Structural Constraints Research Articles

A quantitative evaluation of online translators using Hindi web queries

Understanding machine translation (MT) quality is becoming more and more important as MT usage continues to rise in the translation industry. The acceptance of MT output based on their performance and, ultimately, how acceptable the translators actually are, have received relatively less attention so far. MT plays a vital role in CLIR systems and their retrieval efficiency is directly proportional to the translation accuracy of the queries. The varied meanings of words, sentences carrying multiple interpretations, and differing grammatical structures across languages contribute to the complexity of the MT task. The lack of structural constraints and the presence of ambiguity further compound the complications especially in case of web queries. The objective of this work is to assess the accuracy of free online translators in translating Hindi web queries. The accuracy of the translators has been evaluated on various metrics, i.e., BLEU, NIST, METEOR, hLepor, CHRF and GLEU. Our findings indicate that the translation accuracy for longer queries is higher than the shorter ones. Overall Google translator’s performance has been found the best while Systran performs the worst with 42.06% performance difference between the two. The present work intends to help researchers in further evaluating and analyzing the MT systems specially in context of web query translation, ultimately leading to improved translation quality and retrieval accuracy in CLIR.

Distilling Structural Knowledge for Platform-Aware Semantic Segmentation

Knowledge Distillation (KD) aims to distill the dark knowledge of a high-powered teacher network into a student network, which can improve the capacity of student network and has been successfully applied to semantic segmentation. However, the standard knowledge distillation approaches merely represent the supervisory signal of teacher network as the dark knowledge, while ignoring the impact of network architecture during distillation. In this paper, we found that the student network with a more similar architecture against the teacher network obtains more performance gain from distillation. Therefore, a more generalized paradigm for knowledge distillation is to distill both the soft-label and the structure of the teacher network. We propose a novel Structural Distillation (SD) method which introduces the structural similarity constraints into vanilla knowledge distillation. We leverage Neural Architecture Search technique to search optimal student structure for semantic segmentation from a well-designed search space, which mimics the given teacher both in terms of soft-label and network structure. Experiment results make clear that our proposed method outperforms both the NAS with conventional Knowledge Distillation and human-designed methods, and achieves sota performance on the Cityscapes dataset under various platform-aware latency constraints. Furthermore, the best architecture discovered on Cityscapes also transfers well to the PASCAL VOC2012 dataset.

Joint Inversion of Gravity and Magnetic Data based on the Modified Structural Similarity Index for the Structural Consistency Constraint

Joint inversion is a crucial approach to mitigate the non-uniqueness problem in geophysical inversion. Nevertheless, existing joint inversion methods fall short of meeting the stringent requirements of high-precision exploration, necessitating the development of new techniques. In this paper, we introduce the Structural Similarity Index (SSIM) as a novel structural consistency constraint for the joint inversion of gravity and magnetic data. Compared with the results of cross-gradient inversion, our method demonstrates outstanding performance and stability. SSIM inversion not only introduces a new class of joint inversion with structural constraints but also enhances the consistency of inversion results in the distribution of physical attribute values. The structural constraints of SSIM inversion are more comprehensive and robust, significantly improving the reliability of the inversion. Both synthetic and real data applications demonstrate that the proposed method can effectively handle both synthetic and real data, yielding outstanding results.

The significance of conceptualism in McDowell

To explain perceptual justification, McDowell proposes so-called “conceptualism,” the view that the content of experience is all conceptual. Tony Cheng, in his book, John McDowell on Worldly Subjectivity (2021), suggests that McDowell can do without conceptualism. To support his suggestion, Cheng makes several contentions against McDowell’s thesis of the co-extensiveness of conceptuality and rationality. In this commentary, I focus on two most crucial contentions Cheng makes: (i) conceptualism is an extra commitment for explaining perceptual justification and (ii) it can be replaced by a suitable structural constraint on non-conceptual content. First, I clarify McDowell’s co-extensiveness thesis and his conception of the conceptual. Then, based on my clarifications, I defend conceptualism against the two contentions.

