Alignment Strategy Research Articles

Why Kinematic Alignment Makes Little Sense in Valgus Osteoarthritis of the Knee: A Narrative Review.

There is a debate about the best alignment strategies in total knee arthroplasty (TKA). Mechanical alignment (MA) targets in combination with necessary soft tissue releases are the gold standard for TKA in end-stage valgus osteoarthritis. Some authors propagate kinematic alignment (KA) with the aim of restoring the patient's native alignment and minimizing the need for soft tissue releases. Our previous studies showed that MA with standardized soft tissue release produces reproducible results, and that the preoperative phenotype does not influence the results of patients with valgus osteoarthritis. These data suggest that there is no functional advantage to preserving valgus alignment in patients with valgus osteoarthritis. Many patients with valgus osteoarthritis present with a compromised medial collateral ligament and leaving the knee in valgus could increase the risk of secondary instability. The current literature supports MA TKA with soft tissue release as the gold standard. While using more sophisticated enabling technologies like robotic surgery might allow for aiming for very slight (1-2°) valgus alignment on the femoral side, any valgus alignment outside this range should be avoided. This review paper summarizes our current knowledge on the surgical techniques of TKA in patients with valgus osteoarthritis.

Multi-scale alignment to buried atom-scale devices using Kelvin probe force microscopy

Abstract Fabrication of quantum devices by atomic-scale patterning with scanning tunneling microscopy (STM) has led to the development of single/few atom transistors, few-donor/quantum dot devices for spin manipulation, and arrayed few-donor devices for analog quantum simulation. We have developed atomic precision lithography, dopant incorporation, device encapsulation, ex situ device re-location, and contact processes to enable high-yield device fabrication. In this work, we describe a multiscale alignment strategy using Kelvin probe force microscopy to enable the alignment of buried device components to electronic support structures such as source/drain leads, in-plane and top gates, and waveguides while preserving flexibility in the placement of fabricated STM patterns. The required spatial accuracy to bridge the sub-micrometer scale central region of the device to millimeter scale large wire-bond pads is achieved through a multi-step alignment process at various stages of fabrication, including atom-scale device fabrication using STM, re-location and registration, and electron beam lithography for contact leads and pads. This alignment strategy allows imaging small device regions as well as large-scale fiducial marks, thereby bridging the gap from nanometer STM patterns to the millimeter-scale electrical contact fabrication with a 95% yield on more than 150 devices fabricated to date.

Mispricing and Algorithm Trading

This study relaxes the efficient market hypothesis by introducing a model that accounts for initial mispricing and explores the effects of algorithmic trading. The research finds that algorithmic strategies can cause significant market volatility and affect financial stability, particularly when they amplify overpricing, leading to bubbles and crashes. Key insights include: Initial mispricing is crucial for algorithmic trading to impact market prices. Market reactions vary with the direction of the trading strategy relative to the asset’s true value. Informed traders can benefit from mispricing, whereas noise traders typically incur losses. Policy implications suggest that algorithmic trading is not universally harmful; its effects depend on the alignment of trading strategies with accurate pricing. The study advises regulators to differentiate between stabilizing and destabilizing trading practices. For traders, the research highlights the importance of adaptive strategies that help correct mispricing to ensure long-term profitability and market health. This research advances our understanding of algorithmic trading’s dual potential and informs the development of more nuanced financial regulations and trading strategies.

Value Co-Creation: A Strategy for Enhancing Inclusiveness in Special Education

This qualitative study examines the educational management practices of a municipality in Thailand by evaluating the current delivery of special educational services with underlying principles rooted in value co-creation (VCC)—a business concept often employed to enhance engagement and participation with the aim of creating mutual value. The study’s objective is to identify gaps in real-life practices. The research methodology encompasses in-depth interviews with key stakeholders, focus group discussions involving school board members and parents, and content analysis to extract core concepts for policy recommendations. The study’s findings reveal that the municipality is increasingly recognizing the potential of VCC in enhancing student outcomes, particularly for those with special educational needs. However, challenges stemming from schools’ financial constraints and the influx of students from lower socio-economic backgrounds impede the full realization of VCC principles. Consequently, it is crucial for the government to prioritize the alignment of VCC strategies with special education. This can be achieved by considering reengineering the education budget allocation and making investments, whether direct or indirect, to support the VCC process. These measures are essential to promote the evolution of VCC and sustain special educational service operations effectively.

