission of traditional culture.

Musculoskeletal Disorders Among Surgeons and Attitudes Towards Wearable Devices for Ergonomic Optimization

Objective: To assess the burden of ergonomic strain and to examine factors influencing intention to use wearable technology that may improve ergonomics during surgery. Background: Surgical ergonomic strain leads to high rates of work-related musculoskeletal disorders (MSDs) and pain, contributing to early surgeon retirement and an epidemic of burnout. Methods: A cross-sectional survey of surgeons at a single institution was conducted using two validated instruments, the Nordic Musculoskeletal Questionnaire and Unified Theory of Acceptance and Use of Technology (UTAUT2), assessing musculoskeletal strain and facilitators of wearable sensor use, respectively. Additional survey items examined preferred ergonomic device features. Partial least squares structural equation modeling tested model validity. Results: 161 participants completed the entire survey. Most respondents were male (63.4%) and were more likely to be from General Surgery (26.1%) than from other subspecialties. The majority of surgeons had never received prior ergonomic training (n=107, 72.3%). Twenty surgeons (14.1%) required hospitalization and 31 (21.8%) surgeons changed operating responsibilities due to musculoskeletal injury with 77 (59.1%) reporting an impact of ergonomic pain on their ability to work as a surgeon. Perceived efficacy (β=0.190, P=0.005), social influence (β=0.135, P=0.039), hedonic motivation (β=0.274, P<0.001), and innovativeness (β=0.243, P<0.001) were significantly associated with behavioral intention to use a wearable ergonomic device. The waist (n=36, 24.3%), followed by the neck/upper back (n=29, 19.6%), were the most preferred sensor locations. Conclusions: Surgeons report a high burden of MSDs impacting their ability to work. Wearable sensors that provide feedback on ergonomic strain represent an opportunity to reduce MSDs among surgeons.

Optimizing Autism Spectrum Disorder Identification with Dimensionality Reduction Technique and K-Medoid

This research addresses the challenges of diagnosing and treating Autism Spectrum Disorder (ASD) using dimensionality reduction techniques and machine learning approaches. Challenges in social interaction, communication, and repetitive behaviours characterize ASD. The dimension reduction used in this research aims to identify what features influence autism cases. Several dimension data reduction techniques used in this research include PCA, Isomap, t-SNE, LLE, and factor analysis, using metrics such as Purity, silhouette score, and the Fowlkes-Mallows index. The machine learning approach applied in this study is k-medoid. By employing this method, our goal is to pinpoint the unique characteristics of autism that may facilitate the detection and diagnosis process. The data used in this research is a dataset collected for autism screening in adults. This dataset contains 20 features: ten behavioural features (AQ-10-Adult) and ten individual characteristics. The results indicate that Factor Analysis outperforms other methods based on purity metrics. However, due to data structure issues, the t-SNE method cannot be evaluated using purity metrics. PCA and LLE consistently provide stable silhouette scores across different values. The Fowlkes-Mallows index results closely align, but t-SNE tends to yield lower values. The choice of algorithm requires careful consideration of preferred metrics and data characteristics. Factor analysis is adequate for Purity, while PCA and LLE consistently perform well. This research aims to improve the accuracy of ASD identification, thereby enhancing diagnostic and treatment precision.

Melting Separation of Metalized Pellets of Vanadium–Titanium Magnetite

To realize the efficient and clean comprehensive utilization of vanadium–titanium magnetite in the Baima mining area of Panzhihua, an innovative hydrogen‐based shaft furnace direct reduction followed by a melting separation process is proposed. In this study, the melting separation behaviors of vanadium–titanium magnetite affected by the temperature, time, basicity, and C/O (mole ratio of carbon to reducible oxygen) are systematically investigated. The chemical composition, phase composition, and microstructure of slag using modern analytical techniques such as X‐ray diffraction, scanning electron microscopy, and energy dispersive spectroscopy are also revealed. The results revealed that under the optimization conditions including the melting temperature of 1600 °C for 30 min, basicity of 0.50, and C/O of 1.10, the corresponding separation rates of Fe and V in pig iron are 99.31% and 89.73%, and the separation rate of TiO2 in slag is 92.83%, Meanwhile, a preferable characteristic of slag with viscosity of 0.116 Pa s and melting temperature of 1374.54 °C was obtained. The directed migration of valuable elements in vanadium–titanium magnetite is successfully realized.

