Practical Blueprint Research Articles

Leveraging Data Analytics for Financial Stability: A Blueprint for Sustaining SMEs in Economically Distressed Regions of the U.S.

Purpose: Our research explores how data analytics can empower small- and medium-sized enterprises (SMEs) in economically distressed regions of the U.S. by improving financial decision-making, operational efficiency, and risk mitigation. The study aims to provide a blueprint for leveraging data-driven insights to ensure long-term financial stability and economic growth in underserved areas, particularly in the Midwest and Southern states. Methodology: The study employs a case-study approach to analyze the real-world applications of data analytics platforms in SMEs within economically distressed regions. It uses statistical analysis to compare the performance of businesses that have adopted data analytics tools with those relying on traditional methods. The methodology also includes the collection of data from various sources such as financial performance records, customer analytics, and predictive models. Findings: The research demonstrates that SMEs using data analytics experience significant improvements in financial performance. For instance, predictive analytics increased financial forecasting accuracy by 25%, and real-time business intelligence tools reduced operational costs by 15%. Businesses using customer analytics tools saw a 20% rise in customer retention and a 15% increase in revenue. The study shows that data-driven strategies can improve overall financial stability by 30% and enable SMEs to proactively mitigate risks in volatile markets. Unique Contribution to Theory, Policy, and Practice: The study contributes to the emerging body of literature on the transformative potential of data analytics in financially distressed regions, providing new insights into how real-time and predictive analytics can enhance financial decision-making in SMEs’ practices within the economically distressed regions. The study provides a practical blueprint for SMEs to adopt data analytics tools, demonstrating their impact on financial stability, operational efficiency, and long-term growth. It offers actionable recommendations for business leaders in distressed regions to integrate analytics equitably into their decision-making processes.

ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science

ExaFEL is an HPC-capable X-ray Free Electron Laser (XFEL) data analysis software suite for both Serial Femtosecond Crystallography (SFX) and Single Particle Imaging (SPI) developed in collaboration with the Linac Coherent Lightsource (LCLS), Lawrence Berkeley National Laboratory (LBNL) and Los Alamos National Laboratory. ExaFEL supports real-time data analysis via a cross-facility workflow spanning LCLS and HPC centers such as NERSC and OLCF. Our work therefore constitutes initial path-finding for the US Department of Energy's (DOE) Integrated Research Infrastructure (IRI) program. We present the ExaFEL team's 7 years of experience in developing real-time XFEL data analysis software for the DOE's exascale supercomputers. We present our experiences and lessons learned with the Perlmutter and Frontier supercomputers. Furthermore we outline essential data center services (and the implications for institutional policy) required for real-time data analysis. Finally we summarize our software and performance engineering approaches and our experiences with NERSC's Perlmutter and OLCF's Frontier systems. This work is intended to be a practical blueprint for similar efforts in integrating exascale compute resources into other cross-facility workflows.

Cultivating Collective Armor: Towards a Collaborative Cybersecurity Resilience Framework for SMEs

As Small and Medium-sized Enterprises (SMEs) increasingly integrate digital technologies into their operations, they face escalating cybersecurity threats that challenge their limited financial and human resources. Historically, cybersecurity was not a primary concern for many SMEs due to their minimal reliance on cyberinfrastructure. However, the growing dependency on digital systems has exposed them to significant risks, necessitating robust cybersecurity measures. This research introduces a cybersecurity resilience framework specifically designed to enhance the cybersecurity posture of SMEs through collaborative efforts. Drawing upon principles of collective intelligence, shared resources, and shared responsibility, the proposed framework promotes a holistic approach that merges advanced technological solutions with collaborative practices among SMEs, industry stakeholders, and governmental entities. By enabling SMEs to pool their cybersecurity resources and capabilities, the framework aims to provide a comprehensive defence against cyber threats that could not be achieved by individual entities alone. The research delves into key collaborative literature to ground the framework in proven strategies and existing successful models of collaboration. It highlights how collective action can be a powerful tool in overcoming the inherent vulnerabilities of SMEs to cyber threats. To validate the effectiveness and applicability of the framework, it went through an expert review process involving cybersecurity professionals and stakeholders in the SME sector. This study not only underscores the necessity for SMEs to adapt to the evolving cybersecurity landscape but also provides a practical blueprint for collective defence. The framework’s emphasis on shared responsibility and mutual aid presents a shift from traditional, isolated cybersecurity approaches to a more integrated and cooperative strategy. This has the potential to significantly enhance the resilience of SMEs against cyber threats, safeguarding their operations and contributing to the overall stability of the digital economy. Future research directions include the application of the framework across various industries and global contexts to evaluate its versatility and impact under different regulatory and threat environments.

