Alignment Strategy Research Articles

Financial Analysis, SWOT Analysis and Poter Five Forces Analysis for Nike

This essay provides a comprehensive analysis of Nike, Inc., a participant in the athletic footwear and apparel industry, to evaluate its financial performance, competitive positioning, and strategic outlook. The analysis begins with a detailed financial assessment using ratio analysis, focusing on profitability, liquidity, investment, and efficiency ratios over a three-year period. This quantitative evaluation reveals insights into Nike’s financial health, operational efficiency, and resource utilization, offering a basis for informed decision-making for investors and stakeholders. Following the financial assessment, the essay explores the competitive landscape through Porter’s Five Forces Analysis, examining the industry dynamics, market trends, and competitive pressures that shape Nike’s strategic approach. This analysis provides a thorough understanding of the external factors influencing Nike’s business operations and market position, including competition, potential new entrants, and the influence of buyers and suppliers. A SWOT analysis is done to give an overview of Nike's internal strengths and weaknesses, along with external opportunities and threats. This holistic evaluation identifies areas of excellence and potential challenges, enabling the formulation of aligned and effective strategies to leverage strengths and mitigate weaknesses.

Evolutionary multitasking cooperative transfer for multiobjective hyperspectral sparse unmixing

Evolutionary multiobjective optimization is vigorous but not efficient in solving the hyperspectral sparse unmixing problem, while most related algorithms suffer from high computational complexity. This limitation becomes more pronounced in dealing with multiple complex hyperspectral sparse unmixing tasks (HSUTs) concurrently. In fact, different HSUTs are interrelated on spectral libraries and search spaces. However, there is currently no evolutionary algorithm-based approach that explores the cooperativity of multiple HSUTs. Taking advantage of this opportunity, we propose a novel evolutionary multitasking cooperative transfer (EMCT) framework for multiobjective hyperspectral sparse unmixing in this paper. EMCT can handle different HSUTs simultaneously with a uniformly coded population, and discover the correlation between different tasks to promote their convergence. To overcome the issue of unequal dimensions and non-corresponding spectra in the spectral libraries utilized by different tasks, we propose a novel library alignment strategy to alleviate the multitasking negative transfer. In addition, we design novel initialization strategy and genetic operators to improve the population diversity and the efficiency of population evolution between different HSUTs. We utilize the reconstruction error of every endmember in the spectral library for each task as prior knowledge to evenly distribute the population within the sparsity interval to be explored. In initialization and population evolution, EMCT prefers those decision variables with lower error scores, guiding the population to explore more probable active endmember sets. On the three most commonly used simulated datasets and one real dataset, EMCT achieves state-of-the-art (SOTA) or competitive experimental results. Furthermore, the potential of the EMCT framework is also demonstrated in combination with other multiobjective sparse unmixing algorithms and evolutionary multitasking explicit transfer strategy.

Incorporating attribute-level aligned comparative network for generalized zero-shot learning

The key challenge of zero-shot learning (ZSL) is to sufficiently disentangle each latent attribute from the class-level semantic annotations of images, thereby achieving a desirable semantic transfer to unseen classes with the disentangled attributes. However, most existing studies tackle the ZSL task with a strict class-level alignment strategy that may yield insufficient disentanglement: (1) this strategy simply aligns holistic visual feature with its associated class-level semantic vector for each image; (2) the class-level semantic vectors have limited diversity and complex compositions of attributes. To address these issues, we propose an incorporating attribute-level aligned comparative network, i.e., IAAC-net, that develops the alignment strategy of ZSL to the attribute level. IAAC-net aims to establish diversified attribute-level and refined class-level alignments to facilitate attribute disentanglement and simultaneously improve zero-shot generalization. By further proposing a confusion-aware loss, the model is forced to rectify the disentanglement of indistinguishable attributes, which leads to a more accurate attribute disentanglement. The proposed IAAC-net yields significant improvements over the strong baselines, leading to new state-of-the-art performances on three popular challenging benchmarks, i.e., CUB, SUN, and AWA2.

