Limited Number Research Articles

The perception of Mandarin speech conveying communicative functions in Chinese heroin addicts.

Drug addiction can cause severe damage to the human brain, leading to significant problems in cognitive processing, such as irritability, speech distortions, and exaggeration of negative stimuli. Speech plays a fundamental role in social interaction, including both the production and perception. The ability to perceive communicative functions conveyed through speech is crucial for successful interpersonal communication and the maintaining good social relationships. However, due to the limited number of previous studies, it remains unclear whether the cognitive disorder caused by drug addiction affects the perception of communicative function conveyed in Mandarin speech. To address this question, we conducted a perception experiment involving sixty male participants, including 25 heroin addicts and 35 healthy controls. The experiment aimed to examine the perception of three communicative functions (i.e., statement, interrogative, and imperative) under three background noise conditions (i.e., no noise, SNR [Signal to Noise Ratio] = 10, and SNR = 0). Eight target sentences were first recorded by two native Mandarin speakers for each of the three communicative functions. Each half was then combined with Gaussian White Noise under two background noise conditions (i.e., SNR = 10 and SNR = 0). Finally, 48 speech stimuli were included in the experiment with four options provided for perceptual judgment. The results showed that, under the three noise conditions, the average perceptual accuracies of the three communicative functions were 80.66% and 38% for the control group and the heroin addicts, respectively. Significant differences were found in the perception of the three communicative functions between the control group and the heroin addicts under the three noise conditions, except for the recognition of imperative under strong noise condition (i.e., SNR = 0). Moreover, heroin addicts showed good accuracy (around 50%) in recognizing imperative and poor accuracy (i.e., lower than the chance level) in recognizing interrogative. This paper not only fills the research gap in the perception of communicative functions in Mandarin speech among drug addicts but also enhances the understanding of the effects of drugs on speech perception and provides a foundation for the speech rehabilitation of drug addicts.

Identification of aquifer heterogeneity through inverse methods

The paper underlines the contributions of Ghislain de Marsily (GdM) to the identification of aquifers heterogeneity using inverse methods mainly for modeling subsurface flow. Inverse methods require an objective function to express the goodness of fit of the chosen model, a parameterization to describe the spatial distribution of model parameters, and a minimization algorithm. The resulting inverse problem, which consists in seeking model parameters’ values that render model outputs close to the observations, is usually unstable. GdM developed seminal ideas for the two key inversion issues that are: to stabilize the inverse problem through regularization, and to parameterize it to reproduce the natural heterogeneity of the subsurface with a limited number of parameters. GdM conducted pioneering works that are the basis of current parameterization methods relying upon adaptive zonation and/or interpolation based on pilot points. We take here the opportunity to highlight the GdM’s contributions inspiring currently used techniques.

Can sodium and potassium measured in timed voids be used as reference instruments for validating self-report instruments? Results from a urine calibration study

BackgroundSodium and potassium measured in 24-h urine collections are often used as reference measurements to validate self-reported dietary intake instruments. ObjectivesTo evaluate whether collection and analysis of a limited number of urine voids at specified times during the day (“timed voids”) can provide alternative reference measurements, and to identify their optimal number and timing. MethodsWe used data from a urine calibration study among 441 adults aged 18–39 y. Participants collected each urine void in a separate container for 24 h and recorded the collection time. For the same day, they reported dietary intake using a 24-h recall. Urinary sodium and potassium were analyzed in a 24-h composite sample and in 4 timed voids (morning, afternoon, evening, and overnight). Linear regression models were used to develop equations predicting log-transformed 24-h urinary sodium or potassium levels using each of the 4 single timed voids, 6 pairs, and 4 triples. The equations also included age, sex, race, BMI (kg/m2), and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-h levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-h dietary recall. These estimates were compared. ResultsOptimal combinations found were as follows: single voids—evening; paired voids—afternoon + overnight (sodium) and morning + evening (potassium); and triple voids—morning + evening + overnight (sodium) and morning + afternoon + evening (potassium). Predicted 24-h urinary levels estimated 24-h recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-h urinary levels. To recover lost precision, it was estimated that sample sizes need to be increased by ∼2.6–2.7 times for a single void, 1.7–2.1 times for paired voids, and 1.5–1.6 times for triple voids. ConclusionsOur results provide the basis for further development of new reference biomarkers based on timed voids. Clinical Trial Registryclinicaltrials.gov as NCT01631240.

