Successful Application Research Articles

Multimodal Polymeric Crosslinker Capable of Dual Crosslinking for Enhanced Cohesion, Adhesion, and Debonding of Waterborne Acrylic Adhesives

AbstractWaterborne acrylic adhesives, despite their widespread usage, often suffer from insufficient cohesive and adhesive properties, especially compared to solvent adhesives. Therefore, in order to improve the performance of waterborne adhesives, it is important to enhance and control these properties in a refined manner. For this purpose, herein presents a polymeric crosslinker presenting a variable number of methacrylic groups intended to control the degree of crosslinking. Polysuccinimide (PSI), which consists of a series of succinimidyl rings, is conveniently modified with methacrylic functional groups by ring‐opening nucleophilic reaction to develop methacrylic PSI (“MA‐PSI”). The cohesion and adhesion of MA‐PSI crosslinked adhesive is comprehensively controlled by both concentration and degree of methacrylic substitution (DSMA) of MA‐PSI. Moreover, the dual crosslinking, accomplished by the successive application of thermal‐ and photo‐crosslinking, further increases the cohesion to the extent that significantly reduces the adhesion strength, which is ideal for debonding applications. Overall, the multimodal MA‐PSI is a versatile crosslinker allowing broad controllability of cohesion and adhesion as well as imparting debonding capability.

Immediate and residual effect of tobacco powder compost and of NPK on N 2 O emissions and on N use in a wheat/corn crop succession

ABSTRACT Organic fertilization with tobacco powder compost produced via solid-state fermentation (SSF) is a recent practice that needs to be evaluated through research. This study aimed to evaluate the effect of two nitrogen sources – tobacco powder compost or mineral fertilizer (NPK) – and of compost doses on N use and loss in an immediate and residual way in a wheat/corn crop succession in a subtropical Argissolo. The wheat/corn crop succession was established after three successive fertilizer applications with different doses of the compost and of NPK. The following treatments were evaluated: control without application of the compost or mineral fertilizer (C0); mineral fertilization (NPK); and three doses of tobacco powder compost calculated to supply half (C50), the same amount (C100), and double (C200) the N applied via mineral NPK fertilization. To evaluate the residual effect of the fertilizer applications, the C50, C100, C200, and NPK treatments gave rise to additional treatments that did not receive more fertilization: rC50, rC100, rC200, and rNPK, respectively. The C0 generated an additional treatment that came to receive compost, identified as C0-C100. Yield and N accumulation in the aerial part of wheat and corn and N 2 O emission were evaluated throughout the two crop periods. Compost, in comparison to NPK, maintained the grain yield of wheat (3.0 vs 2.5 Mg ha -1 ) and of corn (10.2 vs 9.5 Mg ha -1 ) and increased N accumulation in the wheat plants (96 vs 68 kg ha -1 ) and corn plants (191 vs 164 kg ha -1 ). Compost increased cumulative N 2 O emissions by up to three times compared to NPK (6.84 vs 1.93 kg N-N 2 O ha 1 yr 1 ). Increasing the compost dose increased cumulative N 2 O emissions by 52 % (9.36 vs 6.16 ha -1 yr -1 ); however, it did not change the emission factor (EF) of N 2 O, the yield-scaled emission, or N use efficiency. The compost residual effect increased wheat yield up to 93 % and corn yield up to 102 %, whereas no residual effect was observed from NPK. Compost applied on the previous crops did not affect N 2 O emissions in wheat or in corn; consequently, a residual effect did not impact the EF of N 2 O. Therefore, tobacco powder compost has the potential to replace mineral fertilization in wheat and in corn. Considering its residual effect is an important strategy to optimize its use and mitigate N 2 O emissions to the atmosphere.

A Method for Calculating Small Sizes of Volumes in Postsurgical Thyroid SPECT/CT Imaging

Differentiated thyroid cancer treatment typically involves the surgical removal of the whole or largest part of the thyroid gland. Diagnostic procedures are useful both before and after treatment to determine the need for radioiodine ablation, re-stage the disease, monitor disease progression, or evaluate treatment efficacy. SPECT/CT imaging can be utilized to identify small, distant iodine-avid metastatic lesions and assess their uptake and volume for the above purposes as well as for performing lesion-based dosimetry when indicated. The objective of this study was to develop and validate a method for calculating small sizes of volumes in SPECT/CT imaging as well as to perform calculations utilizing I-131 and I-123 postsurgical SPECT/CT images from a neck–thyroid phantom. In this approach, the calculated volume was unaffected by radiation spillover from high-uptake voxels since it was the result from the successive application of the gray-level histogram technique to SPECT and CT 3D matrices. Beforehand, the SPECT 3D matrix was resized and aligned to the corresponding CT one. The method was validated following the clinical protocols for postsurgical thyroid imaging by using I-123 and I-131 scatter and attenuation-corrected SPECT/CT images from a neck–thyroid phantom. The phantom could accommodate two volumes of different sizes (0.5, 1, 1.5, 3, and 10 mL) and enclose anatomical tissue-equivalent main scattering structures. For the 0.5 and 10 mL volumes, the % differences between the actual and the calculated volumes were 15.2% and 1.2%, respectively. Radiation spillover was only present in SPECT images, and it was more profound at higher administered activities, in I-131 than in I-123 images, and in smaller volumes. When SPECT/low-dose-CT imaging is performed, this method is capable of accurately calculating small volumes without the need of additional modalities.

