Successful Application Research Articles

Digital technology‐based business model design and innovation to address grand challenges: A process model

AbstractResearch SummaryThe question of how a new venture designs and innovates on its business model (BM) to leverage digital technology to address multiple grand challenges has received little attention in the literature. We address this through a longitudinal case study of a new social enterprise in China. We develop an accessing‐embedding‐strengthening‐expanding process model of BM design and innovation by which a venture may increase both the scale and scope of its impact as a complementor in ecosystems addressing grand challenges. We identify the specific actions and contingencies underlying this process. Our findings contribute to the understanding of BM design and innovation based on digital technologies and propose that assessment of a BM's impact on grand challenges may be in terms of scope as well as scale.Managerial SummaryHow may founders build a BM that exploits a technology's potential to address diverse sustainability challenges? Our study derives insights from one for‐profit Chinese venture whose founders had the explicit objective of leveraging remote sensing data and analytics to address important and diverse social and environmental challenges. We identify the steps in its ecosystem‐based strategy (accessing, embedding, strengthening, and expanding) by which the founders targeted and entered successive application ecosystems as a complementor. This strategy has enabled them to achieve financial goals and also increase their contribution to sustainability objectives in terms of both scale (vis‐à‐vis one grand challenge) and scope (across multiple grand challenges). We also identify the specific questions that the founders had to answer in the process of executing this strategy.

Bacillus clausii: A Review into Story of Its Probiotic Success and Potential Food Applications

Probiotics are live microbiota that can confer their hereditary health benefits upon the host. They can positively alter the diversity of the host’s gut microbiota population. Bacillus clausii is a spore-producing potential probiotic. Its application in the food industry has been highly recommended. Clausii spores are greatly resistant to harsh food processing treatment (heat and industrial pressure) and they can maintain their physiological traits (acid, bile salt) inside the human gut. The utilization of spores can enhance the nutritional viability of livestock, as well as the functionality of on-shelf products. This contemporary review covers the great attributes of B. clausii, including physiological characteristics, modes of action, probiotics benefits, a safety assessment, and the bacteria food industry applications for the purposes of producing enhanced functional foods known as probiotic foods. To our knowledge, although B. clausii has been widely applied in food industry, the amount of the literature that is dedicated to its role in sustainable food production and addresses its potential sporogenous probiotic behavior is fewer as compared to non-sporogenous lactic acid bacteria (Lactobacillus spp. and Bifidobacterium spp.). In our review, we also discovered a lack of empirical evidence on the synbiotic and synergistic behavior of clausii in combination with other active nutrients. Therefore, our review suggests that more studies should be conducted on the potential probiotic contribution of B. clausii in sustainable food production and its synergistic and synbiotic behavior in conjunction with other active nutrients.

Pengujian Aplikasi Homestay Kosasih: Analisis Fungsionalitas Dan Penerimaan Pengguna Menggunakan Metode Black Box Dan UAT

The research focuses on evaluating the functionality of the Homestay Kosasih application, particularly in terms of user interaction, data management, and user satisfaction. This study combines Black box Testing and UAT methods to provide a comprehensive understanding of the application's quality and performance. The research aims to assess the application's functional features, focusing on user interface, ease of use, and the application's ability to meet user expectations. Through analysis, this study will provide a thorough understanding of the application's quality and also measure the level of user satisfaction with the hotel reservation application, involving assessments of user interface, ease of use, and the application's success in meeting user expectations. The results show that the Homestay Kosasih application exhibits good functionality with 80.9% valid test cases and good user acceptance with an average score of 74.39% from 40 respondents. Therefore, it can be concluded that the entire series of application testing for Homestay Kosasih is deemed good.

Comparison of academic achievement, graduation competency and academic sustainability of veterinary students based on college admission processes

A university’s admission policy is established to select applicants who can successfully complete the curriculum. Thus, it is important to develop optimal admissions processes for applicant selection. This study was conducted to infer the most suitable admission processes for veterinary school applicants by examining academic achievement, graduation competency and academic sustainability. It was conducted on students admitted from 2009 to 2018 at one of the veterinary colleges in Korea. The admission methods were divided into, first the regular admission (RA) process, primarily reflecting the results of the College Scholastic Ability Test, and second, the rolling admission processes. The rolling processes were further divided into admission officer-based (AO), high school grade point average-based (GA), essay test-based (ET), admission for the underprivileged (UP) and admission for overseas Koreans and foreigners (OKF). The final grade point average was observed to be higher in the order of the GA, AO and RA groups. The survey on the veterinary graduation competency did not reveal any difference between the regular and rolling admission processes. However, among the latter, AO-based admission showed a higher ratio of outstanding competency levels. In terms of academic sustainability, OKF admission demonstrated a higher rate of failure while RA showed a higher rate of expulsion and dropout. Summarizing the above results, the most successful veterinary applicants were selected through AO-based admission. These results are meaningful in providing the basic information required to establish admission policies for veterinary colleges in Korea.