Meta-learning to calibrate Gaussian processes with deep kernels for regression uncertainty estimation

Although Gaussian processes (GPs) with deep kernels have been successfully used for meta-learning in regression tasks, its uncertainty estimation performance can be poor. We propose a meta-learning method for calibrating deep kernel GPs for improving regression uncertainty estimation performance with a limited number of training data. The proposed method meta-learns how to calibrate uncertainty using data from various tasks by minimizing the test expected calibration error, and uses the knowledge for unseen tasks. We design our model such that the adaptation and calibration for each task can be performed without iterative procedures, which enables effective meta-learning. In particular, a task-specific uncalibrated output distribution is modeled by a GP with a task-shared encoder network, and it is transformed to a calibrated one using a cumulative distribution function of a task-specific Gaussian mixture model (GMM). By integrating the GP and GMM into our neural network-based model, we can meta-learn the model parameters in an end-to-end fashion. Our experiments demonstrate that the proposed method improves uncertainty estimation performance while keeping high regression performance compared with the existing methods using real-world datasets in few-shot settings.

Scour Protection Measures for Offshore Wind Turbines: A Systematic Literature Review on Recent Developments

Offshore wind energy is considered as one of the most promising resources of clean and renewable energy to replace fossil fuels. Additionally, its cost is expected to be lower than onshore wind energy as the technology matures. Offshore wind turbines (OWTs) normally operate in harsh ocean environments, which could impact their structural integrity. Scour erosion around foundations of OWTs can substantially change the overall stiffness of these structures and shorten their lifetime. Currently, there are a limited number of studies on countermeasures and their engineering requirements for decreasing the scouring effect; this is due to their different hydraulic circumstances, such as their stability, reliability, and resistance capacity. To this end, advancements in scour protection measures in the offshore energy sector are evaluated in this paper through a thorough and critical review following the PRISMA systematic literature mapping approach. This includes 68 papers on scour protection and over 30 scour protection designs for various types of wind turbine foundations. Here, we aim to provide an overview of the latest scouring protection measures and their comprehensive assessment, as well as their prospects and future challenges. The findings of this study will provide key insights into scour protection measures for OWTs and will subsequently contribute to the future growth of the offshore renewable energy sector.

Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals.

Stereotactic body radiotherapy for oligoprogression with or without switch of systemic therapy

BackgroundOligoprogression is defined as cancer progression of a limited number of metastases under active systemic therapy. The role of metastasis-directed therapy, using stereotactic body radiotherapy (SBRT), is controversial as is the continuation versus switch of systemic therapy. We report outcomes of oligoprogressive patients after SBRT, and compare those patients that continued or switched their current line of systemic therapy. Material/Methods.We included patients who developed up to 5 progressive extracranial metastases under systemic therapy for any solid organ malignancy and were treated with SBRT to all lesions at our institution between 01/2014 and 12/2019. Overall survival (OS) and progression-free survival (PFS) were analyzed using the Kaplan-Meier method, and the interval to the next systemic therapy line determined using cumulative incidence functions. Multivariable Cox regression models were used to analyze the influence of baseline and post-progression variables on OS, PFS and survival with the next systemic therapy after SBRT. ResultsAmong 135 patients with oligoprogressive disease of which the most common primary tumor was lung cancer (n = 46, 34.1 %), 96 continued their current line of systemic therapy after oligoprogression. Among 39 who switched systemic therapy, 28 (71.8 %) paused or discontinued, while 11 (28.2 %) immediately started another systemic treatment. After a median follow-up of 27.2 months, patients that switched and those who continued systemic therapy after oligoprogression had comparable median OS (32.1 vs. 38.2 months, p = 0.47) and PFS (4.3 vs. 3.4 months, p = 0.6). The intervals to the next systemic therapy line were comparable between both cohorts (p = 0.6). An ECOG performance status of 2 and immediately starting a new systemic therapy after oligoprogression were associated with a poorer survival without next systemic therapy, while the de-novo OMD state was associated with better survival without next systemic therapy compared to the induced state. ConclusionOncological outcomes of patients that continued or switched systemic therapy after SBRT for oligoprogression were comparable, potentially indicating that further lines of treatment may be safely delayed in selected cases.

