Good Application Research Articles

Application-based big data development framework for health sciences libraries.

The study is a part of a student research project on performance-based evaluation of big data application in health sciences libraries. It presents a theoretical framework of the performance-based evaluation system for health institutes' libraries in the digital environment. The health sciences libraries' systematic approach was judged based on the five main components (data culture, organisational infrastructure, responsibilities, skills and technology competence) of big data analytics (BDA). A comprehensive literature review of the published studies was undertaken related to BDA, including the diffusion of innovation theory, and the theoretical background of the technology acceptance model to produce an application-based big data development framework for the health sciences libraries. The application-based evaluation model integrates BDA in health sciences libraries for improving library services and performance. The study proposed a need for skilled professionals with the knowledge and experience both professionally and technically. Finally, the study proposed a model that will help to measure the organisation's ability to analyse vast amounts of data to empirically validate the association concerning big data analysis and analytical practices in health libraries.

A real-world study of the clinical application of the Paris system for reporting urinary cytology in cancer hospital

Objectives: To evaluate the clinical value of the Paris system for reporting urinary cytology (TPS) in the diagnosis of urothelial carcinoma (UC). Methods: A total of 1 744 cytological diagnostic records (from 751 cases) were collected retrospectively. All specimens were voided urines and histopathology as the gold standard. The sensitivity and specificity of urinary cytological diagnosis of UC and risk of high grade malignant (ROHM) in each diagnostic category were compared. Results: There were 360 cases with histopathology. The percentage of negative for high-grade urothelial carcinoma (NHGUC) was 30.1% (226/751), atypical urothelial cells (AUC) was 29.8% (224/751), suspicious for high-grade urothelial carcinoma (SHGUC) was 16.8% (126/751), high grade urothelial carcinoma (HGUC) was 21.2% (159/751), and non-urothelial malignancy (NUM) was 2.1% (16/751). The histpathologic ROHM corresponding to each cytological diagnosis category were 27.3% for NHGUC, 32.7% for AUC, 74.7% for SHGUC, 96.6% for HGUC and 100.0% for NUM, respectively. ROHM of SHGUC was significantly higher than that of AUC group, and the difference between the two groups was statistically significant (P＜0.001). ROHM of HGUC group was significantly higher than that of SHGUC group, and the difference was statistically significant (P＜0.001). With SHGUC as the cut-off value, the sensitivity and specificity of cytological diagnosis of HGUC were 76.7% (165/215) and 85.7% (18/21), and with HGUC as the cut-off value, the sensitivity and specificity of cytological diagnosis of HGUC were 53.0% (114/215) and 100.0% (21/21), respectively. Conclusions: Urine cytology has high sensitivity and specificity in the diagnosis of HGUC. The malignant risk of TPS varies with different diagnosis category. The high malignant risk population in cancer hospital leads to the relatively high malignant proportion and ROHM in each diagnosis category. Urinary cytology TPS reporting system is helpful to clinical management and has good clinical application value.

New Insights into Sources, Bioavailability, Health-Promoting Effects, and Applications of Chitin and Chitosan.

Chitin and chitosan are mostly derived from the exoskeletons of crustaceans, insects, and fungi. Chitin is the second most abundant biopolymer after cellulose, and it is a fibrous polysaccharide which resists enzymatic degradation in the stomach but undergoes microbial fermentation in the colon, producing beneficial metabolites. Chitosan, which is more soluble in the alkaline small intestine, is more susceptible to enzymatic action. Both biopolymers show limited absorption into the bloodstream, with smaller particles exhibiting better bioavailability. The health effects include anti-inflammatory properties, potential in immune system modulation, impacts on cholesterol levels, and antimicrobial effects, with a specific focus on implications for gut health. Chitin and chitosan exhibit anti-inflammatory properties by interacting with immune cells, influencing cytokine production, and modulating immune responses, which may benefit conditions characterized by chronic inflammation. These biopolymers can impact cholesterol levels by binding to dietary fats and reducing lipid absorption. Additionally, their antimicrobial properties contribute to gut health by controlling harmful pathogens and promoting beneficial gut microbiota. This review explores the extensive health benefits and applications of chitin and chitosan, providing a detailed examination of their chemical compositions, dietary sources, and applications, and critically assessing their health-promoting effects in the context of human well-being.

