Analysis Of Strategies Research Articles

Понимание подростками электронного учебного гипертекста (в сравнении с печатным гипертекстом)

<p>The article presents the results of a study comparing reading and understanding of two identical versions of educational hypertext – electronic and printed. The relevance of the work is determined by the important role of hypertext structures in the system of modern digital technologies, the need to teach schoolchildren effective strategies for navigation, search and analysis of hypertext information. The study involved 108 students in grades 7-8 of secondary schools in Moscow and the Moscow region. A specially designed educational hypertext of humanitarian content was used as a stimulus material. A comparative analysis of electronic and printed hypertext was carried out according to the following parameters: understanding of the general content of the text, understanding of conceptual and factual information. It was found that students who worked with electronic hypertext coped with the diagnostic test in general and with understanding factual information in particular significantly better (p <0.05) than those who worked with the paper version. Conceptual information understanding was also more successful when reading from the screen, although the differences were insignificant (p>0.05). However, regardless of the reading format, most students demonstrated superficial comprehension: only 9.7% of them were able to fully correctly understand and formulate the main idea of ​​the text, and about half (50.5%) failed to complete the task. The data obtained allow us to conclude that, although working with hypertext is more successful in a digital environment, the prevalence of incomplete and inaccurate understanding indicates the need for special regular work in schools to teach students effective strategies for interacting with hypertext both from screens and from paper.</p>

"When I gave him the insulin injection, I felt the pain in my heart": Experiences of parents of young children with type 1 diabetes in an Arab country.

Type 1 diabetes mellitus in children is one of the most common chronic health conditions, requiring constant monitoring and care. Living with children who are diagnosed with chronic illness affects multiple aspects of parents' daily lives, including the physical, mental, and social aspects. Due to their child's diabetes, parents may experience many special difficulties with their daily responsibilities. This study describes parental experiences caring for a child with type 1 diabetes. The study was based on semi-structured interviews with 10 parents caring for a child with type 1 diabetes. We analyzed the interviews using Colaizzi's strategy of descriptive phenomenological data analysis in nursing research. Participants were described a profoundly challenging situation, and they were in need of support to help them handle a challenging life situation, especially during the first years of a child's diagnosis. The following themes emerged from the data: adapting to the new reality after diagnosis, keeping up with the child's treatment regimen, psychological, social, and financial burdens, and staying tuned for complications.

Analisis dan Rancangan di PT Honda

This article describes the analysis and development strategy at PT Astra Honda Motor, one of the leaders of the Indonesian two-wheel industry. This article uses SWOT analysis and Porter's Five Forces model to examine the strengths, weaknesses, opportunities and threats facing the company. PT Astra Honda Motor is known for its product quality and strong brand image, but faces challenges from competitors such as Yamaha. Strategic design includes product differentiation, design innovation, and price adjustments to maintain market share.

Strategic Public Relations: Illuminating Corporate Visibility of Higher Learning Institutions in Tanzania through Internal Influence, Internal Collaboration, Media Engagement and Stakeholder’s Confidence

In a current state where the international higher education system has put colleges and universities under pressure of acquiring resources, corporate visibility has become vital for their sustainability and survival. This study aims at exploring the role public relations strategies can play on corporate visibility, focusing on four key themes namely internal influence, internal collaboration, media engagement and stakeholder confidence. Using a qualitative research approach, data were collected from 26 participants through in-depth interviews and supplemented by the review of relevant institutional documents. The findings reveal that internal influence magnified by effective communication play essential role in shaping the corporate visibility of the institution among the public. Internal collaboration, particularly with the management, employees and students emerged as key analytical strategy for extending the institution’s reach and influence. Also findings reveal that media engagement was a powerful tool for amplifying the institution’s achievements and initiatives, while stakeholder confidence was found to be essential in sustaining long-term corporate visibility and credibility of the institution. This study contributes to the understanding of how public relations strategies can be effectively used to illuminate the corporate visibility of Higher Learning Institutions (HLIs) particularly in Tanzania. The insights gained provide practical implications for HLIs and public relations seeking to manage the complexities of reputation management in the education sector. The study further recommends future research to explore these themes in diverse institutional contexts.