Graph-SeTES: A graph based search task extraction using Siamese network

Search task extraction is a sub-process for query suggestion/reformulation, personalized recommendation, and advertisement in search engines and e-commerce platforms. However, they face both internal and external challenges. Internal challenges include short and misspelled queries and incomplete keywords. External challenges include an unknown number of clusters and a limited number of labeled datasets. Deep-learning models require a large amount of data for training; however, search task datasets are rare and small. To overcome these limitations, we proposed Graph-SeTES, which integrates feature extraction with a decision network that utilizes both distance metrics and decision networks. The existing graph-clustering algorithms use the similarities between search query pairs. Because the Siamese network (SN) finds similarities between two objects, it fits search task extraction. Compared with existing models, SN requires fewer parameters for training due to the shared weights. However, it yields good results even with less labeled data, overcoming the external challenges. We benefit from both distance metrics and narrowing of the linear layers for decision networks. Graph-SeTES was compared with state-of-the-art models, and it outperformed its counterparts. The results were 6% better than those of the best baseline on the CSTE dataset, which maintained this performance difference on the WSMC12 dataset.

Systematic Review of Interventions with Some School Involvement for School Refusal in High School–Age Adolescents

Abstract Chronic absenteeism is a public health concern. School refusal due to emotional distress is one reason students exhibit chronic absenteeism. The objective of this systematic review was to determine potential aspects of interventions, in school settings or involving a school-based component, that are successful in addressing school refusal among high school–age adolescents. After duplicated records were excluded, 1,864 studies were identified from searches. The abstracts and full text articles were independently reviewed and received two votes from each of the five reviewers using the eligibility criteria. Two reviewers independently evaluated the remaining articles and met to discuss findings with a third reviewer. The 10 articles included eight studies that noted techniques in cognitive–behavioral therapy (CBT) as promising and two studies that considered CBT as effective as other approaches. The study design for three studies were randomized controlled trials. The remaining seven studies were quasiexperimental. Only three of the 10 studies received a high rating using the Quality Appraisal Tool. There was a limited number of quality studies that used rigorous scientific methods and variation in how each study engaged schools. Further exploration and development of interventions with an integral school-based component are needed.

Comparison of Bagging and Sparcity Methods for Connectivity Reduction in Spiking Neural Networks with Memristive Plasticity

Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local learning and limited numbers of connections, respectively. In this work, we compare two methods of connectivity reduction that are applicable to spiking networks with local plasticity; instead of a large fully-connected network (which is used as the baseline for comparison), we employ either an ensemble of independent small networks or a network with probabilistic sparse connectivity. We evaluate both of these methods with a three-layer spiking neural network, which are applied to handwritten and spoken digit classification tasks using two memristive plasticity models and the classical spike time-dependent plasticity (STDP) rule. Both methods achieve an F1-score of 0.93–0.95 on the handwritten digits recognition task and 0.85–0.93 on the spoken digits recognition task. Applying a combination of both methods made it possible to obtain highly accurate models while reducing the number of connections by more than three times compared to the basic model.

Targeting latent viral infection in EBV-associated lymphomas.

