Search

Existing studies on gait phase estimation generally involve walking experiments using inertial measurement units under limited walking conditions (WCs). In this study, a gait phase estimation algorithm is proposed that uses data from force sensing resistors (FSRs) and a Bi-LSTM model. The proposed algorithm estimates gait phases in real time under various WCs, e.g., walking on paved/unpaved roads, ascending and descending stairs, and ascending or descending on ramps. The performance of the proposed algorithm is evaluated by performing walking experiments on ten healthy adult participants. An average gait estimation accuracy exceeding 90% is observed with a small error (root mean square error = 0.794, R2 score = 0.906) across various WCs. These results demonstrate the wide applicability of the proposed gait phase estimation algorithm using various insole devices, e.g., in walking aid control, gait disturbance diagnosis in daily life, and motor ability analysis. [ABSTRACT FROM AUTHOR]

The social restriction measures implemented due to the COVID-19 pandemic have impacted the pattern of occurrences of respiratory viruses. According to surveillance results in the Gwangju region of South Korea, respiratory syncytial virus (RSV) did not occur during the 2020/2021 season. However, there was a delayed resurgence in the 2021/2022 season, peaking until January 2022. To analyze this, a total of 474 RSV positive samples were investigated before and after the COVID-19 pandemic. Among them, 73 samples were selected for whole-genome sequencing. The incidence rate of RSV in the 2021/2022 season after COVID-19 was found to be approximately three-fold higher compared to before the pandemic, with a significant increase observed in the age group from under 2 years old to under 5 years old. Phylogenetic analysis revealed that, for RSV-A, whereas four lineages were observed before COVID-19, only the A.D.3.1 lineage was observed during the 2021/2022 season post-pandemic. Additionally, during the 2022/2023 season, the A.D.1, A.D.3, and A.D.3.1 lineages co-circulated. For RSV-B, while the B.D.4.1.1 lineage existed before COVID-19, both the B.D.4.1.1 and B.D.E.1 lineages circulated after the pandemic. Although atypical RSV occurrences were not due to new lineages, there was an increase in the frequency of mutations in the F protein of RSV after COVID-19. These findings highlight the need to continue monitoring changes in RSV occurrence patterns in the aftermath of the COVID-19 pandemic to develop and manage strategies in response. [ABSTRACT FROM AUTHOR]

The non-pharmaceutical interventions implemented to prevent the spread of COVID-19 have affected the epidemiology of other respiratory viruses. In South Korea, Human metapneumovirus (HMPV) typically occurs from winter to the following spring; however, it was not detected for two years during the COVID-19 pandemic and re-emerged in the fall of 2022, which is a non-epidemic season. To examine the molecular genetic characteristics of HMPV before and after the COVID-19 pandemic, we analyzed 427 HMPV-positive samples collected in the Gwangju area from 2018 to 2022. Among these, 24 samples were subjected to whole-genome sequencing. Compared to the period before the COVID-19 pandemic, the incidence rate of HMPV in 2022 increased by 2.5-fold. Especially in the age group of 6–10 years, the incidence rate increased by more than 4.5-fold. In the phylogenetic analysis results, before the COVID-19 pandemic, the A2.2.2 lineage was predominant, while in 2022, the A2.2.1 and B2 lineage were observed. The non-pharmaceutical interventions implemented after COVID-19, such as social distancing, have reduced opportunities for exposure to HMPV, subsequently leading to decreased acquisition of immunity. As a result, HMPV occurred during non-epidemic seasons, influencing the age distribution of its occurrences. [ABSTRACT FROM AUTHOR]

Stride length (SL), foot clearance (FC), and foot progression angle (FPA) are the key parameters for diagnosing gait disorders. This study used the distance data between two feet measured by ultra-wideband (UWB) sensors installed on shoes and proposed a method for estimating the three gait parameters. Here, a method of compensating the offset of the UWB sensor and estimating the distances between a base sensor installed on one foot during the stance phase and three UWB sensors on the other during the swing phase was applied. Foot trajectory was acquired in a gait experiment with ten healthy adults walking on a treadmill. The results were compared with those obtained using a motion capture system (MCS). The UWBs sensor displayed average errors of 45.84 mm, 7.60 mm, and 2.82° for SL, FC, and FPA, respectively, compared with the MCS. A similar accuracy level was achieved in a previous study that used an inertial measurement unit (IMU). Thus, these results suggest that UWB sensors can be extensively applied to sensor systems used to analyze mobile gait systems. [ABSTRACT FROM AUTHOR]