Climate-Responsive Urban Design: Innovations and Strategies for Sustainable Buildings and Construction in Afghanistan

The global challenge of climate change resonates profoundly across diverse sectors, notably urban development and construction standing prominently in its ripple effects. The complex integration of architecture, planning, and landscape elements in urban development aims to sculpt functional and sustainable cities. Within the context of Afghanistan, the responsibility for applying climate-responsive regulations and policies falls upon the National Environmental Protection Agency (NEPA). This study employs a descriptive research method to scrutinize the implementation and alignment of NEPA's regulations, policies, and strategies in the current urban landscape. The findings show a significant shift from past building practices to contemporary global norms, indicating a noteworthy exploration and adaptation within the construction industry. There is also a significant deficiency in local awareness, leading to shortcomings in climate innovations and strategies, revealing a notable gap between construction practices and the adoption of climate-responsive measures. Furthermore, a remarkable shortfall exists in aligning urban elements with sustainable building principles, prompting concerns regarding the sustainability and construction standards established by NEPA. A critical focal point emerges in the form of uncontrolled carbon emissions in urban areas, primarily originating from heating and cooling systems, transportation, waste management, and industrial factories, leading to significantly impactful high emissions that affect local temperature and precipitation, resulting in changes to living conditions.

Measuring Culture in Projects: Scale Development and Validation in a Sample of Turkish Professionals

The purpose of this study is to develop a reliable and valid instrument that can be used in organizations to measure project culture and its effects. Since a project culture is formed within the scope of project goals and activities, the organizational culture also affects this culture. For this purpose, a scale has been developed to determine the level of project culture in organizations and to benefit both academia and practitioners. With Exploratory Factor Analysis (EFA) of the data obtained at the end of the pilot study, a structure consisting of 38 questions in 5 dimensions (1) Project Value and Awareness, (2) Organization and Systems, (3) Process and Method, (4) Stakeholder and Communication, and (5) Strategy Alignment) was created. As a result of the data received from a sample of 311 Turkish project management professionals, the analyzes were completed with SPSS22 package using factor analyses, confidence analysis and Cronbach Alpha analyses. Additionally, AMOS version 19 was used for confirmatory factor analysis and Structural Equation Modeling. At the end of all these studies, a project culture scale consisting of 22 questions in 5 dimensions was created.

Subdomain-Alignment Data Augmentation for Pipeline Fault Diagnosis: An Adversarial Self-Attention Network

Data augmentation (DA) has the potential to address the issue of imbalanced and insufficient datasets (I&ID) in pipeline fault diagnosis. However, the majority of existing DA methods for time series are inspired by computer vision techniques, ignoring the temporal dynamic properties and fine-grained fault features, which leads to limited performance of the augmentation. To tackle this problem, we introduce a novel DA approach called the subdomain-alignment adversarial self-attention network (SA-ASN), which takes into account both temporal association and semantic correlation. Our approach features a novel temporal association learning (TAL) mechanism, which transfers temporal information from the discriminator to the generator via a customized knowledge-sharing structure, improving the reliability of synthetic long-range associations. Additionally, we introduce a prototype-assisted subdomain alignment (PASA) strategy that forms a hierarchical structure in the synthetic dataset by incorporating local semantic correlation into the model training. With the support of TAL and PASA, our SA-ASN algorithm enhances the authenticity of temporal structure at the instance level and improves the discriminability of fault features at the category level. Our experimental results show that the SA-ASN algorithm provides a more diverse and accurate augmentation of pipeline data. The effectiveness of our SA-ASN algorithm encourages the use of data-driven diagnostic models in complex real-world oilfield pipeline networks.