Integrating a dimensional perturbation module into exponential distribution optimizer for solving optimization problems

Exponential distribution optimizer (EDO) is a recently proposed optimization technique that is based on the exponential probability distribution model. As most swarm intelligence algorithms (SIAs), EDO is very good at handling common optimization problems. However, when addressing multimodal and centre-bias problems, it exhibits limitations like imbalanced global and local search capabilities and poor solution accuracy. Building upon this, we introduce an improved version of exponential distribution optimizer that incorporates a dimensional perturbation module (DPM) called EDO_DPM. By testing the impact of dimensional perturbation, the K-dimensional evolution strategy is adopted in our approach. Concurrently, to modulate the usage frequency of DPM, an adjustment parameter, named evolutionary state factor (ESF), is designed contingent on the population’s evolutionary state. Moreover, to alleviate the deficiency of non full-dimensional perturbation, a bounce out operation is embedded in the algorithm. The efficacy of EDO_DPM has been substantiated through testing on 20 different type of functions and CEC2017 benchmark suite. Comparative analyses with the state-of-the-art algorithms have demonstrated a marked enhancement in EDO_DPM’s capability to manage multimodal problems and its proficiency in resolving centre-bias problems. Meanwhile, it can achieve excellent results on CEC2017 by comparing excellent improved variants. Furthermore, EDO_DPM is applied to the problem of parameter estimation for photovoltaic (PV) generation systems. Comparison results show that EDO_DPM has good application capability.

Effectiveness of a Mobile Phone Messaging-Based Message Framing Intervention for Improving Maternal Health Service Uptake and Newborn Care Practice in Rural Jimma Zone, Ethiopia: Protocol for a Cluster Randomized Controlled Trial.

Ethiopia has high rates of maternal and neonatal mortality. In 2019 and 2020, the maternal and newborn mortality rates were estimated at 412 per 1,000,000 births and 30 per 10,000 births, respectively. While mobile health interventions to improve maternal and neonatal health management have shown promising results, there are still insufficient scientific studies to assess the effectiveness of mobile phone messaging-based message framing for maternal and newborn health. This research aims to examine the effectiveness of mobile phone messaging-based message framing for improving the use of maternal and newborn health services in the Jimma Zone, Ethiopia. A 3-arm cluster-randomized trial design was used to evaluate the effects of mobile phone-based intervention on maternal and newborn health service usage. The trial arms were (1) gain-framed messages (2) loss-framed messages, and (3) usual care. A total of 21 health posts were randomized, and 588 pregnant women who had a gestational age of 16-20 weeks, irrespective of their antenatal care status, were randomly assigned to the trial arms. The intervention consisted of a series of messages dispatched from the date of enrolment until 6-8 months. The control group received existing care without messages. The primary outcomes were maternal health service usage and newborn care practice, while knowledge, attitude, self-efficacy, iron supplementation, and neonatal and maternal morbidity were secondary outcomes. The outcomes will be analyzed using a generalized linear mixed model and the findings will be reported according to the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth) statement for randomized controlled trials. Recruitment of participants was conducted and the baseline survey was administered in March 2023. The intervention was rolled out from May 2023 till December 2023. The end-line assessment was conducted in February 2024. This trial was carried out to understand how mobile phone-based messaging can improve maternal and newborn health service usage. It provides evidence for policy guidelines around mobile health strategies to improve maternal and newborn health. Pan African Clinical Trials Registry PACTR202201753436676; https://tinyurl.com/ykhnpc49. DERR1-10.2196/52395.

A novel comprehensive method for weak signal blind detection based on adaptive quasi-periodic potential stochastic resonance and its application in bearing fault detection

In order to solve the common output saturation of stochastic resonance systems and the limitation of classical index SNR for blind detection, a novel adaptive quasi-periodic potential stochastic resonance blind detection method is proposed. First, a model of quasi-periodic potential stochastic resonance (QPPSR) possessing infinite steady state is constructed and analyzed for its structure change pattern. The superior performance of the model is verified by using the fourth-order Runge–Kutta algorithm. Secondly, the mechanism of QPPSR is analyzed using the probability flow method, which reveals the relationship between system parameters and performance. Again, a novel comprehensive blind detection index (CBDI) is exquisitely constructed to make up for the shortcomings of each indicator. Finally, CBDI and QPPSR are constructed into an adaptive blind detection system and applied to bearing fault detection. The results analyzed by experiments verify the good engineering application prospect of CBDI-QPPSR.