Generalized zero-shot learning for classifying unseen wafer map patterns

A data-driven approach for classifying defect patterns in wafer maps is crucial for automating the quality assurance process during semiconductor fabrication. Existing works have proposed machine learning-based methods to accomplish this important problem. However, many of these approaches assume model training from a supervised learning perspective, in which a substantial number of annotated wafer maps, each with its actual defect pattern, need to be available. Due to the cost and time required to generate such an annotated dataset, recently a few works started to explore the problem of identifying previously unseen defect patterns. However, the methods proposed in those works are able to only detect the presence of unseen defect patterns without identifying what those defect patterns are. To address this issue, this paper introduces a generalized zero-shot learning (GZSL)-based framework capable of classifying and identifying both seen and unseen defect patterns. It consists of a visual feature extraction phase based on semi-supervised contrastive learning (CL), a clustering phase using distance-based initialization, and an alignment phase based on the Wasserstein-Distance-based Visual Structure Constraint method. Our framework further reduces the need for expensive annotated datasets previously required to train supervised learning-based models, thereby improving the practicality of the data-driven approach for wafer map pattern classification. Furthermore, our framework introduces an effective way of incorporating additional semantic information in wafer map pattern classification that traditionally relied on training models solely from wafer maps. To the best of the authors’ knowledge, our work is the first work to adopt GZSL in wafer map pattern classification.

The politics of agro-industrial greening: policy processes, market institutions and local power relations in the sugarcane sector of French overseas departments

ABSTRACT In France, the agenda of agro-industrial greening has resulted in limited progress towards environmentally sustainable agriculture, while the prevailing productivist model remains dominant across agro-industrial systems. This article deploys an interdisciplinary analytical framework focused on two key explanatory variables – market and policy incentives – to explain the emergence or absence of agro-industrial greening initiatives. Additionally, it introduces a crucial intervening variable, power relations along the value chain, to understand the success or failure of such initiatives. Applying this framework empirically, a comparative analysis of greening dynamics within the sugarcane agro-industry across three French overseas departments is conducted. Following the liberalization of the European sugar market, private greening initiatives emerged in some of these territories but not others. I first demonstrate that policy processes did not play a determining role in instigating greening initiatives. Rather, local representations of market opportunities proved decisive. However, the ultimate outcomes of these initiatives were intricately tied to local power dynamics within territorially embedded agro-industrial value chains. Therefore, territorialized political economies not only impose structural constraints on greening initiatives, but also offer localized opportunities for eco-innovation through shifting discourses, representations and practices.

Achieving a Double Advantage Through Design of TiO2/C Interlaced Coating for Si Anode

AbstractMultifunctional coating strategy shows promising prospective in advancing Si‐based anodes towards practical applications in Li‐ion batteries. Dual coating of amorphous carbon and TiO2 shows demonstrable advantages owing to high elasticity of amorphous carbon and mechanical toughness of TiO2. However, for past design of Si@C@TiO2 composite electrode, wherein C and TiO2 are configured layer‐by‐layer, a long‐standing problem exists as that a thin TiO2 coating is insufficient to stabilize the electrode's architecture while a thick one prevents the core active material of Si far from fully electrochemically utilization due to the too strong structural constraint effect. Herein, it presents that a facial heat treatment of Si@C@TiO2 with thick enough TiO2 can readily avert the problem. Such a strategy promotes the capacity utilization rate from 39% to 61% for the initial cycle and from 43% to 85% after 200 cycles. Model structure of C/TiO2 multilayer films is employed to reveal the role of the heat treatment. It finds that the heat treatment can transform a layer‐by‐layer structure of C@TiO2 into an interlaced structure of C/TiO2 which exhibits a dual advantage in withstanding mechanical strain and simultaneously promoting Li‐ion storage and electron/Li‐ion transport.