Similarity propagation based semi-supervised entity alignment

Entity alignment aims to identify entities referring to the same real world object among multiple knowledge graphs. Current embedding based approaches suffer from the lack of labeled entity pairs as training data. Some works attempt to boost the training process with semi-supervised methods, which add confidently predicted entity pairs into training data iteratively. Though the effectiveness of this strategy has been confirmed, the current semi-supervised methods suffer from the problem of incorrect newly labeled entity pairs. This paper presents a novel semi-supervised entity alignment framework Similarity Propagation based Semi-supervised Entity Alignment (SPSEA), which improves the precision of labeled entity pairs by propagating alignment information from seed entity pairs to their direct neighbors. The key idea is to combine dependency between entities and entity embeddings to obtain entity similarities, which alleviates the problem of mislabeling when the entity embeddings are of low quality. And it finds aligned entities from direct neighbors of seed entity pairs with the help of relation alignment, which narrows the search space and not hurting the recall of true pairs. In addition, we propose novel deferred-acceptance algorithm and bilateral alignment strategy to further guarantee the quality of obtained entity pairs. Through extensive experiments, we show that the quality of labeled entity pairs obtained by SPSEA is high than current semi-supervised methods.

Magnetically assisted construction of Al2O3 platelets dual network and its excellent thermal conductivity in epoxy resin composites

Constructing three-dimensional thermally conductive networks in polymer matrix is a key strategy to improve the thermal management performance of electronic packaging materials. However, optimizing the thermal conductivity of the composite while retaining its electrical insulation properties still requires further structural design. Herein, the heat-transferring Al2O3 platelets dual network was constructed by using a simple compression moulding method combined with a magnetic alignment strategy. Al2O3@Fe3O4 was used for the magnetic orientation arrangement in the vertical direction of the laminated Al2O3 framework. And the obtained Al2O3/Al2O3@Fe3O4/Epoxy composites possessed satisfactory thermal conductivity. The thermal conductivity of the composites containing 34.63 vol% filler was as high as 2.30 W/mK (K‖) and 1.83 W/mK (K⊥), which were significantly higher by 945 % and 732 % compared to epoxy resin. In addition, the introduction of Al2O3@Fe3O4 in the composite promoted isotropic thermal conductivity and provided electromagnetic shielding properties.

Abandonment issues: A hazard analysis of high‐performance work practices

AbstractThis study examines usage of high‐performance work practices (HPWPs) over time using a unique longitudinal survey of Canadian organizations. Human resource management (HRM) studies tell us HPWPs require long‐term commitment but rarely measure it. Using event history analysis this study finds high rates of HPWP abandonment. The study also examines organizational supports for high‐performance work system (HPWS). A Cox regression analysis finds rates of abandonment are reduced when HPWPs are accompanied by aligned business strategies and HR professional support. The results inform process research on HRM strategies and raise troubling questions for findings in studies that fail to measure duration. For managers, the findings highlight the importance of ensuring strategic alignment with and organizational support for HPWPs if they are to endure.

Correction of Coronal Deformity and Intercompartmental Imbalance through Bone Resection.

This review discusses the correction of coronal deformity and intercompartmental imbalance through bone resection in total knee arthroplasty (TKA). To achieve functional knee balance, coronal, rotational, and sagittal alignment are critical to successful patient outcomes. Though variations in coronal alignment are debated as a correlate to positive results, advancements in utilizing implant position, alignment, and soft tissue balance to improve patient outcomes and function are an evolving discussion. This study draws upon existing literature, clinical studies, and surgical techniques related to TKA, including advancements in navigation and robotics. The study highlights the importance of individualized alignment strategies for varus and valgus knees. While mechanical neutral alignment has shown excellent long-term data, reevaluating the anatomic recreation of the patient's joint line obliquity is now being studied extensively. Sensor data and navigation systems contribute to improved outcomes and patient satisfaction. The evolution of navigation and robotics has led surgeons to achieve their target angles consistently and accurately; now, the discussion is around the most effective alignment targets. The classification of various phenotypes assists in the proposed starting points for implant position, but soft tissue tension is required in the input data to achieve global balance and stable motion. Each approach's advantages and limitations are considered. In conclusion, achieving optimal coronal alignment, joint line obliquity, and soft tissue balance is crucial for successful TKA outcomes. Personalized alignment philosophies, supported by three-dimensional data and sensor technology, are evolving to minimize critical errors and enhance functional results. Robotic assistance and future advancements in artificial intelligence and machine learning hold promise for further improving TKA outcomes in the quest for soft tissue stabilization.