A Hybrid Approach for CT Image Noise Reduction Combining Method Noise-CNN and Shearlet Transform

The presence of gaussian noise commonly weakens the diagnostic precision of low-dose CT imaging. A novel CT image denoising technique that integrates the non-subsampled shearlet transform (NSST) with Bayesian thresholding, and incorporates a modern method noise Deep Convolutional neural network (DCNN) based post-processing operation on denoised images to strengthen low-dose CT imaging quality. The hybrid method commences with NSST and Bayesian thresholding to mitigate the initial noise while preserving crucial image features, such as corners and edges. The novel aspect of the proposed approach is its successive application of a DnCNN on initial denoised image, which learns and removes residual noise patterns from denoised images, thereby enhancing fine detail preservation. This dual-phase strategy addresses both noise suppression and image-detail preservation. The proposed technique is evaluated through the use of metrics, such as PSNR, SNR, SSIM, ED, and UIQI. The results demonstrate that the hybrid approach outperforms standard denoising techniques in preserving image quality and fine details.

Effects of Successive Top-Dressing Application of Lime on a Sweet Cherry Orchard in Southern Chile

Annual top-dressing application of agricultural lime is a common practice in fruit orchards on acidic soils in southern Chile, which could result in surface over-liming and base imbalances. A trial was performed in a cherry orchard with an 8-year history of surface liming to evaluate the effectiveness of lime materials in neutralizing acidity in the soil profile and the effect on the tree nutritional status. No-lime (NL), calcitic (AgL), hydrated (HL), and liquid (LL) lime treatments were applied on soil surface at commercial rates, and soil acidity variables were measured at depths of 0–5, 5–10, and 10–20 cm in samples collected at 0, 15, 30, 60, and 225 days after application. Tree nutritional status was evaluated through foliar analysis. Top-dressing application of AgL was ineffective in ameliorating subsoil acidity at depths >5 cm, even in high-rainfall conditions. HL did not exhibit greater alkalinity mobility compared to AgL, although it had a faster but shorter-lived reaction. At the manufacturer-recommended rates, LL application was ineffective. After 8 years of top-dressing liming with AgL, a significant stratification of soil pH, Al, and Ca was observed. However, foliar concentration of bases did not reflect the surface Ca accumulation in soil, discarding an antagonistic cation competition for tree uptake.

Peer-Reviewed Publications by Successfully Matched Orthopedic Surgery Residency Applicants in the 2022-2023 Match.

The Orthopaedic Surgery Match is highly competitive, with more applicants than residency spots. With the Step 1 Exam moving to a pass/fail result, residency programs and applicants have fewer objective data to determine applicants' relative competitiveness. Through this study, we sought to provide the mean number of accepted publications on PubMed a successfully matched orthopedic surgery applicant has by the time of submission of their application. Orthopedic surgery residency programs participating in the National Resident Matching Program were identified by their ranking on the Doximity Residency Navigator. Each program's intern class and their medical schools were identified. Their names were searched in PubMed and Scopus and articles with their name and affiliations were recorded. In total, 877 orthopedic surgery interns published a mean of 3.30±5.27 articles each on PubMed. They were first or second author on 1.44±2.58, and 1.96±3.89 publications were related to orthopedic surgery. There were no statistical differences between degree, sex, or residency program rank from the Doximity Residency Navigator. The mean number of publications from a successful applicant was approximately 3. There was a great range in the number of publications, and 27.3% of successful applicants did not have a single publication. Future applicants and programs can use this number to gauge relative research output. [Orthopedics. 202x;4x(x):xx-xx.].

