Integral Feature Research Articles

The Future of Municipal Wastewater Reuse Concentrate Management: Drivers, Challenges, and Opportunities.

Water reuse is rapidly becoming an integral feature of resilient water systems, where municipal wastewater undergoes advanced treatment, typically involving a sequence of ultrafiltration (UF), reverse osmosis (RO), and an advanced oxidation process (AOP). When RO is used, a concentrated waste stream is produced that is elevated in not only total dissolved solids but also metals, nutrients, and micropollutants that have passed through conventional wastewater treatment. Management of this RO concentrate─dubbed municipal wastewater reuse concentrate (MWRC)─will be critical to address, especially as water reuse practices become more widespread. Building on existing brine management practices, this review explores MWRC management options by identifying infrastructural needs and opportunities for multi-beneficial disposal. To safeguard environmental systems from the potential hazards of MWRC, disposal, monitoring, and regulatory techniques are discussed to promote the safety and affordability of implementing MWRC management. Furthermore, opportunities for resource recovery and valorization are differentiated, while economic techniques to revamp cost-benefit analysis for MWRC management are examined. The goal of this critical review is to create a common foundation for researchers, practitioners, and regulators by providing an interdisciplinary set of tools and frameworks to address the impending challenges and emerging opportunities of MWRC management.

A multi-stage approach for high-precision measurement of cervical curvature in X-ray images

As people’s lifestyles change, cervical spondylosis affects people more often. Early diagnosis is thus crucial in the management of cervical spondylosis. Cervical curvature abnormalities may serve as an objective diagnostic standard in radiographs, commonly assessed by the Cobb angle measurement in clinical practice. The method involves the clinician marking four corners of each vertebra and then drawing lines to calculate the Cobb angle using specific rules. The whole process takes a long time and mainly relies on subjective experience. In this paper, we propose an automatic multi-stage method for measuring cervical spine Cobb angle, which is referred to as MSM-CSCA that can perform the precise measurement of cervical curvature. In Stage 1, the region of interest (ROI) of the cervical spine is carefully cropped automatically. In Stage 2, an improved CA-UNet is used to achieve detailed segmentation of the cervical spine region for the ROI region, which extracts features from the edges of the vertebrae and integrates them with the original ROI to highlight the vertebrae edges more distinctly. At Stage 3, the integrated features are input into a dual-branch landmark detection network, with segmentation and detection branches. Within this stage, the segmentation branch convolutional results are fed back into the detection branch after feature augmentation to provide a more accurate detection area. Finally, the accurately identified cervical landmarks from this network are used to calculate the Cobb angle. Experimental results show that the proposed method achieves SMAPE 15.05% and MAE 3.19 on X-ray images of the cervical spine, significantly better than the other benchmark methods. The proposed method can provide clinicians with an efficient, accurate, and reliable way for the estimation of cervical curvature.

Field-Measurement of Surface Wind and Sediment Transport Patterns in a Coastal Dune Environment, Case Study of Cala Tirant (Menorca, Spain)

Blowouts are integral features of coastal dune fields. Their presence enhances both geomorphological and ecological diversity and enables the movement of sand by wind. Their role as a ‘transport corridor’ may be, however, considered negative from a coastal management perspective in heavily touristic areas, where the existence of blowouts close to the foredune can enhance the loss of sediment from the beach. This paper investigated the relationship between airflow dynamics and patterns of sediment transport from the beach to established dunes through a trough blowout located on the foredune. Seven three-cup anemometers were used to measure wind speed and direction over a 24 h sampling period at a frequency of 1 min under onshore (parallel to the blowout axis) medium and high wind speeds (max of 17.9 ms−1). To measure sediment transport, a total of 12 vertical sand traps were located at three positions along the length of the deflation basin. The results indicated that small amounts of sediments went into the blowout from the beach and that the highest rates of sediment remobilization took place within the deflation basin. These results highlight two processes: (a) flow channelization induced by the blowout topography caused an increase in wind speed and sediment transport toward the depositional lobe, and (b) the presence of embryo dunes and herbaceous vegetation at the beach–blowout boundary effectively reduced the amount of sediment transport from the beach to the landform. The results confirmed the significant role that vegetation plays in controlling sediment movement and conserving the beach–dune system.

