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This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.

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Quantitative monitoring of the concentrations of epigallocatechin gallate (EGCG) and cysteine (Cys) is of great significance for promoting human health. In this study, iron/aluminum bimetallic MOF material MIL-53 (Fe, Al) was rapidly prepared under room temperature using a co-precipitation method, followed by investigating the peroxidase-like (POD-like) activity of MIL-53(Fe, Al) using 3,3',5,5'-tetramethylbenzidine (TMB) as a chromogenic substrate. The results showed that the Michaelis -Menten constants of TMB and H2O2 as substrates were 0.167 mM and 0.108 mM, respectively. A colorimetric sensing platform for detecting EGCG and Cys was developed and successfully applied for analysis and quantitative detection using a smartphone. The linear detection range for EGCG was 15∼80 µM (R2=0.994) and for Cys was 7∼95 µM (R2=0.998). The limits of detection (LOD) were 0.719 µM and 0.363 µM for EGCG and Cys, respectively. This work provides a new and cost-effective approach for the real-time analysis of catechins and amino acids.

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Norfloxacin is widely used owing to its strong bactericidal effect on Gram-negative bacteria. However, the residual norfloxacin in the environment can be biomagnified via food chain and may damage the human liver and delay the bone development of minors. Present work described a reliable and sensitive smartphone colorimetric sensing system based on cobalt-doped Fe3O4 magnetic nanoparticles (Co-Fe3O4 MNPs) for the visual detection of norfloxacin. Compared with Fe3O4, Co-Fe3O4 MNPs earned more remarkably peroxidase-like activity and TMB (colorless) was rapidly oxidized to oxTMB (blue) with the presence of H2O2. Interestingly, the addition of low concentration of norfloxacin can accelerate the color reaction process of TMB, and blue deepening of the solution can be observed with the naked eye. However, after adding high concentration of norfloxacin, the activity of nanozyme was inhibited, resulting in the gradual fading of the solution. Based on this principle, a colorimetric sensor integrated with smartphone RGB mode was established. The visual sensor exhibited good linearity for norfloxacin monitoring in the range of 0.13-2.51 µmol/L and 17.5-100 µmol/L. The limit of visual detection was 0.08 µmol/L. In the actual water sample analysis, the spiked recoveries of norfloxacin were over the range of 95.7%-104.7 %. These results demonstrated that the visual sensor was a convenient and fast method for the efficient and accurate detection of norfloxacin in water, which may have broad application prospect.

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The majority of Black women with hypertension in the United States have smartphones or tablets and use social media, and many use wearable activity trackers and health or wellness apps, digital tools that can be used to support lifestyle changes and medication adherence.

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This article explores the intricate relationship between smartphone usage and mental health and the unintended consequences of the rapid integration of this technology into daily life. It explores the ways in which smartphones disrupt opportunities for introspection and self-reflection, decrease engagement in external reality, increase engagement with realities of the virtual world, precipitate ego destabilization, and interfere with sleep and dreaming. The author explores ways in which the split between the real self and the ideal self is impacted by social media. The influence of smartphones on mental health is a complex and evolving issue, demanding ongoing research, understanding, and a heightened awareness of the potentially deleterious consequences of overusing technology in our ever-changing world.

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The growing demand for wearable healthcare devices has led to an urgent need for cost-effective, wireless and portable breath monitoring systems. However, it is essential to explore novel nanomaterials that combine state-of-the-art flexible sensors with high performance and sensing capabilities along with scalability and industrially acceptable processing. In this study, we demonstrate a highly efficient NiS2-based flexible capacitive sensor fabricated via a solution-processible route using a novel single-source precursor [Ni{S2P(OPr)2}2]. The developed sensor could precisely detect the human respiration rate and exhibit rapid responsiveness, exceptional sensitivity and selectivity at ambient temperatures, with an ultra-fast response and recovery. The device effectively differentiates the exhaled breath patterns including slow, fast, oral and nasal breath, as well as post-exercise breath rates. Moreover, the sensor shows outstanding bending stability, repeatability, reliable and robust sensing performance and is capable of contactless sensing. The sensor was further employed with a user-friendly wireless interface to facilitate smartphone-enabled real-time breath monitoring systems. This work opens up numerous avenues for cost-effective, sustainable and versatile sensors with potential applications for Internet of Things-based flexible and wearable electronics.This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

