Alignment Strategy Research Articles

An Active Learning Exercise for Teaching Process Strategy Alignment and Process Improvement

Abstract This hands-on active learning exercise uses an origami activity to teach students how to (1) achieve competitive priorities through process design and (2) monitor and evaluate process performance for potential improvement. Students are expected to explore current work processes for areas of improvement by focusing on process structure and resource management in process design and evaluation. Through the activity, students are exposed to linking process design with competitive priorities and challenged to evaluate their process for potential improvement. We provide several extensions of the activity that can be utilized to further its pedagogical utility.

Aligning Simulation-Based Education With Didactic Learning in Prelicensure Baccalaureate Nursing Education.

Research has shown there is a gap between academia and practice. Nursing stakeholders support the use of the clinical judgment model in developing competency for practice. This study analyzed the effectiveness of simulation-based education on the development of clinical judgment and explored new graduate nurses' perceptions of their preparation for practice. A longitudinal, explanatory, mixed-methods design with survey points at onset and end of the intervention and interviews 6 months postgraduation. The Simulation Effectiveness Tool-Modified (SET-M) was utilized for the pre- and posttest surveys. Two-tailed Mann-Whitney 2-sample rank sum tests were conducted and analyzed for all items on the SET-M. Significant differences were seen in pre- and posttest responses for 12 of the 19 SET-M survey items. The findings of this study support the deliberate alignment of didactic and practical teaching strategies to strengthen practice performance and bridge the gap.

Open set transfer learning through distribution driven active learning

Domain adaptation enables effective transfer between source and target domains with different distributions. The latest research focuses on open set domain adaptation; that is, the target domain contains unknown categories that do not exist in the source domain. The existing open set domain adaptation cannot realize the fine-grained recognition of unknown categories. In this paper, we propose an uncertainty analysis evidence model and design a distribution driven active transfer learning (DATL) algorithm. DATL realizes fine-grained recognition of unknown categories with no requirements on the source domain to contain the unknown categories. To explore unknown distributions, the uncertainty analysis evidence model was adopted to divide the high uncertainty space. To select critical instances, a cluster-diversity query strategy was proposed to identify new categories. To enrich the label categories of the source domain, a global dynamic alignment strategy was designed to avoid negative transfers. Comparative experiments with state-of-the-art methods on the standard Office-31/Office-Home/Office-Caltech10 benchmarks showed that the DATL algorithm: (1) outperformed its competitors; (2) realized accurate identification of unknown subcategories from a fine-grained perspective; and (3) achieved outstanding performance even with a very high degree of openness.

Corporate social responsibility, new challenges with new development, and new structural work of management framework business development cycles

The study focuses on the impact Of Corporate Social Responsibility on Sustainable Enterprise Development. In addition, it also focuses on Corporate Social Responsibility and institutional theory for new Perspectives on private governance. Lastly, it examines reimagining Corporate Social Responsibility in the Era of COVID-19 by Promoting Corporate Social Competence.As this paper is based on reviews so that secondary data collection method will be used in which various past articles, books, journals, and reviews will be used to collect all the reliable data based on the topic. This study is qualitative in nature.The integration of CSR into the core business strategy is more efficacious than its treatment as a distinct initiative. This strategy aims to synchronize corporate social responsibility endeavors with the objectives of the organization, thereby guaranteeing enduring viability. Nevertheless, it presents difficulties as enterprises must devise strategies to integrate CSR deliberations into all facets of their activities.Corporate Social Responsibility (CSR) initiatives are frequently characterized by their voluntary nature and self-regulatory approach, which may not be subject to enforceable standards. This phenomenon may lead to incongruity and fluctuating degrees of dedication among corporations. In the absence of well-defined guidelines, certain organizations may resort to "greenwashing" tactics, whereby they purport to exhibit social responsibility without effecting significant alterations.Through a comprehensive comprehension and proficiency in tackling emerging challenges in Corporate Social Responsibility (CSR), enterprises can augment their standing and foster confidence among their stakeholders. A robust corporate social responsibility (CSR) strategy that is in line with societal expectations has the potential to enhance brand reputation, entice customers, and cultivate enduring relationships with stakeholders. The academic perspective suggests that the study of Corporate Social Responsibility (CSR) enables businesses to comprehend the wider societal and ecological consequences of their operations. The aforementioned statement highlights the significance of comprehending the effects of corporate choices on stakeholders, thereby facilitating judicious and conscientious decision-making. Consequently, this facilitates the alignment of corporate strategies with sustainable development objectives and enables decision-making that is mutually advantageous for the enterprise and the community.