Large multiplanar changes to native alignment have no apparent impact on clinical outcomes following total knee arthroplasty.

This study sought to examine if achieved postoperative alignment when compared to the native anatomy would lead to a difference in Patient Reported Outcome Measures (PROMs), and whether the achieved alignment could be broadly categorised by an accepted alignment strategy. A retrospective cohort study of prospectively collected data on patients undergoing single primary or bilateral simultaneous total knee arthroplasty (TKA) was carried out. CT scans were used to determine the mean change ("delta values") between the pre and postoperative; hip-knee-ankle angle, lateral distal femoral angle, medial proximal tibial angle and femoral implant rotation. Femoral implant flexion and tibial implant slope were measured postoperatively. The primary outcome was the relationship of the variables to the change in KOOS pain subscale after one year. The secondary outcome was the number of knees which could be categorised postoperatively to an alignment strategy, and the mean PROMs in each cohort. A total of 296 knees in 261 patients were available for analysis. With regards to the primary outcome, the delta values for each variable did not demonstrate any association with the change in knee injury and osteoarthritis outcome score (KOOS) pain score. Approximately 46% of knees could not be categorised to an alignment strategy based on postoperatively measured alignment, with no significant difference between each cohort with regards to the change in KOOS Pain score. Achieved alignment does not consistently match accepted alignment strategies, and appears to confer no benefit to clinical outcomes when the native anatomy is most closely approximated, nor results in poorer outcomes in outliers. This study highlights the importance of routine three dimensional pre and postoperative imaging in clinical practice and for the valid analysis of outcomes in studies on alignment. Level III, retrospective cohort study.

Renewable energy projects in Africa: A review of climate finance strategies

This paper provides a concise overview of the comprehensive examination undertaken to assess climate finance strategies associated with renewable energy projects in Africa. The continent, endowed with abundant renewable resources, faces the dual challenge of meeting its rising energy demand while addressing climate change concerns. This review delves into the diverse landscape of renewable energy initiatives across Africa, emphasizing the pivotal role of climate finance mechanisms in driving sustainable development. The study explores a myriad of renewable energy projects, encompassing solar, wind, hydro, and geothermal technologies, implemented to mitigate the environmental impact of conventional energy sources. It investigates the financial instruments deployed to support these projects, including international funds, grants, loans, and public-private partnerships. The analysis considers the effectiveness of climate finance in fostering the adoption and scaling-up of renewable energy solutions within the unique socio-economic contexts of African nations. Furthermore, the research investigates challenges and opportunities associated with climate finance strategies in the African renewable energy sector. Common barriers, such as regulatory frameworks, institutional capacity, and market dynamics, are examined, alongside success stories that highlight innovative financing models and project implementations. The study also sheds light on the role of regional collaborations and the involvement of local communities in ensuring the sustainability and resilience of renewable energy projects. In addition, the review critically assesses the alignment of climate finance strategies with international climate goals, including the Paris Agreement. It analyzes the extent to which these strategies contribute to both mitigating greenhouse gas emissions and promoting adaptation measures in the face of climate change impacts. The findings aim to provide policymakers, investors, and stakeholders with valuable insights into refining and enhancing climate finance strategies for a more impactful and inclusive renewable energy transition in Africa. This review synthesizes a comprehensive understanding of the current state of renewable energy projects in Africa, emphasizing the critical role played by climate finance in advancing sustainable development. The insights generated from this analysis contribute to the ongoing discourse on effective climate finance strategies, offering a roadmap for fostering resilient and low-carbon energy systems on the African continent.