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BACKGROUND: Timely diagnosis and treatment for ST-elevation myocardial infarction (STEMI) requires a coordinated response from multiple providers. Rapid intervention is key to reducing mortality and morbidity. Activation of the cardiac catheterization laboratory may occur through verbal communication and may also involve the secure sharing of electrocardiographic images between frontline health care providers and interventional cardiologists. To improve this response, we developed a quick, easy-to-use, privacy-compliant smartphone app, that is SMART AMI-ACS (Strategic Management of Acute Reperfusion and Therapies in Acute Myocardial Infarction Acute Coronary Syndromes), for real-time verbal communication and sharing of electrocardiographic images among health care providers in Ontario, Canada. The app further provides information about diagnosis, management, and risk calculators for patients presenting with acute coronary syndrome. OBJECTIVE: This study aims to integrate the app into workflow processes to improve communication for STEMI activation, resulting in decreased treatment times, improved patient outcomes, and reduced unnecessary catheterization laboratory activation and transfer. METHODS: Implementation of the app will be guided by the Reach, Effectiveness, Acceptability, Implementation, and Maintenance (RE-AIM) framework to measure impact. The study will use quantitative registry data already being collected through the SMART AMI project (STEMI registry), the use of the SMART AMI app, and quantitative and qualitative survey data from physicians. Survey questions will be based on the Consolidated Framework for Implementation Research. Descriptive quantitative analysis and thematic qualitative analysis of survey results will be conducted. Continuous variables will be described using either mean and SD or median and IQR values at pre- and postintervention periods by the study sites. Categorical variables, such as false activation, will be described as frequencies (percentages). For each outcome, an interrupted time series regression model will be fitted to evaluate the impact of the app. RESULTS: The primary outcomes of this study include the usability, acceptability, and functionality of the app for physicians. This will be measured using electronic surveys to identify barriers and facilitators to app use. Other key outcomes will measure the implementation of the app by reviewing the timing-of-care intervals, false "avoidable" catheterization laboratory activation rates, and uptake and use of the app by physicians. Prospective evaluation will be conducted between April 1, 2022, and March 31, 2023. However, for the timing- and accuracy-of-care outcomes, registry data will be compared from January 1, 2019, to March 31, 2023. Data analysis is expected to be completed in Fall 2024, with the completion of a paper for publication anticipated by the end of 2024. CONCLUSIONS: Smartphone technology is well integrated into clinical practice and widely accessible. The proposed solution being tested is secure and leverages the accessibility of smartphones. Emergency medicine physicians can use this app to quickly, securely, and accurately transmit information ensuring faster and more appropriate decision-making for STEMI activation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05290389; https://clinicaltrials.gov/study/NCT05290389. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55506.

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Over 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Self-assessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application.

