Real Complex Research Articles

A video object segmentation-based fish individual recognition method for underwater complex environments

Currently, aquaculture methods tend to combine scale and intelligence, which saves manpower and improves the survival rate of seafood at the same time. High-precision and high-efficiency fish individual recognition can provide key technical support for fish disease detection, feeding habits, body condition, etc. In the realm of intelligent aquaculture, it provides robust data support for precision fish farming. However, the current research methods for individual fish recognition struggle to maintain the network model's focus on the fish body in real marine underwater complex environments (e.g., environmental background interference such as coral reefs, overlap between fish bodies, light noise, etc.), leading to unsatisfactory recognition results. To this end, this paper proposes a method for fish individual recognition in underwater complex environments based on video object segmentation, which consists of three parts, including a fish individual segmentation detection module, a fish individual recognition module, and an all-in-one visualization module. The work adopts a combination of deep learning methods and video object segmentation algorithms to solve the problem of low attention and poor detection accuracy of fish individuals in real underwater complex environments, which effectively improves the accuracy and efficiency of fish individual recognition, and analyzes and discusses the comparison of recognition effects using different weights. The results of the simulation experiments show that the key metric Rank1 value of the method achieves more than 96% accuracy on the public datasets DlouFish, WideFish, and the Fish-seg dataset produced in this paper, and improves over the state-of-the-art methods for fish individual recognition by 2.23%, 1.33%, and 1.25%, respectively.

New development: Public service innovation

IMPACT To meet the future fitness of a changing public service, local government practitioners and public managers can advance more values-based approaches that are responsive to workforce planning needs and more accessible to the communities that they serve. First, co-designing human-centric public services with users to target genuine areas of need. Second, promoting good governance to embrace partnership and spread public service financial risk in finding sustainable solutions to real world complex challenges. Third, leveraging data-driven technologies that informs public service decision-making on areas of performance and improvement. Fourth, investing in training and lifelong learning to promote continuous professional development and building capacity to equip public workers with the skills for innovation is crucial. Finally, to attract gender equality, diversity and inclusion in public service recruitment strategies a more balanced approach to conflicting ‘care’ and ‘control’ practices.

Robust and transferable end-to-end navigation against disturbances and external attacks: an adversarial training approach

Purpose This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments. Design/methodology/approach In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models. Findings Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world. Originality/value The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

Temporal local clustering coefficient uncovers the hidden pattern in temporal networks.

Identifying and extracting topological characteristics are essential for understanding associated structures and organizational principles of complex networks. For temporal networks where the network topology varies with time, beyond the classical patterns such as small-worldness and scale-freeness extracted from the perspective of traditional aggregated static networks, the temporality and simultaneity of time-varying interactions should also be included. Here we extend the traditional analysis on the local clustering coefficient C in static networks and study the dynamical local clustering coefficient of temporal networks. We demonstrate that the temporal local clustering coefficient TC conveys the hidden information of nodes' neighboring connectance when interactions occur at various rhythms. By systematically analyzing various empirical datasets, we find that TC uncovers different interaction patterns in different types of temporal networks. Specifically, we show that TC has a strong positive correlation with C in efficiency-related networks, whereas they are uncorrelated in social activity-related networks. Moreover, TC helps to exclude interference from accidental interactions and reflect the actual clustering properties of network nodes. Our results shed light on the importance of digging into dynamical characteristics to fundamentally understand the underlying temporal structures of real complex systems.

Assessing the impact of urban expansion on carbon emission

Urban expansion is not only a driving force for economic growth and development but also a perilous element for anthropogenic carbon emission coinciding with population growth, urban expansion, land use pattern, urban sprawl. The study compares urban expansion and its impact on the carbon emission pattern for Rajshahi, a city of Bangladesh where land cover data is an important determinant. At first, the study aims to evaluate the pattern of urban expansion and the second step quantifies the carbon emission trend. The satellite images are used for land cover detection of urban expansion and Support Vector Machine (SVM) algorithm is applied for its classification. Similar carbon emission data are examined to relate carbon emission quantification rate by remote sensing algorithm. It indicates that urban expansion significantly explains carbon emission by comparing with similar secondary data to extract carbon emission and energy consumption. After accentuating urban expansion and carbon emission rate, the spatiotemporal pattern is analyzed in 16 different directions revealing that the urban expansion and emission trend expands to north-western and north-eastern directions of Rajshahi, turning it into a low carbon absorbed city. The responsible landmarks are real estate housing complex, educational institutions, student dormitories, industrial areas, brickfields and mango wholesale business. In order to understand the effects of uncontrolled urban expansion, this study will assist policymakers and urban planners in developing a comprehensive and well-organized strategy.