Revitalizing university performance evaluation: the case of SETARA model in Malaysia

PurposeSETARA is a well-known university rating tool in Malaysia. The study aims to enhance the transparency, accuracy, and reliability of SETARA assessment instrument by improving its weighting scheme for the domains, sub-domains, criteria, and indicators.Design/methodology/approachThe study utilized a quantitative research design and collected responses from 29 academic administrators in both public and private higher education institutions in Malaysia. The four steps of the Analytic Hierarchy Process (AHP) were employed. An AHP questionnaire was developed, adopting the domains, sub-domains, criteria, and indicators of the SETARA assessment instrument. The “Superdecision” software was used to synthesize the pairwise comparison judgments and compute the consistency ratio for all group pairwise comparison matrices.FindingsThe AHP-assigned weights differed significantly from those assigned by the SETARA rating system. For instance, the weight for “Input” increased from 20 to 41.05 out of 100, and the weight for “Output” decreased from 40 to 25.52. The new SETARA rating instrument is expected to be used by the Ministry of Higher Education (MOHE) due to its scientifically justified foundation to ensure transparency, accuracy, and reliability.Research limitations/implicationsThe study is limited by small sample size for data collection. Future research could benefit from a larger and more diverse sample, including participants from a number of national-level universities and relevant agencies like Malaysia Qualification Agency (MQA) and MOHE. Additionally, the research focused solely on recalculating weights and did not consider modifying the criteria set. Further investigation is needed to determine if some criteria can be added or removed.Practical implicationsThis research offers practical implications for refining the SETARA assessment mechanism and improving the quality of higher education in Malaysia. The modified weights provide guidance to Higher Education Institutions (HEIs) on where to focus their efforts to ensure quality. The transparent methodology introduced by this study can serve as a model for enhancing educational quality evaluations in Malaysia and potentially in other higher education systems worldwide.Originality/valueThe originality of this study lies in its innovative application of the AHP to the SETARA assessment tool, addressing a crucial gap in the assessment process. By providing a comprehensive and transparent methodology for assigning percentages and weights, this research offers a practical blueprint for enhancing the precision and impact of educational quality evaluations in Malaysia and beyond.

Flexible Humidity Sensor Based on a Graphene Oxide-Carbon Nanotube-Modified Co3O4 Nanoparticle-Embedded Laser-Induced Graphene Electrode.

To meet evolving humidity monitoring needs, the development of flexible, high-performance humidity sensors is crucial. This study introduces an innovative flexible humidity sensor using a single-step laser scribing technique to fabricate a flexible in situ Co3O4 nanoparticle-embedded laser-induced graphene (Co3O4-LIG) composite electrode. Compared to conventional LIG electrodes, the Co3O4-LIG electrode exhibits improved conductivity and hydrophilicity, enhancing charge transfer and water molecule affinity. The unique two-dimensional structure and exceptional water permeability of graphene oxide (GO) combine with the rapid water response and high specific surface area of carboxylated multiwalled carbon nanotubes (MWCNTs), thereby assuming a crucial function in the modification and optimization of the performance of humidity sensors. Through the application of a homogenously blended aqueous solution comprising GO and MWCNTs in precise proportions onto the Co3O4-LIG composite electrode, an excellent humidity-responsive layer is established, culminating in the realization of a cutting-edge GO-MWCNTs@Co3O4-LIG flexible humidity sensor. Noteworthy attributes of this sensor include a heightened sensitivity [959.1% (ΔR/R0)], rapid response and recovery times (within 5 and 26 s, respectively), and a noteworthy linearity (R2 = 0.994) across a relative humidity range of 14 to 95%. The findings presented herein offer valuable insights and a practical blueprint for the design and production of flexible humidity sensors.

