Alignment Capability Research Articles

Interactive dual-stream contrastive learning for radiology report generation

Radiology report generation automates diagnostic narrative synthesis from medical imaging data. Current report generation methods primarily employ knowledge graphs for image enhancement, neglecting the interpretability and guiding function of the knowledge graphs themselves. Additionally, few approaches leverage the stable modal alignment information from multimodal pre-trained models to facilitate the generation of radiology reports. We propose the Terms-Guided Radiology Report Generation (TGR), a simple and practical model for generating reports guided primarily by anatomical terms. Specifically, we utilize a dual-stream visual feature extraction module comprised of detail extraction module and a frozen multimodal pre-trained model to separately extract visual detail features and semantic features. Furthermore, a Visual Enhancement Module (VEM) is proposed to further enrich the visual features, thereby facilitating the generation of a list of anatomical terms. We integrate anatomical terms with image features and proceed to engage contrastive learning with frozen text embeddings, utilizing the stable feature space from these embeddings to boost modal alignment capabilities further. Our model incorporates the capability for manual input, enabling it to generate a list of organs for specifically focused abnormal areas or to produce more accurate single-sentence descriptions based on selected anatomical terms. Comprehensive experiments demonstrate the effectiveness of our method in report generation tasks, our TGR-S model reduces training parameters by 38.9% while performing comparably to current state-of-the-art models, and our TGR-B model exceeds the best baseline models across multiple metrics.

Gamma Spot Monitor at SLEGS beamline

The Gamma Spot Monitor(GSM) at SLEGS beamline is designed to provide a direct view of the gamma beam profile. The main part of the GSM is a LYSO crystal and an astronomical camera. A set of experimental tests on its performance were carried out. The results show that the magnification are 21.0% in X and 21.2% in Y direction, and the relative distortion is proved to be lower than 0.7%. The spatial resolution of the GSM evaluated by the Modulation Transfer Function(MTF) is better than 6.4 lp/mm. The contrast resolution is better than 14%. Beamline alignment is essential in beam modulation, and there are several ways for beamline alignment. In this paper, alignment capability of the GSM is demonstrated, also, the absorption coefficient distribution of a model composed of different materials is shown.

Unveiling knowledge ecosystem dimensions for MSMEs’ digital transformation, toward a location-based brokerage

This study explores how knowledge ecosystems (KEs) led by a knowledge broker (KB) can support the digital transformation of Micro-, Small and Medium-sized Enterprises (MSMEs). By employing an exploratory research design and action research methodology, the paper proposes and applies an analytical framework to investigate the characteristics of KEs and the knowledge managed by the KB as the innovation intermediary acting as leader of the KE The case study selected uncovers the dimensions of a regional KE guided by a digital innovation hub (DIH) supporting MSMEs towards the adoption of the “Digital Artisan platform” deriving from an Industry 4.0 project.The findings highlight the importance of trust, strategic alignment, and dynamic capabilities of KBs in enhancing MSMEs' digital journey and emphasize the location dimension of the regional KE as a means for providing tailored support. This research contributes to the theoretical and practical understanding of regional KEs enabling the digital transformation through KBs who are the innovation intermediaries covering the role of leaders of the ecosystem.

VLM-guided Explicit-Implicit Complementary novel class semantic learning for few-shot object detection

Few-shot object detection (FSOD) aims at learning a novel class object detector with abundant base class samples and a limited number of novel class samples. Some recent methods assume that base class images contain unlabeled novel class instances and mine these instances to address the data scarcity of novel classes. Despite achieving competitive results, these methods face two issues: First, mined instances are sampled around the provided few-shot instances, establishing biased data distributions to represent novel classes. Second, the rich novel class semantics contained in base class instances are largely ignored. Benefits from vision-language models (VLMs), we propose a VLM-guided Explicit-Implicit Complementary novel class semantic learning (VEIC) method for FSOD. VEIC incorporates a VLM-cooperated sample mining module that leverages the powerful semantic alignment capability of VLMs to mine representative unlabeled explicit novel instances. In addition, for implicit novel instances (ambiguous base instances contain novel class semantics), we employ a spatial-wise feature re-expression module to capture prominent novel class local patches. Then a dynamic class-transition label assign strategy is proposed to learn from implicit novel instances by utilizing deep network memorization, where implicit novel instances are regarded more as novel classes during the early training and later regarded more as base classes. Experimentally, learning novel class semantics from two complementary types of instances reaches the best performance. Furthermore, learning exclusively from implicit novel instances extends the pseudo-labeling method to a practical scenario where base class images do not contain unlabeled explicit novel instances.

