Academic Research Settings Research Articles

Parameter Optimization for Dissimilar Aluminum Alloys Joined Using Friction Stir Additive Manufacturing: A Screening Study

ABSTRACTDue to the requirement for better strength‐to‐weight ratios, the utilisation of aluminum alloys is rapidly expanding. Lightweight components are of utmost importance in most industries, particularly in transportation, aviation, maritime, automotive, and other industries. These lightweight engineering materials are hard to be joined utilising traditional fusion joining techniques, necessitating the development of alternative joining techniques. The novel friction‐stir additive manufacturing (FSAM) technique, based on the concept of friction stir welding (FSW), can be used to combine aluminum alloys additively in their solid state. This work examines the effects of several process parameters (tool rotational speed, tool tilt angle, and tool transverse speed) on tensile strength and hardness using a 3‐factor L9 Taguchi designed experiment. Three cases were explored, one were AL 6061 was welded to AL 7075, one where AL 7075 was welded to AL 6061, and one where the data were mixed to get an “average” effect representative of large additively manufactured parts. A detailed ANOVA (including both main effects and interactions analyses) provided clear guidance on the optimization of the parameters for several objectives. This work will contribute to the development and wider use of FSAM in both industrial and academic research settings by providing a useful dataset and clear parameter selection guidance. The results of this research indicate that the FSAM methodology could be utilized to fabricate large defect‐free structures, which can be a suitable replacement for the traditional Al6061 material used in automotive and aerospace sectors.

Person in Uniforms Re-Identification

Person in Uniforms Re-identification (PU-ReID) is an emerging computer vision task for various intelligent video surveillance applications. PU-ReID is much understudied due to the absence of large-scale annotated datasets, also this task is extremely challenging because many individuals captured in surveillance videos wear same clothing, introducing significant interference for retrieval tasks owing to the high visual similarity of outfits and subtle differences among individuals. This research initiates the exploration of person in uniform re-identification, a novel and challenging task tailored for real industrial scenarios. To address these issues, a novel framework is proposed for PU-ReID, which aims to reduce the visual impact of similar uniforms and learn the unique cues derived from human parts and detailed visual features. Specifically, several novel techniques are built in this study: first, a uniform feature separation method with orthogonal constraints is proposed to extract non-uniform features. Second, multi-view subspace feature alignment is introduced to integrate soft-biometrics including optics-related visual features, contextual information of human parts, and cloth-invariant biometric features. In addition, to close the gap between academic research and real world settings, a new person in uniforms ReID dataset named PU-151 is constructed, which consists of 151 gas station employees in uniforms from 1,488 videos. At last, extensive experiments conducted on five datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art methods. This advancement can drive further developments in re-identification and person search technologies.

Computational Tools for Hydrogen-Deuterium Exchange Mass Spectrometry Data Analysis.

Hydrogen-deuterium exchange (HDX) has become a pivotal method for investigating the structural and dynamic properties of proteins. The versatility and sensitivity of mass spectrometry (MS) made the technique the ideal companion for HDX, and today HDX-MS is addressing a growing number of applications in both academic research and industrial settings. The prolific generation of experimental data has spurred the concurrent development of numerous computational tools, designed to automate parts of the workflow while employing different strategies to achieve common objectives. Various computational methods are available to perform automated peptide searches and identification; different statistical tests have been implemented to quantify differences in the exchange pattern between two or more experimental conditions; alternative strategies have been developed to deconvolve and analyze peptides showing multimodal behavior; and different algorithms have been proposed to computationally increase the resolution of HDX-MS data, with the ultimate aim to provide information at the level of the single residue. This review delves into a comprehensive examination of the merits and drawbacks associated with the diverse strategies implemented by software tools for the analysis of HDX-MS data.