No Benefit of Robotic-Assisted over Computer-Assisted Surgery for Achieving Neutral Coronal Alignment in Total Knee Arthroplasty.

The use of robotic-assisted surgery (RAS) in total knee arthroplasty (TKA) is becoming increasingly popular due to better precision, potentially superior outcomes and the ability to achieve alternative alignment strategies. The most commonly used alignment strategy with RAS is a modification of mechanical alignment (MA), labeled adjusted MA (aMA). This strategy allows slight joint line obliquity of the tibial component to achieve superior balancing. In the present study, we compared coronal alignment after TKA using RAS with aMA and computer-assisted surgery (CAS) with MA that has been the standard in the center for more than 10 years. We analyzed a prospectively collected database of patients undergoing TKA in a single center. Lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) were compared for both techniques. In 140 patients, 68 CASs and 72 RASs, we observed no difference in postoperative measurements (median 90 degrees for all, LDFA p = 0.676, MPTA p = 0.947) and no difference in outliers <2 degrees (LDFA p = 0.540, MPTA p = 0.250). The present study demonstrates no benefit in eliminating outliers or achieving neutral alignment of both the femoral and the tibial components in robotic-assisted versus computer-assisted TKA if MA is the target. To utilize the precision of RAS, it is recommended to aim for more personalized alignment strategies. The level of evidence is level III retrospective study.

Transformative Curriculum Design through Open Educational Resource Creation

At their best, open educational resources (OER) are known to foster equity, accessibility, and flexibility for users and include multiple perspectives from a collaborative community for creators and contributors (Hylén, 2006). Therefore, when planning our university’s first offering of a week-long course (re)design workshop (based on Saroyan &amp; Amundsen, 2004), recognizing we had been primarily using materials from other post-secondary institutions, we opted to create resources specific to our university through a one-day facilitated writing sprint. Moving beyond offering information simply for course design, we decided to create an OER that encompasses three main areas of curriculum planning and design: composition, mapping, and alignment of learning outcomes; choice and alignment of instructional strategies and learning activities; and alignment of outcomes assessment at all levels. During the OER content development stage, the group also recognized the opportunity to position the OER, named CRICKET, as a community building tool, focusing on learning re-design. The site not only hosts curriculum planning and design information, it also features an OER authoring tool that invites participants to share their work with their peers. Through our OER creation process, we determined that OER have the potential to transform not only how information is disseminated and used, but also how it is created.

Inter-domain mixup for semi-supervised domain adaptation

Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available, achieving better classification performance than unsupervised domain adaptation (UDA). However, existing SSDA work fails to make full use of label information from both source and target domains for feature alignment across domains, resulting in label mismatch in the label space during model testing. This paper presents a novel SSDA approach, Inter-domain Mixup with Neighborhood Expansion (IDMNE), to tackle this issue. Firstly, we introduce a cross-domain feature alignment strategy, Inter-domain Mixup, that incorporates label information into model adaptation. Specifically, we employ sample-level and manifold-level data mixing to generate compatible training samples. These newly established samples, combined with reliable and actual label information, display diversity and compatibility across domains, while such extra supervision thus facilitates cross-domain feature alignment and mitigates label mismatch. Additionally, we utilize Neighborhood Expansion to leverage high-confidence pseudo-labeled samples in the target domain, diversifying the label information of the target domain and thereby further increasing the performance of the adaptation model. Accordingly, the proposed approach outperforms existing state-of-the-art methods, achieving significant accuracy improvements on popular SSDA benchmarks, including DomainNet, Office-Home, and Office-31.