Epstein-Barr virus (EBV) contributes to the development of a significant subset of human lymphomas. As a herpes virus, EBV can transition between a lytic state which is required to establish infection and a latent state where a limited number of viral antigens are expressed which allows infected cells to escape immune surveillance. Three broad latency programs have been described which are defined by the expression of viral proteins RNA, with latency I being the most restrictive expressing only EBV nuclear antigen 1 (EBNA1) and EBV-encoded small RNAs (EBERs) and latency III expressing the full panel of latent viral genes including the latent membrane proteins 1 and 2 (LMP1/2), and EBNA 2, 3, and leader protein (LP) which induce a robust T-cell response. The therapeutic use of EBV-specific T-cells has advanced the treatment of EBV-associated lymphoma, however this approach is only effective against EBV-associated lymphomas that express the latency II or III program. Latency I tumors such as Burkitt lymphoma (BL) and a subset of diffuse large B-cell lymphomas (DLBCL) evade the host immune response to EBV and are resistant to EBV-specific T-cell therapies. Thus, strategies for inducing a switch from the latency I to the latency II or III program in EBV+ tumors are being investigated as mechanisms to sensitize tumors to T-cell mediated killing. Here, we review what is known about the establishment and regulation of latency in EBV infected B-cells, the role of EBV-specific T-cells in lymphoma, and strategies to convert latency I tumors to latency II/III.

The Micronucleus Assay on Cryopreserved Whole Blood.

The in vitro cytokinesis-block micronucleus (CBMN) assay is a widely used technique in radiobiology research, biological dosimetry, genotoxicity studies, and in vitro radiosensitivity testing. This cytogenetic method is based on the detection of micronuclei in binucleated cells resulting from chromosomal fragments lagging during cell division. Fresh whole blood samples are the most preferred sample type for the CBMN assay. However, the disadvantages of working with fresh blood samples include immediate processing after blood collection and the limited number of repeated analyses that can be performed without extra blood sampling. As the need for fresh blood samples can be logistically challenging, CBMN assay on cryopreserved whole blood samples would be of great advantage, especially in large-scale patient studies. This paper describes a protocol to freeze whole blood samples and to perform the CBMN assay on these frozen blood samples. Blood samples from healthy volunteers have been frozen and thawed at different time points and then, subjected to a modified micronucleus assay protocol. The results demonstrate that this optimized procedure allows the performance of the CBMN assay on frozen blood samples. The described cryopreservation protocol may also be very useful for other cytogenetic assays and a variety of functional assays requiring proliferating lymphocytes.

Selected Filipino Faculty Members' Online Shopping Experience in Manila's Online Marketplace


 This study is current and applicable to the new norm of comprehending the experiences of online consumers on online marketplace pages. This study concentrated on the online shopping experiences and reactions of selected faculty members by identifying key observations, and consumer experiences on the web that emphasize browsing, purchasing, and online flow. The researchers used phenomenology as a qualitative research method to collect responses from twenty buyers who varied in consumer experience, gender, and age. The interview was tailored to each participant based on online information and a guide question. Results of the interviews revealed that some faculty members at a local university in Manila find online businesses to be both popular and convenient, while they can readily access online marketplaces. In addition, they purchase frequently using this platform due to the low prices, which is advantageous for them as consumers, although they are wary of con artists and fake buyers/sellers. Meanwhile, the challenges encountered by buyers include product complexities and meet-up changes, as well as the central issues of online trust and social connection. Despite the fact that some product acquisitions are difficult, the study found that appropriateness, price setting, and seller connections kept customers purchasing on the online market. Despite offering valuable insights into the faculty members’ online purchasing experience, this research has a number of limitations. The study is predicated on a twenty-participant qualitative investigation that was conducted primarily in Manila. Consequently, future research should involve populations from different regions. Numerous opportunities exist for future research to build on these findings. The development of a typology of online encounters and circumstances is one of the suggested areas for future study. 

Critical and distinct roles of cell type-specific NF-κB2 in lung cancer.