A multiscale feature fusion network based on attention mechanism for motor imagery EEG decoding

The decoding of motor imagery (MI) electroencephalogram (EEG) is an essential component of the brain–computer interface (BCI), which can help patients with motor impairment communicate directly with the outside world through assistive devices. The key to motor imagery electroencephalogram (MI-EEG) classification is to extract multiple temporal, spatial, and spectral features, to obtain more comprehensive and representative information. However, current deep learning methods must fully consider the depth of temporal features and multi-spectral knowledge in EEG and often ignore the temporal or spectrum dependence in MI-EEG. In addition, the lack of effective feature fusion methods can lead to information redundancy, which affects decoding performance. To solve the above problems, this paper proposes a novel MI-EEG decoding method, named multi-scale feature fusion network based on attention mechanism (MSFF-SENet). Firstly, the multi-scale spatio-temporal module (MS-STM) and the multi-scale temporal module (MSTM) extract spatial and high-dimensional temporal features from the original signal. Then, the power spectral density estimation (PSD) convolution module (PSD-Conv module) acquires the multi-spectral features of the MI-EEG signal. Secondly, the feature fusion module fuses spatio-temporal and multi-spectral features to generate integrated feature mappings and establish the dependencies between different features. Finally, we conducted a visual analysis of the results, explaining the neural activity patterns of various motor imagery tasks in different frequency ranges and revealing the potential relationship between body movement and changes related to brain activity. The experimental results show that the classification accuracy of this model in BCI Competition IV 2a (BCI IV 2a) and High Gamma (HGD) datasets is 85.37% and 96.60%, respectively, which is superior to the most advanced methods.

Engineering self-assembled 2D nano-network membranes through hierarchical phase separation for efficient air filtration

Air pollution has garnered significant worldwide attention; however, the existing air filtration materials still suffer from issues related to monotonous structure and the inherent trade-off between PM rejection and air permeability. Herein, a spider web-inspired composite membrane with continuous monolayer structured 2D nano-networks tightly welded on nanofibers in the electrospun membrane scaffold is designed via a hierarchical phase separation strategy. The resultant biomimetic hierarchical-structured membranes possess the integrated features of hierarchical multiscale structures of 2D ultrafine networks composed of nanowires with a diameter of 31 nm self-assembled by nanoparticles, exceptional characteristics involving small average aperture, extremely low network thickness, high porosity and promising pore channel connectivity, combined with rich surface polar functional groups (3.02D dipole moment). Consequently, the composite membrane exhibits a high PM0.3 capture efficiency of 99.6 % and low pressure drop of 58.8 Pa, less than 0.06 % of atmosphere pressure, with outstanding long-term PM2.5 recycling filtration performance. The hierarchical phase separation–driven 2D nano-networks construction strategy, by virtue of their feasibility and tunability, holds great promise for widespread application across diverse membrane-related domains for air filtration.

Automatic Topic-Based Web Page Classification Using Deep Learning

The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.

How far do indirect signal production mechanisms influence regularity in the three-dimensional Keller–Segel–Navier–Stokes system?

This paper reconsiders the Keller–Segel–Navier–Stokes model with indirect signal production and weak logistic-type degradation in a three-dimensional (3D) bounded and smooth domain. One recent literature has asserted that for all reasonably regular initial data, the associated initial-boundary value problem with certain sub-quadratic degradation possesses at least one global generalized solution which is uniformly bounded and converges to the constant equilibrium in [Formula: see text] with any [Formula: see text]. Nevertheless, the knowledge on regularity properties of solution has not yet exceeded some information on fairly basic integrability features. The present study firstly elevates the regularities of some solution components under the same assumption on degradation exponent, especially establishes the global boundedness in [Formula: see text] with [Formula: see text], and secondly asserts that each of these generalized solutions becomes eventually classical and bounded in [Formula: see text] under some suitably strong sub-quadratic degradation assumption and an explicit smallness condition on the intrinsic growth rate of cells relative to some powers of the degradation coefficient. As a by-product of the latter, these solutions are shown to stabilize toward the corresponding spatially homogeneous state in [Formula: see text]. Our results indeed provide a more in-depth understanding on the global dynamics of solutions, and significantly improve previously known ones. In comparison to the related contributions in the case of direct signal production, our findings inter alia rigorously reveal that the indirect signal production mechanism results in some genuine regularizing effects in the sense that the possibly destabilizing action of chemotactic cross-diffusion in the 3D Keller–Segel–Navier–Stokes system can be greatly weakened or even completely excluded by this indirectness.