Same-day dischargeafter intracranial shunt revision: a retrospective propensity-matched safety analysis.

Patients are often observed overnight after ventricular shunt revision for hydrocephalus. We believe that a same-day discharge after a shunt revision is safe in the appropriate population. The purpose of this study was to determine the appropriate patient population and the safety profile for a same-day discharge following a ventricular shunt revision. We conducted a retrospective chart review of all shunt revisions at a single center from 2014 to 2021. We compared rates of emergency department visits, hospitalization, and revision at 30 and 90days between same-day discharge and control patients following shunt revision. Non-parametric, logistic regression, and chi-squared testing were performed to measure statistical significance with and without propensity score matching. A total of 996 patients were examined, with 56 (5.6%) patients being discharged on the same day as the surgery. Patients with a same-day discharge were older, more likely to have distal shunt failure, more likely to have undergone scheduled surgery, and less likely to have visited the emergency department in the prior 30days. Propensity score matching compared 29 revisions in each group; all covariates were non-significant (p > 0.05), and 30-day and 90-day outcomes were not significantly different the between same-day and non-same day discharge. These findings validate the safety profile for the same-day discharge in post-operative shunt patients and describe our patient cohort's preferred characteristics. Scheduled, distal shunt failure patients are preferred for early discharge post-operatively. When validated by further studies, this would provide improved patient comfort and decreased healthcare expenses for a shunt failure.

Antioxidant Properties and Sensory Preference of Broccoli Affected by Culinary Practices

Broccoli provides a good source of many nutrients and bioactive compounds. However, bitterness of broccoli deterred sensory desirability and furthermore impacted its consumption. Cooking and seasoning addition can be used to reduce the bitterness and to increase the preference of vegetables. The objective of this study aims to investigate the effect of blanching and flavor enhancement with various sauces on the bioactive and organoleptic properties. Broccoli was prepared: raw (C), blanched (B), blanched and flavored with Japanese style sauce (F1), with cheese (F2), and with Chinese style sauce (F3). The bioactive compounds (polysaccharide and phenolic compounds), antioxidant properties (DPPH and ABTS scavenging activities), and sensory preference characteristic were evaluated. The results showed blanching and combined with seasonings significantly altered the color parameters (a*, C* and ho) of broccoli. While blanching and sauces additions improved antioxidant contents and capacity as well as all sensory acceptances tested. Compared to the control, blanching and sauces additions increased the contents of phenolic compounds and polysaccharides from 8.3 to 8.4-10.0 and from 78 to 93-106 mg/g dw, respectively. While blanching and sauces increased the scavenging capacity of DPPH and ABTS from 1.29 to 1.43-1.83 and from 3.28 to 4.52-5.27mg Trolox/100g, respectively. Principal component analysis (PCA) analysis further showed blanching and sauces additions were positively correlated with the acceptance of flavor, taste and overall but negatively correlated to color acceptance. In conclusion, blanching or flavorings retained color acceptance (sensory score >5) and enhanced antioxidant capacity and sensory preference.

Spatial Agglomeration, Industrial Land Price and Enterprise Innovation: Evidence from Micro-data of Land Transactions in Industrial Enterprises

It has become a consensus in academic circles that enterprise spatial agglomeration promotes technological innovation through comparative advantage of factor cost and knowledge spillover effect. With the increase of agglomeration degree, the advantage of low cost is gradually lost, and even under the constraint of financing, it will inhibit the innovation and development of enterprises. By constructing a new micro-level spatial agglomeration index, this paper investigates the dynamic impact of corporate spatial agglomeration on corporate innovation from the perspective of agglomeration cost. On the basis of using the longitude and latitude of industrial enterprises to construct a new index of agglomeration, this paper further carries out a comprehensive matching on the land market transaction data, patent application data and industrial enterprise data, obtains the micro-data at the enterprise level from 2007 to 2014, and constructs a non-linear intermediary effect model with industrial land price as the breakthrough point. The empirical results show that corporate spatial agglomeration has an “inverted U” effect on corporate innovation. The reason is that agglomeration costs such as increased competition caused by excessive spatial agglomeration and the rise in industrial land prices tighten corporate capital constraints, affect corporate resource allocation, and cause enterprises to reduce research and development investment. Research on heterogeneity shows that industrial enterprises have obvious preference characteristics for the direction of innovation investment in different stages of spatial agglomeration. According to this, the local government should scientifically control the agglomeration layout, on the one hand, give full play to the knowledge spillover effect of agglomeration, and on the other hand, mitigate the adverse impact of the rapid increase in land prices on innovation.