Highly efficient Rh-doped MnO2 nanocatalysts for complete elimination of CO at room temperature.

Obliteration of carbon monoxide is significant due to its hazardous effect on human health and potential application in different fields. Catalytic CO oxidation at lower temperature is the most convenient method to diminish the toxicity of CO. The low-cost catalysts which are exhibiting higher activity at lower temperature with good stability are in demand. The nanosized Rh-doped MnO2 catalysts have been prepared by dextrose-assisted co-precipitation method. Catalytic CO oxidation reaction was carried out over these prepared nanocatalysts under environmentally suitable conditions. XRD confirms the phase formation of prepared catalysts. These samples exhibit rod-like morphology with thickness of rods of less than 10nm which is substantiated from electron microscopy images. XPS data reveals the oxidation state of Mn (+ 4) and Rh (+ 3). These catalysts are highly active for CO oxidation reaction at lower temperature, and one showed complete CO conversion at room temperature. The time-on-stream studies revealed that these catalysts are highly stable for CO oxidation for several hours. These catalysts are decidedly stable in moist condition and also showed higher activity in the presence of moisture, indicating participation of moisture in the oxidation reaction at above room temperatures.

Restoration of a wet corrosion-resistant composite filament for material extrusion process

Purpose Material extrusion (MEX) is one of the most known techniques in the additive manufacturing (AM) sector to produce components with a wide range of polymeric and composite materials. Moisture causes alterations in material properties and for filaments strongly hygroscopic like nylon-based composites this means greater ease of deterioration. Drying the filament to reduce the moisture content may not be sufficient if the humidity is not controlled during printing. The purpose of this study is to achieve the recovery of a commercial nylon-based composite filament by applying process optimization using an open source MEX machine. Design/methodology/approach A statistical approach based on Taguchi’s method allowed to achieve an ultimate tensile strength (UTS). A verification of the geometrical capabilities of the process has been performed according to the standard ISO/ASTM 52902-2019. Chemical tests were also carried out to test the resistance to corrosion in acid and basic solutions. Findings An UTS of 71.37 MPa was obtained, significantly higher than the value declared by the filament’s manufacturer (Stratasys Inc., USA). The best configuration of process parameters leads to good geometrical deviations for flat surfaces, in a range of 0.01 and 0.38 for flatness, while cylindrical faces showed more important deviations from the nominal values. The good applicability of the material in corrosive environments has been confirmed. Originality/value This study examined the performance restoration potential of a nylon composite filament that was significantly affected by storage conditions. For the filament manufacturer, if the material remains in ambient air for an hour or idle in the machine for more than 24 h, the material may no longer be suitable for printing. The study highlighted that the drying of the filament must not be temporary but constant to guarantee printability, and, by acting on the process parameters, it is possible to obtain better mechanical properties than declared by the manufacturer.

A Highly Responsive Graphene Oxide Humidity Sensor Based on PVA Nanofibers.

Graphene oxide (GO) humidity sensors based on poly(vinyl alcohol) (PVA) nanofibers have been proposed. The PVA nanofiber layers of different densities were obtained by adjusting the electrospinning time. Then, GO films were deposited on PVA nanofibers by a spin-coating method. The electrical properties of GO films are improved due to the increased distribution of PVA nanofibers in the GO films. The humidity sensors exhibit good sensitivity under a high relative humidity range of 40-80%. The response of sensors has reached 98.44% at a humidity level of 80% RH. The GO/PVA sensors have good stability at various humidity levels for 1 week. Furthermore, the GO/PVA sensors were used for respiration monitoring under different statuses. These sensors have good application prospects in the respiratory detection and analysis of diseases.

Attitudes of the general population and mental health practitioners towards blended therapy in Austria.

Mental health problems are steadily increasing worldwide. In Austria, the overall supply of mental health services is low, especially in rural areas. Mobile technology and ablended care approach have the potential to overcome problems with service provision. The aim of this study was to map the attitudes of practitioners and people living in Austria towards blended therapy. Two individual online questionnaires (including the Unified Theory of Acceptance and Use of Technology, advantages and disadvantages, useful features) were distributed to practitioners and the general population in Austria. The questionnaires were answered by 152 members of the general public and 129 practitioners. The general population and practitioners seem to be cautious, but slightly positive about blended therapy. Previous experience of practitioners with blended therapy was low. Practitioners are most worried about the therapeutic process and their work-life balance, while the general population is worried about being overwhelmed by the concept, mainly due to the time investment. Tracking, recording and reminding functions (e.g., for mood, homework) were seen as especially valuable features and accessibility was deemed the biggest advantage by both samples. Practitioners' attitudes are important for implementation of blended therapy. More awareness might help against the cautiousness as well as implementing digital health applications in Austrian health policies.