Using social media in disaster management: The perceptions of emergency responders versus the public

AbstractThis paper investigates the perceptions of professionals (emergency organizations: authorities and first responders) regarding the usage of social media versus the perceptions of the public regarding their expectations concerning the use of social media in communicating emergencies and disasters. It provides a novel perspective on the alignment between perceptions and expectations in disaster social media usage, delving into the structural constraints and opportunities of current practices. The research incorporates qualitative interviews with emergency professionals, a quantitative survey of the general population, and content analysis of social media interactions across seven countries (Israel, Italy, Spain, France, Sweden, Norway, and Romania), aiming to discern differing viewpoints on social media's role in crises. It underscores the variations and commonalities between authorities/first responders and the public's perspectives, revealing differences in acceptance and concerns regarding social media use among these stakeholders. The analysis identified three primary themes: cognitive needs, integrative needs, and communication flow, each with specific, interconnected subthemes. These insights emphasize the need for a better understanding of these diverse perspectives to enhance disaster communication. The paper suggests that effectively utilizing social media and crowdsourcing technologies can help bridge the gap between emergency services and the public, fostering more disaster‐resilient societies.

Exploring the Role of Healthcare Personnel in Designing Tuberculosis Infection Prevention and Control Measures in Healthcare Settings: A Scoping Review.

Background: Healthcare personnel (HCP) in high TB-burdened countries continue to be at high risk of occupational TB due to inadequate implementation of Tuberculosis Infection Prevention and Control (TB-IPC) measures and a lack of understanding of the context and relevance to local settings. Such transmission in the healthcare workplace has prompted the development and dissemination of numerous guidelines for strengthening TB-IPC for use in settings globally. However, a lack of involvement of healthcare personnel in the conceptualisation and development of guidelines and programmes seeking to improve TB-IPC in high-burden countries generally has been observed. Objectives: The aim of this review was to explore the inclusion of HCP in decision-making when designing the TB-IPC guidelines, in healthcare settings. Methods: A scoping review methodology was selected for this study to gain insight into the relevant research evidence, identifying and mapping key elements in the TB-IPC measures in relation to HCP as implementors. Results: Studies in this review refer to factors related to HCP's knowledge of TB-IPC, perception regarding occupational risks and behaviours, their role against a background of structural resource constraints, and guidelines' adherence. They report several challenges in TB-IPC implementation and adherence, particularly eliciting recommendations from HCP for improved TB-IPC practices. Conclusions: This review highlights a lack of participation in decision-making by the implementers of the policies and guidelines, yet adherence to TB-IPC measures is anticipated. Future research needs to focus more on consultations with users to understand the preferences from both within individual healthcare facilities and the communities. There is an urgent need for research on the participation of the implementers in the decision-making when developing TB-IPC policies and guidelines.

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment

Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model’s superior performance on segmentation and clinical function assessment.

Entangled in complexity: An ethnographic study of organizational adaptability and safe care transitions for patients with complex care needs.

The aim of this study was to visualize vulnerabilities and explore the dynamics of inter-professional collaboration and organizational adaptability in the context of care transitions for patients with complex care needs. An ethnographic design using multiple convergent data collection techniques. Data collection involved document review, participant observations and interviews with healthcare and social care professionals (HSCPs). Narrative analysis was employed to construct two illustrative patient scenarios, which were then examined using the Functional Resonance Analysis Method (FRAM). Thematic analysis was subsequently applied to synthesize the findings. Inconsistencies in timing and precision during care transitions pose risks for patients with complex care needs as they force healthcare systems to prioritize structural constraints over individualized care, especially during unforeseen events outside regular hours. Such systemic inflexibility can compromise patient safety, increase the workload for HSCPs and strain resources. Organizational adaptability is crucial to managing the inherent variability of patient needs. Our proposed 'safe care transition pathway' addresses these issues, providing proactive strategies such as sharing knowledge and increasing patient participation, and strengthening the capacity of professionals to meet dynamic care needs, promoting safer care transitions. To promote patient safety in care transitions, strategies must go beyond inter-professional collaboration, incorporating adaptability and flexible resource planning. The implementation of standardized safe care transition pathways, coupled with the active participation of patients and families, is crucial. These measures aim to create a resilient, person-centred approach that may effectively manage the complexities in care transitions. The recommendations of this study span the spectrum from policy-level changes aimed at strategic resource allocation and fostering inter-professional collaboration to practical measures like effective communication, information technology integration, patient participation and family involvement. Together, the recommendations offer a holistic approach to enhance care transitions and, ultimately, patient outcomes. Findings are reported per the Consolidated Criteria for Reporting Qualitative research (COREQ). No patient or public contribution.