Stakeholder mapping of precision weeding commercialization ecosystem in California

CONTEXTPrecision weeding, a sector of agricultural technology in which drones and/or automated weeders use chemical, mechanical, or thermal means to eradicate weeds, has moved from academic research settings to commercialization. Because of labor shortage pressures, the push to gain competitive advantages, and the environmental impacts of excessive chemical inputs, many California growers have been interested in adopting precision weeding technologies in their operations. OBJECTIVEUsing semi-structured qualitative interviews, this study investigated the viewpoints of three key stakeholder groups involved in the diffusion and adoption of precision weeding technologies: California growers, precision weeding startups, and agricultural technology venture capital firms. With the supplemental viewpoints of large agricultural firms and their corporate venture capital arms and government agencies, this study seeks to understand the compatible motivations between stakeholders, current collaborative models between stakeholders and their limitations, and the user journey for growers adopting precision weeding technology. METHODSWe conducted 17 semi-structured qualitative interviews with diverse stakeholders in the precision weeding sector to gather textual data and gain high data saturation. Data collection balanced rigorous criteria for participant selection with adaptive interview questions to ensure depth and relevance. The analysis procedure involved coding and thematic framework development, complemented by grounded theory for iterative data examination and stakeholder map creation for distilling cross-organizational insights and stakeholder dynamics. RESULTS AND CONCLUSIONSThe results indicated that compatible motivations include addressing current labor issues, reducing costs, and the potential of precision weeding to transform agriculture. Less cited but still popular motivations included having more weeding options, meeting specific field needs, and environmental sustainability. The individual and stakeholder group cognitive maps demonstrated that concerns such as startup longevity, the high expenses of precision weeding machinery, and some startups lacking a direct connection to growers commonly limit the growers' adoption of precision weeding technologies. SIGNIFICANCEThe procedure and cognitive mapping presented in this study can be applied to other emerging agtech technologies and ecosystems.

What Lies in ‘Business Incubation and SMEs’ Research? A Literature Review and Roadmap for Future Research

The concept of ‘Business Incubation and SMEs’ has recently received a lot of interest in academic research and real-world business settings. Business incubator is a critical instrument for promoting entrepreneurial ecosystems and the growth of small and medium-sized enterprises (SMEs). Business incubation is a process that nurtures and promotes the growth of start-ups and early-stage companies. The aim of this article was to examine the existing literature on business incubators and SMEs in order to identify and highlight major as well as relevant themes and trends that can influence future research. The Scopus database was used to extract the data file. To develop network structures biblioshiny package of RStudio and VOSviewer was used. The results from the literature survey show some key observations highlight business incubators’ changing nature, challenges, and impact on economic and entrepreneurship development. In addition, SMEs can be better served if incubation programmes are tailored to specific industries or technologies, for this, governments should encourage the establishment of specialised incubation centres.

Psychometric properties of the Italian version of the Occupational Fatigue Exhaustion/Recovery (OFER) scale among healthcare professionals

ABSTRACT Objective The purpose of this study was to analyze the psychometric properties of the Italian version of the Occupational Fatigue Exhaustion/Recovery (OFER). Methods A cross-sectional study among 434 healthcare workers was undertaken. The factorial validity of the OFER, was compared using standard confirmatory factor analysis (CFA) with exploratory structural equation modeling (ESEM). Internal consistency, convergent validity, and factorial invariance (across gender) were assessed. Results A three-factor first-order ESEM solution provided the best and most parsimonious representation of the data: χ2(63) = 212.81, p < .001, RMSEA = 0.074 (90% CI 0.063–0.085), CFI = 0.949, TLI = 0.915. The three-factors were chronic fatigue, acute fatigue, and intershift recovery. Chronic and acute factors showed excellent internal consistency (ω > 0.80), however intershift recovery showed less internal consistency (ω < 0.70). OFER scores were found to be positively correlated with FAS scores, supporting its convergent validity. Conclusions The OFER was factorially invariant across gender. Overall, our findings provided evidence for the satisfactory psychometric properties of the OFER among Italian workers, confirming its value for use in both practical occupational and academic research settings.

SAS: Split antibiotic selection for identifying chaperones that improve protein solubility