Functional alignment in total knee arthroplasty best achieves balanced gaps and minimal bone resections: an analysis comparing mechanical, kinematic and functional alignment strategies.

Key concepts in total knee arthroplasty include restoration of limb alignment and soft-tissue balance. Although differences in balance have been reported amongst mechanical alignment (MA), kinematic alignment (KA) and functional alignment (FA) techniques, it remains unclear whether there are differences in gap imbalance or resection thicknesses when comparing different constitutional alignment subgroups. MA (measured resection technique), KA (matched resections technique) and FA (technique based on the restricted KA boundaries) were compared in 116 consecutive patients undergoing 137 robotic-assisted cruciate-retaining total knee arthroplasties. The primary outcome was the proportion of balanced gaps (differential laxities ≤ 2mm) for extension, flexion, medial and lateral gap measurements. Manual pre-resection laxity measurements were obtained for MA and KA and manual post-resection measurements were obtained for FA in 10° and in 90° of knee flexion. Secondary outcomes were resection depths and implant alignment. All outcomes were analysed per constitutional coronal alignment and joint line obliquity subgroups. The proportions of balance in all four gap measurements were 54.7%, 66.4% and 96.5%, with MA, KA and FA, respectively. Across all constitutional alignment types, FA achieved the highest proportion of balance. MA resected the least amount of bone from the medial tibial plateau. KA had femoral components in most valgus and most internally rotated, tibial components in most varus and was the most bone-preserving for the posteromedial femoral condyle. FA had the most externally rotated femoral components and was most bone-preserving for the distal femoral resections. The study shows that implant alignment to the mechanical axis or joint line anatomy (equal resections) alone does not guarantee a balanced total knee arthroplasty. FA resulted in the highest proportion of balanced knees across all analysed subgroups. Future research will consider whether one alignment philosophy leads to superior outcomes for different constitutional alignment subgroups. Level II.

Trust-aware conditional adversarial domain adaptation with feature norm alignment

Adversarial learning has proven to be an effective method for capturing transferable features for unsupervised domain adaptation. However, some existing conditional adversarial domain adaptation methods assign equal importance to different samples, ignoring the fact that hard-to-transfer samples might damage the conditional adversarial adaptation procedure. Meanwhile, some methods can only roughly align marginal distributions across domains, but cannot ensure category distributions alignment, causing classifiers to make uncertain or even wrong predictions for some target data. Furthermore, we find that the feature norms of real images usually follow a complex distribution, so directly matching the mean feature norms of two domains cannot effectively reduce the statistical discrepancy of feature norms and may potentially induce feature degradation.In this paper, we develop a Trust-aware Conditional Adversarial Domain Adaptation (TCADA) method for solving the aforementioned issues. To quantify data transferability, we suggest utilizing posterior probability modeled by a Gaussian-uniform mixture, which effectively facilitates conditional domain alignment. Based on this posterior probability, a confidence-guided alignment strategy is presented to promote precise alignment of category distributions and accelerate the learning of shared features. Moreover, a novel optimal transport-based strategy is introduced to align the feature norms and facilitate shared features becoming more informative. To encourage classifiers to make more accurate predictions for target data, we also design a mixed information-guided entropy regularization term to promote deep features being away from the decision boundaries. Extensive experiments show that our method greatly improves transfer performance on various tasks.

Contrastive Learning Assisted-Alignment for Partial Domain Adaptation.