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BACKGROUND: Smartphone-delivered attentional bias modification training (ABMT) intervention has gained popularity as a remote solution for alleviating symptoms of mental health problems. However, the existing literature presents mixed results indicating both significant and insignificant effects of smartphone-delivered interventions. OBJECTIVE: This systematic review and meta-analysis aims to assess the impact of smartphone-delivered ABMT on attentional bias and symptoms of mental health problems. Specifically, we examined different design approaches and methods of administration, focusing on common mental health issues, such as anxiety and depression, and design elements, including gamification and stimulus types. METHODS: Our search spanned from 2014 to 2023 and encompassed 4 major databases: MEDLINE, PsycINFO, PubMed, and Scopus. Study selection, data extraction, and critical appraisal were performed independently by 3 authors using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. When necessary, we pooled the standardized mean difference with a 95% CI. In addition, we conducted sensitivity, subgroup, and meta-regression analyses to explore moderator variables of active and placebo ABMT interventions on reducing symptoms of mental health problems and attentional bias. RESULTS: Our review included 12 papers, involving a total of 24,503 participants, and we were able to conduct a meta-analysis on 20 different study samples from 11 papers. Active ABMT exhibited an effect size (Hedges g) of -0.18 (P=.03) in reducing symptoms of mental health problems, while the overall effect remained significant. Similarly, placebo ABMT showed an effect size of -0.38 (P=.008) in reducing symptoms of mental health problems. In addition, active ABMT (Hedges g -0.17; P=.004) had significant effects on reducing attentional bias, while placebo ABMT did not significantly alter attentional bias (Hedges g -0.04; P=.66). CONCLUSIONS: Our understanding of smartphone-delivered ABMT's potential highlights the value of both active and placebo interventions in mental health care. The insights from the moderator analysis also showed that tailoring smartphone-delivered ABMT interventions to specific threat stimuli and considering exposure duration are crucial for optimizing their efficacy. This research underscores the need for personalized approaches in ABMT to effectively reduce attentional bias and symptoms of mental health problems. TRIAL REGISTRATION: PROSPERO CRD42023460749; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=460749.

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BACKGROUND: Specialized studies have shown that smartphone-based social interaction data are predictors of depressive and anxiety symptoms. Moreover, at times during the COVID-19 pandemic, social interaction took place primarily remotely. To appropriately test these objective data for their added value for epidemiological research during the pandemic, it is necessary to include established predictors. OBJECTIVE: Using a comprehensive model, we investigated the extent to which smartphone-based social interaction data contribute to the prediction of depressive and anxiety symptoms, while also taking into account well-established predictors and relevant pandemic-specific factors. METHODS: We developed the Corona Health App and obtained participation from 490 Android smartphone users who agreed to allow us to collect smartphone-based social interaction data between July 2020 and February 2021. Using a cross-sectional design, we automatically collected data concerning average app use in terms of the categories video calls and telephony, messenger use, social media use, and SMS text messaging use, as well as pandemic-specific predictors and sociodemographic covariates. We statistically predicted depressive and anxiety symptoms using elastic net regression. To exclude overfitting, we used 10-fold cross-validation. RESULTS: The amount of variance explained (R2) was 0.61 for the prediction of depressive symptoms and 0.57 for the prediction of anxiety symptoms. Of the smartphone-based social interaction data included, only messenger use proved to be a significant negative predictor of depressive and anxiety symptoms. Video calls were negative predictors only for depressive symptoms, and SMS text messaging use was a negative predictor only for anxiety symptoms. CONCLUSIONS: The results show the relevance of smartphone-based social interaction data in predicting depressive and anxiety symptoms. However, even taken together in the context of a comprehensive model with well-established predictors, the data only add a small amount of value.

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BACKGROUND: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment. OBJECTIVE: This review aims to evaluate existing studies on hand function evaluation using smartphones. METHODS: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions. RESULTS: In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation. CONCLUSIONS: This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses.

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A simple method has been developed for semi-quantitative analysis of the colorimetric output of loop-mediated isothermal amplification (LAMP) using a 3D-printed tube holder with a smartphone and notebook for the detection of Raillietina, which is the cause of Raillietiniasis affecting free-range chicken farming. In this method, a light is directed from a notebook screen to the LAMP products in the tube holder and the color absorption of the LAMP products is measured by using the appropriate smartphone application. It was found that the malachite green dye-coupled LAMP (MaG-LAMP) assay showed the highest sensitivity and specificity for detecting Raillietina without any cross-reaction with other related parasites and hosts. The limit of detection was 10 fg/µL of DNA. A total of 60 fecal samples were infectively confirmed by microscopic examination and the results of microscopy compared with those of MaG-LAMP and triplex PCR assays. Microscopy and MaG-LAMP based on the color absorption demonstrated high agreement in Raillietina detection with kappa = 1. Rapid, simple, cost-effective, and easy interpretation of colorimetric LAMP assays and their high sensitivity make them superior to PCR and morphological investigation, demonstrating the feasibility of this assay in point-of-care screening to support farm management and solve chicken health problems. Our study presents is an alternative diagnostic method using semi-quantitative analysis of colorimetric LAMP based on the differing solution color absorptions between positive and negative reactions for infectious disease diagnosis.