Robust image segmentation and bias field correction model based on image structural prior constraint

In this paper, we propose an advanced variational model for image segmentation and bias correction. In contrast to the majority of existing level set segmentation models that only consider illumination bias fields, we additionally consider the impact of image reflectance on segmentation accuracy. Our method is capable of effectively segmenting images with blurry edge structures affected by non-uniform illumination. In order to enhance segmentation efficiency, we directly segment the underlying structures of the images, construct spatial prior and apply adaptive regularization constraints on the structural component. Therefore, in the process of segmentation, the proposed algorithm can accurately identify object boundaries without being affected by the environment. Besides, the GL operator is applied to enhance the robustness of the model against noise. Furthermore, we use the alternating direction method of multipliers and the operator splitting algorithm for numerical solution. The experimental results obtained from various sorts of images illustrate that our model outperforms many leading-edge level set models with regard to robustness, corrected results and accuracy.

Application of evolutionary algorithms to design economic flat slab buildings based on the intended function

Numerous studies revealed optimization techniques' applicability in minimizing the costs of reinforced concrete buildings. However, the existing literature has narrowly focused on optimizing buildings with a single function, such as residential or office buildings, hindering the generalization of the results. This paper aims to bridge the gap between optimization and structural engineering by obtaining the minimum-cost design of flat slab buildings with different intended functions. In this context, the optimal designs of 120 alternatives were obtained, considering various spans (4–8 m), live loads (2–10 kPa), and concrete compressive strength (25–40 MPa). The optimization was executed using the evolutionary algorithm provided in Microsoft Excel’s Solver tool. The optimization model permits the utilization of drop panels to resist punching stresses developed from the slab-column interaction. The objective function is the cost of materials and labor involved in constructing floors and columns. The decision variables are the floor dimensions and column configurations in dimensions and reinforcement. The structural constraints were applied per the Egyptian design code (ECP203-2020). Eventually, guidelines were developed to help the designers choose the economic floor system and quantities of materials based on the building's intended function.

Niemieckie i polskie nazwy szkół wyższych w ujęciu języko- i przekładoznawczym (XX i XXI wiek)

GERMAN AND POLISH NAMES OF HIGHER SCHOOLS FROM A LINGUISTIC AND TRANSLATIONAL PERSPECTIVE (20TH AND 21ST CENTURIES) The present paper is an attempt to describe the nomination usus (namegiving linguistic custom) regarding higher schools of the German-speaking area on the one hand, and of the Polish-speaking one on the other. The specific semantic categories of nominal components correspond to grammatical realisations, which are subject to certain structural constraints, as well as exhibit more and less typical forms. Informed by such observations – initiated by Jan Iluk’s overview paper [2000] and refined by an analysis of much broader material, encompassing Austrian and Swiss names as well – I tried to show the name-giving possibilities and tendencies in this field in both German and Polish, mainly with a view to a translation-related application of such contrastive analysis. The findings presented here, based on the material of a considerable number of higher school names in the examined area and combined with the presentation of about 100 German and some 45 Polish examples (provided with translations), should help both in evaluating already published equivalents, hardly ever official in the case of German, and proposing one’s own ones – also because of the non-existence of given equivalents in publications, be it only Internet-based ones.