Settling Time vs. Accuracy Tradeoffs for Clustering Big Data

We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets. Since effectively all clustering methods are slower than the time it takes to read the dataset, the fastest approach is to quickly compress the data and perform the clustering on the compressed representation. Unfortunately, there is no universal best choice for compressing the number of points -- while random sampling runs in sublinear time and coresets provide theoretical guarantees, the former does not enforce accuracy while the latter is too slow as the numbers of points and clusters grow. Indeed, it has been conjectured that any sensitivity-based coreset construction requires super-linear time in the datase size. We examine this relationship by first showing that there does exist an algorithm that obtains coresets via sensitivity sampling in effectively linear time -- within log-factors of the time it takes to read the data. Any approach that significantly improves on this must then resort to practical heuristics, leading us to consider the spectrum of sampling strategies across both real and artificial datasets in the static and streaming settings. Through this, we show the conditions in which coresets are necessary for preserving cluster validity as well as the settings in which faster, cruder sampling strategies are sufficient. As a result, we provide a comprehensive theoretical and practical blueprint for effective clustering regardless of data size. Our code is publicly available at https://github.com/Andrew-Draganov/Fast-Coreset-Generation and has scripts to recreate the experiments.

Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector

ABSTRACT Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. Our comprehensive analysis reveals that while not every re-sampling technique improves model performance, all feature selection methods markedly bolster predictive accuracy. Notably, the combination of the Gradient Boosting Machine (GBM) algorithm with NCR re-sampling and GBMVI feature selection emerges as the most effective configuration, offering superior fraud detection capabilities. This study not only advances the theoretical framework for combating insurance fraud through AI but also provides a practical blueprint for insurance companies aiming to incorporate advanced AI strategies into their fraud detection arsenals, thereby mitigating financial risks and fostering trust systems.

Ambulatory Quality Improvement Despite COVID-19: Blueprint for a Successful System for Continuous Improvement.

In this article, we describe our experience developing and implementing a multipronged approach to improve performance across a strategic subset of quality measures within primary care. Detailed techniques include data visualization and analytics, process reengineering, team engagement, visual project management, continuous improvement methods and training, and incentives and recognition. We achieved positive change across 12 high priority measures which we deemed the "High Value Framework (HVF)" by fostering a collaborative, nonpunitive, problem-solving culture. We focused on measures that had the greatest potential for impact from a clinical, reimbursement, and reputational perspective. More importantly, we sustained gains despite the challenges posed by the COVID-19 pandemic, thereby demonstrating programmatic resilience and high process reliability. This systematic approach serves as a practical blueprint for other healthcare entities seeking to navigate the complexities of quality improvement in a dynamic environment. The model provides a strategic framework for prioritizing and standardizing quality measures, effectively engaging stakeholders, and managing organizational change. Our model emerged from a need to address real-world operational challenges, rather than as an academic or theoretical exercise, and was developed independently of existing literature on measure prioritization and standardization at the time of its inception.

Enhancing self-learning skills and quality through formative actions and feedback within chemistry classes in the laboratory – A useful model