26. Youth-Driven Systems Alignment Strategies to Optimize Health Among Youth Experiencing Homelessness

26. Youth-Driven Systems Alignment Strategies to Optimize Health Among Youth Experiencing Homelessness

A Framework for Valuation and Portfolio Optimization of Venture Capital Deals with Contractual Terms

Venture capitalists invest not only in the business aspect of a deal but also in its contractual terms. Therefore, the selection of deals and the combination of contractual terms pose challenging decisions for them. This paper consists of two main sections. The first section introduces a novel framework for the valuation of venture capital (VC) deals, including startups and their contractual terms. By taking into account risk situations, this section presents the valuation of combined contractual terms, including call options, liquidity preference, and participant rights. In the second section, a new multiobjective mathematical model for VC deals and contractual terms portfolio selection is developed using right-tail probability, strategy alignment, and a utility function. To solve the proposed model, three metaheuristic algorithms—Non-Dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Binary Harmony Search Algorithm, and Dynamic Tuning Parameter Binary Harmony Search Algorithm (DTPBHS)—are applied. Based on numerical examples, DTPBHS outperforms other algorithms in the “Mean Ideal Distance” index, but NSGA-II demonstrates the best performance in the “Rate of Achievement of two objectives simultaneously” index. Furthermore, we demonstrate that the proposed utility function is more robust than the right-tail probability function under default deals conditions.

Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment

Research on transfer learning in rolling bearing fault diagnosis can help overcome challenges such as different data distributions and limited fault samples. However, most existing methods still struggle to address the zero-shot cross-domain problem within the same equipment and the few-shot cross-machine problem. In response to these challenges, this paper introduces a transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment. The model consists of two stages. In the first stage, the original vibration signals undergo continuous wavelet transform to obtain time–frequency diagram. Subsequently, a convolutional neural network extracts features from the diagram. The obtained deep features are decomposed into four types: uncertain features, domain-shared features, domain-specific features and category features. Multiple loss functions are then employed to remove extraneous features beyond the category features. In the second stage, category features are further extracted, and convolutional block attention module is introduced to further reduce the potential interference of unexcluded irrelevant information within the category features with classification results. Simultaneously applying a class-level alignment strategy effectively alleviates inter-domain class distribution discrepancies. Experimental validation was conducted on three distinct datasets, revealing a significant improvement in the classification performance of the proposed method over alternative methods. Furthermore, the model demonstrated robustness and noise resistance.

Key success factors for implementing strategy in the Icelandic fisheries industry

PurposeThis study focuses on the key success factors (KSFs) for strategy implementation in the fisheries industry in Iceland identified by chief executive officers within the industry. The purpose is to provide a comprehensive categorization of KSFs that influence how strategy is mobilized. The secondary aim is to uncover the level of priority that companies place on the dimensions of the United Nations (UN) Sustainable Development Goals (SDGs).Design/methodology/approachThe methodology involves qualitative case studies based on in-depth elite interviews with nine chief executive officers of Icelandic fishing companies.FindingsThe research indicates strategy implementation can be improved in four main areas. First, by engaging and involving all employees in the implementation process. Second, by enhancing bottom-up innovation and communication. Third, through alignment of the corporate strategy and the UN SDGs, and fourth, by following rigorous action plans with clear, measurable and prioritized objectives and timeframes for the managers to follow. These improvements have both theoretical and practical implications for the fishing industry. Consequently, a conceptual framework for integrated strategy implementation in the fisheries industry is proposed.Research limitations/implicationsA limited number of in-depth elite interviews were conducted since access to the chief executive officers of the country’s largest fishing companies proved challenging. However, the nine companies collectively hold nearly 50% of the country’s total quota, thereby proving a deep understanding of the topic relevant to the industry. The research uncovered a substantial cross-section of viewpoints, and as such, the results are relevant for both academia and practitioners alike.Originality/valueThis study contributes to the debate on KSFs relevant to strategy implementation within a specific industry but also aligns with the UN SDGs by proposing a dedicated framework for implementing strategies in the fisheries industry. Overall, this study can help managers achieve strategy implementation.