AXIOLOGICAL ASPECT OF THE CONCEPT IDENTITY OPPOSITION REALIZATION (BY THE EXAMPLE OF THE CREATION ‒ DESTRUCTION DICHOTOMY)

Background. The global redistribution of the balance of power, caused by the imbalance of the geopolitical interests of the leading world powers, contributed to the actualization within the boundaries of the Russian linguistic and cultural space of phenomena associated with the accentuation of existential significance categories among which the concept IDENTITY occupies a special position.
 Purpose. The article is devoted to the study of the implementation of the value aspect of the concept IDENTITY opposition in Russian linguistic culture.
 Materials and methods. The material for this study was the article of the President of the Russian Federation V.V. Putin On the historical unity of Russians and Ukrainians. July, 12, 2021; interview with the Minister of Foreign Affairs of the Russian Federation S.V. Lavrov; Internet publication of the Russian journalist I.A. Yakovenko Disintegration of the Russian Federation: Scenarios and Prospects; speeches of state and political figures of Russia in the television political show Evening with Vladimir Solovyov. When writing the article, the following methods were used: lexical-semantic, comparative-contrastive, discursive, contextual analysis.
 Results. In the course of the study the specifics of the differentiated objectification of the concept IDENTITY in the Russian language were analyzed; various theoretical approaches to the substantiation of the oppositional nature of the language implementation of the concept under study were systematized; the factual interdependence of the studied concept with the category “linguistic consciousness” was established; an integrative feature of the implementation of the concept IDENTITY in the Russian language space was revealed; the indicative characteristic of the concept IDENTITY in the composition of the subjects of the opposition CREATION ‒ DESTRUCTION was described.
 Practical implications. The results of the study can find practical application in the preparation of teaching aids and textbooks on the linguo-cognitive analysis of concepts based on dichotomous models.

Reconceptualizing the curation of prescribed learning resources in an immersive block teaching model

The presentation will describe how the curation of prescribed learning resources has been reconceived as an enabler of an immersive block teaching model. Contemporary higher education curriculum relies on high quality learning resources to engage students, to encourage them to adopt deeper learning approaches (Gledhil et al., 2017) and to support students’ academic adjustment to university (Owusu-Ageyman & Mugume, 2023). Little has been written about learning resources and reading lists within the digital transformation of higher education in the post-pandemic environment. The presentation will showcase a new way to position prescribed learning resources accessed as an integral feature of constructively aligned pedagogy within an immersive teaching model. Through the lens of an institutional case study at Southern Cross University, the presentation will show how prescribed learning resources delivered via integrated reading list technology are an enabling element of an innovative, student-centred, university-wide curriculum renewal project. At a time of increasing student expectations of teaching quality, academic, educational design and library staff need to review whether reading lists of learning resources are fit for purpose (Brewerton, 2014) and consider the cognitive load placed on students by reading (Barile et al., 2022). The presentation will outline the evidence-based, policy-led approach adopted by Southern Cross University and will demonstrate how learning resources and reading lists can contribute to student success. Prescribed learning resources and reading lists are now seen as pedagogical tools within the immersive teaching model, providing a variety of media-rich learning resources aligned to unit learning outcomes. Reading list parameters ensure that a manageable volume of learning is implemented, reducing cognitive load and barriers to participation. The approach to reading lists taken by Southern Cross University to empower immersive teaching practices provides a refreshed model for the use of prescribed learning resources in unit and curriculum design, development, and delivery.