Different from the well-studied canonical NF-κB member RelA, the role of the noncanonical NF-κB member NF-κB2 in solid tumors, and lung cancer in particular, is poorly understood. Here we report that in contrast to the tumor-promoting role of RelA, NF-κB2 intrinsic to lung epithelial and tumor cells had no marked effect on lung tumorigenesis and progression. On the other hand, NF-κB2 limited dendritic cell number and activation in the lung but protected lung macrophages and drove them to promote lung cancer through controlling activation of noncanonical and canonical NF-κB, respectively. NF-κB2 was also required for B cell maintenance and T cell activation. The antitumor activity of lymphocyte NF-κB2 was dominated by the protumor function of myeloid NF-κB2; thus, NF-κB2 has an overall tumor-promoting activity. These studies reveal a cell type-dependent role for NF-κB2 in lung cancer and help understand the complexity of NF-κB action and lung cancer pathogenesis for better design of NF-κB-targeted therapy against this deadliest cancer.

Construction of a sustainability reporting score index integrating sustainable development goals (SDGs). The case of Sri Lankan listed firms

PurposeAlthough publicly listed firms in Sri Lanka have been increasingly adapting sustainability reporting into their annual reporting practices, a limited number of firms prepare sustainability reports by integrating sustainable development goals (SDGs) into reporting mechanisms. This study attempts to develop an index to monitor firms' sustainability reporting practices based on Global Reporting Institute (GRI) guidelines integrating SDGs.Design/methodology/approachThis paper develops a sustainability score index using the 17 SDGs utilising the results of content analysis of corporate annual reports of a selected sample of 100 firms listed on the Colombo Stock Exchange (CSE). Principal component analysis was employed to examine the reliability of data in the developed index.FindingsFindings show that the developed scoring index is efficient for evaluating the contents of the sustainability reports of Sri Lankan firms. Sustainability reporting practises with regard to the SDGs were observed to have a turbulent period from 2015 to 2019 and the SDGs 12 and 15 were identified to be mostly reported in Sri Lankan corporate sustainability reports.Research limitations/implicationsThe results of the study add to knowledge on the monitoring of sustainability reporting practises with reference to SDGs. The study outcomes are useful for the investors, stakeholders, and statutory bodies to measure the sustainable performance of business firms and assess the firm’s commitment towards the global sustainability agenda.Originality/valueTo the best of our knowledge, this is the first study that constructs a sustainability reporting score index integrating SDGs.

Role of minimally invasive surgery in the management of localized pancreatic ductal adenocarcinoma: a review.

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with a minority of patients eligible for curative-intent surgical intervention. Pancreatic resections are technically demanding operations associated with considerable morbidity and mortality. Minimally invasive pancreatic resections (MIPRs), which include laparoscopic and robotic approaches, may enhance postoperative outcomes by lessening physiological impact of open surgery. A limited number of randomized-controlled trials as well as numerous retrospective reports have focused on MIPR outcomes and role in management of a variety of tumors, including PDAC. Today, MIPRs are generally considered acceptable alternatives to open surgery as a trend towards improved short-term metrics is observed. However, several questions remain regarding the oncological adequacy of MIPR's as long-term experience is less extensive compared to open techniques. This review aims to summarize existing evidence on MIPRs with a focus on PDAC.

Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) are a promising approach for solving expensive multiobjective optimization problems, but they often cannot address high-dimensional problems. Although one common approach to overcoming this challenge is to construct reliable surrogates, their accuracy inevitably deteriorates in a high-dimensional search space. Thus, this paper presents a novel SAEA based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems. The intent is to decrease the risk of search performance degradations caused by unreliable approximations and retain the redundancy of the surrogate-assisted search to hedge the risk of over-fitting. Furthermore, each approximation model is adapted to a promising region of its corresponding decomposed problem to reduce the complexity of model fitting given a limited number of training samples. Experimental results show that the proposed algorithm is competitive with state-of-the-art SAEAs adapted for high-dimensional problems.

Identification of an Antagonist Targeting G Protein and β-Arrestin Signaling Pathways of 5-HT7R.