The biopsychosocial perspective on designing mobile health apps for self-management of chronic headaches

ABSTRACT This study identifies user-preferred features in apps for self-management of chronic headaches from the biopsychosocial perspective and examines the extent to which such features are represented in the current apps. We first conducted semi-structured interviews to identify user-preferred features that tap into the biopsychosocial domains of health. Then, we conducted a landscape analysis to review existing apps with respect to the identified features. Our findings revealed participants’ preferences for features in apps to self-manage chronic headaches and were categorised based on the biopsychosocial model. Further, our landscape analysis showed that several features (e.g. physical symptom tracker) are highly present in existing apps, while most of the preferred features (e.g. journaling) are scarce. The identified features appear to align with theory-based behaviour change techniques and, therefore, have implications for health behaviour change. In addition, our findings demonstrate that most of the user-preferred biopsychosocial features are not widely present in the existing headache apps. Overall, our study highlights the importance of incorporating user-preferred features that align with the biopsychosocial needs of headache app users. By acknowledging and addressing these needs, we can broaden the existing perspectives concerning app design and evaluation and cater to the holistic health experiences of users.

Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied-An Exploratory Study.

Background/Objectives: This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals-cardiological patients, healthy participants, and athletes-based on cardiorespiratory features from short-term static measurements. It also examined the impact of cardiorespiratory coupling (CRC)-related features (from causal and information domains) on the modeling accuracy to identify a preferred cardiorespiratory feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions. Methods: We utilized six self-prepared datasets that comprised various subsets of cardiorespiratory parameters and applied several ML algorithms to classify subjects into three distinct groups. This research also leveraged explainable artificial intelligence (XAI) techniques to interpret model decisions and investigate feature importance. Results: The highest accuracy, over 89%, was obtained using the dataset that included most important demographic, cardiac, respiratory, and interrelated (causal and information) domain features. The dataset that comprised the most influential features but without demographic data yielded the second best accuracy, equal to 85%. Incorporation of the causal and information domain features significantly improved the classification accuracy. The use of XAI tools further highlighted the importance of these features with respect to each individual group. Conclusions: The integration of ML algorithms with a broad spectrum of cardiorespiratory features provided satisfactory efficiency in classifying pediatric individuals into groups according to their actual health status. This study underscored the potential of ML and XAI in advancing the analysis of cardiorespiratory signals and emphasized the importance of CRC-related features. The established set of features that appeared optimal for the classification of pediatric patients should be further explored for their potential in assessing individual progress through training or rehabilitation.

Evaluating the adoption of handsearching, citation chasing, and screening tools in education research: a survey study.

The rapid development of software tools to assist systematic reviewers has led to varying degrees of adoption and selection among researchers. However, the actual usage patterns of these tools, their preferred features, and the criteria for selecting the most suitable tools remain unclear. To understand these aspects, we collected 175 responses from researchers across different continents. In terms of handsearching, despite new tools developed, our findings reveal that manual handsearching remains prevalent among more than half of the participants. Databases are the most popular tools for citation searching, followed by citation management tools and spreadsheets. This reliance on citation management tools and spreadsheets is concerning as they are not specifically designed for systematic reviews. The primary factors influencing tool selection are the research environment and ease of use. Barriers stopping researchers from adopting alternative tools include limited awareness, challenges in learning new tools, and the financial costs associated with acquiring licenses. Moreover, researchers located in Europe show greater familiarity with a wider range of tools compared to their North American counterparts. This preregistered study contributes valuable insights into the tool usage patterns of education researchers, emphasizing the importance of promoting awareness and facilitating the broader adoption of existing tools.