BackgroundHeterologous expression of active, native-folded protein in Escherichia coli is critical in both academic research and biotechnology settings. When expressing non-native recombinant proteins in E. coli, obtaining soluble and active protein can be challenging. Numerous techniques can be used to enhance a proteins solubility, and largely focus on either altering the expression strain, plasmid vector features, growth conditions, or the protein coding sequence itself. However, there is no one-size-fits-all approach for addressing issues with protein solubility, and it can be both time and labor intensive to find a solution. An alternative approach is to use the co-expression of chaperones to assist with increasing protein solubility. By designing a genetic system where protein solubility is linked to viability, the appropriate protein folding factor can be selected for any given protein of interest. To this end, we developed a Split Antibiotic Selection (SAS) whereby an insoluble protein is inserted in-frame within the coding sequence of the hygromycin B resistance protein, aminoglycoside 7″-phosphotransferase-Ia (APH(7″)), to generate a tripartite fusion. By creating this tripartite fusion with APH(7″), the solubility of the inserted protein can be assessed by measuring the level of hygromycin B resistance of the cells. ResultsWe demonstrate the functionality of this system using a known protein and co-chaperone pair, the human mitochondrial Hsp70 ATPase domain (ATPase70) and its co-chaperone human escort protein (Hep). Insertion of the insoluble ATPase70 within APH(7ʹʹ) renders the tripartite fusion insoluble and results in sensitivity to hygromycin B. Antibiotic resistance can be rescued by expression of the co-chaperone Hep which assists in the folding of the APH(7ʹʹ)-ATPase70-APH(7ʹʹ) tripartite fusion and find that cellular hygromycin B resistance correlates with the total soluble fusion protein. Finally, using a diverse chaperone library, we find that SAS can be used in a pooled genetic selection to identify chaperones capable of improving client protein solubility. ConclusionsThe tripartite APH(7ʹʹ) fusion links the in vivo solubility of the inserted protein of interest to hygromycin B resistance. This construct can be used in conjunction with a chaperone library to select for chaperones that increase the solubility of the inserted protein. This selection system can be applied to a variety of client proteins and eliminates the need to individually test chaperone-protein pairs to identify those that increase solubility.

Agile by accident: how to apply Agile principles in academic research projects

Sustainability science and sustainability transition studies are interdisciplinary fields. Interdisciplinarity is understood to support out-of-the-box thinking and thus facilitate scientific advancements. Interdisciplinary research projects dealing with complex issues might need project management approaches that support learning, adaptation, and innovation. Agile is a management approach that has been developed to facilitate collaboration, learning, creativity, innovation, and reflectivity. Due to these qualities, Agile might be a suitable approach to managing interdisciplinary research projects. Agile project management is still new in academic research settings. Only limited information about how to apply it in an academic setting exists. To fill this gap the Agile principles are translated to fit the academic context. The article then outlines how Agile principles can be applied to an academic research program, that did not consciously plan to apply this approach. The translation of the principles will permit other researchers to intentionally use them in their interdisciplinary research projects. Furthermore, other researchers can build on the insights provided in this article to overcome the challenges that Agile can bring.

From clinical data management to clinical data science: Time for a new educational model.

The purpose of this article is to propose and provide a blueprint for a graduate-level curriculum in clinical data science, devoted to the measurement, acquisition, care, treatment, and inferencing of clinical research data. The curriculum presented here contains a series of five required core courses, five required research courses, and a list of potential electives. The coursework draws from but does not duplicate content from the foundational areas of biostatistics, clinical medicine, biomedical informatics, and regulatory affairs, and may be reproduced by any institution interested in and capable of offering such a program. This new curriculum in "clinical" data science will prepare students for work in academic, industry, and government research settings as well as offer a unifying knowledge base for the profession.

The Subject Is a Patient.

Although it is common in academic and government research settings to speak of study participants as "subjects," this perspective piece argues against doing so. In particular, the relationship of the study physician with study participants is unique and still retains many elements of the usual patient-physician relationship that exists in general clinical practice.

Alternative routes for 64Cu production using an 18 MeV medical cyclotron in view of theranostic applications

Radiometals play a fundamental role in the development of personalized nuclear medicine. In particular, copper radioisotopes are attracting increasing interest since they offer a varying range of decay modes and half-lives and can be used for imaging (60Cu, 61Cu, 62Cu and 64Cu) and targeted radionuclide therapy (64Cu and 67Cu), providing two of the most promising true theranostic pairs, namely 61Cu/67Cu and 64Cu/67Cu. Currently, the most widely used in clinical applications is 64Cu, which has a unique decay scheme featuring β+-, β−-decay and electron capture. These characteristics allow its exploitation in both diagnostic and therapeutic fields. However, although 64Cu has extensively been investigated in academic research and preclinical settings, it is still scarcely used in routine clinical practice due to its insufficient availability at an affordable price. In fact, the most commonly used production method involves proton irradiation of enriched 64Ni, which has a very low isotopic abundance and is therefore extremely expensive. In this paper, we report on the study of two alternative production routes, namely the 65Cu(p,pn)64Cu and 67Zn(p, α)64Cu reactions, which enable low and high 64Cu specific activities, respectively. To optimize the 64Cu production, while minimizing the mass of copper used as a target in the first case, or the co-production of other copper radioisotopes in the second case, an accurate knowledge of the production cross sections is of paramount importance. For this reason, the involved nuclear reaction cross sections were measured at the Bern medical cyclotron laboratory by irradiating enriched 65CuO and enriched 67ZnO targets. On the basis of the obtained results, the production yield and purity were calculated to assess the optimal irradiation conditions. Several production tests were performed to confirm these findings.