In consideration of the limited number of FDA-approved drugs for autism spectrum disorder (ASD), significant efforts have been devoted to identifying novel drug candidates. Among these, 5-HT7R modulators have garnered considerable attention due to their potential in alleviating autism-like behaviors in ASD animal models. In this study, we designed and synthesized biphenyl-3-ylmethylpyrrolidines 3 and biphenyl-3-yl-dihydroimidazoles 4 as 5-HT7R modulators. Through extensive biological tests of 3 and 4 in G protein and β-arrestin signaling pathways of 5-HT7R, it was determined that 2-(2'-methoxy-[1,1'-biphenyl]-3-yl)-4,5-dihydro-1H-imidazole 4h acted as a 5-HT7R antagonist in both signaling pathways. In in vivo study with Shank3-/- transgenic (TG) mice, the self-grooming behavior test was performed with 4h, resulting in a significant reduction in the duration of self-grooming. In addition, an immunohistochemical experiment with 4h restored reduced neurogenesis in Shank3-/- TG mice, which is confirmed by the quantification of doublecortin (DCX) positive neurons, suggesting the promising therapeutic potential of 4h.

Business Opportunities and Drivers for Health and Spa Tourism: A Qualitative Research on Baile Felix Spa Resort, Romania

The tourism sector has witnessed continuous development over the last few decades, overcoming various challenges and crises. This resilience underscores the remarkable diversity of managerial and entrepreneurial initiatives capable of meeting the ever-growing and unpredictable global demand. Our objective is to explore the perceptions of factors and opportunities that encourage the launch and management of businesses in health and spa tourism utilizing a qualitative methodology. Our conclusions are based on the analysis of 32 hotel managers’ and small business owners’ interviews in a relevant tourist resort in Romania. This study reveals that all four types of identified opportunities and drivers influencing business creation and management in health and spa tourism (i.e., improving work quality and independence, professional and personal development, responses to opportunities, and upgrading existing services) are substantially influenced by gender, relevant work experience, and education. The novelty of our research lies in highlighting the influence of these drivers according to a specific context given by the size, management type, and ownership structure of tourism ventures, as well as the difference between large hotel managers and small business owners in terms of their perception and goals. We acknowledge that the limited number of interviewees and the spatially bound context of the research may have an effect on the relevance of the results. This study’s findings underscore the necessity for compromise and collaboration among diverse stakeholders in the tourism industry, particularly when developing long-term strategies for health and wellness destinations. This collaborative approach plays a critical role in promoting the well-being and prosperity of individuals, businesses, and the entire local community.

Transfer learning model for estimating site amplification factors from limited microtremor H/V spectral ratios

Summary Site amplification factors (SAFs) of seismic ground motions are essential in evaluating and estimating seismic hazards. In our previous study, the authors proposed a simple and cost-effective method to estimate a SAF based on a deep neural network (DNN) model and microtremor horizontal-to-vertical spectral ratio (MHVR). Since the previous DNN model was based on the observed SAFs and MHVRs within a limited district in Japan, the applicability of the previous model to non-source regions with different site conditions was limited. This study explored the application of a transfer learning (TL) technique to adapt an existing (pre-trained) DNN model to new regions and a different database. The SAFs obtained through generalized spectral inversion technique (GIT) at the seismic observation stations (K-NET and KiK-net) in Japan were collated as the ground truth for site effects. MHVRs recorded at the stations in several districts of Japan were collected to construct a dataset for the development of the TL model. Subsequently, a TL model was constructed, leveraging the neural network layers and their weights from the pre-trained model while incorporating additional neural network layers to enhance the performance. During the training process, a total data set of 112 sites was divided into training set, validation set, and external test set by 1:1:5. Utilizing a cross-validation strategy, the residuals between pSAFs (pseudo-SAFs) estimated by the TL model and the observed SAFs were analyzed for the external test set containing 80 sites. The results showed that the TL model outperformed the pre-trained DNN model. The cross-validation results demonstrated that almost consistent prediction results were obtained regardless of any combination of 16 sites selected as the training set. Furthermore, by contrasting the influence of varying training set sizes on the performance of the TL model and comparing the TL model to a DNN model with an extended training set, the effectiveness of constructing the model with the limited number of data (16 sites) was ascertained. Finally, the effectiveness and limitations of the TL model were evaluated using MHVRs with peak frequencies falling outside the training set's range.