Analysis of skin health management through telemedicine and mobile health in dermatology in the post-COVID era

Mobile health has made significant strides in the field of tele-dermatology (TD) following the developments post the COVID-19 pandemic. The application of telemedicine and mobile health to dermatology holds the potential to enhance the quality of healthcare for citizens and streamline workflows in the healthcare domain, hence this subject is of great importance. This research encompasses the last three years. A comprehensive overview examining the opportunities, perspectives, and encountered challenges regarding the integration of TD with mHealth has been provided. The narrative review methodology is based on (I) conducting research via PubMed and Scopus, and (II) conducting compliance assessment using the proposed parameters. The outcome of the research indicates rapid progress in the integration of TD with mobile health during the COVID-19 pandemic. This integration has facilitated the monitoring of dermatological issues and allowed for remote specialist visits, thereby reducing face-to-face interactions. Artificial intelligence and mobile applications have empowered citizens to take a more active role in their own healthcare, which is distinct from other imaging areas where information exchange is limited only to professionals. Opportunities that TD can offer in the field of mobile health include enhancing service quality, increasing the efficiency of healthcare processes, reducing costs, and providing more accessible care. This is applicable not only to conditions like acne, vitiligo, psoriasis, and skin cancers but also to other conditions. Integration with artificial intelligence and augmented reality (AR), along with the use of wearable sensors, are expected as future developments. However, the integration of TD with mobile health brings along issues and challenges related to regulation, ethics, cybersecurity, data privacy, and device management. These issues, along with the involvement of citizens in the process, should be addressed by scientists and policymakers.

Behavioral therapy in migraine: Expanding the therapeutic arsenal.

The US Headache Consortium developed evidence-based guidelines for the treatment of migraine and found grade A evidence in support of behavior therapy (BT). Understanding the mechanisms of BT may improve the management of migraine and reduce its burden. We performed a narrative review to define the current evidence of BT and determine its usefulness in migraine management. The information was obtained from 116 publications, with 56 of them retrieved through direct searches in PubMed (2011-2020) and the remainder selected by the authors to complete the content. BT might reduce migraine impact by decreasing the sympathetic nervous system's response to stress and increasing pain tolerance. Acting in headache-related surroundings can be improved, together with headache duration and self-efficacy. Applications such as mobile health and electronic health applications can help to carry out healthier lifestyle patterns. Regarding medication overuse, BT seems to be a good choice, with similar results to pharmacological prophylaxis. Advantages of using BT are the lack of adverse effects and the unrestricted use in children, where BT is postulated to be even more effective than the standardized pharmacopeia. BT is an interesting tool that can be used as an add-on therapy in migraine. Through BT, the autonomy and empowerment of migraine patients is enhanced. BT may not cure migraine, but it could help to reduce pain severity perception, disability, and migraine impact, adding an emotive and cognitive approach to the perceptive role of pharmacopeia. Thus, a better approach in migraine, implementing specific therapeutic management, can improve migraine control.

G-T correcting: an improved training of image segmentation under noisy labels.

Data-driven medical image segmentation networks require expert annotations, which are hard to obtain. Non-expert annotations are often used instead, but these can be inaccurate (referred to as "noisy labels"), misleading the network's training and causing a decline in segmentation performance. In this study, we focus on improving the segmentation performance of neural networks when trained with noisy annotations. Specifically, we propose a two-stage framework named "G-T correcting," consisting of "G" stage for recognizing noisy labels and "T" stage for correcting noisy labels. In the "G" stage, a positive feedback method is proposed to automatically recognize noisy samples, using a Gaussian mixed model to classify clean and noisy labels through the per-sample loss histogram. In the "T" stage, a confident correcting strategy and early learning strategy are adopted to allow the segmentation network to receive productive guidance from noisy labels. Experiments on simulated and real-world noisy labels show that this method can achieve over 90% accuracy in recognizing noisy labels, and improve the network's DICE coefficient to 91%. The results demonstrate that the proposed method can enhance the segmentation performance of the network when trained with noisy labels, indicating good clinical application prospects.

