Complex Process Research Articles

A CONSTRUÇÃO DA IDENTIDADE CULTURAL NA ERA DIGITAL

The construction of cultural identity in the digital era is a dynamic and multifaceted phenomenon, driven by technological advancement and globalization. The internet and social networks, as the main contemporary communication vehicles, play a central role in the formation of cultural identities, promoting interaction between people of different cultures and values. In this scenario, cultural identity is no longer static or limited to a geographic space, but becomes fluid, shaped by diverse experiences, including online and offline interactions. The digital age offers unlimited possibilities for cultural expression and recognition, allowing individuals and groups to share their traditions, beliefs and customs with a global audience. At the same time, this constant exposure to different cultures can generate tension between the preservation of local identities and the influence of a dominant global culture. Furthermore, cultural identity in the digital age is often negotiated, where individuals and communities choose which aspects of their culture they want to emphasize or adapt. However, this transformation can also generate challenges, such as cultural homogenization or the risk of cultural appropriation. Therefore, the construction of cultural identity in the digital age is a complex process that involves both the strengthening and transformation of cultural traditions. New digital platforms allow for greater democratization of cultural discourse, but they also pose challenges that need to be recognized and discussed, especially in relation to the preservation of cultural diversity.

FAM20C Promotes Papillary Thyroid Cancer Proliferation and Metastasis via Epithelial-Mesenchymal Transition.

Thyroid cancer (TC) is the prevailing malignancy that impacts the endocrine system, accounting for 1% of all recently diagnosed malignancies in humans. The incidence of TC has been continuously increasing, which can be attributed to advancements in clinical diagnostic technology. However, the mechanisms behind the development of TC are still not well understood. TC is classified into four pathological forms: medullary thyroid cancer, papillary thyroid cancer (PTC), follicular thyroid cancer, and poorly differentiated TC. PTC constitutes more than 80% of all TC cases globally. Current research indicates that complex genetic and cellular processes could be responsible for the growth and spread of TC. Next-generation sequencing (RNA-seq) of 79 PTC samples and their corresponding normal thyroid tissues was performed to investigate the molecular mechanisms of PTC. An analysis of RNA-seq data from a local cohort from The Cancer Genome Atlas (TCGA) revealed that, compared with normal tissues, PTC tissues presented elevated FAM20C expression levels. In vitro, the function of FAM20C was validated with small interfering RNA (siRNA). Gene set enrichment analysis (GSEA) revealed the pathways influenced by FAM20C. A western blot experiment was used to investigate protein expression levels associated with epithelial‒mesenchymal transition (EMT). In conclusion, by regulating EMT, FAM20C facilitates PTC cell proliferation and metastasis.

The Effects of SSRIs and Antipsychotics on Long COVID Development in a Large Veteran Population

The development of Long COVID is a complex disease process that may be partially driven by neuroinflammation. Antipsychotics have been shown to exert neuroprotective effects under certain conditions. Our study aimed to determine if veterans treated with antipsychotics and/or selective serotonin reuptake inhibitors (SSRIs) for a psychiatric condition had a reduced risk of developing long-term COVID. We conducted a retrospective cohort study with two cohorts of patients based on the COVID-19 wave in which the patient’s initial infection occurred (Cohort 1: alpha/beta waves, and Cohort 2: delta/omicron waves) with stratification by age. A multivariate logistic regression model was used to evaluate the association between the use of antipsychotics and Long COVID diagnosis. In Cohort 1, antipsychotic use was associated with 43% and 34% reductions in the odds of developing Long COVID in patients aged <65 and >65 years, respectively. This association was reduced in the second cohort to 11% in patients aged <65 years and without an association over 65 years of age. SSRIs showed no benefit in either age group or cohort. Our results show that antipsychotic use for the treatment of a mental health condition was associated with a reduction in the risk of developing Long COVID, and the magnitude of this reduction varied between COVID-19 cohorts.

Flexible TiO2-WO3−x hybrid memristor with enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

Tungsten oxide (WO3)-based memristors show promising applications in neuromorphic computing. However, single-layer WO3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO3−x and TiO2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO2 not only enhances the memristor’s memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The “learning-forgetting-relearning” characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%.