In here a novel method is described to improve student success rates in a first-year basic chemistry theoretical/practical hybrid course (n = 31 students) by implementing simple ways of formative assessment. This to reduce student dropout rates following the philosophy of encouraging students’ self-control. Essential is to train first-year bachelor students in their self-learning skills and to enhance their evaluative judgment. As a result, students are able to provide better quality of the assessment products at the end of the course. In practice the course is redesigned and intervention tools are integrated at multiple levels throughout the course. The lecturers’ role was adapted to a coaching role, thereby introducing low-effort personalized micro-interventions to meet the personalized needs of students. To clarify these learning needs for students, awareness of the quality desired for the final assessment products is important. Awareness was improved by providing examples of varying quality and introducing multiple peer- and self-assessment moments during the course. The final evaluation of the course examination products showed that the quality of the laboratory notebook was substantially higher after following this approach. Additionally students learned other important skills such as self-learning skills, collaborating in practical work and giving and receiving feedback. Unexpectedly, the high perceived lecturers’ workload decreased. The work presented here provides a novel approach in the form of a model and a practical blueprint with tools for a practical chemistry course design which develops students’ self-learning skills thereby substantially improving student success rates. In our example course, the ultimate student success rate increased form 83 % to 95 % after using this novel approach.

Physician Assistant Educators' Production Blueprint for Video Pedagogy.

This article presents a blueprint for effective video media production in physician assistant (PA) education based on validated pedagogical practices found in the literature. Using the cognitive load theory and a practical blueprint for video production designed for PA educators, this method aims to improve video production practices and better engage students within a format that improves learning outcomes for a diverse body of PA students. Students are interacting with videos, and there is an opportunity for educators to hone practices in video production to enhance student learning. A literature review of pedagogical practices in video production guides the production blueprint for video production. The practical principles of cognitive load theory improve efficiency in assimilating new information, enhance student engagement, and facilitate active and deep learning for a student learner engaging with the instructional video. Based on the literature and the author's educational video creation experience, a guide in the form of a production blueprint specific to PA education is proposed.

The Design of a MOOC on Health Behaviors: A Practical Blueprint for the Instructional Design of MOOCs

Massive Open Online Courses (MOOCs) are a primary mode of scaled-up online learning. They have enjoyed great popularity, which grew significantly during the Covid-19 pandemic. Despite the growing demand for MOOCs and the desire of institutions to develop high-quality courses, research indicates that the educational impact of MOOCs is limited. Low instructional design quality has been cited as a culprit for the failure of MOOCs to support learning and skill development, prompting calls for a more thorough design of MOOCs. The aims of our work are to describe the systematic development of guiding principles that informed instructional design decisions for our MOOC on health behaviors and to showcase how these principles were implemented in practice. Through triangulation of various sources of evidence on anticipated learners and the MOOC learning environment, we identified four key aspects we needed to address with our instructional design to facilitate learning. We formulated guiding principles that both addressed these aspects and served as a reference point during instructional design. Our approach to making instructional design decisions can serve as a practical blueprint for other MOOC developers.

A country's efforts toward creating an advanced practice nurse in public health.

The study goal was to inform the creation of a blueprint for an advanced practice nurse (APN) in public health. No internationally accepted standard for an APN in public health exists. Activities of public health nurses (PHN) traditionally have centered on health promotion and disease prevention, but many have added other population-based activities such as chronic and acute disease treatment. An APN in public health is needed to address the global challenges threatening the physical, social, and mental health of populations worldwide. This qualitative study was comprised of six focus groups, each containing a different group of stakeholders (n=40). Study results followed the requirements of the consolidated criteria for reporting qualitative research (COREQ). Two major themes emerged: the APN role in public health and core expectations. From the APN role theme, four subthemes emerged on APN domains of public health practice and functions. From the core expectations theme, nine subthemes emerged on the APN's qualifications and behaviors. Agreement among stakeholders was found in the nine core expectations; however, among the four different visions of an APN in public health, two fit a population-based model rather than the traditional PHN model. A single APN role in public health is insufficient to address the breadth and complexity of today's global challenges as detailed by the sustainable development goals. Due to the interaction between health and the biopsychosocial environments, we need APNs with different areas of expertise. Nurses working at universities, in public health services, and as healthcare policymakers are needed to create a multistage strategy that gradually introduces several different types of APNs in public health.