DOP27 Systemic antibody responses against gut microbiota flagellins implicate shared and divergent immune reactivity in Crohn's Disease and chronic fatigue syndrome

Abstract Background Patients with Crohn’s disease (CD) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) exhibit elevated antibody responses against gut microbiota flagellins. However, flagellin-specific antibody repertoires and functional roles in the diseases remain incompletely understood. Bacterial flagellins can be categorized into three types depending on their interaction with toll-like receptor 5 (TLR5): (1) "stimulator" and (2) "silent" flagellins, binding TLR5 through a conserved N-terminal motif, with only stimulators activating TLR5 due to a specific C-terminal domain; (3) "evader" flagellins of pathogens, which circumvent TLR5 activation via mutated N-terminal TLR5 binding motifs. Here we studied the characteristics, epitope binding, and sequence (dis)similarity of anti-flagellin antibody responses in CD and ME/CFS. Methods Since conventional antibody profiling methods like enzyme-linked immunosorbent assays [ELISAs] do not allow for large-scale measurements of antibody repertoires, we leveraged phage-display immunoprecipitation sequencing (PhIP-Seq) to characterize 344,000 rationally selected peptide antigens in 256 patients with CD, 40 patients with ME/CFS and in two equally sized groups of age- and sex-matched healthy controls from population-based cohorts in the Netherlands and U.K., respectively. Different sequence alignment strategies were employed to compare flagellin peptide structures with observed antibody-bound flagellin peptide reactivity. Results Both patients with CD and ME/CFS exhibited elevated antibody responses against distinct regions of flagellin peptides compared to healthy individuals (P<0.001). N-terminal binding to Lachnospiraceae flagellins was comparable in both diseases, while C-terminal binding was more prevalent in CD. N-terminal antibody-bound flagellin sequences were similar across CD and ME/CFS, resembling ‘stimulator’ and ‘silent’ flagellins more than evaders. However, C-terminal antibody-bound flagellins showed higher resemblance to stimulator than to silent flagellins in CD, but not in ME/CFS. This group of antibody-bound flagellins was exclusively identified in a subset (10-20%) of patients with CD and characterized by its strong overrepresentation (exceeding 20-fold), underscoring its potential significance in distinguishing pathophysiologic subtypes of CD. Conclusion Antibody binding to the N-terminal domain of stimulator and silent flagellins may impact TLR5 activation in both CD and ME/CFS patients. Furthermore, elevated antibody binding to the C-terminal domain of stimulator flagellins in CD may explain pathophysiological differences between diseases. Our results highlight the diagnostic potential of these antibody responses and their impact on innate/adaptive immunity balance.

DIGITAL NATIVES AND MIGRANTS

Contemporary information technology requires the reformulation of school routines aimed at optimizing student, teacher, and institutional performance. Concurrently, it necessitates the development and alignment of pedagogical and curricular strategies shaped by Web 3.0 platforms, which enable collective synchronous and asynchronous virtual interactions and the use of cognitive tools such as generative artifi cial intelligences. Such a scenario is natural for the contemporary generation of students, known as digital natives, while it can hinder the relationships, interactions, curricula, and didactic pedagogical performance of the preceding generation of teachers, referred to as digital migrants. This study aims to investigate how technologies are being applied in educational institutions and what their pedagogical and curricular contributions are. Google Scholar, Digital Library, and University Periodicals were used as support for the literature review and theoretical construction of this study. Additionally, other databases, books, theses, and articles from online journals were also explored. Finally, this article underscores the urgency for educational actors to master the new technologies that are emerging in educational spaces, considering that various activities previously carried out in non-virtual modalities are now taking place in digital environments, the native habitat of future generations.

Cross-machine deep subdomain adaptation network for wind turbines fault diagnosis

Recently, subdomain adaptation has gained extensive interest in addressing the problem of wind turbine (WT) fault diagnosis. However, current methods mainly focus on the subdomain adaptation of statistical features and scenarios with constant rotation speed. To overcome these limitations, a new cross-machine deep subdomain adaptation network (CMDSAN) is proposed in this paper for fault diagnosis of WT under multiple operating conditions. CMDSAN contains an improved subdomain adaptive (ISA) mechanism. In ISA, a subdomain distribution shift measure of jointed statistical and geometric features is constructed to boost domain confusion. Meanwhile, to further capture fine-grained information and discriminative features, a local correlation alignment (LCA) strategy is proposed. Additionally, a two-stage training trade-off factor is designed for balancing classification and ISA loss during the training process to improve the transferability of features. Subsequently, test rigs are constructed, i.e., a planetary gearbox test rig and a scaled-down test rig for WT gearbox with a reduction ratio of 110.11, to validate the effectiveness and superiority of CMDSAN. The case studies conducted under constant rotation speed, acceleration, and deceleration demonstrate that the proposed CMDSAN exhibits better fault transfer diagnostic ability than other domain adaptation methods.