Gwforge: a user-friendly package to generate gravitational-wave mock data

Abstract Next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary coalescence signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge, a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. gwforge allows users to seamlessly simulate data while abstracting away technical complexities, enabling more efficient testing and development of analysis pipelines. Additionally, the package’s data generation process is optimized using high-throughput systems like HTCondor, significantly speeding up the simulation of large populations of gravitational-wave events. We demonstrate the package’s capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation.

The gender and age perspectives of allostatic load

The role of chronic stress in the development of chronic diseases, especially multimorbidity, through the pathways of increasing allostatic load, and finally, allostatic overload (the state when a compensatory mechanism is likely to fail) is being emphasized. However, allostatic load is a dynamic measure that changes depending on sex, gender, age, level and type of stress, experience of a stressful situation, and coping behaviors. Many other factors such as race, ethnicity, working environment, lifestyle, and circadian rhythm of sleep are also important. The aim of this paper was to synthesize the available information on allostatic load differences, especially those connected to sex/gender and age, and to provide a model for the future study of allostatic load, with a focus on these differences. By carefully studying allostatic load factors, we realized that many studies do not take this allostatic load difference into account in the analysis methods. In this paper, we also support the idea of further research to develop new allostatic load analysis strategies that will include all knowledge about sex/gender differences and that will, in more detail, explain numerous changeable social and educational factors that are currently accepted as biological ones. Furthermore, specific allostatic load biomarkers are expressed differently in different age groups, indicating that the discrepancies cannot be attributed solely to sex/gender disparities. This kind of approach can be valuable, not only for better explaining the differences in the frequency and age of onset of chronic diseases and multimorbidity, but also for the potential planning and development of preventive actions based on the aforementioned sex/gender and age disaparities, in order to prevent the most frequent diseases and to establish specific biomarker cut-off values for each sex/gender and age group.

Can LLMs deeply detect complex malicious queries? A framework for jailbreaking via obfuscating intent

Abstract This paper delves into a possible security flaw in large language models (LLMs), particularly in their capacity to identify malicious intent within intricate or ambiguous inquiries. We have discovered that LLMs might overlook the malicious nature of highly veiled requests, even without alterations to the malevolent text in those queries, thus exposing a significant weakness in their content analysis systems. To be specific, we pinpoint and scrutinize two aspects of this vulnerability: (i) LLMs’ diminished capability to perceive maliciousness when parsing extremely obscured queries, and (ii) LLMs’ inability to discern malicious intent in queries that have been intentionally altered to increase their ambiguity by modifying the malevolent content itself. To illustrate and tackle this problem, we propose a theoretical framework and analytical strategy, and introduce a novel black-box jailbreak attack technique called IntentObfuscator. This technique exploits the identified vulnerability by concealing the genuine intentions behind user prompts, thereby compelling LLMs to inadvertently produce restricted content and circumvent their inherent content safety protocols. We elaborate on two specific applications within this framework: ”Obscure Intention” and ”Create Ambiguity,” which skillfully manipulate the complexity and ambiguity of queries to effectively dodge the detection of malicious intent. We empirically confirm the efficacy of the IntentObfuscator approach across various models, including ChatGPT-3.5, ChatGPT-4, Qwen, and Baichuan, achieving an average jailbreak success rate of 69.21%. Remarkably, our tests on ChatGPT-3.5, boasting 100 million weekly active users, yielded an impressive success rate of 83.65%. Additionally, we verify our approach across a range of sensitive content categories, including graphic violence, racism, sexism, political sensitivity, cybersecurity threats, and criminal techniques, further highlighting the considerable impact of our findings on refining ”Red Team” tactics against LLM content security frameworks.

Simple, accurate, and precise detection of total nitrogen in surface waters using a smartphone-based analytical method.