DNA Replication and Polymer Chain Duplication Reshape the Genome in Space and Time

In eukaryotes, DNA replication constitutes a complex process whereby multiple origins are stochastically fired, and from which the replication machinery proceeds along chromosomes to achieve the faithful synthesis of two identical copies of the genome during the S phase of the cell cycle. Experimental evidence shows a functional correlation between the dynamics of replication and the spatial organization of the genome inside cell nuclei, suggesting that the process of replicating DNA may impact chromosome folding. However, the theoretical and mechanistic bases of such a hypothesis remain elusive. To address that question, we propose a quantitative, minimal framework that integrates the dynamics of replication along a polymer chain by accounting explicitly for the progression of the replication machinery and the resulting formation of sister chromatids. By systematically characterizing the 3D structural consequences of replication, and of possible interactions between active replication machineries, we show that the formation of transient loops may potentially impact chromosome organization across multiple temporal and spatial scales, from the level of individual origins to that of the global polymer chain. Comparison with available microscopy and chromosome conformation capture data in yeast suggests that a replication-dependent loop extrusion process may be acting , and may shape chromosomes as loose polymer bottle brushes during the S phase. Lastly, we explore the postreplication relative organization of sister chromatids and demonstrate the emergence of catenations and intertwined structures, which are regulated by the density of fired origins. Published by the American Physical Society 2024

An Analysis on Domestic Tourism Development Prospective in Karnali Province, Nepal

The main thrust of this paper is to elucidate various aspects of domestic tourism in the specific context of Karnali Province in Nepal. To elucidate the complex interface between tourism and people, attempt have been made to provide an analysis covering various aspects related to domestic tourism policies, programs, prospects and problems. This research was conducted under Mixed Method research design because the study was described systematically the major problems and prospects of tourism development in the study area. The districts and places for domestic tourism importance have been purposively selected in order to collect valid and reliable data from the local level. Tourism is a complex process related to the work and life of people who live in the tourism potential areas. The tourism phenomenon has to make a perfect match in order to harness the benefits of tourism related activities. This research based article covers various facts of tourism development related phenomena and processes as social, cultural, religious, and natural aspects when come together make a complex form of an integrated rural network.

Optimizing neural network models for predicting nuclear reactor channel temperature: A study on hyperparameter tuning and performance analysis

This study emphasizes how important accurate prediction of channel temperatures in nuclear reactors is for safety and operational efficiency. While traditional methods require long and complex processes such as kernel modeling and mathematical simulations, artificial neural networks (ANN) provide more efficient predictions by accelerating this process. The superior ability of ANNs to process large data sets is intended to demonstrate that this study will provide a valuable alternative compared to conventional methods and increase the accuracy of reactor temperature predictions. In this study, the training performances of Artificial Neural Network (ANN) developed to determine the nuclear reactor channel temperature with different hyperparameter combinations were analysed. It was conducted several experimental studies to assess the influence of hyperparameters on our model for nuclear reactor parameter data prediction. The training and validation results indicates that learning rate, hidden layer sizes and number have critical effects for the more precisive prediction. It was observed that models with a learning rate of 0.05 and 0.5 achieved successful learning with less fluctuation in training and validation errors. When looking at hidden layer sizes, networks with 32 and 64 neurons performed better than networks with 16 neurons. For the test phase our model can successfully predict data with slight error margin. As a result, we demonstrated that neural networks are a powerful tool in nuclear reactor channel temperature prediction through our proposed model.

Aspiration after critical illness: role of endotracheal tube, tracheostomy, swallowing disorders.

Swallowing is a complex process that involves over 50 muscles and nerves and has two critical roles: passing food from the oral cavity through the pharynx and into the esophagus and preventing contents from entering the airway. If a patient's swallowing physiology or airway protective mechanisms are disturbed, the airways and the lungs have innate defense systems to protect against injury and infection. However, critically ill patients are more likely to develop dysphagia, which is an impairment or malfunction in any aspect of the swallowing mechanism, due to the numerous interventions they undergo. When airway reflexes fail, commonly in the presence of dysphagia, aspiration can occur, which is the entry of a fluid or solid below the level of the true vocal cords. If left unmanaged, dysphagia has been associated with aspiration pneumonia, pneumonitis, airway obstruction, delayed enteral nutrition, prolonged length of ICU and hospital stay, reduced quality of life, and even death; in some cases, dysphagia is an independent risk factor for mortality. It is important to routinely assess dysphagia in all critically ill patients using a multimodal approach, including systematic assessments, scoring indices, trained specialists, and intensive care unit nurses. Several interventions are crucial for preventing and managing dysphagia and its associated problems. Further research is necessary to help determine the best ways to prevent and manage pulmonary aspiration in critically ill patients. Several interventions are essential in preventing and managing dysphagia and the sequelae of swallowing dysfunction. Further research is needed to help elucidate the best way to avoid and manage pulmonary aspiration in critically ill patients.