Humanitarian supply chain resilience: does organizational flexibility matter?

PurposeThe COVID-19 epidemic caused significant disruptions to numerous supply chains. In order to enhance the resilience of supply chains, Collaboration (CO), Information Alignment (IA), and Big Data Analytics Capability (BDAC) have emerged as contemporary strategies within the humanitarian context. This study was conducted to explore the mechanism via which the effect of BDAC, IA and CO on Humanitarian Supply Chain Resilience (HSCR) in the humanitarian space could be optimized through Organizational Flexibility (OF).Design/methodology/approachA model of six hypotheses was developed based on the Organizational Information Processing Theory (OIPT). Data from 127 supply chain managers in humanitarian organizations were used to test the hypotheses. The analysis employed both descriptive and inferential statistics using SPSS version 26 and Smart-PLS version 3.FindingsThe study revealed that BDAC, IA, and CO individually influence supply chain resilience in the humanitarian setting while OF did not moderate the relationship between BDAC, IA, CO, and HSCR.Practical implicationsIt is essential that humanitarian stakeholders prioritize factors that could increase supply chain resilience by employing contemporary BDA technologies, effective information flow, and collaborative strategies to set up a robust humanitarian SC system that could help lessen the impact of disasters.Originality/valueThis presents interesting insights that advance theoretical debates on how CO, IA, and BDAC under varying levels of OF could influence SCR in the humanitarian context. The paper further offers some useful guidance to managers in relief organizations who desire to build resilient supply chains by leveraging BDAC, collaboration and information alignment. Finally, the paper may also provoke future humanitarian scholars to replicate the study using different approaches.

Doping PCBM with fullerene phosphinate derivatives enhances the interface energy alignment and synergistic passivation capability

Phenyl-C61-butyric acid methyl ester (PCBM) serves as a common electron transport layer (ETL) in inverted p-i-n structure perovskite solar cells (IPSCs), yet energy barriers and insufficient passivation at the PCBM-perovskite interface hinder device effectiveness and durability. In this study, we present a series of novel Fullerene Phenylacid Ester Derivatives (FPEDs: FPP, FTPP, FDPP) incorporated into PCBM. Our investigations illustrate that FPEDs effectively act to passivate the perovskite surface by forming robust interactions with uncoordinated Pb2+ ions via the phosphine oxide groups present in their molecular structures, thereby enhancing the stability of the devices. Moreover, these additives elevate the energy level of the lowest unoccupied molecular orbital (LUMO) of ETL, diminish the electron injection barrier, and enhance the efficiency of interlayer electron transport. Incorporating FPEDs enhances ETL coverage on the perovskite layer, reducing leakage current significantly. Notably, Devices with PCBM/FTPP achieved a peak PCE of 23.62% and showed superior stability, maintaining 96.8% of the initial PCE after 500 h, while control devices retained merely 80.7% over the same period.

Mergers and Acquisitions: Strategic Fit and Post-Merger Performance in Vietnam

Purpose: The aim of the study was to investigate the mergers and acquisitions: strategic fit and post-merger performance in Vietnam. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Strategic fit, encompassing alignment in organizational culture, business goals, and operational synergies, emerges as a key determinant of M&A success. However, challenges such as cultural integration issues, regulatory complexities, and managerial resistance can impede integration efforts and undermine anticipated synergies. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV), agency theory & transaction cost economics (TCE) may be used to anchor future studies on the mergers and acquisitions: strategic fit and post-merger performance in Vietnam. Encourage organizations to enhance due diligence processes by integrating comprehensive assessments of cultural compatibility, strategic alignment, and operational capabilities. Policymakers should streamline regulatory frameworks to facilitate cross-border M&A activities while ensuring compliance with local laws and regulations

Firm-Sponsored Online Communities: Building Alignment Capabilities for Participatory Governance

Firm-Sponsored Online Communities: Building Alignment Capabilities for Participatory Governance