This work addresses unsupervised partial domain adaptation (PDA), in which classes in the target domain are a subset of the source domain. The key challenges of PDA are how to leverage source samples in the shared classes to promote positive transfer and filter out the irrelevant source samples to mitigate negative transfer. Existing PDA methods based on adversarial DA do not consider the loss of class discriminative representation. To this end, this article proposes a contrastive learning-assisted alignment (CLA) approach for PDA to jointly align distributions across domains for better adaptation and to reweight source instances to reduce the contribution of outlier instances. A contrastive learning-assisted conditional alignment (CLCA) strategy is presented for distribution alignment. CLCA first exploits contrastive losses to discover the class discriminative information in both domains. It then employs a contrastive loss to match the clusters across the two domains based on adversarial domain learning. In this respect, CLCA attempts to reduce the domain discrepancy by matching the class-conditional and marginal distributions. Moreover, a new reweighting scheme is developed to improve the quality of weights estimation, which explores information from both the source and the target domains. Empirical results on several benchmark datasets demonstrate that the proposed CLA outperforms the existing state-of-the-art PDA methods.

Laxity, Balance, and Alignment of a Simulated Kinematic Alignment Total Knee Arthroplasty.

Kinematic alignment (KA) and related personalized alignment strategies in total knee arthroplasty (TKA) target restoration of native joint line obliquity and alignment. In practice, deviations from exact restoration of the prearthritic joint surface are tolerated for either the femur or tibia to achieve ligamentous balance. It remains unknown what laxity, balance, and alignment would result if a pure resurfacing of both femur and tibia were performed in a KA TKA technique. We used data from 382 robot-assisted TKA performed with a digital joint tensioner to simulate TKA with a pure resurfacing KA technique for both femur and tibia. All knees had the posterior cruciate ligament retained. Knees were subdivided into 4 groups based on preoperative coronal alignment: valgus, neutral, varus, and high varus. Medial and lateral laxity in extension and flexion, balance in extension and flexion, and coronal plane alignment were compared between groups using analysis of variance testing. In simulated pure resurfacing KA TKA across a range of preoperative coronal plane deformities, only 11%-31% of knees would have mediolateral extension ligament balance within ±1 mm, and 20%-41% would have a medial flexion gap that is looser than the lateral flexion gap. Over 45% of knees would have coronal hip-knee-ankle angle >3 degrees from mechanical neutral. In simulations of pure resurfacing KA TKA, there was wide variability in the resulting laxity and alignment outcomes. Most knees had alignment and balance outcomes outside of normally accepted ranges. Techniques that deviate from pure resurfacing in order to achieve balance appear favorable.

Alignment of CSR Strategies with Business Objectives in IT Sector, Focusing on the Competitive Advantage

While numerous researchers have focused on examining the impact of Corporate Social Responsibility (CSR) on various aspects of business in different industries, there still remains a research gap in conducting comprehensive analyses of CSR strategies in Information Technology (IT) sector. Therefore, this study aims to address this gap by evaluating the effects of CSR on competitive advantage in the IT companies. In order to analyze data, AMOS structural equation modeling (AMOS-SEM) method was applied, and data were collected during February-April 2023 from 143 Iranian IT firms where CSR has not been appropriately dealt within theoretical as well as practical terms. The findings reveal that among the four components of CSR — legal, ethical, economic, and discretionary—, ethical, legal, economic, and discretionary dimensions have the most positive impact on competitive advantage, respectively. Taken together, these findings suggest a significant role for CSR in enhancing the level of competitive advantage in IT companies. In addition to the knowledge contribution, this study holds significant practical implications for IT company managers to strengthen their decision-making processes and develop effective CSR strategies.

Evolution of distributional alignment goals

Concepts regarding sagittal plane alignment in adult spinal deformity (ASD) surgery are evolving in the pursuit of individualized care with personalized targets. We have moved past simple targets of mismatch between lumbar lordosis and pelvic incidence, as these ignore the distribution, and the subsequent shape, of lumbar lordosis. Several classification systems and alignment strategies exist, with some proposing alignment to “normal” while others seek age-appropriate spinal alignment. While differences exist, the importance of the pelvic incidence as a fixed parameter from which one may build the spine is common to all theories. The purpose of this narrative review is to summarize the literature regarding the current concepts behind spinal alignment theories and the data supporting these theories.

Can we nudge to reduce the perioperative low value care? Decision making factors influencing safe practice implementation.