Security situational awareness of power information networks based on machine learning algorithms

To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception model outlines the consequences of the abstracted perception problem. Sample data is initially pre-processed using linear discriminant analysis methods to optimise the data, get integrated features, and ascertain the best projection. To assess system safety posture and find mapping relationships with network posture values, the cleaned data is subsequently input into an RBF neural network as training data. The reliability of the suggested technique for network security posture analysis is finally shown by simulations using the KDD Cup99 dataset and attack data from power information networks, with detection rates frequently surpassing 90%.

GCCF: A lightweight and scalable network for underwater image enhancement

Underwater images suffer from color deviation and low contrast arising from light attenuation and scatter, making them act as a drag on exploring and exploiting ocean resources. Existed underwater image enhancement algorithms are either inflexible due to scene-dependent design or redundant for ignoring prior knowledge. To address the above problems, we propose a lightweight and scalable deep learning-based network that consists of a Grouped Color Compensation encoder and channel Fusion decoder, abbreviated GCCF. Concretely, the least absorbed green channel contains abundant information compared to the red and blue channels. Taking this into account, the encoder extracts information independently from three grouped features according to the color. Simultaneously, the encoder flexibly compensates for the red and blue channels from the green channel with a proposed learnable module. Then, the decoder gradually fuses the grouped features into a whole part and produces competitive outputs from the integrated features. Extensive experiments on real-world underwater images demonstrate that the proposed GCCF achieves outstanding results in subjective visual comparisons and objective metrics.

A derivative, integral, and proportional features extractor for fault detection in dynamic processes

Detecting faults in dynamic systems is challenging due to temporal dependencies and signal correlations. Feature extraction from time-series data is a common step in fault detection, which is usually performed according two main approaches: knowledge-based and statistical. Knowledge-based methods provide interpretable features but require significant design time; also, they usually lack generality. Statistical methods are faster but lead to dimensionality issues and lack of interpretability. To address these challenges, we propose an interpretable and automated feature extraction method. It combines the benefits of knowledge-based and statistical methods, offering a fast solution for extracting effective and interpretable indicators without requiring prior domain knowledge. Also, this method consistently computes a fixed set of interpretable features describing the process’s dynamic behavior.We extensively compare our approach with state-of-the-art methods considering nine open-source datasets spanning various domains. Our results show that classifiers trained on features extracted with the proposed method achieve performance comparable to those provided by state-of-the-art automatic features extractors, according to F1-score, true positive rate, and false positive rate. In addition, our approach proves to be more robust to dimensionality issues and enhances interpretability, extracting a reduced set of features effective at providing insight into the detected anomalies’ characteristics. Additionally, we demonstrate that the selected features maintain consistent performance across different classifiers, showcasing their versatility.

A pilot study comparing immersive virtual reality simulation and computerized virtual patient simulation in undergraduate medical education

Simulation-based teaching is an integral feature within medical education and following the emergence of virtual simulation, an array of possibilities exists for educators to choose between. However, evidence informing their use is scarce, particularly regarding outcomes assessing user experience and knowledge acquisition, and experimental studies comparing different approaches to virtual simulation. Therefore, this study compared immersive virtual reality (VR) simulation to computerized virtual patient (VP) simulation measuring their effect on knowledge acquisition and retention, as well as user experience, in fifth-year medical students. This pilot study, of a randomized crossover design, comprised 18 participants independently completing an immersive VR simulation and a computerized VP simulation. All participants completed the same two scenarios and received an induction to both modalities. Multiple-choice questions were employed to assess knowledge acquisition and retention, with participants completing the questions immediately before and after the simulation and following a 12-week interval. User experience questionnaires were completed after the simulation, utilizing both Likert scale and open-ended questions. Statistical analysis comprised a Student’s Both interventions achieved statistically significant levels of knowledge acquisition and retention. However, VR simulation achieved higher levels of acquisition (2.11; 95% CI = 0.89, 3.32, The findings indicated that both interventions are effective and acceptable educational tools. However, learning does not appear to be uniform across different virtual simulators, with participants achieving higher levels of learning following immersive VR simulation. Moreover, participants reacted significantly more positively to VR simulation, though potential applications were identified for both interventions. This study highlights the importance of an evidence-based approach to the implementation of novel simulation technologies. The findings contribute to an underexplored area of the literature and offer a step towards enabling medical educators to make an informed decision regarding the application of virtual simulation in their context.