In this study, a simple, accurate, and precise analytical strategy was developed for the determination of total nitrogen (total dissolved nitrogen + particulate nitrogen) in surface waters using smartphone technology. The spectrophotometric method based on digestion with persulfate, commonly used in water analyses, was integrated with a digital image-based colorimetric detection system (DIC). To evaluate the accuracy of the total nitrogen-digital image-based colorimetric detection system (TN-DIC), results obtained from the Hach method and UV-VIS spectrophotometer were compared with the RGB values obtained from the smartphone imaging system. T-Test results indicated no statistically significant difference between the results obtained from the TN-DIC method and the standard methods. The linear range of the TN-DIC method was determined to be 0.25-5mg/L. The analytical quality indicators of the system were established as follows: limit of detection (LOD) 0.095ppm, limit of quantification (LOQ) 0.29ppm, %RSD 0.32, and R2 value 0.9982. Recovery percentages (99-112%) used to measure the analytical performance of the method were calculated through spike applications at concentrations of 1 and 2ppm in real water samples. Unlike traditional spectrophotometric methods, this smartphone-based approach is fast, simple, and cost-effective, requiring no complex equipment during analysis.

Digital outcome measures in Duchene muscular dystrophy: Lessons learnt from clinical trials

Duchenne muscular dystrophy is a severe neuromuscular disorder characterized by progressive muscle degeneration resulting from mutations in the dystrophin gene. Digital outcome measures offer a promising alternative to traditional outcome measures used in clinical trials. This review explores the development and application of digital outcome measures in Duchenne muscular dystrophy, emphasizing the feasibility, reliability, sensitivity, and validity of these measures. The stride velocity 95th centile has been validated as a robust endpoint and has been approved for use in clinical evaluation of drugs for the treatment of Duchenne muscular dystrophy by the European Medicines Agency. Although digital outcome measures have the potential to enhance the efficiency and accuracy of clinical trials, challenges such as limited sample sizes and patient compliance persist. The integration of artificial intelligence into the data analysis is in progress, but further validation is required before these analysis strategies can be incorporated into future clinical trial methodologies.

Theoretical Analysis and Optimization Strategies of Wavefront Errors and Chromatic Aberrations Induced by Optical Coatings

This research addresses the frequently overlooked wavefront errors and chromatic aberrations in optical coatings. It identifies intrinsic phase shifts within the coating system as the primary cause of these aberrations, rather than errors in surface form accuracy. This study investigates the impact of group delay (GD) on wavefront errors in optical coatings. The results reveal that GD contributes significantly to wavefront errors in two cases: when coatings exhibit thickness non-uniformity and when they operate over a wide range of incident angles. At a single incident angle, wavefront errors are only induced when both GD and thickness non-uniformity exist. In the case of a wide range of incident angles, even in the absence of thickness non-uniformity, GD still introduces wavefront errors. This study introduces novel optimization strategies for thin-film components through theoretical derivation, aiming to minimize wavefront errors by optimizing GD.

Asymmetric N-Glycosylation in the Tailpiece of Recombinant IgA1.

Here, we employed a variety of mass spectrometry (MS)-based approaches, both (glyco)peptide-centric and protein-centric, to resolve the complex glycoproteoform landscape of recombinant IgA1 produced in HEK293 cells. These key immunoglobulins harbor several N- and O-glycosylation sites, making them considerably more heterogeneous than their IgG counterparts. We provide quantitative data on the occupancy and glycan composition for each IgA1 glycosylation site. Combining all data, we revealed that IgA1 molecules consist of at least three distinct populations with varying N-glycosylation site occupancies at the C-terminal tailpiece, namely, one with both glycosylation sites occupied, another with both glycosylation sites unoccupied, and a third asymmetric population with one glycosylation site occupied and the other unoccupied, challenging the prevailing acceptance that IgA1 N-glycosylation is symmetrical. This finding is significant, given that the tailpiece is involved in interactions with the J-chain and the Polymeric Immunoglobulin Receptor, and in general as antibody glycosylation is a quality attribute that needs to be carefully monitored, as the presence and nature of these modifications can affect the antibody's efficacy, lifetime, stability, and binding and/or neutralizing capacities. Optimizing strategies to produce recombinant IgA1 requires efficient and specific quality control analytical strategies, as presented here, which is essential for therapeutic IgA1-based antibody development. We expect that the integrated MS-based strategy presented here may be beneficial to comprehensively characterize the glycoproteoform profiles of IgA1-based therapeutics, thereby improving their production and optimization processes and facilitating the pathway to bring more IgA1-based therapeutics into clinical applications.