Investigating the role of functional mutations in leucine binding to Sestrin2 in aging and age-associated degenerative pathologies using structural and molecular simulation approaches

Leucine is the native known ligand of Sestrin2 (Sesn2) and its interaction with Sesn2 is particularly important, as it influences the activity of mTOR in aging and its associated pathologies. It is important to find out how leucine interacts with Sesn2 and how mutations in the binding pocket of leucine affect the binding of leucine. Therefore, this study was committed to investigating the impact of non-synonymous mutations by incorporating a broad spectrum of simulation techniques, from molecular dynamics to free energy calculations. Our study was designed to model the atomic-scale interactions between leucine and mutant forms of Sesn2. Our results demonstrated that the interaction paradigm for the mutants has been altered thus showing a significant decline in the hydrogen bonding network. Moreover, these mutations compromised the dynamic stability by altering the conformational flexibility, sampling time, and leucine-induced structural constraints that consequently caused variation in the binding and structural stability. Molecular dynamics-based flexibility analysis revealed that the regions 217–339 and 371–380 demonstrated a higher fluctuation. Noteworthy, these regions correspond to a linker (217–339) and a loop (371–380) that cover the leucine binding cavity that is critical for the ‘latch’ mechanism in the N-terminal, which is essential for leucine binding. Further validation of reduced binding and modified internal motions caused by the mutants was obtained through binding free energy calculations, principal components analysis (PCA), and free energy landscape (FEL) analysis. By unraveling the molecular intricacies of Sesn2-leucine interactions and their mutations, we hope to pave the way for innovative strategies to combat the inevitable tide of aging and its associated diseases. Communicated by Ramaswamy H. Sarma

Comparative Analysis of Deep Generative Model for Industrial Enzyme Design

Abstract: Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the design of novel enzymes tailored to specific conditions and requirements. It is beneficial to de novo design industrial enzymes using computational methods. Currently, only one tool explicitly designed for the enzyme-only generation performs unsatisfactorily. We have selected several general protein sequence design tools and systematically evaluated their effectiveness when applied to specific industrial enzymes. We investigated the literature related to protein generation. We summarized the computational methods used for sequence generation into three categories: structure-conditional sequence generation, sequence generation without structural constraints, and co-generation of sequence and structure. To effectively evaluate the ability of six computational tools to generate enzyme sequences, we first constructed a luciferase dataset named Luc_64. Then we assessed the quality of enzyme sequences generated by these methods on this dataset, including amino acid distribution, EC number validation, etc. We also assessed sequences generated by structure-based methods on existing public datasets using sequence recovery rates and root-mean-square deviation (RMSD) from a sequence and structure perspective. In the functionality dataset, Luc_64, ABACUS-R, and ProteinMPNN stood out for producing sequences with amino acid distributions and functionalities closely matching those of naturally occurring luciferase enzymes, suggesting their effectiveness in preserving essential enzymatic characteristics. Across both benchmark datasets, ABACUS-R and ProteinMPNN, have also exhibited the highest sequence recovery rates, indicating their superior ability to generate sequences closely resembling the original enzyme structures. Our study provides a crucial reference for researchers selecting appropriate enzyme sequence design tools, highlighting the strengths and limitations of each tool in generating accurate and functional enzyme sequences. ProteinMPNN and ABACUS-R emerged as the most effective tools in our evaluation, offering high accuracy in sequence recovery and RMSD and maintaining the functional integrity of enzymes through accurate amino acid distribution. Meanwhile, the performance of protein general tools for migration to specific industrial enzymes was fairly evaluated on our specific industrial enzyme benchmark.