RS Transformer: A Two-Stage Region Proposal Using Swin Transformer for Few-Shot Pest Detection in Automated Agricultural Monitoring Systems

Agriculture is pivotal in national economies, with pest classification significantly influencing food quality and quantity. In recent years, pest classification methods based on deep learning have made progress. However, there are two problems with these methods. One is that there are few multi-scale pest detection algorithms, and they often lack effective global information integration and discriminative feature representation. The other is the lack of high-quality agricultural pest datasets, leading to insufficient training samples. To overcome these two limitations, we propose two methods called RS Transformer (a two-stage region proposal using Swin Transformer) and the Randomly Generated Stable Diffusion Dataset (RGSDD). Firstly, we found that the diffusion model can generate high-resolution images, so we developed a training strategy called the RGSDD, which was used to generate agricultural pest images and was mixed with real datasets for training. Secondly, RS Transformer uses Swin Transformer as the backbone to enhance the ability to extract global features, while reducing the computational burden of the previous Transformer. Finally, we added a region proposal network and ROI Align to form a two-stage training mode. The experimental results on the datasets show that RS Transformer has a better performance than the other models do. The RGSDD helps to improve the training accuracy of the model. Compared with methods of the same type, RS Transformer achieves up to 4.62% of improvement.

Evidence surveillance for a living clinical guideline: Case study of the Australian stroke guidelines.

Continual evidence surveillance is an integral feature of living guidelines. The Australian Stroke Guidelines include recommendations on 100 clinical topics and have been 'living' since 2018. To describe the approach for establishing and evaluating an evidence surveillance system for the living Australian Stroke Guidelines. We developed a pragmatic surveillance system based on an analysis of the searches for the 2017 Stroke Guidelines and evaluated its reliability by assessing the potential impact on guideline recommendations. Search retrieval and screening workload are monitored monthly, together with the frequency of changes to the guideline recommendations. Evidence surveillance was guided by practical considerations of efficiency and sustainability. A single PubMed search covering all guideline topics, limited to systematic reviews and randomised trials, is run monthly. The search retrieves about 400 records a month of which a sixth are triaged to the guideline panels for further consideration. Evaluations with Epistemonikos and the Cochrane Stroke Trials Register demonstrated the robustness of adopting this more restrictive approach. Collaborating with the guideline team in designing, implementing and evaluating the surveillance is essential for optimising the approach. Monthly evidence surveillance for a large living guideline is feasible and sustainable when applying a pragmatic approach.

Linking supply chain performance with organizational strategic performance – a review and research agenda

PurposeThis article aims to comprehensively review the measurement and management of supply chain performance (SCP) and strategic performance (SP). It strives to identify integrable features regarding frameworks, measurement approaches, practices and emerging research issues in these areas to integrate SCP and SP for measuring and managing performance. It intends to develop a dynamic-integrated-performance-system by incorporating integrable aspects of SCP and SP to link these domains for organizational performance improvement.Design/methodology/approachUsing systematic-literature-review, this study analyzes 154 articles published in selected peer-reviewed international journals from 2000 to 2023 regarding SCP and SP. It assesses existing knowledge regarding research-design followed, challenging areas and imperatives in these critical business domains to investigate the prior conceptual, empirical, case study-based and literature-review-based articles.FindingsThe study identifies integrable features regarding key theoretical and measurement frameworks, critical objectives, significant measures, effective practices for measuring and managing SCP and SP and emerging research issues common to these areas. The findings help develop a dynamic-integrated-performance-system that uses the theoretical lenses of resource-based-view/dynamic-capability-theory and adopts a comprehensive framework like DBSC (system-dynamic-model with BSC perspectives). It incorporates identified integrable measures and best practices to monitor, measure, manage and improve organizational performance for sustainable competitive advantage. The article reveals that earlier studies have overlooked analyzing SCP and SP integration aspects.Research limitations/implicationsFrom the theoretical viewpoint, the present SLR is unique in three ways: first, in investigating both the measurement and management of SCP and SP holistically; second, in identifying integrative features of these two; and third, in proposing a DIPS to link SCP and SP for performance improvement. The study reveals that existing literature has focused on measuring and managing SCP and SP in isolation without attempting a comprehensive and unified approach to integrate the respective domains. The present SLR adopts a holistic approach to link SCP and SP from SCM and strategic-management perspectives. The study proposes a dynamic-integrated-performance-system to measure, manage and improve performance in a unified method.Practical implicationsThis study provides SC and strategy practitioners with an understanding of strategy-performance pathways for achieving strategic objectives and executing risk mitigation initiatives to counter disruptions. It enables SC managers to comprehend SC practices and SCP leading to dynamic SC capabilities development. Operationalizing the proposed DIPS will help firms link SCP and SP, align operational SC practices with strategic sustainability and circularity objectives and meet sustainable development goals while benefiting social and environmental stakeholders.Originality/valueAssessing relationships and identifying a unified approach integrating SCP with SP have not been addressed earlier. This study's uniqueness is finding integrable features of SCP and SP and constructing a dynamic-integrated-performance-system to link these domains for achieving strategic competitiveness.