Assessing motivations and barriers to science outreach within academic science research settings: A mixed-methods survey

The practice of science outreach is more necessary than ever. However, a disconnect exists between the stated goals for science outreach and its actual impact. In order to examine one potential source of this disconnect, we undertook a survey-based study to explore whether barriers to participation (either intrinsic or extrinsic) in science outreach exist within the academic community. We received responses to our survey from 530 individuals, the vast majority of whom engage in some type of science outreach activity on an annual basis. Those who engage in outreach report doing so for both personal and altruistic reasons, and having high (yet varied) levels of comfort with performing outreach activities. Respondents also report the existence of several significant yet surmountable barriers to participation, including lack of time and funding. Our findings demonstrate that both levels of participation in, and attitudes toward, science outreach within the academic community are generally favorable, suggesting that the general ineffectiveness of science outreach is due to other causes. We place our findings within the context of the broader science outreach, science communication and public engagement literature. We make recommendations on how existing approaches and infrastructure can, and must, be changed in order to improve the practice.

Translating the Recommended Multicomponent Intervention for Childhood Overweight and Obesity into Practice: Implementation Challenges.

A multicomponent, family-based intervention with ≥ 26 contact hours is recommended for the treatment of childhood overweight and obesity. This intervention utilizes behavioral strategies to improve diet, physical activity, and sedentary behaviors. The evidence-based recommendations for this treatment have predominantly come from randomized trials in which the intervention is implemented by research-trained staff in academic research settings, with the intervention delivered to fairly homogeneous samples that are limited in being inclusive of those experiencing health disparities. Thus, there are challenges in implementing the recommended intervention into practice. In particular, there are implementation challenges related to providers, contact time, and settings that impact all children. Specifically, the structure of the intervention may diminish its ability to be delivered by many types of providers in different settings, limiting overall accessibility. There are implementation challenges affecting children who experience health disparities, as it is not clear how efficacious the recommended intervention is for African American or Latinx children, or children from households with low income. Several strategies to reduce identified implementation challenges, such as reducing contact time and intensity of the dietary intervention, are discussed. However, use of these strategies may reduce the effect size of the weight improvements commonly seen with the recommended intervention. Suggestions for future research regarding implementation, specifically using study designs that enhance the ability to create cost-efficient and adaptive interventions that can generalize to many different children and families, are provided.

The COVID-19 Pandemic: Reflections of Science, Person, and Challenge in Academic Research Settings.

In spring of 2021, the Society on NeuroImmune Pharmacology (SNIP) organized a virtual workshop on the coronavirus disease 2019 (COVID-19). The daylong event’s fourth and final symposium, “Well-being and reflections,” offered a glimpse at the pandemic’s impact on the lives of our scientists and educators. This manuscript includes a brief summary of the symposium, a transcription of our incoming president Dr. Santosh Kumar’s lecture, titled “Intervention and improved well-being of basic science researchers during the COVID-19 era: a case study,” and the panel discussion that followed, “Reflection and sharing,” featuring Drs. Jean M. Bidlack, Sylvia Fitting, Santhi Gorantla, Maria Cecilia G. Marcondes, Loyda M. Melendez, and Ilker K. Sariyer. The conclusion of this manuscript includes comments from SNIP’s president Dr. Sulie L. Chang and our Chief Editor, Dr. Howard E. Gendelman. Drs. Sowmya Yelamanchili and Jeymohan Joseph co-chaired the symposium.Graphical abstract

CE: Nursing Orientation to Data Science and Machine Learning.

Nurses collect, use, and produce data every day in countless ways, such as when assessing and treating patients, performing administrative functions, and engaging in strategic planning in their organizations and communities. These data are aggregated into large data sets in health care systems, public and private databases, and academic research settings. In recent years the machines used in this work (computer hardware) have become increasingly able to analyze large data sets, or "big data," at high speed. Data scientists use machine learning tools to aid in analyzing this big data, such as data amassed from large numbers of electronic health records. In health care, predictions for patient outcomes has become a focus of research using machine learning. It's important for nurses and nurse administrators to understand how machine learning has changed our ways of thinking about data and turning data into knowledge that can improve patient care. This article provides an orientation to machine learning and data science, offers an understanding of current challenges and opportunities, and describes the nursing implications for nurses in various roles.