A multilevel graph approach for IoT-based complex scenario management through situation awareness and semantic approaches

Modern reality and the environments in which we live are characterized by technology components geared toward automatic management through pervasive services. Thanks to the advent of the Internet of Things, such environments can provide information such as pollution levels, public transport conditions, efficiency of energy distribution networks, and identification of suspicious activities by generating complex scenarios. The profitable management of such scenarios can be performed through context modeling and methodologies that can extract and understand environmental information by preventing certain events through artificial intelligence techniques by increasing Situation Awareness. This paper focuses on developing a methodology with predictive capabilities and context adaptability for managing complex scenarios. The use of semantic and graph-based approaches, unlike many approaches used, leads to better integration of knowledge, resulting in improved system performance. In addition, such approaches allow understanding of what is happening in the system at a given time, enabling manipulation and integration of semantic information. Graph-based approaches chosen for this purpose are Ontologies, Context Dimension Trees, and Bayesian Networks, which are able to support the end-user or expert user in handling complex scenarios. The proposed methodology has been validated and applied to real complex scenarios based on the IoT paradigm. The proposed approach validation was conducted using open data from the city of London; a practical scenario case study was conducted in the field of automated management of a Smart Home. In both cases, the system achieved promising results.

Thermal decomposition mechanism of lithium methyl carbonate in solid electrolyte interphase layer of lithium-ion battery

To predict the early stages of thermal runaway and improve the safety of lithium-ion batteries, it is necessary to examine the elementary reaction steps for the thermal decomposition of individual solid electrolyte interphase (SEI) components. This study elucidates a detailed decomposition mechanism of lithium methyl carbonate (LMC) which is one of SEI components and accounts for the majority of SEI components formed in commercial electrolytes. The detailed reaction mechanism for the thermal decomposition of LMC is proposed based on the in-situ/ex-situ experiments and density functional theory calculations. LMC underwent six reactions before finally converting to Li2CO3 at 300 °C. To supplement the reliability of the proposed reaction mechanism, the actual gas composition measured using mass spectrometry (MS) are compared with the chain reactions of radicals generated through the thermal decomposition reactions, which is calculated using GRI-MECH 3.0, a gas-phase reaction mechanism. The methodology proposed in this study can be used both for analyzing the reaction mechanisms of different SEI components and their coupling effects with the electrolyte in the future. Understanding these reaction mechanism sets will help us to understand the degradation reactions of real complex SEI and the initial self-heating stage during thermal runaway.

Automating data classification for label-free point-of-care biosensing in real complex samples

Surface-based affinity biosensors present a promising avenue for point-of-care (POC) pathogen detection in real-world samples. While laboratory-based devices commonly employ various techniques to mitigate noise, signal drifts, fluidic artifacts, and other system imperfections, their simple cost-effective POC counterparts designed for field use frequently lack such capabilities. This paper addresses this gap by introducing a procedure for automatically classifying pathogen presence in unprocessed liquids from direct detection data measured by a simple POC quartz crystal microbalance sensor device. The procedure integrates classical analytical tools such as filtering, data selection, baseline de-drifting, and result calculation in tailored successive steps, considering the nature of the sensor signal and the challenges posed by real-world media. We show that the developed procedure exhibits exceptional robustness across different biosensing assays and complex real-world media. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8 % and 90.9 % for E. coli and SARS-CoV-2, respectively, without extensive machine learning. Furthermore, we analyse the sensitivity of the procedure to variations of input parameters and with examples discuss key factors influencing overall procedure accuracy. Our results suggest that this exceptionally robust method holds potential as a straightforward tool for automating sample classification in point-of-care diagnostics, underpinning its promising broader applicability.

Theoretical Perspectives Underpinning Research on the Physician-Patient Relationship in a Digital Health Practice: Scoping Review.