Enhancing AAA capabilities in humanitarian supply chains through 4PL adoption

PurposeThis paper aims to explore the impact of fourth-party logistics (4PL) adoption on the agility, adaptability and alignment (AAA) capabilities within humanitarian supply chains (HSCs).Design/methodology/approachSemi-structured interviews with individuals from a large non-government organisation were combined with secondary data to assess the influence of 4PL adoption on AAA capabilities in HSCs.FindingsThe results indicate that HSCs exhibit some of the AAA antecedents but not all are fully realised. While 4PL positively affects the AAA capabilities of HSCs, its adoption faces challenges such as the funding environment, data security/confidentiality and alignment with humanitarian principles. The study suggests an AAA antecedent realignment, positioning alignment as a precursor to agility and adaptability. It also identifies three core antecedents in HSCs: flexibility, speed and environmental uncertainty.Practical implicationsThe study shows the positive impact 4PL adoption can have on the AAA capabilities of HSCs. The findings have practical relevance for those wishing to optimise HSC performance through 4PL adoption, by identifying the inhibiting factors to its adoption as well as strategies to address them.Originality/valueThis research empirically explores 4PL’s impact on AAA capabilities in HSCs, highlighting the facilitating and hindering factors of 4PL adoption in this environment as well as endorsing a realignment of AAA antecedents. It also contributes to the growing research on SC operations in volatile settings.

Highly area-selective atomic layer deposition of device-quality Hf1-xZrxO2 thin films through catalytic local activation

Area-selective atomic layer deposition (AS-ALD) has garnered significant attention as a promising bottom-up patterning process for sub-10 nm scale technology, offering unmatched atomic-scale precision and pattern alignment capabilities in 3D nanofabrication. In this study, we present a groundbreaking methodology for achieving highly selective deposition of Hf1-xZrxO2 (HZO) thin films through catalytic local activation on noble metal surfaces (Ru and Pt) and TiN surfaces, without the need for surface inhibitory materials. To achieve inherent selectivity on metal surfaces, we employ O2 gas as a mildly oxidizing reactant and utilize cyclopentadienyl-tris(dimethylamido)-hafnium(zirconium) [Hf(Zr)Cp(NMe2)3] precursors, which require strong oxidizing agents, for HZO film formation. By catalytically dissociating O2 molecules, we successfully achieved area-selective deposition of HZO films greater than ∼7 nm on both blanket Ru versus Si substrates and Pt/Si-patterned substrates. Furthermore, we demonstrate the selective deposition of antiferroelectric HZO thin films with high dielectric constants of 34 and 31 on Ru and TiN substrates, respectively, using the inherent AS-ALD method combined with post-ozone treatment. Importantly, this catalytic local activation approach for achieving inherent deposition selectivity expands the potential utility of 3D bottom-up nanopatterning processes in next-generation nanoelectronic applications.

Building digital technology and innovative lean management capabilities for enhancing operational performance: an empirical study

The study examines the direct effect of digital technology capability on innovative lean management capability and the indirect effect on operational performance. We considered continuous learning as a moderator, such that continuous learning could influence the relationships (i.e. digital technology capability and innovative lean management capability). We considered the overall satisfaction of employees as a moderator and empirically tested the relationships. The theoretical model was conceptualised by keeping the dynamic capability view in mind. The samples for the online survey were selected from food supply chain (FSC) firms. Hypothesis testing was performed and findings indicate that lower-order capabilities, such as adaptability, agility, and alignment capabilities, play a critical role in developing dynamic capability, that is, digital technology capability, which helps in achieving superior operational performance. However, innovative lean management capability plays a partial mediation role between digital technology capability and operational performance. A moderating role for continuous learning and overall satisfaction of employees was also supported. FSC firms should consider these factors to enhance operational performance in a dynamic business environment.

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method’s effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities.

Bridging the Cross-Modality Semantic Gap in Visual Question Answering.

The objective of visual question answering (VQA) is to adequately comprehend a question and identify relevant contents in an image that can provide an answer. Existing approaches in VQA often combine visual and question features directly to create a unified cross-modality representation for answer inference. However, this kind of approach fails to bridge the semantic gap between visual and text modalities, resulting in a lack of alignment in cross-modality semantics and the inability to match key visual content accurately. In this article, we propose a model called the caption bridge-based cross-modality alignment and contrastive learning model (CBAC) to address the issue. The CBAC model aims to reduce the semantic gap between different modalities. It consists of a caption-based cross-modality alignment module and a visual-caption (V-C) contrastive learning module. By utilizing an auxiliary caption that shares the same modality as the question and has closer semantic associations with the visual, we are able to effectively reduce the semantic gap by separately matching the caption with both the question and the visual to generate pre-alignment features for each, which are then used in the subsequent fusion process. We also leverage the fact that V-C pairs exhibit stronger semantic connections compared to question-visual (Q-V) pairs to employ a contrastive learning mechanism on visual and caption pairs to further enhance the semantic alignment capabilities of single-modality encoders. Extensive experiments conducted on three benchmark datasets demonstrate that the proposed model outperforms previous state-of-the-art VQA models. Additionally, ablation experiments confirm the effectiveness of each module in our model. Furthermore, we conduct a qualitative analysis by visualizing the attention matrices to assess the reasoning reliability of the proposed model.