(110)-Surface Strained-Channel MOSFETs

Improvement of semiconductor device performances have been brought about by innovations in device structures such as SOI, FinFET and gate-all around FET. The FinFET devices saw a remarkable success owing to their high hole mobility. FinFET devices attain this preferable feature by having the (110)-surface channel. Recently, considerable progress has been made in the nanosheet transistor technology. Owing to the gate-all-around structure and the added dimension, the nanosheet transistor technology has an enormous potential for realization of the electronic devices with a significantly high performance. An issue for the nanosheet transistor is the degradation of the hole mobility. This problem stems from the following factors. First, currently developed nanosheet transistors are characterized as (100)-channel devices which have inferior character regarding the hole mobility. Secondly, for fabrication of nanosheet transistors, crystal growth of multiply stacked Si/SiGe layers is an essential process, which involves deterioration of the crystalline quality. The degradation of the crystalline quality is severer in the case of crystal growths on the (110)-surface than those on the (100)-surface. Therefore, the use of the (110)-wafer is challenging and methods to grow Si/SiGe multilayers on (110)-wafers with high quality is to be developed. The authors have been involved in the crystal growth and characterizations of Si/SiGe heterostructures on Si(110) wafers [1-4]. In these studies, a significant enhancement of the hole mobility in the strained Si layer was demonstrated. The results indicate the effect of the lattice strain on the energy band structure can overcome the adverse effects of the crystalline defects. In addition, characteristic features of the crystalline defects generated in the (110)-oriented heterostructures have been revealed. Along with the knowledge on the defect generation and the formation process of the surface morphology, the effect of strain is also a focus of this talk. We discuss first the effect of strain on the valence band structure of Si. It is shown that the effect of strain in reducing the hole cyclotron effective mass is more significant when the surface is (110) compared to the case where the surface is (100). Interestingly, the strain dependences of the hole cyclotron effective mass differ in these two cases. The computational investigation indicates that the lattice strain combined with the (110)-oriented surface is an effective method to increase the hole mobility. Next, a formation mechanism of the crystalline defects in SiGe grown on the (110) wafer is discussed. The TEM observations clearly show that the dominant crystalline defect induced by strain is a twin structure having the {111} twin boundary. The reason for the twin generation is understood in the framework of a dislocation theory. The shear stress acting on a (111) plane drives the atomic layer to form a stacking fault and/or a twin structure. The strength of the shear stress is higher in the case of the (110)-oriented SiGe film compared to the case of the (100) counterpart, which explains the small critical layer thickness of the SiGe on a Si(110) wafer. Finally, the formation process of the surface morphology during the growths of SiGe on Si(110) wafers is discussed. Even at the initial stages of the SiGe, difficulty in obtaining a flat surface is recognized [4]. Based on currently obtained experimental results, we discuss the effect of a surface inclination on the surface morphology.[1] K. Arimoto et al., Jpn. J. Appl. Phys. 59, SGGK06 (2020).[2] D. Namiuchi et al., Materials Science in Semiconductor Processing 113, 105052 (2020).[3] K. Arimoto et al., ECS Transactions 98 (5), 277 (2020).[4] S. Saito et al., Materials Science in Semiconductor Processing 113, 105042 (2020).

Understanding older adults’ needs for and perceptions of shared autonomous vehicle interior features: A focus group and user enactment study

Shared autonomous vehicles (SAVs) can help older adults maintain mobility and independence throughout the later stages of life. However, research is critically needed to assess the design of SAV interior features and quantify potential mobility challenges for older populations. This paper presents a study that adopted a mixed-methods approach to evaluate the needs and perceptions of adults aged 65 years and older regarding interior features of SAVs during user enactment, and offers data-driven insights on task performance to inform design decisions. Thirty participants interacted with an SAV mock-up and participated in subsequent focus group discussions regarding their interaction experiences and general perceptions of SAV. Quantitative video-based time-motion analysis and qualitative thematic analysis of the focus group discussion highlighted concerns related to a) ingress and egress accessibility; b) seat and cabin comfort; c) appropriate social interactions with co-riders; and d) SAV operational functionality. Staggered seating arrangements and multi-functional use of available space were preferred features. The analysis also identified potential improvements, such as the inclusion of additional handholds and efficient HVAC systems, and underscored the need for central monitoring for emergency interventions during SAV operations. Ultimately, findings from this research can provide valuable insights for researchers and engineers to design equitable transportation solutions tailored to a broad range of traveler abilities and needs.