The advent of digital health technologies has transformed the landscape of health care, influencing the dynamics of the physician-patient relationship. Although these technologies offer potential benefits, they also introduce challenges and complexities that require ethical consideration. This scoping review aims to investigate the effects of digital health technologies, such as digital messaging, telemedicine, and electronic health records, on the physician-patient relationship. To understand the complex consequences of these tools within health care, it contrasts the findings of studies that use various theoretical frameworks and concepts with studies grounded in relational ethics. Using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a scoping review. Data were retrieved through keyword searches on MEDLINE/PubMed, Embase, IEEE Xplore, and Cochrane. We screened 427 original peer-reviewed research papers published in English-language journals between 2010 and 2021. A total of 73 papers were assessed for eligibility, and 10 of these were included in the review. The data were summarized through a narrative synthesis of the findings. Digital health technologies enhance communication, improve health care delivery efficiency, and empower patients, leading to shifts in power dynamics in the physician-patient relationship. They also potentially reinforce inequities in health care access due to variations in technology literacy among patients and lead to decreases in patient satisfaction due to the impersonal nature of digital interactions. Studies applying a relational ethics framework have revealed the nuanced impacts of digital health technologies on the physician-patient relationship, highlighting shifts toward more collaborative and reciprocal care. These studies have also explored transitions from traditional hierarchical relationships to mutual engagement, capturing the complexities of power dynamics and vulnerabilities. Other theoretical frameworks, such as patient-centered care, and concepts, such as patient empowerment, were also valuable for understanding these interactions in the context of digital health. The shift from hierarchical to collaborative models in the physician-patient relationship not only underscores the empowering potential of digital tools but also presents new challenges and reinforces existing ones. Along with applications for various theoretical frameworks and concepts, this review highlights the unique comprehensiveness of a relational ethics perspective, which could provide a more nuanced understanding of trust, empathy, and power dynamics in the context of digital health. The adoption of relational ethics in empirical research may offer richer insights into the real-life complexities of the physician-patient relationship, as mediated by digital technologies.

Activation Functions for Neural Networks: Application and Performance-based Comparison

Past decade has seen explosive growth of Deep Learning (DL) algorithms based on Artificial Neural Networks (ANNs) and its applications in vast emerging domains to solve real world complex problems. The DL architecture uses Activation Functions (AFs), to perform the task of finding relationship between the input feature and the output. Essential building blocks of any ANN are AFs which bring the required non-linearity of the output in the Output layer of network. Layers of ANNs are combinations of linear and nonlinear AFs. Most extensively used AFs are Sigmoid, Hyperbolic Tangent (Tanh), Rectified Linear Unit (ReLU) etc to name a few. Choosing an AF for a particular AF depends on various factors such as Nature of Application, Design of ANN, Optimizers used in the network, Complexity of Data etc. This paper presents a survey on most widely used AFs along with the important consideration while selecting an AF on a specific problem domain. A broad guideline on selecting an AF based on the literature survey has been presented to help researchers in employing suitable AF in their problem domain.

Complex symmetry in the Fock space of several variables

In this paper we study the complex symmetry in the several variable Fock space by using the techniques of weighted composition operators and semigroups. We characterize unbounded weighted composition operators that are (real) complex symmetric with respect to a concrete conjugation. Using this characterization, we study complex symmetric semigroups and their generators. We realize such generators as first-order differential operators.

Person re‐identification via deep compound eye network and pose repair module

AbstractPerson re‐identification is aimed at searching for specific target pedestrians from non‐intersecting cameras. However, in real complex scenes, pedestrians are easily obscured, which makes the target pedestrian search task time‐consuming and challenging. To address the problem of pedestrians' susceptibility to occlusion, a person re‐identification via deep compound eye network (CEN) and pose repair module is proposed, which includes (1) A deep CEN based on multi‐camera logical topology is proposed, which adopts graph convolution and a Gated Recurrent Unit to capture the temporal and spatial information of pedestrian walking and finally carries out pedestrian global matching through the Siamese network; (2) An integrated spatial‐temporal information aggregation network is designed to facilitate pose repair. The target pedestrian features under the multi‐level logic topology camera are utilised as auxiliary information to repair the occluded target pedestrian image, so as to reduce the impact of pedestrian mismatch due to pose changes; (3) A joint optimisation mechanism of CEN and pose repair network is introduced, where multi‐camera logical topology inference provides auxiliary information and retrieval order for the pose repair network. The authors conducted experiments on multiple datasets, including Occluded‐DukeMTMC, CUHK‐SYSU, PRW, SLP, and UJS‐reID. The results indicate that the authors’ method achieved significant performance across these datasets. Specifically, on the CUHK‐SYSU dataset, the authors’ model achieved a top‐1 accuracy of 89.1% and a mean Average Precision accuracy of 83.1% in the recognition of occluded individuals.