Transitory sensitivity in automatic chemical kinetic mechanism analysis

AbstractDetailed chemical kinetic mechanisms are necessary for resolving many important chemical processes. As the chemistry of smaller molecules has become better grounded and quantum chemistry calculations have become cheaper, kineticists have become interested in constructing progressively larger kinetic mechanisms to model increasingly complex chemical processes. These large kinetic mechanisms prove incredibly difficult to refine and time‐consuming to interpret. Traditional sensitivity analysis on a large mechanism can range from inconvenient to practically impossible without special techniques to reduce the computational cost. We first present a new time‐local sensitivity analysis we term transitory sensitivity analysis. Transitory sensitivity analysis is demonstrated in an example to accurately identify traditionally sensitive reactions at an 18,000x speed up over traditional sensitivities. By fusing transitory sensitivity analysis with more traditional time‐local branching, pathway, and cluster analyses, we develop an algorithm for efficient automatic mechanism analysis. This automatic mechanism analysis at a time point is able to identify the reactions a target is most sensitive to using transitory sensitivity analysis and then propose hypotheses why the reaction might be sensitive using branching, pathway, and cluster analyses. We implement these algorithms within the reaction mechanism simulator (RMS) package, which enables us to report the automatic mechanism analysis results in highly readable text formats and in molecular flux diagrams.

Quantitative Analysis of Protein-Protein Equilibrium Constants in Cellular Environments Using Single-Molecule Localization Microscopy.

Current methods for determining equilibrium constants often operate in three-dimensional environments, which may not accurately reflect interactions with membrane-bound proteins. With our technique, based on single-molecule localization microscopy (SMLM), we directly determine protein-protein association (Ka) and dissociation (Kd) constants in cellular environments by quantifying associated and isolated molecules and their interaction area. We introduce Kernel Surface Density (ks-density,) a novel method for determining the accessible area for interacting molecules, eliminating the need for user-defined parameters. Simulation studies validate our method's accuracy across various density and affinity conditions. Applying this technique to T cell signaling proteins, we determine the 2D association constant of T cell receptors (TCRs) in resting cells and the pseudo-3D dissociation constant of pZAP70 molecules from phosphorylated intracellular tyrosine-based activation motifs on the TCR-CD3 complex. We address challenges of multiple detection and molecular labeling efficiency. This method enhances our understanding of protein interactions in cellular environments, advancing our knowledge of complex biological processes.

Deciphering the potential role of post-translational modifications of histones in gastrointestinal cancers: a proteomics-based review with therapeutic challenges and opportunities

Oncogenesis is a complex and multi-step process, controlled by several factors including epigenetic modifications. It is considered that histone modifications are critical components in the regulation of gene expression, protein functions, and molecular interactions. Dysregulated post-translationally modified histones and the related enzymatic systems are key players in the control of cell proliferation and differentiation, which are associated with the onset and progression of cancers. The most of traditional investigations on cancer have focused on mutations of oncogenes and tumor suppressor genes. However, increasing evidence indicates that epigenetics, especially histone post-translational modifications (PTMs) play important roles in various cancer types. Mass spectrometry-based proteomic approaches have demonstrated tremendous potential in PTMs profiling and quantitation in different biological systems. In this paper, we have made a proteomics-based review on the role of histone modifications involved in gastrointestinal cancers (GCs) tumorigenesis processes. These alterations function not only as diagnostic or prognostic biomarkers for GCs, but a deeper comprehension of the epigenetic regulation of GCs could facilitate the treatment of this prevalent malignancy through the creation of more effective targeted therapies.

Partial regression of conventional renal cell carcinoma displays markers of wound repair

AimsDuring detailed analysis of H&E-stained histological slides of 710 unbiased conventional renal cell carcinomas (cRCCs), 141 tumours displayed partial regressive changes showing strong similarity to that of wound healing. We...