The Role of Contractual Provisions in Managing Delays and Disputes: A Review

Construction projects are inherently complex and often experience delays, significantly impacting timelines and costs. Effective delay management is crucial, with contracts playing a key role in defining responsibilities and remedies, including provisions like Extension of Time (EoT) clauses and liquidated damages to mitigate financial risks. Delay analysis techniques, such as Time Impact Analysis (TIA) and the Relative Importance Index (RII), are critical for identifying delay causes, assigning accountability, and ensuring fair dispute resolution. However, challenges like unclear contract language, poor record-keeping, and inconsistent analysis methods often complicate the resolution of disputes, leading to prolonged conflicts. Establishing clear documentation requirements, fostering strong communication among stakeholders, and adopting standardized delay analysis methods are vital for minimizing disputes and facilitating efficient resolutions. Furthermore, advancements in technology, including AI and Building Information Modelling (BIM), present opportunities to improve delay management and enhance contractual effectiveness. A well-structured contractual framework, supported by robust delay analysis and dispute resolution strategies, is essential for minimizing conflicts and ensuring the successful delivery of construction projects.

The intrinsic problems in EQ damage prognosis: the experience from a systematic evaluation of L’Aquila databases and different levels of data availability

AbstractWithin the European TURNkey project, a knowledge-based exposure-modelling framework was developed, enabling the consideration of different levels of investigation and data availability. In particular, the proposed framework recognizes various levels and origins of uncertainties, as well as the completeness of a building stock catalogue. Despite substantial efforts, the main question still needs to be answered: How reliable can the developed tools and instruments be if they are not tested and validated by actual events? The L’Aquila 2009 earthquake has been the subject of several analytical strategies to enrich earthquake engineering knowledge. In this study, the information provided by the Italian Observed Damage Database is analyzed, explicitly focusing on the seismic sequence of the L’Aquila 2009 earthquake within the delimited area of the city’s historical center. A second dataset, where the European Macroseismic Scale (EMS-98) was used as a reference, is integrated into the study, and the results are compared. A methodology is implemented for a systematically evaluating the database based on the EMS-98. From the data analysis, a proposal is made to define a comparable EMS-98 building typology and to assign vulnerability classes considering optimistic, pessimistic and most likely criteria. The reliability of the sample is then explored using the knowledge-based exposure modelling framework established by the TURNkey Project. Accuracy is then evaluated through an empirical inspection of frontal (lateral) views available in Google Street View (2022). Images before and after the event are collected and compared with the available data. Intrinsic problems encountered during the process are then listed and discussed, particularly regarding the use of the database, the joint between the studied datasets, and the post-processing required to use the data for damage prognosis. This paper intends to demonstrate how reliable datasets for the building stock, including structural types and corresponding vulnerability classes, can be elaborated. Not least, exposure modelling has to transform the available data into a descriptive form that can be linked directly with the Fragility or Vulnerability Functions, expecting that these assignments are the best suited or representative ones. The data layers provided by the study enable the testing and training of exposure modelling techniques for the selected event and target region.