As the scale of online news and social media expands, attempts to analyze the latest social issues and consumer trends are increasing. Research on detecting spatio-temporal event sentences in text data is being actively conducted. However, a document contains important spatio-temporal events necessary for event analysis, as well as non-critical events for event analysis. It is important to increase the accuracy of event analysis by extracting only the key events necessary for event analysis from among a large number of events. In this study, we define important 'representative spatio-temporal event documents' for the core subject of documents and propose a BiLSTM-based document classification model to classify representative spatio-temporal event documents. We build 10,000 gold-standard training datasets to train the proposed BiLSTM model. The experimental results show that our BiLSTM model improves the F1 score by 2.6% and the accuracy by 4.5% compared to the baseline CNN model. [ABSTRACT FROM AUTHOR]

In order to operate a gait rehabilitation device, it is necessary to accurately classify the states appearing in activities of daily living (ADLs). In the case of force sensing resistors (FSRs), which are often used as pressure sensors in gait analysis, it is desirable to replace them with other sensors because of their low durability. In the present study, capacitive-type pressure sensors, as an alternative to FSRs, were developed, and their performance was evaluated. In addition, the timed up and go test was performed to measure the ground reaction force in healthy individuals, and a machine learning technique was applied to the calculated biosignal parameters for the classification of five types of ADLs. The performance evaluation results showed that a sensor with thermoplastic polyurethane (substrate and dielectric layer material) and multiwall carbon nanotubes (conductive layer) has sufficient sensitivity and durability for use as a gait analysis pressure sensor. Moreover, when an overlapping filter was applied to the four-layer long short-term memory (LSTM) or the five-layer LSTM model developed for motion classification, the precision was greater or equal to 95%, and unstable errors did not occur. Therefore, when the pressure sensor and ADLs classification algorithm developed in this study are applied, it is expected that motion classification can be completed within a time range that does not affect the control of the gait rehabilitation device. [ABSTRACT FROM AUTHOR]

Demand for healthy diets has led researchers to explore new saccharide as sucrose alternatives. ᴅ-Psicose, the C-3 epimer of ᴅ-fructose, has a similar sweetness intensity to sucrose but contributes fewer calories. This study proposes a disaccharide with a stable structure derived from ᴅ-psicose. The compound with a spiro-tricyclic core was generated at 32% conversion via caramelization of ᴅ-psicose under acidic anhydrous conditions. The compound was identified by high-resolution mass spectrometry and multi-dimensional nuclear magnetic resonance (NMR). The molecular formula was established as C12H20O10 from the molecular weight of m/z 324.1055. Twelve signals were observed by the 13C NMR spectrum. This compound, denoted di-ᴅ-psicose anhydride (DPA), exhibited a lower water solubility (40 g/L) and higher thermal stability (peak temperature = 194.7 °C) than that of ᴅ-psicose (peak temperature = 126.5 °C). The quantitatively evaluated metal ion scavenging ability of DPA was the best in magnesium (average 98.6 ± 1.1%). This synthesis methodology can provide disaccharides with high stability-reducing heavy metals. [ABSTRACT FROM AUTHOR]

Gait assessment is an important tool for determining whether a person has a gait disorder. Existing gait analysis studies have a high error rate due to the heel-contact-event-based technique. Our goals were to overcome the shortcomings of existing gait analysis techniques and to develop more objective indices for assessing gait disorders. This paper proposes a method for assessing gait disorders via the observation of changes in the center of pressure (COP) in the medial–lateral direction, i.e., COPx, during the gait cycle. The data for the COPx were used to design a gait cycle estimation method applicable to patients with gait disorders. A polar gaitogram was drawn using the gait cycle and COPx data. The difference between the areas inside the two closed curves in the polar gaitogram, area ratio index (ARI), and the slope of the tangential line common to the two closed curves were proposed as gait analysis indices. An experimental study was conducted to verify that these two indices can be used to differentiate between stroke patients and healthy adults. The findings indicated the potential of using the proposed polar gaitogram and indices to develop and apply wearable devices to assess gait disorders. [ABSTRACT FROM AUTHOR]