Pollen-Modified Flat Silk Cocoon Pressure Sensors for Wearable Applications

Microstructures have been proved as crucial factors for the sensing performance of flexible pressure sensors. In this study, polypyrrole (PPy)/sunflower pollen (SFP) (P/SFP) was prepared via the in situ growth of PPy on the surface of degreased SFP with a sea urchin-like microstructure; then, these P/SFP microspheres were sprayed onto a flat silk cocoon (FSC) to prepare a sensing layer P/SFP-FSC. PPy-FSC (P-FSC) was prepared as an electrode layer through the in situ polymerization of PPy on the FSC surface. The sensing layer P/SFP-FSC was placed between two P-FSC electrode layers to assemble a P/SFP-FSC pressure sensor together with a fork finger electrode. With 6 mg/cm2 of optimized sprayed P/SFP microspheres, the prepared flexible pressure sensor has a sensitivity of up to 0.128 KPa−1 in the range of 0–13.18 KPa and up to 0.13 KPa−1 in the range of 13.18–30.65 KPa, a fast response/recovery time (90 ms/80 ms), and a minimum detection limit as low as 40 Pa. This fabricated flexible P/SFP-FSC sensor can monitor human motion and can also be used for the encrypted transmission of important information via Morse code. In conclusion, the developed flexible P/SFP-FSC pressure sensor based on microstructure modification in this study shows good application prospects in the field of human–computer interaction and wearable electronic devices.

The Information and Communication Technology Maturity Assessment at Primary Health Care Services Across 9 Provinces in Indonesia: Evaluation Study.

Indonesia has rapidly embraced digital health, particularly during the COVID-19 pandemic, with over 15 million daily health application users. To advance its digital health vision, the government is prioritizing the development of health data and application systems into an integrated health care technology ecosystem. This initiative involves all levels of health care, from primary to tertiary, across all provinces. In particular, it aims to enhance primary health care services (as the main interface with the general population) and contribute to Indonesia's digital health transformation. This study assesses the information and communication technology (ICT) maturity in Indonesian health care services to advance digital health initiatives. ICT maturity assessment tools, specifically designed for middle-income countries, were used to evaluate digital health capabilities in 9 provinces across 5 Indonesian islands. A cross-sectional survey was conducted from February to March 2022, in 9 provinces across Indonesia, representing the country's diverse conditions on its major islands. Respondents included staff from public health centers (Puskesmas), primary care clinics (Klinik Pratama), and district health offices (Dinas Kesehatan Kabupaten/Kota). The survey used adapted ICT maturity assessment questionnaires, covering human resources, software and system, hardware, and infrastructure. It was administered electronically and involved 121 public health centers, 49 primary care clinics, and 67 IT staff from district health offices. Focus group discussions were held to delve deeper into the assessment results and gain more descriptive insights. In this study, 237 participants represented 3 distinct categories: 121 public health centers, 67 district health offices, and 49 primary clinics. These instances were selected from a sample of 9 of the 34 provinces in Indonesia. Collected data from interviews and focus group discussions were transformed into scores on a scale of 1 to 5, with 1 indicating low ICT readiness and 5 indicating high ICT readiness. On average, the breakdown of ICT maturity scores was as follows: 2.71 for human resources' capability in ICT use and system management, 2.83 for software and information systems, 2.59 for hardware, and 2.84 for infrastructure, resulting in an overall average score of 2.74. According to the ICT maturity level pyramid, the ICT maturity of health care providers in Indonesia fell between the basic and good levels. The need to pursue best practices also emerged strongly. Further analysis of the ICT maturity scores, when examined by province, revealed regional variations. The maturity of ICT use is influenced by several critical components. Enhancing human resources, ensuring infrastructure, the availability of supportive hardware, and optimizing information systems are imperative to attain ICT maturity in health care services. In the context of ICT maturity assessment, significant score variations were observed across health care levels in the 9 provinces, underscoring the diversity in ICT readiness and the need for regionally customized follow-up actions.

CRISPR/Cas12a-coupled multiplexed strand displacement amplification for miRNA155 one-tube detection: via a dual-cavity PCR tube.