Strategic Financial Risk Management: Implementing the COSO-ERM Framework at ABC Biotech Company

This paper explores financial risk management through the lens of the COSO-ERM framework at ABC Biotech Company. It emphasizes the integration of risk management with corporate governance, strategy, and performance. Key aspects of the COSO-ERM framework, including Governance and Culture, Strategy and Objective Setting, Performance, and Information Communication and Reporting, are examined. The study delves into ABC Biotech's implementation of COSO-ERM, covering strategies for risk management, corporate strategy alignment, risk identification, and assessment enhancements. It underscores the importance of financial risk management as a dynamic, cyclical process aligned with strategic corporate growth, illustrating the framework's effectiveness in managing financial risks at ABC Biotech Company.

AUTOMATIC COARSE CO-REGISTRATION OF POINT CLOUDS FROM DIVERSE SCAN GEOMETRIES: A TEST OF DETECTORS AND DESCRIPTORS

Abstract. Point clouds are collected nowadays from a plethora of sensors, some having higher accuracies and higher costs, some having lower accuracies but also lower costs. Not only there is a large choice for different sensors, but also these can be transported by different platforms, which can provide different scan geometries. In this work we test the extraction of four different keypoint detectors and three feature descriptors. We benchmark performance in terms of calculation time and we assess their performance in terms of accuracy in their ability in coarse automatic co-registration of two clouds that are collected with different sensors, platforms and scan geometries. One, which we define as having the higher accuracy, and thus will be used as reference, was surveyed via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox Horizon over a walking path with un-even ground.The novelty in this work consists in comparing several strategies for fast alignment of point clouds from very different surveying geometries, as the drone has a bird’s eye view and the bicycle a ground-based view. An added challenge is related to the lower cost of the bicycle sensor ensemble that, together with the rough terrain, reasonably results in lower accuracy of the survey. The main idea is to use range images to capture a simplified version of the geometry of the surveyed area and then find the best features to match keypoints. Results show that NARF features detected more keypoints and resulted in a faster co-registration procedure in this scenario whereas the accuracy of the co-registration is similar to all the combinations of keypoint detectors and features.

Learning feature alignment and dual correlation for few‐shot image classification

AbstractFew‐shot image classification is the task of classifying novel classes using extremely limited labelled samples. To perform classification using the limited samples, one solution is to learn the feature alignment (FA) information between the labelled and unlabelled sample features. Most FA methods use the feature mean as the class prototype and calculate the correlation between prototype and unlabelled features to learn an alignment strategy. However, mean prototypes tend to degenerate informative features because spatial features at the same position may not be equally important for the final classification, leading to inaccurate correlation calculations. Therefore, the authors propose an effective intraclass FA strategy that aggregates semantically similar spatial features from an adaptive reference prototype in low‐dimensional feature space to obtain an informative prototype feature map for precise correlation computation. Moreover, a dual correlation module to learn the hard and soft correlations was developed by the authors. This module combines the correlation information between the prototype and unlabelled features in both the original and learnable feature spaces, aiming to produce a comprehensive cross‐correlation between the prototypes and unlabelled features. Using both FA and cross‐attention modules, our model can maintain informative class features and capture important shared features for classification. Experimental results on three few‐shot classification benchmarks show that the proposed method outperformed related methods and resulted in a 3% performance boost in the 1‐shot setting by inserting the proposed module into the related methods.

Adapting to Lockdown: Exploring Stress Coping Strategies on Short Video Social Media During the COVID-19 Pandemic.

The study addresses a gap in research on media use during the COVID-19 pandemic, focusing on the overlooked role of short video social media platforms in stress management. It builds on transactional stress model and coping flexibility to advance the understanding of the strategy-situation fit, particularly the alignment of coping strategies with platform affordances. Our study investigated the detailed use of short video platforms and coping strategies during the pandemic lockdown through an online survey of 1569 respondents conducted immediately after the lockdown. We characterized lockdown duration as a situational stressor attribute and explored variations in the coping process. Assessing changes in user behavior across the top four Chinese short video platforms, we examined anxiety induced by lockdown and the use of problem-focused and emotion-focused coping strategies. Significant changes in user behavior in response to lockdown-induced stress were observed. Structural model analyses reveal that people employed both coping strategies to manage heightened anxiety, primarily through increased viewing behavior over interaction behavior. The findings underscore the positive implications of short video platforms on individual well-being. This study argues for a deeper research focus on coping flexibility within the realm of social media, accompanied by a comprehensive assessment of platform user behavior. Future research should also consider the influence of algorithms on platform functionality.

DreamStone: Image as a Stepping Stone for Text-Guided 3D Shape Generation.