A high-resolution meshfree particle method for numerical investigation of second-order macroscopic pedestrian flow models

Recent advancements in empirical observations of human psychological responses have revealed that the governing rules of human behavioral aspects can not only be predicted with adequate deterministic precision but also forged into mathematical formulations. These modeling studies enable crisis managers with reliable simulation tools to gain insights into critical facets of crowd disasters and enhance their decision-making abilities to facilitate safe egress in emergency situations. The macroscopic scale of representation in this context is often invoked to comprehend large-scale collective pattern formation in vast built environments, owing to its computational viability. The associated governing equations are typically constructed in a system of hyperbolic conservation law form, and conducting simulations in real-life complex geometric configurations can pose computational challenges. This article puts forward a high-resolution, shock-capturing meshfree particle method in an Eulerian framework for the numerical approximation of several widely adopted macroscopic pedestrian flow models. It serves as a superior alternative to traditional mesh-based methods, eliminating the need for specific mesh structures and connectivity between them. A classical Generalized Finite Difference Method (GFDM) in conjunction with several consistency conditions on spatial derivative coefficients is adopted to obtain a non-oscillatory and fairly conservative Godunov-type discretization of the governing system. The intercell flux is evaluated using a suitable Riemann solver, and a slope-limited reconstruction procedure of the corresponding Riemann states, adapted to arbitrary point distribution, is employed for improved accuracy. Moreover, the preferred direction of human motion is determined from a local density-dependent eikonal-type equation, which is solved using a generalized version of the Fast Marching Method. A thorough numerical investigation of three well-known second-order macroscopic models, namely Payne-Whitham (PW), Aw-Rascle (AR), and Aw-Rascle-Zhang (ARZ) is carried out through two hypothetical scenarios of crowd evacuation, not only to demonstrate the accuracy and applicability of the proposed approach but also to deepen understanding of their distinctive characteristics. A comparison of the flow flux with the fundamental diagram suggests that the density profile obtained with the ARZ model is equivalent to Hughes' first-order model. In contrast, the PW and AR models have proven to effectively replicate complex, unstable pedestrian phenomena such as stop-and-go waves. Finally, Helbing's counter-intuitive idea that strategically positioned barriers upstream of a bottleneck can essentially expedite evacuation is numerically investigated across various parameter choices.

The inhibitory effect of natural organic matters and ions on 17ɑ-ethinylestradiol removal over new TiO2(B)/Bi5O7I and mesoporous carbon/TiO2(B)/BiOI composites: The role of mesoporous carbon

The natural organic matters (NOMs) and ions prevalent in water matrixes always inhibits photocatalytic degradation of pollutants by TiO2. Herein, we prepared TiO2(B)/Bi5O7I and mesoporous carbon/TiO2(B)/BiOI composites to investigate the inhibitory effect of NOMs and ions on photocatalytic degradation of 17ɑ-ethinylestradiol (EE2). The results show that Bi5O7I or BiOI is tightly anchored onto the large surface of TiO2(B) nanobelt to form heterojunction, and metallic Bi forms on the surface of TiO2(B) in MC/TiO2(B)/BiOI composite. This intimate contact is beneficial for the separation of hole-electron pairs and stability. More importantly, the introduction of mesoporous carbon (MC) not only improve the EE2 removal but also eliminate obvious inhibitory effect of NOMs and inorganic ions. The main cause is that MC component in MC/TiO2(B)/BiOI composite can preferentially adsorb EE2 molecules from water via π-π and hydrophobic interaction forces between EE2 and MC. Quenching experiments and electron paramagnetic resonance confirm that •O2- and h+ are main reactive species for EE2 degradation over both TiO2(B)/Bi5O7I and MC/TiO2(B)/BiOI composites. Besides, the degradation mechanism was investigated, and degradation pathways are proposed based on intermediates identification. The MCF-7 cell test shows that the estrogenic activity of the treated solution can be rapidly eliminated. More importantly, MC/TiO2(B)/BiOI has excellent anti-interference capacity for the EE2 removal in real complex water matrixes.

The Blame Game: Understanding Blame Assignment in Social Media

Cognitive and psychological studies on morality have proposed underlying linguistic and semantic factors. However, laboratory experiments in the philosophical literature often lack the nuances and complexity of real life. This paper examines how well the findings of these cognitive studies generalize to a corpus of over 30,000 narratives of tense social situations submitted to a popular social media forum. These narratives describe interpersonal moral situations or misgivings; other users judge from the post whether the author (protagonist) or the opposing side (antagonist) is morally culpable. Whereas previous work focuses on predicting the polarity of normative behaviors, we extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychology theories enables understanding real-life moral situations. Moreover, our results suggest that there exist biases in blame assignment on social media, such as males are more likely to receive blame no matter whether they are protagonists or antagonists.