A CRISPR/Cas12a-coupled multiplexed strand displacement amplification (CMSDA) for the detection of miR155 has been developed. Non-specific amplification was avoided by designing a single-stranded DNA template with a hairpin structure. The detection target miR155 was used as a primer to initiate a multiple-strand displacement reaction to produce abundant ssDNA. ssDNA was recognized by the Cas12a/CrRNA binary complex, activating the trans-cleaving activity of Cas12a. The multiple-strand displacement reaction is more efficiently detected compared with a single-strand displacement reaction. The detection range is from 250pM to 1nM, and the limit of the detection is 6.5pM. The proposed method showed a good applicability in complex serum environments, indicating that the method has a broad prospect for disease detection and clinical application. In addition, we designed a dual-cavity PCR tube, which realized one-tube detection of miRNA155 and avoided open-cap contamination.

Unveiling the Communication Dynamics: A Comprehensive Review of Mobile Health Applications

Unveiling the Communication Dynamics: A Comprehensive Review of Mobile Health Applications

Lipids from the purple and white açaí (Euterpe oleracea Mart) varieties: nutritional, functional, and physicochemical properties

The Brazilian superfruit called Açaí or Assaí has gained interested from researcher and consumers worldwide, due to its health-related properties. In this context, this pioneering study aimed to compare the physicochemical, nutritional, and thermal properties of vegetable oils obtained from two varieties of açaí (Euterpe oleracea), purple and white. Both açaí oils from white (WAO) and purple (PAO) varieties were obtained by using the conventional solid–liquid extraction, which resulted in oil yields ranging from 52 to 61%. WAO and PAO were analyzed by their edibility quality parameters given the recommendations from Codex Alimentarius; their nutritional functionality indices and their composition of fatty acids and triglycerides content were estimated. Both oils showed low levels of acidity and peroxides, <1.8 mg KOH g−1 and < 1.7 mEq kg−1, respectively, which are good indicators of their preservation status, agreeing with the food regulations. PAO and WAO showed differences among the composition of fatty acids, mainly related to the content of monounsaturated fatty acids (MUFAs), which were 62.5 and 39.5%, respectively, mainly oleic acid. Regarding the polyunsaturated fatty acids (PUFAs), the WAO showed up to 23% of linoleic acid, whereas the PAO exhibited up to 11% of it. These differences reflect on the values of the nutritional functionality indices, atherogenic (AI), thrombogenic (IT), and hypocholesterolemic/hypercholesterolemic ratio (H/H). Both PAO and WAO showed low levels of AI and TI and superior values of H/H than other oilseeds from the literature. These results indicate the nutritional properties of açaí oils regarding a potential cardioprotective effect when included in a regular dietary intake. The thermogravimetric behavior and the evaluation of oxidation status by infrared spectroscopy (FTIR) were also studied. Both açaí oils demonstrated higher thermal stability (with an onset temperature ranging from 344 to 350 °C) and low indications of oxidation status, as no chemical groups related to it were noted in the FTIR spectrum, which agrees with the determined acidity and peroxide content. Moreover, the FTIR analysis unveiled characteristic chemical groups related to fatty acids and triglycerides, agreeing with the literature reports. These findings collectively contribute to a deeper comprehension of the nutritional and functional properties between white and purple açaí oils, offering valuable insights into their potential health, food, and industrial applications.

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information

Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics. On the other hand, the constructed initial static graph structure is more dependent on human labor and domain expertise, and the introduced errors cannot be corrected. To solve these problems, we introduce a dynamic graph structure framework (SIF) based on the complementarity of semantic and structural information. Specifically, for the first problem, SIF integrates the semantic and structural information of the text, and constructs two graph structures, structural information graph and semantic information graph, respectively, based on specialized knowledge, which complements the information between the two graph structures, provides rich semantic features for the downstream identification task, and helps to understanding of the contextual information between implicit sentiment semantics. To deal with the second issue, SIF dynamically learns the initial static graph structure to eliminate the noise information in the graph structure, preventing noise accumulation that affects the performance of the downstream identification task. We compare SIF with mainstream natural language processing methods in three publicly available datasets, all of which outperform the benchmark model. The accuracy on the Puns of day dataset, SemEval-2021 task 7 dataset, and Reddit dataset reaches 95.73%, 85.37%, and 65.36%, respectively. The experimental results demonstrate a good application scenario for our proposed method on implicit sentiment identification tasks.

Adoption of Mobile Health Applications for Diabetes Management From a Push–Pull–Mooring Perspective

Adoption of Mobile Health Applications for Diabetes Management From a Push–Pull–Mooring Perspective