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Introduction: This study examines the impact of digital new media art on the health literacy and digital health literacy of older adults. It explores how digital new media art influences the social adaptability of the older adult, with a focus on variations in their engagement with digital technologies and community activities. Methods: The research employed interviews and observations of older adult participants from communities A and B. Data were collected on their smartphone usage, community engagement, and access to technological infrastructure. The study also assessed their interaction with digital new media across various domains, including interpersonal communication, information retrieval, entertainment, practical applications, and mobile payments. Results: The study found significant differences in engagement with digital new media art among the older adult. Participants with prior computer experience were generally more skilled in using smartphones and more active in community events. In contrast, individuals in community B showed lower acceptance of digital new media art and no clear association with community participation. There was substantial variability in their use of digital media for information retrieval, entertainment, practical applications, and mobile payments. Some older adult individuals demonstrated proficiency with these technologies, while others were more reserved. Discussion: The findings suggest that digital new media art can enhance community participation and social adaptability among older adults, particularly those with prior computer experience. However, disparities in digital media usage highlight the need for targeted interventions to improve digital health literacy and engagement across different community settings. The study underscores the importance of addressing these disparities to ensure that all older adults can benefit from digital advancements, thereby improving their overall well-being and health literacy.

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BACKGROUND: In past 20 years, there is increase in mobile phone users from 12.4 million to about 5.6 billion i.e 70 % of the world's population.[1] Electromagnetic radiations emitted from mobile phone damages inner ear, cochlea and outer hair cells of inner ear and auditory pathway (AP).[2]. MATERIALS AND METHODS: Case control study. Group 1, N=30 subjects, using mobile smart phones since past 1-5 years and exposure time more than 2 hours per day. Group II included 30 subjects, using mobile smart phones for more than 5 years and exposure time more than 2 hours per day. Headache, tinnitus, or sensations of burning around phone-using were excluded. Brainstem auditory evoked potential (BAEP) done. Student Unpaired t test was used for analysis and chisquare test. RESULTS: Mean ± SD of absolute latencies (AL) of Brainstem evoked response auditory. (BERA) waves III, V and all interpeak latencies at 80 dB and 4,6,8 KHz in group 2 were delayed and significant as compared to group 1. All parameters were highly significant at 8KHz as compared to 4KHz in group 2. CONCLUSION: Brain stem evoked response audiometry (BERA) detects hearing loss in smart mobile phone using subjects at higher frequencies i.e at 8 KHz early. Hence central neural axis involvement can be detected early by BERA.

Résumé Contexte:Au cours des 20 dernières années, le nombre d'utilisateurs de téléphones mobiles est passé de 12,4 millions à environ 5,6 milliards, soit 70 % de la population mondiale.[1] Les rayonnements électromagnétiques émis par les téléphones portables endommagent l'oreille interne, la cochlée et les cellules ciliées externes de l'oreille interne et des voies auditives (PA).[2]Matériels et méthodes:étude cas-témoins. Groupe 1, N = 30 sujets, utilisant des téléphones intelligents mobiles depuis 1 à 5 ans et temps d'exposition supérieur à 2 heures par jour. Le groupe II comprenait 30 sujets, utilisant des téléphones portables intelligents depuis plus de 5 ans et ayant une durée d'exposition supérieure à 2 heures par jour. Les maux de tête, les acouphènes ou les sensations de brûlure lors de l'utilisation du téléphone ont été exclus. Potentiel évoqué auditif du tronc cérébral (BAEP) réalisé. Le test t non apparié de Student a été utilisé pour l'analyse et le test du chi carré.Résultats:Moyenne ± écart-type des latences absolues (AL) de la réponse auditive évoquée du tronc cérébral. (BERA) les ondes III, V et toutes les latences inter-pics à 80 dB et 4,6,8 KHz dans le groupe 2 étaient retardées et significatives par rapport au groupe 1. Tous les paramètres étaient hautement significatifs à 8 KHz par rapport à 4 KHz dans le groupe 2.Conclusion:L'audiométrie à réponse évoquée du tronc cérébral (BERA) détecte la perte auditive dans un téléphone mobile intelligent en utilisant des sujets à des fréquences plus élevées, c'est-à-dire à 8 KHz plus tôt. Par conséquent, l'implication de l'axe neural central peut être détectée précocement par BERA.