The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain

Abstract The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter which blurs rapid changes. However, the shape and timing of hemodynamic responses are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of fMRI signals, or the ability of fMRI to preserve fast information, could also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing affect the temporal specificity of fMRI. We used ultra-high field (7T) fMRI at high spatiotemporal resolution, studying the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity, and tested whether voxel timing was related to anatomical and vascular features. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxel’s position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05Hz to 0.20Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. While we attribute this variance primarily to hemodynamic effects, neuronal nonlinearities may also influence response timing. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

Axions in Andromeda: Searching for minicluster-neutron star encounters with the Green Bank Telescope

The QCD axion and axionlike particles are compelling candidates for galactic dark matter. Theoretically, axions can convert into photons in the presence of a strong external magnetic field, which means it is possible to search for them experimentally. One approach is to use radio telescopes with high-resolution spectrometers to look for axion-photon conversion in the magnetospheres of neutron stars. In this paper, we describe the results obtained using a novel approach where we used the Green Bank Telescope (GBT) to search for radio transients produced by collisions between neutron stars and dark matter clumps known as axion miniclusters. We used the versatile GBT astronomical spectrometer and the X-band receiver (8–10 GHz) to observe the core of Andromeda. Our measurements are sensitive to axions with masses between 33 and 42 μeV with Δma=3.8×10−4 μeV. This paper gives a description of the search method we developed, including observation and analysis strategies. Given our analysis algorithm choices and the instrument sensitivity (∼2 mJy in each spectral channel), we did not find any candidate signals greater than 5σ. We are currently implementing this search method in other spectral bands. Published by the American Physical Society 2024

Creative Strategy Analysis of SWJ Ambassador Tourism Village Promotion Content (Case Study of Creative Strategy Analysis of Tourism Village Promotion Content by Instagram Account @_nikenaf)

<p><em>Tourism in West Java is very diverse, ranging from massive tourist destinations and alternative tourism. One form of alternative tourism is a tourist village. The presence of a tourist village is an alternative for people who want to visit the beauty and uniqueness of a particular place. Various kinds of tourist villages in West Java have been widely exposed on social media through various kinds of creative content of ordinary social media users and content creators. The West Java Provincial Government formed a community tasked with reaching out to tourist villages and promoting them through social media, the community was named Smilling West Java Ambassador (SWJ Ambassador). The mechanism carried out by SWJ Ambassadors in carrying out promotional activities for tourist villages in West Java is by creating creative content in the form of videos or photos through each ambassador's Instagram. The conceptual framework used includes Creative Content Promotion Strategy in SOSTAC Analysis, social media, SWJ Ambassador program. This research uses a constructivist paradigm with a case study method on creative content promotion of tourist villages by the Instagram account @aqqiki_. Data collection techniques used in-depth interviews with informants and observation of tourist villages that became objects in the selected content. The results found that SWJ Ambassador @_nikenaf carried out a creative content strategy with the SOSTAC model (Situation, Objective, Strategy, Tactics, Actions, Control) and with limited capabilities and the absence of a creative team, sometimes the results and consistency of making promotional content were less massive.</em><em></em></p>

Strategic Insights into Bank Performance: CAMELS Analysis of Indian Banks

The banking sector is a cornerstone of the global economy, driving economic growth, financial sta-bility, and efficient resource allocation. Assessing the financial health and operational effectiveness of banks is crucial for maintaining the stability of the broader financial system. The CAMEL model, a well-established analytical framework, provides a comprehensive approach to evaluating key aspects of bank-ing performance, including Capital Adequacy, Asset Quality, Management Efficiency, Earnings Quality, and Liquidity. This model highlights areas for improvement and ensures a thorough assessment of a bank’s financial soundness. Extensive research has confirmed the model's applicability across various global contexts, including India, Nepal, Pakistan, Saudi Arabia, and Sri Lanka, consistently identifying asset quality, capital adequacy, and earnings as essential drivers of profitability. The model also reveals that private banks generally outperform public banks in most parameters. Moreover, the CAMEL model has proven to be a valuable tool in promoting financial inclusion, particularly through the analysis of Small Finance Banks, and its adaptability to emerging markets further underscores its relevance. Despite its broad applicability, gaps exist in the literature, particularly regarding the model’s adaptation to diverse regulatory environments and market dynamics. Addressing these gaps will enable more customized strategies for financial analysis and risk management, enhancing the resilience of the global banking system. This study aims to contribute to this discourse by exploring the nuanced use of the CAMEL model in assessing the financial performance of banks in varied contexts. The findings aim to offer ac-tionable insights for improving financial performance, managing risks, and fostering sustainable growth in the banking industry.