Self-Organizing Robust Fuzzy Neural Network for Nonlinear System Modeling.

Fuzzy neural network (FNN) is a structured learning technique that has been successfully adopted in nonlinear system modeling. However, since there exist uncertain external disturbances arising from mismatched model errors, sensor noises, or unknown environments, FNN generally fails to achieve the desirable performance of modeling results. To overcome this problem, a self-organization robust FNN (SOR-FNN) is developed in this article. First, an information integration mechanism (IIM), consisting of partition information and individual information, is introduced to dynamically adjust the structure of SOR-FNN. The proposed mechanism can make itself adapt to uncertain environments. Second, a dynamic learning algorithm based on the α -divergence loss function ( α -DLA) is designed to update the parameters of SOR-FNN. Then, this learning algorithm is able to reduce the sensibility of disturbances and improve the robustness of Third, the convergence of SOR-FNN is given by the Lyapunov theorem. Then, the theoretical analysis can ensure the successful application of SOR-FNN. Finally, the proposed SOR-FNN is tested on several benchmark datasets and a practical application to validate its merits. The experimental results indicate that the proposed SOR-FNN can obtain superior performance in terms of model accuracy and robustness.

Effect of Directed Pre-Veterinary Career Education on Applicant Confidence and Perception of the Profession.

Elective undergraduate courses are offered at the University of Georgia and the University of Florida that explore veterinary careers, offer guidance on being a successful veterinary school applicant, and expose students to the reality of the profession through guest speakers. Students (N = 276) from both universities responded to a survey offered at the first and last class period over the course of five semesters. Using 100-point Likert scales, students were asked to gauge their opinion of and interest in the profession and demonstrate their confidence in various aspects of building a successful application. Data were analyzed as a randomized complete block design with survey completion time as the fixed effect. After completing the course, students showed decreased (p < .01) interest in becoming a veterinarian and a reduced (p = .01) opinion of the profession. Despite these decreases, student perception ratings remained in the positive range in both categories. Student opinions of financial commitment, veterinarian mental health, and diversity did not change (p > .20) after taking the course, though ratings in these categories were neutral to somewhat negative. The post survey revealed students demonstrated more confidence in applying to veterinary school (p < .01), including understanding academic requirements, identifying ideal references, composing a statement of purpose, and gaining veterinary experience (p < .01). The survey demonstrated the benefits of a career exploration class for pre-veterinary students by increasing applicant confidence and showed that challenges facing the profession may influence the applicant pool.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Comparing the Pretrained Models of Source Code by Re-pretraining Under a Unified Setup.

Recent years have seen the successful application of large pretrained models of source code (CodePTMs) to code representation learning, which have taken the field of software engineering (SE) from task-specific solutions to task-agnostic generic models. By the remarkable results, CodePTMs are seen as a promising direction in both academia and industry. While a number of CodePTMs have been proposed, they are often not directly comparable because they differ in experimental setups such as pretraining dataset, model size, evaluation tasks, and datasets. In this article, we first review the experimental setup used in previous work and propose a standardized setup to facilitate fair comparisons among CodePTMs to explore the impacts of their pretraining tasks. Then, under the standardized setup, we re-pretrain CodePTMs using the same model architecture, input modalities, and pretraining tasks, as they declared and fine-tune each model on each evaluation SE task for evaluating. Finally, we present the experimental results and make a comprehensive discussion on the relative strength and weakness of different pretraining tasks with respect to each SE task. We hope our view can inspire and advance the future study of more powerful CodePTMs.

Predicting Admission and Future Performance of Veterinary School Applicants: Evaluation of Scores of Self-Reported Animal Experience and Rural Versus Urban Background.

Admission to veterinary school is generally based on academic and nonacademic measures. Descriptions of animal or veterinary experience and rural versus urban background are often sought from applicants, but little is objectively known about their impact on admission success or future performance. We evaluated scores from written descriptions from 590 veterinary school applicants for the nature and extent of self-reported animal experience. For those admitted to the program, we compared animal experience and rural versus urban background to performance in discipline-based courses, professional skills courses, clinical rotations, and the North American Veterinary Licensing Exam (NAVLE). More than 98% of applicants reported animal experience, with small animal veterinary experience most reported. There was no difference in animal experience or background between successful and unsuccessful applicants, but rural and urban applicants reported different experiences. There was a small correlation between small animal experience and performance in clinical rotations (.21), a small negative correlation between rural background and NAVLE performance (-.23), but otherwise, no significant correlations between animal experience or background and future performance. These findings suggest that scores of self-reported animal experience do not provide predictive information on applicants, or, alternatively, that the nature and extent of animal experience, the methods used to score these experiences, and/or the measures assessed during veterinary school need to be explicitly defined to ensure that we are capturing the appropriate information. More investigation into the scoring and impact of animal experience and background on applicant performance in the DVM program and success in a veterinary career is warranted.

Letter to the Editor Regarding “Senior Mentorship and Scholarly Success: Assessing Influences on Successful Neurosurgery Residency Applicant’ H-Index”

Letter to the Editor Regarding “Senior Mentorship and Scholarly Success: Assessing Influences on Successful Neurosurgery Residency Applicant’ H-Index”

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 &gt;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. 2024;47(6):355-358.].

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

It 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. An anonymous electronic survey was sent to medical students through social media of a women's surgical society. Questions reflected aspects of the ERAS application. There 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. Students 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 &amp; Javadi) and EP/V032275/1 (Holt &amp; 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.