Recently, a therapeutic method to stimulate the suprahyoid muscle using peripheral magnetic stimulation for dysphagia rehabilitation has been reported. However, clinical evidence, application protocol, and intervention method remain unclear. Therefore, a systematic review of the published literature is needed. The objective of this study was to systematically review clinical studies of peripheral magnetic stimulation applied for rehabilitation of dysphagia. Issues to be considered in future studies are also suggested. This systematic review performed a literature search of four databases (Medline, Embase, CINAHL, and Web of Science) to identify relevant studies published on the application of repetitive peripheral magnetic stimulation (rPMS) for swallowing-related muscles between 2010 and 2022. Seven studies were reviewed. Randomized controlled trials and one-group pre–post, case study designs were included. In the included studies, rPMS was applied to strengthen the submental suprahyoid muscles. The intervention regime varied. The rPMS was applied at a frequency of 30 Hz for 2 s. Rest time ranged from 8 s to 27–28 s. The number of intervention sessions ranged from 2–3 to 30. The intensity ranged from pain-inducing minimum intensity (90% of maximum stimulus output) to non-painful intensity (70–80% of maximum intensity). The rPMS on the suprahyoid muscles had positive effects on physiological changes in the swallowing function, such as displacement of the hyoid bone, muscle strength (cervical flexor, jaw-opening force), swallowing safety, swallowing performance, and swallowing-related quality of life. Participants also reported little pain and adverse reactions during rPMS. Although rPMS is a therapeutic option that can help improve the swallowing function as a non-invasive stimulation method in the rehabilitation of dysphagia, clinical evidence is needed for the development of clear stimulation protocols and guidelines. [ABSTRACT FROM AUTHOR]

The e-learning environment should support the handwriting of mathematical expressions and accurately recognize inputted handwritten mathematical expressions. To this end, expression-related information should be fully utilized in e-learning environments. However, pre-existing handwritten mathematical expression recognition models mainly utilize the shape of handwritten mathematical symbols, thus limiting the models from improving the recognition accuracy of a vaguely represented symbol. Therefore, in this paper, a context-aided correction (CAC) model is proposed that adjusts an output of handwritten mathematical symbol (HMS) recognition by additionally utilizing information related to the HMS in an e-learning system. The CAC model collects learning contextual data associated with the HMS and converts them into learning contextual information. Next, contextual information is recognized through artificial intelligence to adjust the recognition output of the HMS. Finally, the CAC model is trained and tested using a dataset similar to that of a real learning situation. The experiment results show that the recognition accuracy of handwritten mathematical symbols is improved when using the CAC model. [ABSTRACT FROM AUTHOR]

With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data and utilizing it for event analysis is being actively conducted. However, if spatiotemporal information that does not describe the core subject of a document is extracted, it is rather difficult to guarantee the accuracy of core event analysis. Therefore, it is important to extract spatiotemporal information that describes the core topic of a document. In this study, spatio-temporal information describing the core topic of a document is defined as 'representative spatio-temporal information', and documents containing representative spatiotemporal information are defined as 'representative spatio-temporal documents'. We proposed a character-level Convolution Neuron Network (CNN)-based document classifier to classify representative spatio-temporal documents. To train the proposed CNN model, 7400 training data were constructed for representative spatio-temporal documents. The experimental results show that the proposed CNN model outperforms traditional machine learning classifiers and existing CNN-based classifiers. [ABSTRACT FROM AUTHOR]