In the Era of pass/fail: Medical student perceptions on optimizing their surgical residency applications

BackgroundIt is unclear how changes to the 2023–2024 Electronic Residency Application Service (ERAS) application will impact future applicants. We investigate students’ perceptions about applying to residency during this transition period. MethodsAn anonymous electronic survey was sent to medical students through social media of a women's surgical society. Questions reflected aspects of the ERAS application. ResultsThere were 415 responses, with 68 ​% planning to apply into surgical specialties (SS) and 17 ​% into non-surgical specialties (NSS). Compared with the NSS group, SS students reported decreased confidence in successfully matching, less attainable applicant expectations, and increased personal sacrifices to pursue their residency of interest. Students interested in Plastic and Reconstructive Surgery (PRS) placed 3-times more importance on the number of publications than General Surgery students. Away rotations were rated significantly more important for Otolaryngology, Orthopaedic, and PRS students. ConclusionStudents applying into surgical residencies experience unique stressors. Values among SS students differed across surgical subspecialties in preparation for the match. Further investigation characterizing the relationship between these values and characteristics of successful resident applicants is warranted.

PUBLIC AND PATIENT ENGAGEMENT IN RESEARCH TO DEVELOP TECHNOLOGIES FOR PREDICTING SPINE LOADS IN VIVO

BackgroundOur current research aims to develop technologies to predict spinal loads in vivo using a combination of imaging and modelling methods. To ensure the project's success and inform future applications of the technology, we sought to understand the opinions and perspectives of patients and the public.MethodsA 90-minute public and patient involvement event was developed in collaboration with Exeter Science Centre and held on World Spine Day 2023. The event involved a brief introduction to the project goals followed by an interactive questionnaire to gauge the participants’ background knowledge and interest. The participants then discussed five topics: communication, future directions of the research, concerns about the research protocol, concerns about data, and interest in the project team and research process. A final questionnaire was used to determine their thoughts about the event.ResultsTwelve adults attended the event, many motivated by their experience or interest in back pain. A thematic analysis was used to review participant comments on the research project, identifying the need to relate the research to everyday life, present risks in various ways, and be transparent about funding and data sharing. In terms of future applications, participants felt the technology should be used to understand normal spine behaviour, prevent problems, and improve treatment. Participants agreed that they had got something positive out of engaging in the event.ConclusionEngagement with public and patient stakeholders is an essential activity that can generate vital information to inform and add value to technology development projects.Conflicts of interestNo conflicts of interestSources of fundingEPSRC grants EP/V036602/1 (Meakin, Holsgrove & Javadi) and EP/V032275/1 (Holt & Williams).

A Critical Review of Inductive Logic Programming Techniques for Explainable AI.

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms' explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.

Mobile Assistant Application for Street Food Consumers in Bandung

In the dynamic city of Bandung, the lively street food scene has captured the fascination of tourists, offering a diverse selection of tempting dishes. Nevertheless, a persistent challenge arises from the lack of comprehensive details about these street foods, presenting a hurdle for consumers in making well-informed and health-conscious choices. This predicament underscores the necessity for a solution, leading to the introduction of the Mobile Assistant Application for Street Food Consumers in Bandung. Harnessing cutting-edge computer vision technology, this application seeks to provide a solution by furnishing users with an intuitive and effective tool for accessing in-depth information regarding street foods. The outcomes of thorough experimentation highlight the application's success in precisely identifying a wide array of street foods in Bandung. Users benefit from accurate information on ingredients and nutritional values, empowering them to make informed dietary decisions and elevating the overall street food experience in Bandung. This inventive solution not only addresses the prevailing information gap but also contributes to the well-being of consumers, ushering in a healthier and more enlightened food culture in Bandung at the tip of one's finger.

Competing for research funding: Key elements impacting the evaluation of grant proposal

As funding is one of the key pillars of research activity, identifying the factors that impact the evaluation results in research funding competitions remains challenging, due to the heterogeneity of funding instruments. In this context, our study aims to identify the elements that ensure the application's success, comparing two perspectives: one of the applicant and the other based on the evaluation grid. The empirical investigation focuses on a survey of 243 Romanian researchers. As analysis methods, we use a binary logistic regression model to correlate the success in funding competitions for research projects with a set of factors considered determinants. The results show that the researcher's past performance influences the proposal's future performance/success, with the quality of the project director's previous publications, and its international visibility being the key drivers of successful research project applications.