The COVID-19 Pandemic: Reflections of Science, Person, and Challenge in Academic Research Settings

The COVID-19 Pandemic: Reflections of Science, Person, and Challenge in Academic Research Settings

Making the case for virtual competency-based education

PurposeThe purpose of this paper is to present a competency-based curriculum design model based on a set of ten foundational professional competencies (PCs) that prepare college graduates to meet the needs of global businesses now and in the future.Design/methodology/approachThis phenomenological single-case study reviews literature on the foundational principles of competency-based education (CBE) and comparatively analyzes the results of qualitative interviews to create a set of ten PCs linking employee and business success.FindingsThis study presents a theoretical competency-based curriculum model (competency-based learning, performance and behavior (CBLPB)) designed for online education programs to enable a twenty-first century workforce to succeed. The curriculum design model is tested as applied by the researchers in various courses taught at an online university.Research limitations/implicationsThis is a conceptual model for testing in academic research settings in colleges and universities.Practical implicationsThe study suggests that higher education business curriculum should be designed using a CBE model to develop graduates with the foundational PCs that employers need and desire in educated working professionals.Originality/valueFrom the faculty perspective, the CBLPB curriculum design model can enhance the design and implementation of CBE in business programs.

High Performance Bubble Manipulation on Ferrofluid-Infused Laser-Ablated Microstructured Surfaces.

Manipulation of gas bubbles in an aqueous ambient environment is fundamental to both academic research and industrial settings. Present bubble manipulation strategies mainly rely on buoyancy or Laplace gradient forces arising from the sophisticated terrain of substrates. However, these strategies suffer from limited manipulation flexibility such as slow horizontal motion and unidirectional transport. In this paper, a high performance manipulation strategy for gas bubbles is proposed by utilizing ferrofluid-infused laser-ablated microstructured surfaces (FLAMS). A typical gas bubble (<2 μL) can be accelerated at >150 mm/s2 and reach an ultrafast velocity over 25 mm/s on horizontal FLAMS. In addition, diverse powerful manipulation capabilities are demonstrated including antibuoyancy motion, "freestyle writing", bubble programmable coalescence, three-dimensional (3-D) controllable motion and high towing capacity of steering macroscopic object (>500 own mass) on the air-water interface. This strategy shows terrain compatibility, programmable design, and fast response, which will find potential applications in water treatment, electrochemistry, and so on.

Safety Moments in Chemical Safety Education

Chemical safety education is a critical aspect of an integrated culture of safety in academic research settings. Showcasing best practices and sharing practical solutions for recognizing hazards an...

4079 Lessons learned from implementing Quality Improvement (QI) in academic clinical research setting

OBJECTIVES/GOALS: We describe here the implementation of a pilot Quality Improvement (QI) program in clinical research processes in order to facilitate translation from bench to community. This presentation will also discuss challenges encountered by the research teams during the implementation of QI activities. METHODS/STUDY POPULATION: Miami CTSI collaborated with University of Kansas’ CTSA to test the implementation of a QI program for clinical research processes. The program has a duration of 1 year and consists of multi-modal training and coaching sessions with different research teams. Six teams comprising of Principal investigators, clinical coordinators, and regulatory specialists participated in the program based in applied clinical microsystem theory science. Team coaches and teams worked together to assess current processes, test new and improved processes, and standardize and disseminate applicable best practices of the QI program. RESULTS/ANTICIPATED RESULTS: The implementation of QI activities in large clinical research settings poses numerous challenges for the research team. We will present survey results from the coaching sessions and follow on feedback from the different teams involved in the program to implement the QI activities. We will describe the modifications and adjustments made to the original conceptual framework of QI program in order for it to be applicable and feasible for the settings of the University of Miami. We will provide recommendations for other academic clinical research centers that are considering implementing a QI program. DISCUSSION/SIGNIFICANCE OF IMPACT: The successful adaptation of a QI process to implement in academic clinical research settings relies on early engagement of the institution leadership, careful selection of team members, as well as developing communication skills to enhance team dynamics as a clinical research unit.