With the continuous development of wireless communication technology, the Internet of Things (IoT) is being used in a wide range of fields. The IoT collects and exchanges large amounts of data with objects, either tangible or intangible, such as sensors or physical devices, connected to the Internet. Wireless sensor networks (WSNs) are components of IoT systems. WSNs are used in various IoT systems, such as monitoring, tracking, and detection systems, to extract relevant information and deliver it to users. WSNs consist of sensor nodes with low power, low cost, and multiple functions. Because sensor nodes have limited resources, such as power and memory, a reduction in the energy efficiency of the sensor nodes in WSNs will lead to a decrease in wireless network performance and an increase in packet loss, which affects IoT system performance. Therefore, this study aimed to find an energy-efficient routing method that extends the lifetime of WSNs by minimizing the battery use of sensor nodes to improve the network performance of IoT systems. Conserving energy from sensor nodes and increasing network throughput in WSNs involves having protocols. The low-energy adaptive clustering hierarchy (LEACH) protocol is a well-known hierarchical routing protocol in WSNs that constructs clusters and transmits data. LEACH increases energy efficiency by transmitting data from sensor nodes to the base station (BS) through the cluster head. It is widely adopted in the WSN network field, and many protocols are being studied to improve cluster header selection and data transmission to increase the energy efficiency of sensor nodes. In this study, we attempted to improve energy efficiency by removing unnecessary energy from LEACH. In LEACH, when the sensor node is located between the BS and the cluster head, the sensor node transmits data to the cluster head in the opposite direction of the BS. The data sent to the cluster head are transmitted in the direction of the BS. Thus, transmission in the opposite direction consumes unnecessary energy and affects the WSN performance of IoT systems. In this study, we propose a D-LEACH (direction-based LEACH) protocol based on the received signal strength indicator (RSSI) that improves the efficiency of transmission energy considering the data transmission direction of sensor nodes. D-LEACH aims to balance the energy of the sensor nodes and improve the performance of WSNs in the IoT system by reducing unnecessary energy consumption caused by reverse transmission considering the data transmission direction of the sensor nodes. In the course of the paper, we refer to the routing protocol of WSNs to improve network performance and describe LEACH. We also explain the D-LEACH protocol proposed in this paper and confirm the performance improvement of WSNs in an IoT system through simulation. [ABSTRACT FROM AUTHOR]

Featured Application: Green Communication in Smart City. In upcoming smart urban environments, various things can be interconnected, and the Internet of Things (IoT) can be used to construct a safer and more convenient urban environment. Things in the IoT need an addressing system that can uniquely identify each one; internet protocol (IP) addresses can be used for this purpose. The IP address the two roles of an identifier and a locator. However, this binding has problems related to mobility and multihoming, and it is hard to deploy on a legacy IP system because of some limitations of sensor devices. To solve the problem, we propose a design for software-defined networking (SDN)-based identifier–locator separation architecture on IoT networks. In the proposed scheme, Internet Protocol version 6(IPv6)-based addresses are used for the identifiers and locators. The network is partitioned into a host identity domain for local routing and an IP domain for global routing. The host identity domain operates as an overlaid network over the IP domain, and it makes the unrouteable identifiers routable with a distributed hash table (DHT)-based routing strategy. For the evaluation of the proposed scheme, a packet forwarding cost and signaling cost model is calculated, and the results show that the proposed scheme is conjugable to an IoT network environment. [ABSTRACT FROM AUTHOR]

In manufacturing a product, product defects occur at several stages. This study makes the case that one can build a smart factory by introducing it into the manufacturing process of small-scale scarce products, which mainly solves the defect problem through visual inspection. By introducing an intelligent manufacturing process, defects can be minimized, and human costs can be lowered to enable sustainable growth. In this paper, in order to easily detect defects occurring in the manufacturing process, we studied a deep learning-based automatic defect detection model that can train product characteristics and determine defects using open sources. To verify the performance of this model, it was applied to the disposable gas lighter manufacturing process to detect the liquefied gas volume defect of the lighter, and it was confirmed that the detection accuracy and processing time were sufficient to apply to the manufacturing process. [ABSTRACT FROM AUTHOR]

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data. [ABSTRACT FROM AUTHOR]