Alignment Efficiency of Two Archwires in Adolescents with Moderate to Severe Crowding

There is no consensus in clinical orthodontics on which aligning arch wire should be used at the initial stage of treatment. This randomized clinical trial evaluated the efficiency of multistranded Nitinol (Supercable) aligning arch-wire versus a standard arch-wire (Nitinol) in the first stage of orthodontic treatment in adolescents with severe crowding. Forty patients ages 12 to 17 with an Irregularity Index as proposed by Little between 5 to 17 mm were selected. Vinyl polysiloxane impressions (VPS) were taken before treatment (T1) at 6 weeks (T2) and 12 weeks (T3). Roth prescription .022 x .028 Edgewise brackets were bonded using light cure adhesive to the six anterior teeth of each dental arch and bands were placed on all first molars. Subjects were paired by gender and Irregularity Index into two groups of 20 each and assigned either to the control group (Nitinol) or the experimental archwire (Supercable). The predictor variables in this study were the type of archwire, type of dental arch and the time intervals. The outcome variable was the Irregularity Index score. The Irregularity Index was measured using 3D digital study models (e-models) and GeoDigm software from the VPS impressions taken at T1, T2 and T3. The data was analyzed with both a Two and Three-way Analysis of Variance (p≤0.05) to identify differences in the alignment of the maxillary and mandibular teeth and to determine differences between the two archwires. The Two and Three way ANOVA showed that there was a statistically significant difference (p≤0.05) in the alignment capabilities between the maxilla and the mandible. However, there was no evidence of a statistically significant difference (p=0.301) between NiTi and Supercable archwires at any time interval.

Cross Attention Transformer-Based Domain Adaptation: A Novel Method for Fault Diagnosis of Rotating Machinery with High Generalizability and Alignment Capability

Cross Attention Transformer-Based Domain Adaptation: A Novel Method for Fault Diagnosis of Rotating Machinery with High Generalizability and Alignment Capability

Conceptual Framework of the influence of strategic alignment capability (SAC) and VUCA model as moderator in navigation leadership

Purpose: This study intends to investigate the influence of strategic alignment capability (SAC) and volatile, uncertain, complex, and ambiguous (VUCA) as moderator on navigation leadership in private hospitals of healthcare sector in Pakistan. Utilizing ventakraman’s model of strategic alignment capability and volatility-ambiguity theory by Warren Bennis and Burt Nanus, the research hopes to identify the impact of SAC model and VUCA moderator on navigation leadership in healthcare sector in Pakistan. 
 Design/Methodology/Approach: A total of 400 healthcare executives from the healthcare industry will participate in this study by completing an online self-filled questionnaire. The collected data will undergo various statistical analyses, including normality tests, reliability assessments, correlation analyses, and descriptive examinations. These analyses shall be employed to determine the connection between independent variables (strategic alignment capability model) and the dependent variable of navigation leadership along with volatile, uncertain, complex, and ambiguous context as moderator variable.
 Findings: The research paper serves as a conceptual framework of influence of strategic alignment capability and volatile, uncertain, complex, and ambiguous context as moderator on navigation leadership. Healthcare leaders have the potential to significantly enhance their productivity, making valuable contributions to their respective fields in the healthcare profession. The study underlines the significance of addressing these sensitive elements of healthcare sector to enhance the performance in the Pakistan healthcare sector.
 Practical Implications: This study offers valuable insights for healthcare leadership within the developing countries like Pakistan healthcare industry. By recognizing and fostering influence of strategic alignment capability and vuca moderator on navigation leadership in healthcare sector can create an environment where healthcare leaders are more inclined to increase their productivity and positively impact overall healthcare sector success.
 Originality/Value: The originality of this study lies in its application of venkatraman’s model of strategic alignment capability and volatility-ambiguity theory by Warren Bennis and Burt Nanus within the specific context of the Pakistan healthcare industry. By identifying the unique factors influencing strategic alignment capability model in navigation leadership along with vuca moderator in to evaluate and enhance the performance in this industry, this research contributes to a deeper understanding of the relationship between strategic alignment capability and navigation leadership moderating vuca context.

Flexible experimental platform for dispersion-free temporal characterization of ultrashort pulses.

