Structural Constraints Research Articles

COMPARATIVE STUDY OF THE ISLAMISATION PROCESS IN MALAYSIA AND NIGERIA: A SOCIAL MOVEMENT THEORY APPROACH

This exploratory, diachronic study compares the politics of Islamisation, which have undergone different patterns of developmentin both Nigeria and Malaysia. Islamisation is defined as an ideological translation of Islamism into policies to “Islamise” the state andsociety by expanding the scope of the application of Islamic laws and principles. Islamisation in Malaysia has been implemented steadily and progressively since the 1980s. It involves the “inculcation of Islamic values” in a wide range of aspects of society, including the economy. The Islamic penal code or hudud enactments were legislated in two states, but held in abeyance due to their unconstitutionality. In contrast, Nigeria observed the sudden and erratic implementation of hudud in 12 northern states in 1999, along with other bureaucratic adjustments. Adopting an interpretative case study approach of the comparative method of Arend Lijphart, we explain the two contrastive case studies of the ideological translation of Islamism by analysing the manner in which various political opportunities and structural constraints in the respective cases have shaped the Islamisation process. Data gathering was based on an extensive review of the related body of literature. Analysis was conducted using a social movement theoretical framework based on a structured, focused comparison. The dynamic conceptualisation of the state as a set of institutions, which is continually and dialectically reshaped by contentious processes, captures how the bureaucratisation and judicialisation of Islam have modified the nature of state structures and the structure of political opportunities, which in turn allows for additional channels of influence for Islamic activists.

Livelihood Trajectories of Rural Young People in Southern Africa: Stuck in Loops?

ABSTRACTAttempts to boost rural development in the Global South tend to focus on ways in which people can transform their lives. Interventions are often designed to help overcome specific envisioned constraints and push individuals onto a pathway out of poverty. Research has contributed to nuancing this vision by documenting the non‐linearity of pathways, which often results in people being left in limbo or stuck, rather than moving forward. Based on a study in two villages in Malawi and Lesotho, this article argues that even these nuances do not fully capture the real‐life experiences of the 63 young people who participated. Interviews tracing the course of their lives between 2007–08 and 2016–17 reveal trajectories that are circular rather than linear, and show the detrimental effects of being stuck in these frustrating loops of taking action without progressing. Conceptualizing rural young people's livelihood trajectories in contexts of severe poverty as loops highlights the structural issues that need to be addressed if their lives are to be transformed. Understanding development as emancipation from sources of unfreedom means focusing on the structural constraints that keep some people in poverty, and the importance of attaining agency if they are to put their needs on the agenda and demand basic rights.

Unsupervised Domain Adaptation Depth Estimation Based on Self-attention Mechanism and Edge Consistency Constraints

In the unsupervised domain adaptation (UDA) (Akada et al. Self-supervised learning of domain invariant features for depth estimation, in: 2022 IEEE/CVF winter conference on applications of computer vision (WACV), pp 3377–3387 (2022). 10.1109/WACV51458.2022.00107) depth estimation task, a new adaptive approach is to use the bidirectional transformation network to transfer the style between the target and source domain inputs, and then train the depth estimation network in their respective domains. However, the domain adaptation process and the style transfer may result in defects and biases, often leading to depth holes and instance edge depth missing in the target domain’s depth output. To address these issues, We propose a training network that has been improved in terms of model structure and supervision constraints. First, we introduce a edge-guided self-attention mechanism in the task network of each domain to enhance the network’s attention to high-frequency edge features, maintain clear boundaries and fill in missing areas of depth. Furthermore, we utilize an edge detection algorithm to extract edge features from the input of the target domain. Then we establish edge consistency constraints between inter-domain entities in order to narrow the gap between domains and make domain-to-domain transfers easier. Our experimental demonstrate that our proposed method effectively solve the aforementioned problem, resulting in a higher quality depth map and outperforming existing state-of-the-art methods.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Constraint‐aware optimization model for plane truss structures via single‐agent gradient descent

AbstractThis study introduces the constraint‐aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses this challenge. It does so by incorporating a variety of assistant modules along with the Adam optimizer, a variant of the gradient descent method. Uniquely, CAOM employs the leaky rectified linear unit (ReLU) activation function beyond its conventional use in neural networks, applying it as a mechanism to integrate constraints and losses seamlessly. The model's effectiveness was validated through two numerical examples and a practical application, demonstrating that CAOM can reduce structural weight by up to 84% compared to unoptimized designs while fully adhering to structural, dimensional, and moveable constraints. Furthermore, the study found that while shape optimization plays a key role for stiffness‐governed structures, size optimization is crucial for strength‐governed structures. Optimizing size, shape, and topology together consistently leads to the most weight‐efficient designs. This emphasizes the significance of a holistic approach in the optimization processes.