User Behavior and Satisfaction in AI-Generated Video Tools: Insights from Surveys and Online Comments

Abstract. With the rapid advancement of AI, AI-generated video tools have become essential in digital content creation. However, there are significant differences in user acceptance and satisfaction with these tools. This study aims to explore the underlying reasons for these differences by analyzing user behavior, satisfaction levels, and preference characteristics through a questionnaire survey and online review data. Ridge regression analysis identified usage frequency and data security concerns as key factors influencing satisfaction, with familiarity and education level also playing significant roles. Based on these factors, K-means clustering categorized users into three groups: occasional users with high satisfaction, functionality-focused users with the highest satisfaction, and frequent users with lower satisfaction due to data security concerns. Sentiment analysis and LDA topic modeling of online reviews for five AI video tools-Animoto, Invideo, Lumen5, Pictory, and Synthesia-revealed differences in user evaluations. The findings indicate that Lumen5 excels in social media content creation, while Pictory has a weaker user experience with more negative feedback. The study concludes that developers should focus on increasing tool usage frequency and enhancing data security to improve user satisfaction. Moreover, targeted strategies based on different user segments can aid in more precise product optimization and market promotion. This research provides practical guidance for the further development of AI-generated video tools. Future studies could expand the sample size and data dimensions to further validate and deepen these conclusions.

User Behavior and Satisfaction in AI-Generated Video Tools: Insights from Surveys and Online Comments

Abstract. With the rapid advancement of AI, AI-generated video tools have become essential in digital content creation. However, there are significant differences in user acceptance and satisfaction with these tools. This study aims to explore the underlying reasons for these differences by analyzing user behavior, satisfaction levels, and preference characteristics through a questionnaire survey and online review data. Ridge regression analysis identified usage frequency and data security concerns as key factors influencing satisfaction, with familiarity and education level also playing significant roles. Based on these factors, K-means clustering categorized users into three groups: occasional users with high satisfaction, functionality-focused users with the highest satisfaction, and frequent users with lower satisfaction due to data security concerns. Sentiment analysis and LDA topic modeling of online reviews for five AI video tools-Animoto, Invideo, Lumen5, Pictory, and Synthesia-revealed differences in user evaluations. The findings indicate that Lumen5 excels in social media content creation, while Pictory has a weaker user experience with more negative feedback. The study concludes that developers should focus on increasing tool usage frequency and enhancing data security to improve user satisfaction. Moreover, targeted strategies based on different user segments can aid in more precise product optimization and market promotion. This research provides practical guidance for the further development of AI-generated video tools. Future studies could expand the sample size and data dimensions to further validate and deepen these conclusions.

Rural Tourist Attractions Recommendation Model Based on Multi-Feature Fusion Graph Neural Networks

With the rapid growth of the rural tourism industry, traditional tourism recommendation technologies can no longer meet the necessary requirements. To address the issue of rural tourist attraction recommendations, a rural tourist attraction recommendation model is constructed based on a multi-feature fusion graph neural network. First, construct a feature map based on the relationship between tourists’ preferences and tourist attractions, and incorporate the attention mechanism to enhance the model’s learning capabilities. Second, utilize a two-part graph model to extract positive and negative preference features of tourists, and a conversation graph model to extract tourists’ transfer preference features. Finally, various features are utilized to generate suggested content by computing scores for tourists’ travel preferences. To address the problem of recommending tourist groups, suitable features for random group matching are collected and the cosine function is employed to identify users with similar random group features. Finally, the multi-features are merged, and the tourists’ interest preferences are scored to arrive at content recommendations. In the experiment on individualized attraction recommendations, data from the Chengdu area were used to test the proposed model. The accuracy of the model’s recommendations was 0.822 for five recommendations which outperformed the other models. In the experiment for group-based attraction recommendations, this experiment tested the Chengdu dataset. The proposed model achieved the highest accuracy of 0.972 when the group size was 70, outperforming the other two models. Additionally, with regards to different numbers of recommendations, the proposed model’s accuracy was 0.5241, which was the best performance among the three models when the number of recommendations was set to five. The proposed recommendation model performs optimally in suggesting tourist attractions and meets the needs of rural tourism. The research content provides crucial technical references for tourist traveling and rural tourism development.