This paper presents a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between the text and shape modalities for generating 3D shapes without requiring paired text and 3D data. The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between the rendered images and the input text. Besides, to extend beyond the generative capability of the SVR model, we design the text-guided 3D shape stylization module that can enhance the output shapes with novel structures and textures. Further, we exploit pre-trained text-to-image diffusion models to enhance the generative diversity, fidelity, and stylization capability. Our approach is generic, flexible, and scalable. It can be easily integrated with various SVR models to expand the generative space and improve the generative fidelity. Extensive experimental results demonstrate that our approach outperforms the state-of-the-art methods in terms of generative quality and consistency with the input text. Codes and models are released at https://github.com/liuzhengzhe/DreamStone-ISS.

The Role of Information Technology in Governance Mechanism for Strategic Business Contribution: A Pilot Study

Information Technology Governance (ITG) aligns IT and business transformation in schools. Private schools need to implement a method to evaluate the process of aligning IT strategy with business strategy and whether IT investment supports business objectives. Â What factors influence the ITG of selected Indonesia High School (HS) aligning IT-Business strategy? Partial Least Squares Structural Equation Modeling (PLS-SEM) is a tool for analyzing the ITG model results in this study. The result is the composition of 9 variables with four independent variables (as structure mechanism variables), four dependent variables (as process and relational mechanism variables), and the ITG variable (as the final variable) shows a significant value of 0.75 at the ITG variable. This considerable value means that the ITG supporting variables of four independent and four dependent variables significantly affect the ITG variable by 75%. This study provides information if the system trust variable is increasing due to the influence of good IT strategy (independent) variables and good business (dependent) variables. The recommendation is that this ITG trust model can be used to evaluate the alignment of IT strategy with business strategy and whether IT investment supports business objectives in HS

Strategy Alignment as an Effort to Create an Innovation Process to Improve Organizational Performance

This study aims to investigate the effect of strategy alignment as an effort to create an innovation process to improve organizational performance. The sample used in this study came from the Credit Union Cooperative which is under the auspices of the Indonesian Credit Union Cooperative Center (PUSKOPCUINA) Federation Network. Data was collected using a survey method to the management leaders of the Credit Union Cooperative. By using the path analysis method, the result shows firstly, that strategy alignment has a positive and significant influence on the innovation process. Secondly, strategy alignment has a positive but not significant effect on organizational performance. Thirdly, the innovation process has a positive and significant impact on organizational performance. In addition, by using the Sobel test, the result of the study shows that there is a mediating effect of the innovation process in mediating strategy alignment on organizational performance.

RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network

Rolling bearings are widely used in mechanical equipment, effectively determining the failure time of rolling bearings is particularly significant to ensure the safe performance of mechanical equipment. However, in industrial scenarios, the machine mainly works in the normal state for a long time, it is difficult to accumulate the same distribution of the whole life data, but the use of different distribution of data for forecasting will reduce the performance of deep learning-based prediction methods. Therefore, in order to tackle this problem, a multi-scale convolutional parallel memory domain adaptation network is investigated to forecast the residual useful life (RUL) of bearings across working conditions. Firstly, a new characteristic extractor—multi-scale convolutional parallel memory network is designed to extract spatial and temporal characteristics of bearing degradation data. At the same time, in order to minimize the distribution difference between source domain and target domain, a temporal-spatial feature alignment strategy is proposed to obtain domain invariable characteristics by combining maximum mean difference and domain adversarial learning. Finally, the availability of the proposed approach is verified using two rolling bearing data sets. The results reveal that it can efficiently forecast the RUL of rolling bearings across working conditions.

Tunable Anisotropic Structural Aramid Triboelectric Aerogels Enabled by Magnetic Manipulation

AbstractMaterial designs for wearable sensors are increasingly important due to variable application scenarios and environmental disturbances. The high temperatures pose a significant challenge to the performance of sensing materials. The reasonable anisotropic structure in materials is recognized as a promising approach to address this challenge. Precise control of the orientation of the material remains difficult, owing to the entropy effect. In this work, a tunable anisotropic triboelectric aerogel via an in situ coupled magnetic alignment and protonation reduction strategy is demonstrated. The designed orientation with a fitting degree of 98% can effectively suppress electron thermionic emission, which enables the surface charge density to reach 75 µC m−2 at 300 °C. Such a perfect coordination between self‐powered sensing and thermostability innovates multifunctional wearable sensing design at high temperatures, allowing aramid‐based aerogel to be a candidate for advanced sensing materials for applications in the military and aerospace fields.