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Asthma is a chronic inflammatory airway disorder and is among the top ten causes of morbidity and mortality in Indonesia, highlighting the need for effective management strategies. This study aimed to evaluate changes in asthma knowledge levels before and after using the "Paru Sehat" smartphone application for digital education. A quasi-experimental study using a pre- and post-test design was conducted over three months at the outpatient clinic of Universitas Sumatera Utara Hospital, Medan, Indonesia, among asthma patients aged over 18 years. The patients completed the Patient Asthma Knowledge Questionnaire (PAKQ) before installing the "Paru Sehat" application to establish baseline knowledge. Once a week, patients received reminders to access materials covering an asthma overview, peak flow meter use, medication differences, and management techniques. After four weeks of intervention, the participants' knowledge was reassessed using the PAKQ. Data were analyzed using either paired Student's t-test or Wilcoxon test to assess the improvement in knowledge. The results showed increases in knowledge about asthma in general, asthma triggers, and asthma diagnosis domains, with the median score in each domain rising by one point (p=0.003, p=0.001, and p=0.002, respectively). The asthma management domain exhibited the largest change, with a median increase of two points (p<0.001). The total scores increased by an average of five points (p<0.001), indicating an overall improvement in patient knowledge about asthma. The "Paru Sehat" application demonstrated significant potential in enhancing asthma knowledge across multiple domains. Further research is warranted to explore the long-term effectiveness of the application in diverse populations and settings, ultimately contributing to improved asthma management and patient outcomes.

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Asynchronous teledermatology (TD) has shown promise in improving access to dermatological care, yet studies reveal varied methodologies and results. This study aimed to design a mobile app using the store-and-forward model, marking a pioneering effort in Iran to facilitate written documentation of patient history and skin lesion imagery for accurate diagnosis. Conducted from July 2022 to July 2023, this study involved the development and implementation of a smartphone app to collect clinical data and photographs from 89 patients at a dermatology clinic. Diagnoses made at the clinic served as the gold standard. Three dermatologists independently reviewed the app-recorded data, providing diagnoses that were compared to in-person assessments using the Kappa coefficient and agreement percentage. Lesions were predominantly located on the whole body (47%), with psoriasis (19.1%), acne vulgaris (11.2%), and eczema (10%) being the most common. The overall inter-observer Kappa value demonstrated excellent agreement at 0.856. The study's findings endorse the use of a mobile app for TD as an effective adjunct in regions with scarce dermatological services, such as Iran. To promote the broader application of TD, ongoing research is essential to further establish its reliability and practicality.

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BACKGROUND: Previous studies indicated that excessive engagement in digital devices could lead to negative psychological impacts in general population. We aimed to determine the association of electronic screen exposure with depression among women in early pregnancy. METHODS: A cross-sectional study was conducted from June 2021 to June 2022. A total of 665 women in early pregnancy were recruited and the information included socio-demographic characteristics, screen exposure and Patient Health Questionnaire - 9 depression scale. RESULTS: Among the women in early pregnancy, the total daily smartphone viewing time was the longest (median [P25-P75], 5 [3-6] hours/day) in the three types of electronic screen exposure. The total daily smartphone viewing time (P = 0.015, OR[95%CI] = 1.09[1.11-1.18]), smartphone (P = 0.016, OR[95%CI] = 1.24[1.04-1.47]) and television viewing time (P = 0.006, OR[95%CI] = 1.35[1.09-1.67]) before nocturnal sleep were significantly associated with depression among women in early pregnancy. The thresholds calculated by receiver operator characteristic curves were 7.5 h/day, 1.5 h/day and 1.5 h/day, respectively. In addition, women with higher scores of smartphone addiction were more susceptible to depression (P<0.001, OR[95%CI] = 1.11[1.07-1.16]). The top three smartphone usages in women with depression were watching videos (22.0%), listening to music (20.9%) and playing games (16.7%). CONCLUSIONS: In conclusion, electronic screen exposure, including screen viewing time, smartphone addiction and problematic smartphone use was associated with depression among women in early pregnancy. Further studies are warranted to verify the conclusions.