A rapid and simple analytical method for triflumezopyrim, a new class of mesoionic insecticides and commercialized molecules from DuPont, was developed with a modified QuEChERS method. The pH adjustment was used to improve the extraction efficiency of acetonitrile solvent, and dispersive solid-phase extraction was employed for the clean-up process. The five selected food commodities were used to verify the present optimized method, which displayed good linearity with an excellent correlation coefficient (R2 = 0.9992–0.9998) in the 0.003–0.30 mg/kg calibration range. The method limits of detection (LOD) and quantification (LOQ) were determined to be a value of 0.003 and 0.01 mg/kg, respectively. The mean recovery for the triflumezopyrim was in the 89.7–104.3% range. The relative standard deviations were ≤9.8% for intra- (n = 5) and inter-day (n = 15) precisions at concentrations of 0.01, 0.1, and 0.5 mg/kg in the five representative samples. The matrix effect has been calculated to confirm the effect during ionization of the analyte in the UPLC-MS/MS. The matrix effects of the instrumental analysis showed that triflumezopyrim was less susceptible to matrices. The proposed analytical method in this study has effectively improved the accuracy, selectivity, and sensitivity for the determination of triflumezopyrim in agricultural commodities; therefore, it can serve as a reference method for the establishment of maximum residue limits (MRLs). [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of semi-immersive virtual reality-based cognitive training (VRCT) combined with locomotor activity on cognitive function, balance, and gait ability in older adults. Eighteen community-dwelling older adults participated in this study. Subjects who met the selection criteria were assigned to an experimental group (n = 9) and a control group (n = 9). The experimental group received VRCT combined with locomotor activity for 30 min a day, three times a week, for 6 weeks. The control group received tabletop activity-based cognitive training for the same amount of time. Before and after the training, the Korean Mini-Mental State Examination (K-MMSE), Trail Making Test (TMT; A and B), and Digit Span Test (DST; forward and backward) were used to evaluate cognitive function; and the Timed Up and Go (TUG) test and 10-m Walking Test (10MWT) were used to evaluate the improvement in the balance and gait ability parameters. After the intervention, the experimental group showed a significantly greater improvement in the TMT-A (p = 0.045) and DST-backward (p = 0.012) scores compared with the control group. Regarding the gait ability variable, the experimental group showed a significant improvement in the 10MWT test (p = 0.001). This study confirmed that semi-immersive VRCT combined with locomotor activity is useful for improving cognitive function and gait ability in older adults. Therefore, VRCT combined with locomotor activity can be used as a simultaneous intervention for cognitive rehabilitation and functional capacity improvement in older adults. [ABSTRACT FROM AUTHOR]

Telehealth (TH) intervention is a method to optimize self-management (SM) support in stroke survivors. Objectives of this study included identifying the TH-SM intervention's focus and SM support components, the TH delivery type, and the TH-SM support effects on stroke survivors. Five databases were searched for the years 2005–2020 to identify TH-SM support interventions for stroke survivors. Randomized controlled trials and quasi-experimental, one-group re-post study designs were included. Ten studies were reviewed. TH-SM support focused on post-stroke depression, obesity management, participation, functional mobility, and activities of daily living. The TH delivery type most used in selected studies was messaging. Regarding the SM support components, the education component was used in all studies, and psychological support and lifestyle advice and support were used in 8 out of 10 studies. TH-SM intervention had positive effects in terms of goal achievement for SM behavior, emotional state, and mobility of clinical outcomes, and TH acceptance in stroke survivors. Although the TH-SM-supported intervention effects were not found consistently in all outcomes, this review discovered a positive effect on various SM-related outcomes. In addition, TH delivery types and SM support components showed the possibility of various options to be considered for intervention. Therefore, we suggest that TH-SM supported intervention is a positive alternative for SM support in stroke survivors. [ABSTRACT FROM AUTHOR]

The operation of wearable robots, such as gait rehabilitation robots, requires real-time classification of the standing or walking state of the wearer. This report explains a technique that measures the ground reaction force (GRF) using an insole device equipped with force sensing resistors, and detects whether the insole wearer is standing or walking based on the measured results. The technique developed in the present study uses the waveform length that represents the sum of the changes in the center of pressure within an arbitrary time window as the determining factor, and applies this factor to a conventional threshold method and an artificial neural network (ANN) model for classification of the standing and walking states. The results showed that applying the newly developed technique could significantly reduce classification errors due to shuffling movements of the patient, typically noticed in the conventional threshold method using GRF, i.e., real-time classification of the standing and walking states is possible in the ANN model. The insole device used in the present study can be applied not only to gait analysis systems used in wearable robot operations, but also as a device for remotely monitoring the activities of daily living of the wearer. [ABSTRACT FROM AUTHOR]

It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students' educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students' perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students. [ABSTRACT FROM AUTHOR]