Leveraging Neurospora crassa Fungus and Carboxypeptidase A1 Enzyme to Illuminate Microscale Biodiversity Changes in Response to Global Shifts

The profound impacts of global changes on biodiversity necessitate a more comprehensive documentation, particularly at the microscale level. To achieve precise and rapid insights into this unique diversity, the choice of an ideal species candidate is crucial. Neurospora crassa, a well-established organism in the field of biology, emerges as a promising candidate for this purpose. In our study, we explore the potential of the Carboxypeptidase A1 (CPA1) enzyme as a valuable tool for profiling global diversity. Our investigation has revealed that CPA1 possesses distinctive characteristics, notably its conserved solvent accessibility. This unique feature makes CPA1 an invaluable asset for microscale studies of global changes. The insights presented in our study serve as a practical blueprint, showcasing the application of structural biology in understanding diversity and global changes within microscale environments.

Research on the Reform of English Language Curriculum System in Colleges and Universities Based on SERVQUAL Model

Abstract This investigation assesses the efficacy of the SERVQUAL model in advancing the quality of English language education within higher education institutions. Confronted with the imperative to modernize teaching content and methodologies to enhance student satisfaction, the SERVQUAL model’s application provides a systematic framework for evaluating and improving service quality dimensions. Employing questionnaires and data analysis to compare teaching quality and student satisfaction before and after targeted curriculum reforms, this study documents substantial improvements, notably in “Responsiveness” and “Reliability,” where satisfaction levels rose by 20% and 15%, respectively. The successful implementation of the SERVQUAL model underscores its utility in elevating the caliber of English language instruction and student satisfaction, offering a practical blueprint for ongoing curriculum reforms in higher education.

Applying Cognitive Load Theory to eLearning of Crafts

Craft education and training are important for preserving cultural heritage and fostering artisanal skills. However, the pedagogical challenges in this domain are numerous. This research paper presents a comprehensive framework for applying Cognitive Load Theory to enhance craft education and training via eLearning platforms. In this study, practical guidelines based on CLT principles are provided to optimize the instructional design and content delivery. These guidelines scaffold craft learning experiences within eLearning platforms and encompass strategies to manage cognitive load, promote active learning, and facilitate gradual transition. Subsequently, the paper details the implementation of these guidelines within a popular eLearning platform, Moodle, emphasizing its adaptability and utility for craft education. It discusses the customization of Moodle courses to align with the cognitive load management principles, providing a practical blueprint for educators and instructional designers. The research culminates in a case study, wherein the guidelines are applied to a craft eLearning course using Moodle.

Catalytic stereodivergent allylic alkylation of 2-acylimidazoles for natural product synthesis

The stereocontrolled allylic alkylation of carbonyl compounds with the goal of producing the full range of stereoisomers presents an effective approach for increasing the productivity of collective natural product synthesis and the creation of chiral molecule libraries for drug exploration. The simultaneous control of regio-, diastereo-, and enantioselectivity poses a significant synthetic challenge in contemporary organic synthesis. Herein, we describe a catalytic stereodivergent α-allylation protocol applicable to both aliphatic and aromatic 2-acylimidazoles, thereby providing a practical blueprint for the divergent synthesis of important chiral building blocks. Each of the six isomeric α-allylated compounds can be readily obtained with remarkable yields and exceptional stereoselectivities, by judiciously selecting the appropriate leaving group and permutations of enantiomers adapted from nickel and iridium catalysts. The versatility of this asymmetric allylic alkylation has been successfully utilized in the enantioselective synthesis of (R)-arundic acid and (S,S)-cinamomumolide, as well as in the stereodivergent total synthesis of tapentadol.

The role of the multidisciplinary team in surgical management of intractable tubo-ovarian abscess as a late sequelae of challenging Crohn's disease in the modern era: A case report and review of current literature.