Smart Building Digital Twin for Interior Water Distribution System Management

Abstract. A smart campus integrates the Internet of Things, artificial intelligence, and real-time sensor data to optimize campus functions and create a context-aware decision-support platform for effective campus management. A crucial aspect of a smart campus is the water distribution system, which faces several challenges due to bursts, leaks, and water quality issues. This study uses Digital Twin technology to address these challenges through real-time monitoring, detection, and management of campus water distribution networks. A building-level digital twin is developed for the interior water networks of the Daphne Cockwell Complex at Toronto Metropolitan University. The major components include a 3D network model capable of analysis and simulations, a smart water management system, and real-time visualization. A 3D static model of the interior water utility network is created using the Industrial Foundation Class data. The semantic and topological models based on graph models are developed for network analysis. The models are integrated into the ESRI ArcGIS Pro to facilitate BIM-GIS integration. A dynamic model is created by incorporating automatic meter readings based on IoT. Combined with the 3D model of the utility network, the digital twin monitors and visualizes building water consumption, facilitating anomaly detection and real-time maintenance by the facilities management department. This study provides practical guidance for managing water distribution systems in university buildings, with potential applications in other complex environments. Future work aims to develop a system for detecting complex event processes by integrating different utilities.

The Molecular Mechanism of Embryonic Development in Decapod Crustaceans

ABSTRACTIn recent decades, the decapod aquaculture industry has grown significantly in many parts of the world. Despite this progress, production levels still fall short of market demands, indicating a need for further efforts to increase the production. Investigations into the molecular mechanisms underlying embryonic development in decapods provide valuable insights into the complex embryonic development processes of this economically and ecologically important animal group. In this review, we explore the molecular mechanisms underlying decapod embryonic development, focusing on key genes, signaling pathways, and microRNAs (miRNAs) involved in the embryogenesis. Numerous genes have been identified that regulate various aspects of embryogenesis including egg activation, maternal‐to‐zygotic transition (MZT), mesoderm development, segmentation, nervous system development, sex determination, germline development, and exoskeleton formation. We also discuss the pivotal role of signaling pathways such as Hedgehog, Wnt, Notch, MAPK, TGF‐β, Jak‐STAT, VEGF, and Ecdysteroid in the regulation of embryogenesis. Additionally, this review identifies current research gaps in decapod embryogenesis and proposes directions for future investigation. This review is anticipated to be especially beneficial for researchers delving into the molecular mechanisms driving embryonic development in decapods.

Lack of Association Between BsmI and FokI Polymorphisms of the VDR Gene and Sporadic Colorectal Cancer in a Romanian Cohort-A Preliminary Study.

Colorectal cancer (CRC) is a major public health problem worldwide, currently ranking third in cancer incidence and second in mortality. Multiple genes and environmental factors have been involved in the complex and multifactorial process of CRC carcinogenesis. VDR is an intracellular hormone receptor expressed in both normal epithelial and cancer colon cells at various levels. Several VDR gene polymorphisms, including FokI and BsmI, have been evaluated for their possible association with CRC susceptibility. The aim of our study was to investigate these two SNPs for the first time in Romanian CRC patients. FokI (rs228570 C>T) and BsmI (rs1544410 A>G) were genotyped by real-time polymerase chain reaction (RT-PCR) in 384-well plates using specific TaqMan predesigned probes on a ViiA™ 7 RT-PCR System. A total of 441 subjects (166 CRC patients and 275 healthy controls) were included. No statistically significant difference was observed between CRC patients and controls when we compared the wild-type genotype with heterozygous and mutant genotypes for both FokI (OR 0.85, 95% CI: 0.56-1.28; OR 0.95, 95% CI: 0.51-1.79, respectively) and BsmI (OR 0.97, 95% CI: 0.63-1.49; OR 1.10, 95% CI: 0.65-1.87, respectively) or in the dominant and recessive models. Also, we compared allele frequencies, and no correlation was found. Moreover, the association between these SNPs and the tumor site, TNM stage, and histological type was examined separately, and there was no statistically significant difference. In conclusion, our study did not show any association between FokI and BsmI SNPs and CRC susceptibility in a Romanian population. Further studies including a larger number of samples are needed to improve our knowledge regarding the influence of VDR polymorphism on CRC susceptibility.