Highlight sources of low-value care (LVC) during the perioperative period help understanding the decision making behind its persistence, the barriers for change, and the potential implementation strategies to reduce it. The behavioural economics science spread of use through aligned strategies or nudge units offer an opportunity to improve success in the LVC reduction. LVC, such as unneeded surgeries, or preanaesthesia tests for low-risk surgeries in low-risk patients, is a relevant source of waste and preventable harm, most especially in the perioperative period. Despite the international focus on it, initial efforts to reduce it in the last decade have not clearly shown a sustainable improvement. Understanding the shared decision-making process and the barriers to be expected when tackling LVC is the first step to build the change. Applying a structured strategy based on the behavioural science principles may be the path to increasing high value care in an effective an efficient way. It is time to foster nudge units at different healthcare system levels.

Discriminative subspace embedded dynamic geometrical and statistical alignment based on pseudo-label correction for cross-domain bearing fault diagnosis

Many domain adaptation (DA) approaches have been developed to address the challenge of domain divergence in cross-domain fault diagnosis. However, most of them only attempt to align statistical distribution while neglecting geometric alignment between source and target data. Furthermore, the use of some unreliable pseudo-labels may cause geometrical and statistical distributions mismatching and interfere with the DA model generating correct pseudo-labels during the iterative learning. In this paper, we propose a new model called discriminative subspace embedded dynamic geometrical and statistical alignment based on pseudo-label correction (DSDGSA-PC) for bearing fault diagnosis. Firstly, discriminative subspace alignment is proposed to mitigate feature redundancy and divergence by generating aligned subspaces for two domains, while preserving class discriminative information and global structures of data. Then, DSDGSA-PC leverages the representer theorem and the principle of structural risk minimization to learn a domain-invariant classifier in the subspace, while minimizing statistical and geometrical shift by jointly optimizing dynamic graph embedding and dynamic weighted distribution alignment strategies. Finally, a novel pseudo-label correction mechanism is integrated into DSDGSA-PC to evaluate the credibility of pseudo-labels and rectify the unreliable ones during the iterations. The experimental results illustrate that DSDGSA-PC has higher transfer performance compared to several advanced methods on 24 transfer tasks.

Restoration of preoperative tibial alignment improves functional results after medial unicompartmental knee arthroplasty.

The alignment obtained after unicompartmental knee arthroplasty (UKA) influences the risk of failure. Kinematic alignment after UKA based on Cartier angle restauration is likely to improve clinical outcomes compared with mechanical alignment. The purpose of this study is to analyze the influence of implant alignment and native knee restoration after UKA using the conventional techniques on clinical outcomes. This retrospective study included 144 medial UKA patients from 2015 to 2020. Radiographic measurements were performed pre- and postoperatively. Outliers were defined as follows: Δ Cartier > 3° (difference between the preoperative and postoperative Cartier angle); Δ MPTA (Medial Proximal Tibial angle) and postoperative TCA (Tibial Coronal component Angle) > 3° (difference between the positioning of the tibial implant and the preoperative proximal tibial deformity). The Knee injury and Osteoarthritis Outcome Score (KOOS), the International Knee Society (IKS) Function and Knee score, the Forgotten Joint Score (FJS), and the Subjective Knee Value (SKV) were evaluated. A Student t test or a non-parametric Wilcoxon test was used for non-normal data to compare pre- and postoperative values for functional scores and angular measurements. The correlation of postoperative angles with functional outcomes was assessed by the Spearman's rank correlation coefficient. During the inclusion period, 214 patients underwent medial UKA, 71 patients were excluded, and 19 were lost to follow-up leaving 124 patients with 144 knees (20 bilateral UKA) included for analysis with a mean follow-up of 54.7months ± 22.1 (24-95). The Δ Cartier was significantly correlated with IKS function (R2 = 0.06, p < 0.001) and FJS (R2 = 0.05, p < 0.01) scores. The Δ preoperative MPTA-TCA was significantly correlated (p < 0.001) with KOOS (R2 = 0.38), IKS Knee (R2 = 0.17), IKS function (R2 = 0.34), SKV (R2 = 0.08), and FJS (R2 = 0.37) scores. In subgroup analysis, non-outliers (< 3°) for Δ preoperative MPTA-TCA had better KOOS score (Δ = 23.5, p < 0.001) and IKS Function (Δ = 17.7, p < 0.001) compared to outliers (> 3°) patients. Functional results after medial UKA can be influenced by implant alignment in the coronal plane with slight clinical improvement when positioning the tibial implant close to the preoperative tibial deformity, rather than by restoring the Cartier angle. This series suggests the interest of a more personalized alignment strategy, but these results will have to be confirmed by other controlled studies. IV, retrospective case series.