Inferring disease progressive stages in single-cell transcriptomics using a weakly-supervised deep learning approach

Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in human. However, individual cells in patient-derived tissues are in different pathological stages, and hence such cellular variability impedes subsequent differential gene expression analyses. To overcome such heterogeneity issue, we present a novel deep learning approach, scIDST, that infers disease progressive levels of individual cells with weak supervision framework. The inferred disease progressive cells displayed significant differential expression of disease-relevant genes, which could not be detected by comparative analysis between patients and healthy donors. In addition, we demonstrated that pretrained models by scIDST are applicable to multiple independent data resources, and advantageous to infer cells related to certain disease risks and comorbidities. Taken together, scIDST offers a new strategy of single-cell sequencing analysis to identify bona fide disease-associated molecular features.

Characterization of Guest DNA Transport and Adsorption within Host Porous Protein Crystals.

Nucleic acid transport through protein-based pores is a well-characterized phenomenon due in part to advancements in nanopore sequencing. A less studied area is nucleic acid transport through extended protein-based channels, where the additional surface area and increased contact time allow for the study of prolonged binding interactions. Porous protein crystals composed of "CJ", a putative polyisoprenoid-binding protein from Campylobacter jejuni, represent a favorable, highly ordered material for studying DNA transport and binding/unbinding along protein-based channels. These crystals adopt a hexagonal prism habit and contain a densely packed hexagonal array of 13 nm diameter axial nanopores that run from the top to the bottom of the crystal. After cross-linking, the crystals are easily manipulated for experimentation. An adsorption isotherm between host crystals and guest double-stranded 8 base pair DNA (8mer) revealed a high equilibrium adsorption constant of 206 ± 30 L/g. Fluorescence confocal microscopy tracked the loading of guest DNA into host crystals predominately along the major axial crystal nanopores. Four different computational models based on the finite volume (FV) method were assessed to model the transport process for guest 8mer and 15mer dsDNA loading into empty host crystals in terms of fundamental parameters, such as the intrapore diffusion constant. Fitting the models to the data revealed that the most basic FV model sufficed to describe the observed loading behavior, characterized by a single effective diffusion coefficient. Leveraging Fick's first law, we more directly fit a numerical range for the observed intrapore diffusion coefficient as a function of time, position within the crystal, and relative guest concentration. This new transport analysis strategy was applied to both out-of-equilibrium loading and fluorescence recovery after photobleaching (FRAP) experiments. The intrapore diffusion constants are comparable between 8mer and 15mer dsDNA and were found to be 2 orders of magnitude faster for DNA loading into empty crystals than that observed in FRAP experiments, which averaged (10 ± 4) × 10-11 cm2/s.

360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-View Geometric Consistency Perception.

Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results as the compression process often muddles the semantics between various planes. Besides, these data-driven approaches impose an urgent demand for massive data annotations, which are laborious and time-consuming. For the first problem, we propose an orthogonal plane disentanglement network (termed DOPNet) to distinguish ambiguous semantics. DOPNet consists of three modules that are integrated to deliver distortion-free, semantics-clean, and detail-sharp disentangled representations, which benefit the subsequent layout recovery. For the second problem, we present an unsupervised adaptation technique tailored for horizon-depth and ratio representations. Concretely, we introduce an optimization strategy for decision-level layout analysis and a 1D cost volume construction method for feature-level multi-view aggregation, both of which are designed to fully exploit the geometric consistency across multiple perspectives. The optimizer provides a reliable set of pseudo-labels for network training, while the 1D cost volume enriches each view with comprehensive scene information derived from other perspectives. Extensive experiments demonstrate that our solution outperforms other SoTA models on both monocular layout estimation and multi-view layout estimation tasks.