Practical use of mechanistic modeling to discern real word complex drug interactions

Practical use of mechanistic modeling to discern real word complex drug interactions

Decomposition and factorisation of transients in functional graphs

Functional graphs (FGs) model the graph structures used to analyse the behaviour of functions from a discrete set to itself. In turn, such functions are used to study real complex phenomena evolving in time. As the systems involved can be quite large, it is interesting to decompose and factorise them into several subgraphs acting together. Polynomial equations over functional graphs provide a formal way to represent this decomposition and factorisation mechanism, and solving them validates or invalidates hypotheses on their decomposability. The current solution method breaks down a single equation into a series of basic equations of the form A×X=B (with A, X, and B being FGs) to identify the possible solutions. However, it is able to consider just FGs made of cycles only. This work proposes an algorithm for solving these basic equations for general connected FGs. By exploiting a connection with the cancellation problem, we prove that the upper bound to the number of solutions is closely related to the size of the cycle in the coefficient A of the equation. The cancellation problem is also involved in the main algorithms provided by the paper. We introduce a polynomial-time semi-decision algorithm able to provide constraints that a potential solution will have to satisfy if it exists. Then, exploiting the ideas introduced in the first algorithm, we introduce a second exponential-time algorithm capable of finding all solutions by integrating several ‘hacks’ that try to keep the exponential as tight as possible.

Effect of PFDS on the immobilization of Cs+ by metakaolin-based geopolymers in complex environments

Metakaolin-based geopolymers are very promising materials for improving the safety of low and intermediate level radioactive waste disposal, with respect to ordinary Portland cement, due to their excellent immobilization performance for Cs+ and superior chemical stability. However, their application is limited by the fact that the leaching behavior of Cs+ is susceptible to the presence of other ions in the environment. Here, we propose a way to modify a geopolymer using perfluorodecyltriethoxysilane (PDFS), successfully reducing the leaching rate of Cs+ in the presence of multiple competitive cations due to blocking the diffusion of water. The leachability index of the modified samples in deionized water and highly concentrated saline water reached 11.0 and 8.0, respectively. The reaction mechanism between PDFS and geopolymers was systematically investigated by characterizing the microstructure and chemical bonding of the material. This work provides a facile and successful approach to improve the immobilization of Cs ions by geopolymers in real complex environments, and it could be extended to further improve the reliability of geopolymers used in a range of applications.

A novel, low-cost, and efficient separation strategy based on S-doped C3N4 for the analysis of PCDD/Fs and dioxin-like PCBs

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (dl-PCBs) are widespread and highly toxic persistent organic pollutants with similar chemical structure. The popular process for the separation of PCDD/Fs and dl-PCBs in one sample often relies on imported and expensive activated carbon, giving rise to an urgent need of inexpensive and efficient separation method. Herein, a novel, simple, and low-cost separation strategy based on sulfur-doped carbon nitride (S-C3N4) is reported. The S-C3N4 material was synthesized through simple thermal annealing with precursor of thiocyanuric acid. After recovery experiments, the optimal addition ratio of S-C3N4 was determined to be 40% mixed with silica gel. The recoveries for PCDD/Fs and dl-PCBs congeners are 32% - 105% and 60% - 70% respectively, which not only meets USEPA and Chinese standards but also shows comparable performance to activated carbon. Moreover, the proposed S-C3N4 strategy exhibits excellent performance for the analysis of PCDD/Fs and dl-PCBs in real complex samples (sediment and fish liver) in terms of detected concentrations and toxic equivalents. Overall, this study puts forward a new and low-cost avenue for the separation of PCDD/Fs and dl-PCBs, which is of remarkable meaning for motivating the development of detection techniques and domestic equipment for dioxins and other pollutants in the environment.

What do leaders really want to learn in a workplace? A study of the shifting agendas of leadership coaching

Multiple theories of leadership postulate specific capability requirements with an expectation that leaders recognize the need for such capabilities and become motivated to develop them. In the workplace, leaders’ development is also expected to respond to the immediate demands of the organizational context. However, what leaders end up learning in the workplace remains largely unexplored. Hence our inquiry is into what leaders choose to learn, when they are in role and face the realities and demands of their immediate and wider environment. In line with Vygotsky’s sociocultural theory and concept of ‘perezhivanie’ we explore what actually becomes important for leaders to learn when they receive developmental support from a coach. We do this by identifying the content of coaching conversations: what is demonstrably discussed in coaching - the main themes of the actual coaching conversations and how the predominance of different themes changes over the coaching engagement. Based on the analysis of the sequencing of coaching themes in 153 organizational coaching engagements we discuss the dynamic interplay of the personal and the organizational agendas in the changing foci of leader learning. We propose a novel and theoretically-grounded explanation of leaders’ choices for learning in real complex environments. The results of uniquely gathered data and analysis challenge some current trends in the scholarship and praxis of leader development.