Cross-domain open-set fault diagnosis using prototype learning and extreme value theory

The open-set cross-domain fault diagnosis scenario is similar to the practical industrial application. It assumes that a distribution discrepancy between the training (source) and testing (target) datasets exists, and the testing label set contains the training label set. The developed model transfers knowledge from the source domain to the share-class faults of the target domain and simultaneously rejects unknown fault samples. Previous studies have two shortcomings. First, feature alignment strategies only consider the alignment of the source and target-known distributions but ignore the segregation of the source and target-unknown distributions, resulting in unreasonable boundaries between known and unknown classes. Secondly, the similarity calculation for feature alignment relies on overconfident network predictions, resulting in biased similarities. To address these problems, this study employs two domain discriminators to clarify the boundaries between the known and unknown classes; one for aligning the source and target-known, and the other for segregating the source and target-unknown. In addition, an unbiased similarity calculation strategy using prototype learning and extreme value theory was proposed to quantify the contribution of each target sample to the domain adversarial training loss. Extensive experiments were conducted on a public bearing dataset and a laboratory gas-insulated switchgear vibration signals. The results outperformed those of other reported approaches, indicating that the proposed method is a promising model for practical industrial applications.

High-output moisture-enabled electricity generator for fully self-powered wearable physical and biochemical monitoring

Spontaneous conversion of environmental moisture energy to high-output electricity can help meet the massive electricity demand of wearable electronics. However, low power density and inefficient energy harvesting are the main challenges to power electronics currently. Herein, we developed a flexible moisture-enabled electricity generation (MEG) membrane with high output power. The asymmetrical structure of the moisture-enabled flexible units is composed of a bilayer of polyanion and polycation. The single device produced a sustained voltage of 0.8 V and can be customized by an aligned stacking strategy for scale integration on demand. Enhanced by increasing the diffusion speed of carriers with the metal-air reaction, the output power reached 286.5 μW/cm2, which is enough for long-term wearable health monitoring. Meanwhile, to solve the power supply challenge of the wearables, a fully self-powered system with an energy harvester, a power management module, and a wireless and wearable sensing module was developed. Biochemical indicators and body motion were detected and monitored by the sensor array, demonstrating the promising application in wearable health management, including harvesting energy from giant moisture and powering wearable electronics.

A comparative study on point cloud down-sampling strategies for deep learning-based crop organ segmentation

The 3D crop data obtained during cultivation is of great significance to screening excellent varieties in modern breeding and improvement on crop yield. With the rapid development of deep learning, researchers have been making innovations in aspects of both data preparation and deep network design for segmenting plant organs from 3D data. Training of the deep learning network requires the input point cloud to have a fixed scale, which means all point clouds in the batch should have similar scale and contain the same number of points. A good down-sampling strategy can reduce the impact of noise and meanwhile preserve the most important 3D spatial structures. As far as we know, this work is the first comprehensive study of the relationship between multiple down-sampling strategies and the performances of popular networks for plant point clouds. Five down-sampling strategies (including FPS, RS, UVS, VFPS, and 3DEPS) are cross evaluated on five different segmentation networks (including PointNet + + , DGCNN, PlantNet, ASIS, and PSegNet). The overall experimental results show that currently there is no strict golden rule on fixing down-sampling strategy for a specific mainstream crop deep learning network, and the optimal down-sampling strategy may vary on different networks. However, some general experience for choosing an appropriate sampling method for a specific network can still be summarized from the qualitative and quantitative experiments. First, 3DEPS and UVS are easy to generate better results on semantic segmentation networks. Second, the voxel-based down-sampling strategies may be more suitable for complex dual-function networks. Third, at 4096-point resolution, 3DEPS usually has only a small margin compared with the best down-sampling strategy at most cases, which means 3DEPS may be the most stable strategy across all compared. This study not only helps to further improve the accuracy of point cloud deep learning networks for crop organ segmentation, but also gives clue to the alignment of down-sampling strategies and a specific network.

HyRSM++: Hybrid relation guided temporal set matching for few-shot action recognition

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. However, existing attempts still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmarks demonstrate that our method consistently outperforms existing methods and achieves state-of-the-art performance under various few-shot settings. The source code is available at https://github.com/alibaba-mmai-research/HyRSMPlusPlus.