Acoustic characteristics of bamboo-based guitar – A case study

Bamboo is a fast-growing plant easily obtained in Malaysia. It is commonly used for constructing various solid structures including the guitar. Its unique sound quality makes it different from the traditional wooden guitar. The Yamaha guitar sound was used as reference for the generally preferred guitar characteristics. This work focused on the acoustic characteristics easily obtained using the frequency spectrum analysis via a PicoScope oscilloscope and spectrogram using Adobe Audition. A microphone was used for recording the string sound and yielding the frequency response function. The Fast Fourier Transform (FFT) spectra showed that the Yamaha guitar had less partials compared to the bamboo guitar, except string 4. Strings 1, 2, and 3 showed a regular signal from the Yamaha guitar whereas the bamboo guitar showed an irregular pattern with significant overtone. The intensity of the partials in the bamboo guitar displayed a recognizable pattern, i.e., a reduction of partial intensity amplitude proportional to increasing frequency in strings 4, 5, and 6. Some random partials appeared between the harmonics in string 1, 2, and 3 from bamboo guitar whereas the absence of partials in the Yamaha guitar could be due to the higher radiation coefficient of wood, which displays a different timbre.

Adapting generalized suitability curves from Brown trout to Minnow using 1D and 2D aquatic habitat models

The article focuses on adapting generalized depth preference and flow velocity characteristics from Brown trout (Salmo trutta morpha fario) to the Minnow (Phoxinus phoxinus). The results obtained were used to model habitat suitability with 1D and 2D models. Since 1995, research on assessing aquatic habitat quality has been ongoing on 77 mountain streams in Slovakia. This assessment employs a System of Environmental Flow Analysis (SEFA), which is based on the Instream Flow Incremental Methodology (IFIM). In most sections, Brown trout occurred in representative numbers. Brown trout habitat preferences were derived and generalized, and represented by suitability curves. Minnows were present in sufficient numbers for the derivation of suitability curves only on some streams, making direct generalization from the measured data unrepresentative. The measurement results showed that Minnow has similar depth and flow rate preferences to Brown trout. Therefore, it can be assumed that it is possible to adapt the generalised suitability curves from Brown trout to Minnow. This expansion enabled us to broaden the assessment of habitat quality using the SEFA model to mountain streams that are dominated by Minnows and where there is insufficient Brown trout presence. Verification of parameter adaptation from Brown trout to Minnow was conducted in 11 sections of mountain streams. We discuss the performance of habitat quality modeling, concerning the fundamental hydraulic characteristics of streams, using both 1D and 2D models. The results of the 2D modeling are presented for a sub-mountain stream.

Attenuation of bulk waves using locally resonant soil-coupled metabarriers

Abstract Low frequency ground-borne vibrations generated by transport infrastructure are one of the most serious causes of disturbance to the general population. One possibility to reduce this problem is to use the wave filtering properties of elastic metamaterials. However, their integration in the soil complicates the prediction of their response, and the influence of soil-structure interaction needs to be correctly evaluated for an efficient design. The aim of this work is to experimentally evaluate the efficiency of metamaterial trench barriers set in soil in attenuating vibrations, using low-frequency local resonance mechanisms. A lab scale model is proposed comprising different resonating structures and a cylindrical encasement is adopted to couple the structure to the soil. The influence of various parameters is evaluated, such as metamaterial structure, geometrical characteristics of the resonator, and constituent materials. Finite Element simulations are used to develop a suitable design, analysing mode shapes and resonance frequencies of structures with and without the surrounding encasement. Experimental modal analysis is then performed on the corresponding fabricated samples, providing both model validation and out-of-soil mechanical characterization. Finally, vibration transmission loss measurements are performed in a setup in which different resonant metamaterial barriers are embedded into the soil sample, allowing the evaluation of barrier performance. Results indicate that the metamaterial structures provide good attenuation of vibrations in selected intervals in the low to high frequency range (1–5 kHz), demonstrating the feasibility of the approach in a scaled sample. Preliminary data regarding the structures providing preferable design characteristics is also obtained. These results can be useful for the design of trench barriers scaled to large dimensions in more realistic applicative settings.