Alignment strategy for IFMIF-DONES facility

This article addresses the challenges associated with the alignment of the equipment within the IFMIF DONES installation and presents the strategy devised to achieve acceptable levels of uncertainty. The primary obstacles stem from the need to align equipment located in different rooms, some of which are inaccessible for manual operations. Additionally, the elevated radiation levels in certain areas impose restrictions on the fiducials that can be employed.The article details the outcomes of implementing the proposed alignment procedure, including the specific equipment used and the achieved levels of uncertainty.

Switchable Diffraction Pattern Based on Cascaded Metasurfaces

AbstractMetasurfaces have great advantages in the field of laser detection because of their ability to generate large amounts of point sources or various diffraction patterns. Here,cascaded metasurfaces (CM) for generating switchable spatial light distributions is utilized, which can be used for optical scanning and identifications. The light distribution is composed of several distinct diffraction patterns, and the scanning of the diffraction space is realized by multiple pixel‐scale alignment strategies between two pieces of CM. Through combining Dammann optimization and the gradient descent method, various diffraction patterns under different matching situations can be guaranteed by phase superposition. Various diffraction conditions with highly identifiable intensity distribution are guaranteed by using the optimization method. Such optical scanning devices based on CM have small footprints, high integration, flexible composition, and stable diffractive combination features. By manipulating CM to generate changeable spatial diffraction phenomenon may provide flexible modulation properties to optical scanning, measurement, and other applications.

Driving Revenue Growth: The Role of Strategic Human Resource Practices in 3-5 Star Hotels in Selected Regions of Ghana

This study explores the impact of strategic human resource practices on revenue maximization in Ghana's 3-5-star hotels across eight regions. Employing a descriptive survey method, data was gathered from 60 hotels, focusing on human resource practices and revenue strategies. Results reveal a robust correlation (R = 88.3%) between strategic human resource practices and revenue maximization. Regression analysis indicates a significant impact of "human resource" (β = 0.282, p < 0.001) on "revenue maximization." Factor analyses identify key practices, including pricing strategies, operational efficiency, and guest satisfaction, crucial for revenue optimization in Ghana's hospitality industry. Policy implications underscore the importance of training initiatives, competitive hiring incentives, and technology integration in workforce development for enhanced revenue outcomes. These findings provide essential guidance for policymakers, industry stakeholders, and hotel management, emphasizing the alignment of human resource strategies with revenue objectives in the dynamic Ghanaian hospitality landscape.

Adaptive graph-guided joint soft clustering and distribution alignment for cross-load and cross-device rotating machinery fault transfer diagnosis

Domain adaptation (DA) is an effective solution for addressing the domain shift problem. However, existing DA techniques usually directly match the distributions of the data in the original feature space, where some of the features may be distorted by a large domain shift. Besides, geometric and clustering structures of the data, which play a significant role in revealing hidden failure patterns, are not considered in traditional DA methods. To tackle the above issues, a new joint soft clustering and distribution alignment with graph embedding (JSCDA-GE) method is proposed. Specifically, weighted subspace alignment is proposed to align bases of source and target subspaces by combining instance reweighting and subspace alignment strategies. Then, JSCDA-GE formulates an objective function by incorporating dynamic distribution alignment, soft large margin clustering, and GE in a unified structural risk minimization framework. Ultimately, JSCDA-GE aims to learn a generalization classifier for fault diagnosis. Its effectiveness and superiority have been confirmed through 36 tasks on 2 bearing databases.