The purpose of this study was to demonstrate the time-efficiency and the clinical effectiveness of chairside-fabricated lithium disilicate single crowns by digital impressions compared to the conventional method. Thirteen patients requiring a single crown on the maxillary or mandibular premolar or first molar were assigned as study subjects. The impressions were obtained using the conventional method and two digital methods with intraoral scanners: AEGIS.PO (Digital Dentistry Solution, Seoul, Korea) and CEREC Omnicam (Sirona, Bensheim, Germany). Two types of lithium disilicate single crowns were obtained; a reference crown (by conventional workflow) and a chairside crown (by digital workflow). The total time taken for fabricating the chairside crown was recorded. The replica technique was performed to compare the marginal and internal fit of the two types of crowns. In addition, accuracy of the intraoral scanners was evaluated by the best-fit alignment method. The difference between the groups was analyzed using the two-tailed paired t-test or one-way ANOVA, followed by the Student–Newman–Keuls test for multiple comparisons. Statistical significance was accepted at p < 0.05 for all statistical tests. The time required to obtain the impressions by the AEGIS (7:16 ± 1:50 min:s) and CEREC (7:29 ± 2:03 min:s) intraoral scans was significantly lower than the conventional method (12:41 ± 1:16 min:s; p < 0.001). There was no significant difference between the intraoral scanners. The total working time to fabricate the chairside crown averaged 30:58 ± 4:40 min:s. The average marginal gap was not significantly different between the reference (107.86 ± 42.45 µm) and chairside (115.52 ± 38.22 µm) crowns (p > 0.05), based on results of replica measurement. The average internal gaps were not significantly different. The average value of the root mean square between the AEGIS (31.7 ± 12.3 µm) and CEREC (32.4 ± 9.7 µm) scans was not significantly different (p > 0.05). Intraoral scans required a significantly shorter impression time than the conventional method, and it was possible to fabricate a lithium disilicate crown in a single visit. There were no statistically significant differences in the fit of the restorations and accuracy of the intraoral scanners compared to the conventional workflow. [ABSTRACT FROM AUTHOR]

Background: It has recently been shown that suprahyoid muscle exercise using kinesiology taping (KT) increases the activation of the suprahyoid muscle in healthy adults, suggesting a potential therapeutic clinical exercise for dysphagia rehabilitation. This study investigated the effect of dysphagia rehabilitation using KT in stroke patients with dysphagia. Methods: Thirty subjects in South Korea were enrolled in this prospective placebo-controlled double-blind study. Participants were randomly assigned to the experimental and sham groups. In the experimental group, the tape was attached to the hyolaryngeal complex, pulled downward with approximately 70% tension, and then attached to the sternum and the clavicle bilaterally. In the sham group, the tape was applied similarly but without the tension. Both groups performed voluntary swallowing 50 times (10 times swallowing per set, times 5 sets) a day for 4 weeks with KT applied. Outcome measures were assessed using portable ultrasound equipment. The parameter measured was the change in thickness of the tongue muscle, mylohyoid muscle, and the anterior belly of the digastric muscle. Results: The experimental group showed statistically significant changes in the thickness of the tongue muscle, mylohyoid muscle, and anterior belly of the digastric muscle than the sham group (p = 0.007, 0.002, and 0.001). Conclusion: Dysphagia rehabilitation using KT is a technique that may promote oropharyngeal muscle thickness in patients with dysphagia after stroke. [ABSTRACT FROM AUTHOR]