The precise temporal characterization of laser pulses is crucial for ultrashort applications in biology, chemistry, and physics. Especially in femto- and attosecond science, diverse laser pulse sources in different spectral regimes from the visible to the infrared as well as pulse durations ranging from picoseconds to few femtoseconds are employed. In this article, we present a versatile temporal-characterization apparatus that can access these different temporal and spectral regions in a dispersion-free manner and without phase-matching constraints. The design combines transient-grating and surface third-harmonic-generation frequency-resolved optical gating in one device with optimized alignment capabilities based on a noncollinear geometry.

Cross-modal incongruity aligning and collaborating for multi-modal sarcasm detection

Sarcasm embodies a linguistic phenomenon that highlights a significant incongruity between the literal meanings of words and intended attitudes. With the proliferation of image–text content on social media, the task of multi-modal sarcasm detection (MSD) has gained considerable attention recently. Tremendous progress have been made in developing better MSD models, primarily relying on a straightforward extract-then-fuse paradigm. However, such a setting encounters two potential challenges. First, the utilization of separately pre-trained unimodal models for extracting visual and textual features frequently lacks the fundamental alignment capabilities required for effective multimodal data integration. Second, the detrimental modality gaps between vision and language make it challenging to comprehensively integrate multi-modal information solely via diverse cross-modal fusion techniques. Consequently, this poses a prominent challenge in further capturing cross-modal incongruity and improving the effectiveness of MSD. In this paper, we propose a Multi-modal Mutual Learning (MuMu) network to tackle these issues. Specifically, we initialize the MuMu network with image and text encoders from the large-scale Contrastive Language-Image Pretraining model to enhance the underlying image–text correspondence. Moreover, to improve the capability of capturing cross-modal inconsistency during the fusion process, we design an align-fuse-collaborate mechanism to align disparate modalities before fusion and enhance the collaborative modeling ability between the two modalities with mutual learning after fusion. The proposed MuMu achieves new state-of-the-art results on a public dataset, demonstrating a substantial improvement of approximately 3% to 9% in terms of accuracy, micro-F1, and macro-F1 scores.

Capability and feasibility of the Global Alignment of Immunisation Safety Assessment in pregnancy criteria for the assessment of pregnancy and birth outcomes in Kinshasa, Democratic Republic of the Congo: a prospective cohort study

IntroductionThere is an urgent need to investigate the capabilities of active surveillance in strengthening the development of pharmacovigilance (PV) systems in low-resource settings. Here, we assess the capability and feasibility...

A Conceptual Framework Of The Influence Of Strategic Alignment Capability (SAC) And VUCA Model As Moderator In Navigation Leadership In Private Hospitals In Healthcare Industry, Pakistan

This study intends to investigate the influence of strategic alignment capability (SAC) and volatile, uncertain, complex, and ambiguous (VUCA) as moderator on navigation leadership in private hospitals of healthcare sector in Pakistan. Utilizing ventakraman’s model of strategic alignment capability and volatility-ambiguity theory by Warren Bennis and Burt Nanus, the research hopes to identify the impact of SAC model and VUCA moderator on navigation leadership in healthcare sector in Pakistan. A total of 400 healthcare executives from the healthcare industry will participate in this study by completing an online self-filled questionnaire. The collected data will undergo various statistical analyses, including normality tests, reliability assessments, correlation analyses, and descriptive examinations. These analyses shall be employed to determine the connection between independent variables (strategic alignment capability model) and the dependent variable of navigation leadership along with volatile, uncertain, complex, and ambiguous context as moderator variable.The research paper serves as a conceptual framework of influence of strategic alignment capability and volatile, uncertain, complex, and ambiguous context as moderator on navigation leadership. Healthcare leaders have the potential to significantly enhance their productivity, making valuable contributions to their respective fields in the healthcare profession. The study underlines the significance of addressing these sensitive elements of healthcare sector to enhance the performance in the Pakistan healthcare sector. This study offers valuable insights for healthcare leadership within the developing countries like Pakistan healthcare industry. By recognizing and fostering influence of strategic alignment capability and vuca moderator on navigation leadership in healthcare sector can create an environment where healthcare leaders are more inclined to increase their productivity and positively impact overall healthcare sector success.The originality of this study lies in its application of venkatraman’s model of strategic alignment capability and volatility-ambiguity theory by Warren Bennis and Burt Nanus within the specific context of the Pakistan healthcare industry. By identifying the unique factors influencing strategic alignment capability model in navigation leadership along with vuca moderator in to evaluate and enhance the performance in this industry, this research contributes to a deeper understanding of the relationship between strategic alignment capability and navigation leadership moderating vuca context.