Dynamical complexity and multifractal analysis of geomagnetic activities at high temporal scales over three solar cycles

Activities in geospace occur at different time scales. Understanding geomagnetic activity at high temporal scales will give insight into fast dynamics in geospace. This study aims to investigate dynamical complexities in geomagnetic activities at a high temporal scale across three solar cycles. Geomagnetic activities, as represented by 5-min SYM-H data, were considered in this study over three solar cycles (22–24) from 1986 to 2019. Chaos analysis using sample entropy, Lyapunov exponent, and correlation dimension indicates that the geomagnetic activities are driven by intrinsic complex and chaotic processes. Positive Lyapunov exponent values between 0.13 and 0.18, 0.15–0.18, and 0.16–0.19 were obtained for solar cycles 22, 23, and 24 respectively. Furthermore, geomagnetic activities were also found to have multifractal structures driven by high fractal exponents with fine structures. A positive relationship was obtained between the annual mean values of SYM-H and the degree of complexity. It is concluded that geomagnetic activities have a short prediction horizon.

Numerical modelling and experimental validation of an Enhanced multi-force model of free-fall electrostatic separators

Electrostatic separation is a crucial process for separating mixed materials, especially in recycling discarded electrical and electronic equipment. This study aims to validate an enhanced numerical model for free-fall electrostatic separators by incorporating additional forces. The model simulates particle trajectories under electrostatic forces, considering electrostatic interactions and collisions. The particles’ positions, masses, and charges are recorded to create a comprehensive database for model verification. The model was validated experimentally using three-particle mixtures of PVC-PC, PS-PC, and PS-PVC; experimental validation confirms that the updated model accurately captures the complex physical processes in the electrostatic separator. Including additional forces results in more precise particle behavior, essential for optimizing separator design and improving the purity of the separated products. The results show a concordance of mass and charge 98.812% and 96.907% for PS + PVC, 99.972% and 98.715% for PC + PVC mixtures, and 92.406% for mass and 97.244% for charge in PS+PC mixtures, demonstrating the model’s accuracy and reliability.

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks.

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, whose behaviour was marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised gating. This suggests that insight-like behaviour can arise from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation. These results have potential implications for more complex systems, such as the brain, and guide the way for future insight research.

The Potential of AI in Information Provision in Energy-Efficient Renovations: A Narrative Review of Literature

<p>Energy-efficient renovation (EER) is a complex process essential for reducing emissions in the built environment. This research identifies homeowners as the main decision-makers, whereas intermediaries and social interactions between peers are highly influential in home renovations. It investigates information and communication barriers encountered during the initial phases of EERs. The study reviews AI tools developed within the EERs domain to assess their capabilities in overcoming these barriers and identifies areas needing improvement. This research examines stakeholders, barriers, and the AI tools in the literature for EERs. The discussion compares the functionalities of these tools against stakeholder needs and the challenges they face. Findings show that tools often overlook methodologies in human–computer interaction and the potential of textual and visual AI methods. Digital tool development also lacks insights from social science and user feedback, potentially limiting the practical impact of these innovations. This article contributes to the EERs literature by proposing an AI-supported framework and outlining potential research areas for future exploration, particularly improving tool effectiveness and stakeholder engagement to scale up the EER practice.</p>

Learning by Visualize a Nurse-Led CCOS Using the Functional Resonance Analysis Method.

Quality improvements (QIs) in dynamic and complex health care contexts require resilience and take variability into account in quality improvement. The Functional Resonance Analysis Method (FRAM) helps us understand resilience and gain insight into (un)desirable variability in the complex system of daily practice. We explored how using FRAM in the Deming cycle of a QI project can help professionals and researchers learn from, reflect upon, and improve complex processes. We used FRAM in a Dutch hospital to study a QI: Critical Care Outreach Service (CCOS). The aim was to use FRAM before and after implementation to create a FRAM model and reflect to health care professionals the mismatch between Work As Imagined (WAI) and Work As Done (WAD). The WAI FRAM model was co-created with professionals before the implementation of CCOS. We used descriptions of tasks and processes for ICU nurses and verified them in 30-minute semistructured interviews (N = 2). WAD was created by input of semistructured interviews with key professionals in CCOS (N = 21) and 3 nonparticipant observations of trained CCOS nurses. We validated WAD in 2 dialogue sessions with key professionals (N = 11). Data collection continued until saturation. Juxtaposing the WAI and WAD models showed that WAD contained additional functions and highlighted unexpectedly complex functions. Reflecting on the application of FRAM with health care professionals revealed opportunities and challenges, especially time investment. FRAM helps professionals outline processes and tasks (WAI), learn from, and reflect upon their daily practice (WAD). FRAM models help professionals identify variability proactively to improve practices that enhance resilient performance.