Chronic, severe Crohn's disease in a young female patient can result in surgical complexity. The rarity of the presentation of intractable pelvic abscesses within this etiology with additional considerations given to fertility concerns and hence requirement for input from a multi-disciplinary team makes this a vital case in building a consensus for evidence-based surgical management. A 29-year-old nulliparous woman was referred to our tertiary centre for surgical management of Crohn's disease with known tubo-ovarian abscess and abdominoperineal and abdominal wall sinuses. Her previous surgical history included 4 midline laparotomies, subtotal colectomy and proctectomy with stoma formation. The patient underwent egg collection to preserve fertility. This was followed by midline laparotomy and abdominoperineal resection, which involved a retrograde radical modified hysterectomy using the Hudson technique, alongside excision of the perineal sinus, with reconstruction of the perineal defect using an internal pudendal artery perforator gluteal fold flap, and in addition to excision and drainage of the abdominal wall abscess. Involvement was sought from gynecological oncology, colorectal, urology, plastics, stoma, fertility, microbiology, and gastroenterology teams, which enabled successful preservation of end organ function and improvement in patient psychological well-being. This case is a paradigm of surgical challenge, requiring expert gynecological oncology techniques including a retroperitoneal approach, nerve and vessel sparing considerations alongside colorectal and urological procedures. Moreover, we believe that our blueprint for effective multi-disciplinary practice will inform the future management of gynecological surgery. Therefore this report aims to contribute towards the optimum management of the gynecological sequelae of Crohn's disease.

CLIMATE RESILIENCE IN TOURISM: A SYNTHESIS OF GLOBAL STRATEGIES AND IMPLICATIONS FOR U.S. DESTINATIONS

In an era where climate change poses a formidable threat to global industries, tourism stands at a critical juncture. This study delves into the nexus between climate resilience and tourism, with a concentrated lens on the United States. The purpose is to amalgamate global strategies and discern their implications for U.S. tourism destinations, thereby equipping stakeholders with a blueprint for sustainable practice. By employing a systematic literature review as its methodological backbone, the study meticulously sifts through peer-reviewed literature to extract and thematically synthesize resilience strategies that are pertinent to the tourism industry. The findings reveal a pronounced vulnerability of tourism to climatic variabilities, emphasizing the indispensable role of climate resilience in fostering sustainable tourism. The study identifies and evaluates global best practices, assessing their applicability to the U.S. context, and distills effective practices for U.S. implementation. It also casts light on the economic and social ramifications of adopting climate resilience measures. Conclusively, the study posits strategic policy recommendations, advocating for an integrated approach that encompasses expanding protected areas, formulation of a tourism vulnerability index, and promoting sustainable transport policies. These recommendations are proffered not merely as a defensive mechanism against climate change but as a proactive stance to reinvigorate and future-proof the tourism sector.

Establishing underpinning concepts for integrating circular economy and offsite construction: a bibliometric review

PurposeCircular economy (CE) and offsite construction (OSC) are two innovations for improving the construction industry's overall performance against a myriad of sustainability-driven agenda/initiatives. There is a real opportunity to conjoin OSC and CE to provide new insight and opportunities to deliver more evidence-based sustainable systems. This study analyses extant literature in CE and OSC (between 2000 and 2021) through a bibliometric review to tease out critical measures for their integration and transformation.Design/methodology/approachThis study adopts a science mapping quantitative literature review approach employing bibliometric and visualisation techniques to systematically investigate data. The Web of Science (WoS) database was used to collect data, and the VOSviewer software to analyse the data collected to determine strengths, weights, clusters and research trends in OSC and CE.FindingsImportant findings emerging from the study include extensive focus on sustainability, waste, life cycle assessment and building information modelling (BIM), which currently serve as strong interlinks to integrate OSC and CE. Circular business models, deconstruction and supply chain management are emerging areas, with strong links for integrating CE and OSC. These emerging areas influence organisational and operational decisions towards sustainable value creation, hence requiring more future empirical investigations.Originality/valueThis study is a novel research using bibliometric analysis to unpick underpinning conduits for integrating CE and OSC, providing a blueprint for circular OSC future research and practice. It provides the needed awareness to develop viable strategies for integrating CE in OSC, creating opportunities to transition to more sustainable systems in the construction sector.

Ask a Political Scientist: A Conversation with Catharine A. MacKinnon about Power, Politics, and Political Science

Ask a Political Scientist: A Conversation with Catharine A. MacKinnon about Power, Politics, and Political Science