Can ChatGPT Fool the Match? Artificial Intelligence Personal Statements for Plastic Surgery Residency Applications: A Comparative Study

Introduction: Personal statements can be decisive in Canadian residency applications. With the rise in AI technology, ethical concerns regarding authenticity and originality become more pressing. This study explores the capability of ChatGPT in producing personal statements for plastic surgery residency that match the quality of statements written by successful applicants. Methods: ChatGPT was utilized to generate a cohort of personal statements for CaRMS (Canadian Residency Matching Service) to compare with previously successful Plastic Surgery applications. Each AI-generated and human-written statement was randomized and anonymized prior to assessment. Two retired members of the plastic surgery residency selection committee from the University of British Columbia, evaluated these on a 0 to 10 scale and provided a binary response judging whether each statement was AI or human written. Statistical analysis included Welch 2-sample t tests and Cohen's Kappa for agreement. Results: Twenty-two personal statements (11 AI-generated by ChatGPT and 11 human-written) were evaluated. The overall mean scores were 7.48 (SD 0.932) and 7.68 (SD 0.716), respectively, with no significant difference between AI and human groups ( P = .4129). The average accuracy in distinguishing between human and AI letters was 65.9%. The Cohen's Kappa value was 0.374. Conclusions: ChatGPT can generate personal statements for plastic surgery residency applications with quality indistinguishable from human-written counterparts, as evidenced by the lack of significant scoring difference and moderate accuracy in discrimination by experienced surgeons. These findings highlight the evolving role of AI and the need for updated evaluative criteria or guidelines in the residency application process.

EVALUACIÓN DE ESTRATEGIAS DE MANEJO ECOLÓGICO DE ANTRACNOSIS DE LA PAPAYA EN CAMPO Y POSCOSECHA

<p><strong>Background:</strong> Mexico is the third producer and the first exporter of papaya in the world. The crop has multiple phytosanitary limitations, among which anthracnose stands out, which can cause substantial production losses. The main management method is with chemical synthesis fungicides. <strong>Objective:</strong> To evaluate treatments with biorational characteristics to control anthracnose in papaya crops in pre- and post-harvest. <strong>Methodology:</strong> The causal agent of anthracnose was isolated and identified, the growth inhibitory effect of<em> </em><em>C. gloeosporioides</em><em> </em>was evaluated <em>in vitro</em> with extracts of <em>Datura stramonium</em>, <em>Argemone mexicana</em>, <em>Citrus sinensis</em> essential oil and <em>Eucalyptus globulus</em> essential oil; In addition, biorational treatments were applied pre- and post-harvest (botanical extracts, essential oils and <em>Trichoderma harzianum</em>); The incidence and severity were evaluated and the percentage of efficacy was calculated. A randomized complete block design was used and analysis of variances and comparisons of means were performed (Tukey, P ≤ 0.05). <strong>Results:</strong> The <em>E. globulus</em> oil extract reached the highest in vitro growth inhibition levels, followed by the <em>C. sinensis</em> essential oil and the <em>A.</em><em> mexicana</em> extract; In the field phase, <em>T. harzianum</em> reached 80.7% efficacy and 8% incidence, the essential oil of <em>E. globulus</em> and the essential oil of <em>C. sinensis</em> reached 73 and 66% efficacy, respectively. The effect of the treatments applied in pre-harvest demonstrated that successive applications of <em>T. harzianum</em> reduced severity by 81% and incidence on papaya fruits by 30%, the essential oil of <em>E. globulus</em> and that of <em>C. sinensis</em> achieved efficacy of 64 and 68%, in their order. Finally, in the storage phase, <em>T. harzianum</em> achieved a control efficiency of 89%, <em>C. sinensis</em> oil 84% and 85% for the essential oil of <em>E. globulus</em>, <em>A. mexicana</em> managed to inhibit 76.46%. <strong>Implications:</strong> Comparison of other biorational management treatments such as the use of essential elements (silicon, sulfur, etc.), nanotechnology, UV irradiation and resistance inducers is suggested. <strong>Conclusion:</strong> These results support that a successful and biorational control of anthracnose in papaya must be based on comprehensive management that considered the preharvest and storage phases.</p>

The Human Capital Effects of Access to Elite Jobs

We study the human capital effects of access to elite bureaucratic jobs in Taiwan, where performance on an examination determines entry into the civil service. Historically, quotas for successful applicants were set based on the 1948 populations of individuals’ native provinces in mainland China. This resulted in a higher probability of success on the exam—and, thus, greater access to elite positions—for descendants of certain migrant groups. These preferential quotas were replaced in 1962 with a uniform admissions policy. Using this variation, we find that the incentives created by preferential quotas increased human capital and improved long-run economic outcomes. (JEL D73, H83, J24, J45, M51)

Senior Mentorship and Scholarly Success: Assessing Influences on Successful Neurosurgery Residency Applicant's H-Index