Dеvising a Modеl AI-UTAUT by Combining Artificial Inteligence AI with Unifiеd Thеory of Accеptancе and Usе of Tеchnology (UTAUT)

This paper addresses the gap in understanding how traditional technology acceptance models like UTAUT apply to emerging AI technologies. Based on a review of 30 studies using UTAUT, we propose an original AI-UTAUT model that incorporates new determinants such as transparency, explainability, and value alignment specific to AI. This model provides both theoretical advancements and practical insights for the adoption of AI systems. It serves as a framework for analyzing user acceptance in the context of AI, offering strategies for ethical alignment and implementation. Future research directions for validating the model across various sectors are also highlighted. Thе implications of this rеsеarch transcеnd acadеmia, offеring tangiblе stratеgiеs for thе implеmеntation and еthical alignmеnt of AI tеchnologiеs.

Digital transformation as an enabler of sustainability innovation and performance – Information processing and innovation ambidexterity perspectives

As sustainability gains increased importance among investors and regulators, firms are resorting to digitalization as a means to facilitate innovations and significantly improve their sustainability performances. Drawing on innovation ambidexterity and information processing theory, a framework was curated to assess the effect of digital transformation factors on sustainability innovation ambidexterity as well as sustainability performance. A survey questionnaire was administered to 187 maritime professionals who held relevant roles in innovation, performance management, sustainability, or digitalization. The data collected was then assessed using structural equation modelling, which revealed that the factors influencing sustainability performance, in decreasing order of importance, were digital capabilities, digital technology and business strategy alignment, sustainability exploration innovation, sustainability exploitation innovation, and digital flexibility. These findings contribute to the existing literature by employing the concepts of the identified theories to examine the triadic relationship between digital transformation factors, sustainability innovation ambidexterity, and sustainability performance. This provides a deeper understanding of the interactions between variables that lead to sustainability performance. In addition, it offers directions for managers to enhance sustainability performance through resource optimization in prioritizing training for employees, portfolio management, scheduling regular meetings between information technology and business management teams, and adopting forward-looking approaches to implement digital architecture.

Sustainability Management, Technological Innovation and Corporate Social Responsibility for Social Media Small to Medium-Sized Enterprises (SMEs)

The aim of this paper is to investigate the impact of the social and technological environment on business performance. Focus is placed on SMEs and their effort to achieve sustainable competitive advantage and thereby sustainable business performance. For this purpose, a survey study was carried out, where primary data was collected from a representative sample of SMEs operating in the Social Media industry of Greece. Questionnaires were distributed to managers and entrepreneurs of the Social Media Industry. These managers/entrepreneurs were selected using a simple random sampling methodology. The findings of the study revealed that social and technological developments have a major role in developing a sustainable competitive advantage. Strategy alignment was also found to be key in tackling the problems and in undertaking the opportunities that arise from the current social and technological environment, thereby reaching sustainable business performance. The findings of the study are especially useful to entrepreneurs and professionals in the industry in formulating effective strategies towards aligning their business with current advancements in emerging digital technologies. Such an alignment would bring numerous competitive advantages and in turn sustainable business performance.

A comparative study of teachers values alignment strategies in the classroom: Face-to-face versus distance education

A comparative study of teachers values alignment strategies in the classroom: Face-to-face versus distance education