Third Ventricle Diameter Is Inversely Related to Thalamic Massa Intermedia Thickness in Hydrocephalus Caused by Congenital Aqueductal Stenosis.

In fetuses with lateral ventriculomegaly and normal posterior fossa cerebrospinal spaces, third ventricle distention is a compelling clue that supports a diagnosis of aqueductal stenosis. However, this association assumes normal ventricular anatomy. Structural constraints can impair pressure-induced compliance. We aimed to determine how thalamic massa intermedia size alterations may impact the size of the third ventricle in the setting of congenital aqueductal stenosis. This retrospective study was performed at a single academic pediatric hospital after institutional review board approval. We searched our brain MRI reports for all examinations describing aqueductal stenosis and included all the patients who had both fetal and postnatal examinations. Patients with interhypothalamic adhesions and hydrocephalus unrelated to congenital aqueductal stenosis were excluded from this study. We evaluated all the MRIs for the presence of thalamic massa intermedia and documented third ventricle diameters (supraoptic recess, central and suprapineal recesses) and the thalamic massa intermedia circumference. The Spearman correlation was used to identify the potential relationship between the thalamic massa intermedia circumference and third ventricle size in fetal and postnatal MRIs. Patients were also stratified into 2 groups based on the presence or absence of thalamic massa intermedia. Mann-Whitney U tests were used to compare third ventricle diameters between these groups. The study included both fetal and postnatal studies from 59 patients. The overall third ventricle diameter was inversely proportional to the circumference of the thalamic massa intermedia in both groups (fetal: P = .001, ρ = -0.422; [95% CI, -0.628 to -0.181]; postnatal: P < .001, ρ = -0.653; [95% CI, -0.782 to -0.479]). Nonetheless, dilation of anterior and posterior recesses still occurred when the mid third ventricle was nondilated or less severely dilated in patients with an enlarged thalamic massa intermedia. Third ventricle dilation was most severe in patients lacking a thalamic massa intermedia compared with patients with a thalamic massa intermedia (P < .001). In patients with suspected congenital aqueductal stenosis, lack of marked third ventriculomegaly as conventionally measured can sometimes be explained by thickening of the thalamic massa intermedia. In this circumstance, it is important to evaluate the extreme recesses of the third ventricle for evidence of dilation on fetal MRI.

Makespan minimization for workflows with multiple privacy levels

With the advanced development of Metaverse, various service requests are required to be processed as soon as possible which contains a series of tasks with topology structure (workflow) and different privacy constraints. Due to the diversity and complex hierarchical structure of resources, how to rent suitable resources from cloud, edge, and local nodes to minimize the makespan of workflows is currently one of the key issues in service computing scenarios. Due to the limited computing resources of local devices, tasks need to be offloaded to edge or cloud servers. Based on task privacy constraints, it is challenging to determine the appropriate offloading nodes for each task in the workflow. In this paper, for workflows with multi-privacy level tasks, a privacy-preserving workflow scheduling algorithm based on clustering (PWCSA) is proposed to minimize the workflow makespan with limited bandwidth. Multiple tasks of different privacy levels are clustered into one task pipeline to reduce the data transmission time caused by limited bandwidth, thereby reducing workflow makespan. After statistically calibrating algorithm parameters over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art algorithms over five workflow-type instances. The results indicate that the proposed PWCSA algorithm outperforms other algorithms with acceptable computational time. Through a number of workflow instances, the system parameters are verified by analysis of variance methods.

Between biological age and social age: age of marriage and first birth among Arab-Palestinian women in Israel

The gap between the perceived ideal age for major life events, and the age they actually occur, stems from cultural and structural constraints. These constraints, more restrictive in patriarchal societies, impose gender-based expectations in all aspects of life, affecting career opportunities and gender relations. Focusing on the Arab-Palestinian society in Israel, we used a questionnaire [n(men)=38; n(women)=82], and ‘the social survey 2019’ [n(men)=555; n(women)=574], to examine the perceived ideal age for women to marry, and the perceived importance of career pursuit before marriage. Our findings show that the social norms preserve conservative patterns. The gaps between social and biological time indicate that time in the context of society exerts effects of discipline and classification, and should be considered in the study of gender and family relations.

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.