Research productivity is on the rise as neurosurgical residency positions become increasingly competitive. We explored the relationship between neurosurgical residency applicant's senior author's research productivity and matching into a neurosurgery residency program. A retrospective analysis of bibliometric data for applicants who matched into neurosurgery in 2022-2023 and their senior authors was conducted using Scopus. Logistic regression revealed a significant association between h-index values and top 40 match outcomes (P= 0.038). The maximum h-index of senior authors significantly predicted matches at top 40 programs (P=0.003). Affiliation with a top 40 medical school increased both applicant and senior author h-indices (P=0.05, P < 0.001 respectively). Linear regression of the maximum h-index of senior authors in preresidency publications explained 42% of this variability (P < 0.001). A multiple linear regression model incorporating this with publication number elucidated 69% of the variance in interns' h-index. Authorship data categorized as first, second, and third author positions showed 1847 first author, 1417second author, and 118 third author publications over 2-years. Applicants at top 40 residency programs had more first and second author publications compared to those from nontop 40 programs (P= 0.0158, P= 0.0275). There is a strong correlation between a neurosurgical applicant's academic output and that of their senior authors. The number of publications and the maximum h-index of senior authors significantly predict applicant h-indices. We also demonstrated that there is a significant difference in the academic productivity of applicants and senior authors of applicants who successfully match into a top 40 i(h)5 rated neurosurgical residency.

MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3-D CT Lesions.

With the renaissance of deep learning, automatic diagnostic algorithms for computed tomography (CT) have achieved many successful applications. However, they heavily rely on lesion-level annotations, which are often scarce due to the high cost of collecting pathological labels. On the other hand, the annotated CT data, especially the 3-D spatial information, may be underutilized by approaches that model a 3-D lesion with its 2-D slices, although such approaches have been proven effective and computationally efficient. This study presents a multiview contrastive network (MVCNet), which enhances the representations of 2-D views contrastively against other views of different spatial orientations. Specifically, MVCNet views each 3-D lesion from different orientations to collect multiple 2-D views; it learns to minimize a contrastive loss so that the 2-D views of the same 3-D lesion are aggregated, whereas those of different lesions are separated. To alleviate the issue of false negative examples, the uninformative negative samples are filtered out, which results in more discriminative features for downstream tasks. By linear evaluation, MVCNet achieves state-of-the-art accuracies on the lung image database consortium and image database resource initiative (LIDC-IDRI) (88.62%), lung nodule database (LNDb) (76.69%), and TianChi (84.33%) datasets for unsupervised representation learning. When fine-tuned on 10% of the labeled data, the accuracies are comparable to the supervised learning models (89.46% versus 85.03%, 73.85% versus 73.44%, 83.56% versus 83.34% on the three datasets, respectively), indicating the superiority of MVCNet in learning representations with limited annotations. Our findings suggest that contrasting multiple 2-D views is an effective approach to capturing the original 3-D information, which notably improves the utilization of the scarce and valuable annotated CT data.

Overview of the support of geothermal energy investments in the programming period 2014-2020 in Hungary

Geothermal energy has great potential to reduce the greenhouse gas emissions of the energy sector, thus contributing to the realization of EU targets and, of course, those ones set for Hungary. The country has excellent geological and geographical conditions, well known due to the existing databases as well as the know-how to guarantee a high-level utilization of this renewable energy source. Still, only a small part of the estimated potential is utilized and the previous programming period, 2014-2020 did not change this situation significantly. On one hand, the poor planning of the Széchenyi 2020 Programme, on the other hand, the lack of risk management measures, discourage investors and provide a competitive advantage for other renewable energy systems over geothermal, especially for biomass. The present paper pro- vides a comprehensive overview of the EU co-financed support of geothermal energy in the period 2014-2020, identifying relevant calls and successful projects. A questionnaire survey among successful applicants is the other pillar of the article. The critical analysis and recom- mendations of the development policy may contribute to a more efficient support of geother- mal energy in the following programming period(s).

Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.

The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthcare services. Particularly, multimodal representation learning gains increasing attention thanks to the high temporal and spatial resolution information extracted from neuroimaging fusion. In this work, we propose an efficient multimodality fusion framework to identify multiple mental disorders based on the combination of functional and structural magnetic resonance imaging. A multioutput conditional generative adversarial network (GAN) is developed to address the scarcity of multimodal data for augmentation. Based on the augmented training data, the multiheaded gating fusion model is proposed for classification by extracting the complementary features across different modalities. The experiments demonstrate that the proposed model can achieve robust accuracies of 75.1 ± 1.5 %, 72.9 ± 1.1 %, and 87.2 ± 1.5 % for autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and schizophrenia, respectively. In addition, the interpretability of our model is expected to enable the identification of remarkable neuropathology diagnostic biomarkers, leading to well-informed therapeutic decisions.

Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models.

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.