A multimodal prediction model for suicidal attempter in major depressive disorder.

Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts. Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not. The multimodal predictive model was found to correctly distinguish MDD patients with and without suicidal attempts using integrated features derived from SVM-RFE and RF, with a balanced accuracy of 77.78%, sensitivity of 83.33%, specificity of 70.37%, positive predictive value of 78.95%, and negative predictive value of 76.00%. The strategy of merging the features from two selection methods outperformed traditional methods in the prediction of suicidal attempts in MDD patients, with hippocampal volume, cerebellar vermis volume, and supracalcarine volume being the top three features in the prediction model. This study not only developed a new multimodal prediction model but also found three important brain structural phenotypes for the prediction of suicidal attempters in MDD patients. This prediction model is a powerful tool for early intervention in MDD patients, which offers neuroimaging biomarker targets for treatment in MDD patients with suicidal attempts.

Hybrid Photonics: Integration, Design and Devices feature issue: publisher’s note

“Invited” was added to the title of a published paper [Opt. Mater. Express 13, 3266 (2023)10.1364/OME.501920].

Lightweight Human Motion Recognition Method with Multiscale Temporal Features

To address the problems of the large size of human motion recognition models based on deep learning and insufficient mining of data features and thus low recognition accuracy, a lightweight human motion recognition algorithm based on multi-scale temporal features is proposed, the algorithm automatically extracted features through a multiscale feature fusion model. After, the integrated features are modeled by an improved time convolution network (TCN). In the TCN network structure, In the TCN network structure, the depthwise separable convolution is used instead of the normal convolutional layer to reduce the computational complexity, and the Leaky ReLU activation function is used instead of the ReLU function to improve the training efficiency. The experiments are based on the WISDM public dataset. finally achieve fast real-time recognition of actions, and structural and parametric optimization is performed through experiments to effectively improve the accuracy of results, and the final accuracy rate reaches 99.06%. In comparison with other methods, this method can reduce the model volume while maintaining a high accuracy rate.

Sex differences in avian parental care patterns vary across the breeding cycle

Parental care in birds consists of elaborate forms across stages, including nest building, incubation, and offspring provision. Despite their evolutionary importance, knowledge gaps exist in the extent to which parents contribute disproportionately to these forms and factors that are associated with variations in care patterns between sexes. Here, we analyzed 1533 bird species and discovered remarkable variability in care patterns. We show that parental care should not be viewed as a unitary trait but rather as a set of integrated features that exhibit considerable temporal and sex-specific variation. Our analyses also reveal moderate consistency in care patterns between breeding stages, pointing towards shared intrinsic factors driving sex-specific care. Notably, we found that species experiencing strong sexual selection on males or species facing paternity uncertainty display a tendency towards female-biased care. This work advances our understanding of the temporal variations in sex-specific contributions to avian parental care and their potential evolutionary drivers.