(1) Background: Occupational therapy (OT) practice has a unique perspective that addresses the complex needs of cancer survivors. Despite the expanded research and application of OT services using telehealth (TH) to promote clients' health and well-being, studies on OT services using TH for cancer survivors are rare. This study aimed to review the TH approaches in the scope of OT and the outcome of factors affecting occupational engagement in adult cancer survivors. (2) Materials and Methods: This systematic review performed a literature search of five databases (Medline Complete, PubMed, CINAHL, PsycINFO, Web of Science) using a combination of keywords and cross-referencing. Studies were included if they described a TH intervention within the scope of OT practice to improve occupational engagement. (3) Results: Fifteen studies (12 randomized controlled trials, three quasiexperimental studies) were reviewed. Physical activity had a positive effect on physical and cognitive function. Symptom self-management showed positive effects on the relief of symptom burden. Psychosocial interventions, which included cognitive behavioral therapy, problem-solving, cognitive behavioral therapy for insomnia, mind–body training, reduced sleep disturbance, and improved physical activity. Lifestyle behavior change interventions improved participation in moderate-intensity physical activity and diet quality. In addition, these interventions reduced cancer-related symptoms such as pain, depression, fatigue, distress, and improved quality of life. There were no direct outcomes of occupational engagement, excluding sleep, that could be confirmed through this review. (4) Conclusion: This review explored and confirmed the usefulness of TH approaches in the scope of OT practice in adult cancer survivors. It also supports the notion that OT-specific research using TH interventions for cancer survivors will be needed in the future. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the effects of virtual reality-based cognitive–motor rehabilitation (VRCMR) on the rehabilitation motivation and cognitive function in older adults. This study enrolled 40 older adults with mild cognitive impairment (MCI), living in the community. The subjects were randomly assigned to a VRCMR group (n = 20) or a conventional cognitive rehabilitation (CCR) group (n = 20). The VRCMR group underwent VRCMR using MOTOcog, a computer recognition program, whereas the CCR group underwent conventional cognitive rehabilitation, which included puzzles, wood blocks, card play, stick construction activity, and maze activity. Both interventions were performed 30 min per day, 5 days/week, for 6 weeks. This study performed a cognitive assessment using the Montreal Cognitive Assessment (MoCA) scale, Trail Making Test A and B (TMT-A/B), and Digit Span Test forward and backward (DST-forward/backward). In addition, a 0-to-10 numeric rating self-report scale was used to assess interest and motivation during the rehabilitation training. After the intervention, the VRCMR group showed a significantly greater improvement in the MoCA (p = 0.045), TMT-A (p = 0.039), TMT-B (p = 0.040), and DST-forward (p = 0.011) scores compared to the CCR group, but not in the DST-backward score (p = 0.424). In addition, subjects in the experimental group had significantly higher interest (p = 0.03) and motivation (p = 0.03) than those in the control group. Cohen's d effect size was 0.4, 0.3, 0.35, 0.4, and 0.5 for the MoCA, TMT-A, TMT-B, DST-forward, and DST-backward tests, respectively. This study demonstrates that VRCMR enhances motivation for rehabilitation and cognitive function in older adults with MCI better than CCR. [ABSTRACT FROM AUTHOR]

In daily living, people are challenged to focus on their goal while eliminating interferences. Specifically, this study investigated the pre-frontal cortex (PFC) activity while attention control was tested using the self-made color-word interference test (CWIT) with a functional near-infrared spectroscopy device (fNIRS). Among 11 healthy Korean university students, overall the highest scores were obtained in the congruent Korean condition 1 (CKC-1) and had the least vascular response (VR) as opposed to the incongruent Korean condition 2 (IKC-2). The individual's automatic reading response caused less brain activation while IKC-2 involves color suppression. Across the three trials per each condition, no significant differences (SD) in scores and in VR since there was no intervention did. Meanwhile, SD was observed between CKC-1 and English Congruent Condition 3 (ECC-3) across trials. However, SD was only observed on the third trial of VR. In the connectivity analysis, right and left PFC are activated on ECC-3. In CKC-1 and IKC-2, encompassing dorsomedial and dorsolateral although CKC-1 has less connection and connectivity due to less brain activation as compared. Therefore, aside from VR, brain connectivity could be identified non-invasively using fNIRS without ionizing radiation and at low-cost. [ABSTRACT FROM AUTHOR]

A Web archive system is a traditional subject for preserving web content for the future and the importance is getting more significant due to the explosive growth of web content. The reference model for an open archival information system (OAIS) has been advising guidance for a long-term archiving system and most organizations that archive web content follow this guidance. In addition, the web archive (WARC) ISO standard is for web content archiving. However, there is no way to secure content integrity, and it is hard to identify the original. Because of limitations, a web archive system has a weakness against the dispute of content integrity. In this paper, we proposed the blockchain linked (BCLinked) web archiving system, which uses blockchain technology and an extended WARC field to keep a web content integrity metadata into a blockchain. Furthermore, we designed the BCLinked web archiving system, and we confirmed the proposed system secures content integrity through the experiment. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services. [ABSTRACT FROM AUTHOR]