Cultivating a research culture in Tanzanian higher education

ABSTRACT Higher education institutions (HEIs) are primarily responsible for producing high-quality research and training the next generation of researchers to achieve socio-economic development. However, empirical evidence suggests that a significant number of HEIs in Tanzania and sub-Saharan Africa generally have limited involvement in research. This situation requires an in-depth analysis of how this challenge is addressed. This paper 1 reports the findings of a study that examined how Tanzanian HEIs cultivate a research culture. Data were generated through interviews with senior leaders, academic staff and postgraduate students from four public and private Tanzanian HEIs. The study found that research culture is cultivated by creating research governance tools; reinforcing desired research behaviours; building research capacity; supporting research dissemination; and promoting research collaboration and networking. Overall, these approaches to research development were found to be less rigorous in fostering a robust research culture because many structural constraints remain that need to be addressed by key stakeholders in the higher education sector. Universities committed to cultivating a research culture should prioritise strategies that promote an equitable, transparent and inclusive research environment that recognises and supports academics as critical thinkers, researchers, educators and learners rather than simply as producers of measurable research outputs.

Use of Digital Tools (Wikihouse System) in Multi-Local Social Housing

The primary objective of this study is to formulate a comprehensive digital and physical model, at a scaled level, for a social housing unit utilizing the open-source Wikihouse system. The construction industry is currently grappling with the dual challenges of a real estate crisis and climate change. In response to this scenario, the integration of industrialized methods in construction processes is advocated to enhance the overall quality of the end product, streamline construction timelines, and curtail production costs. The algorithm developed for this purpose leverages Rhino and Grasshopper programs, thereby optimizing material efficiency when compared to traditional individual pieces. Noteworthy among the features of the Wikihouse system is its remarkable versatility, allowing implementation in diverse locations. This flexibility stems from its efficient assembly characteristics, which liberate it from the constraints of rigid modular structures, contributing significantly to architectural design flexibility. The paramount finding of this research is the demonstrated efficiency of the proposed system, requiring 44% less time compared to conventional construction practices and exhibiting a commendable 29% reduction in costs. These outcomes position the Wikihouse-based approach as an appealing and competitive alternative within the real estate sector.

Concatenating Structural Constraint Effects at Tin for the Sequential Generation, Stabilization, and Transfer of Acyclic Aminocarbenes.

Structural constraint approaches have been employed toward different ends in recent years, from augmenting the nucleophilicity in pyramidalized low-valent p-block compounds to enhancing the Lewis acidities at planarized tetravalent p-block elements. While previous studies exploited these effects separately, this work introduces a strategy to concatenate structural constraint approaches at individual stages of a reaction sequence in a row to unlock a synthetic path unattainable by conventional methodologies. The boosted nucleophilicity resulting from the constrained tetracoordinated calix[4]pyrrolato stannate(II) dianion enables the reductive formation of sterically unprotected acyclic aminocarbenes. These amino carbenes are stabilized at the concomitantly formed square-planar stannane(IV) as air-stable adducts. Transfer of the carbenes onto copper(I) by cooperativity of the calix[4]pyrrole ligand finalizes this protocol to hitherto unreported yet prototypical carbene complexes. Detailed spectroscopic and quantum theoretical analyses establish the synergy of structural constraints and element-ligand cooperation as the linchpin to this reaction path and its selectivity.

Informality and insecurity in the Sahel: unravelling the hybrid political orders of Northern Mali and Northern Niger

Since the Sahelian crisis broke out in 2012, Northern Mali and Northern Niger have been identified as the two potential epicentres of violence in the region. Nevertheless, while sharing very similar structural conditions and constraints, during the last decade these two subregions have scored very differently in terms of stability and armed conflict along the centre-periphery divide. Aiming to understand the divergent security patterns in Northern Mali and Northern Niger, this work highlights the importance of informal institutions as politically – and spatially contingent networks whose mechanisms for regulating authority and power are key for the appreciation of state capacity and political ordering, and essential for understanding statehood in the Sahel. We stress the importance of hybrid political orders and informality for the definition of political settlements which result in (a) the cooptation of political/economic competitors and the stabilisation of viable governance arrangements; or (b) an open contestation of authority and value-extraction that paves the way for destabilising dynamics. The diverging pattern exhibited by the two cases demonstrates that hybrid networks that straddle political and informal institutions are crucial in determining either the stabilisation or destabilisation of political settlements.