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Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of convolutional neural networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (95% CI: 84.2-91.0) compared to body-based classification 78.9% (95% CI: 77.7-80.0). Nevertheless, there are notable limitations of CNNs as they perform reliably across multiple devices only when trained specifically on those devices, resulting in an average decline of mean accuracy by 14%, even with extensive image augmentation. Additionally, we also estimate the required training data volume for effective classification, noting a reduced requirement for wing-based classification compared to body-based methods. Our study underscores the viability of both body and wing classification methods for mosquito species identification while emphasizing the need to address practical constraints in developing accessible classification systems.

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OBJECTIVE: This study aims to examine the impact of e-WOM on customer purchase intentions in Facebook fan pages using theories of trust, value co-creation and brand attitude. The present research has set out to explore this emerging domain of study and has thus developed & tested propositions which attempt to establish a relationship between e-WOM and customer's purchase intentions. A deeper understanding of this possible association is obtained by studying the mediating roles of Trust, Value Co-Creation, Brand Image and Brand Attitude. METHODOLOGY: The context for exploring this phenomenon is chosen to be the fan pages of smartphone brands on Facebook. The study involved conducting a sample survey of 490 respondents, comprising of both male and female, who belong to 5 smartphone brands Facebook fan pages-Samsung, Moto G, Lenovo, MI and ASUS are considered for the study. Out of which sample of 100 each has been targeted individually. FINDINGS: The findings suggested that e-WOM significantly predicts the purchase intentions of the customers of a specific product and considerable impacted on the purchase decision. The findings of the study also reveal that customer 's trust beliefs, perceived value co-creation, brand image and brand attitude partially mediate in between relationships of e-WOM and purchase intention. CONCLUSION: The actual presence of different types of consumer electronics brands on the social media, more prominently, the smartphones, which undoubtedly are the most ubiquitous product of this segment. In fact, this indicates that presence on social media is a well- thought organizational strategy developed by companies to gain partial control over the customer 's decision- making process by establishing a close connect with the customers for a long period. IMPLICATION: This consequence will significantly impact the decision-making process of marketers or practitioners in relation to their marketing tactics. This research also indicates that marketers could devise more effective methods for distributing marketing content through social networking sites, while corporations can cultivate favorable electronic word-of-mouth for their products or services. Through the implementation of social media marketing strategies, companies can increase their sales volume and generate higher revenue. The study examined the role of trust, virtual community participation, and desire to purchase as mediators on smartphone brand fan sites on Facebook. It was observed that these factors had a partial influence on customer purchase intention.

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The purpose of the current study was to examine the risk factors of young children's smartphone addiction in a longitudinal study design. Data collected from 313 participants (mean age, 4.5 ± 0.82 years; male, 49.8%) over 4 years for Kids Cohort for Understanding of Internet Addiction Risk Factors in Early Childhood were analyzed in this study. Mixed effect models were used to evaluate the influence of various variables on the repeated measures of smartphone addiction tendency in young children over time. The multi-level analysis showed that parents' lack of control over children's smartphone use (t = -4.523; 95% confidence interval [CI], -7.32, -1.72), and parents' higher smartphone addiction proneness (t = 6.340; 95% CI, 0.23, 0.440) predicted higher smartphone addiction tendency in young children. The responsibility of the parents to prevent their children from becoming addicted to smartphones should start in a very early age when they start using the smartphones.

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