Dynamic Management Research Articles

Stretchable Metamaterials with Tunable Infrared Emissivity for Dynamic Thermal Management.

Manipulation of infrared emissivity, which is closely related to surface structure and optical parameters of materials, is a crucial approach for realizing dynamic thermal management. In this study, we design a metamaterial consisting of an array of aluminum disks embedded on a surface of a stretchable elastomeric substrate. Mechanical stretching-induced deformation allows dynamic modification of the surface structure and equivalent optical parameters, thus enabling dynamic control of the emissivity. By utilizing the elastomer polydimethylsiloxane (PDMS) as the substrate, the microstructure interdisk gap can be altered by stretching the PDMS. Through theoretical calculations, the plausibility of this approach is explained by the excitation of plasmon resonance and the variation in the exposed area of highly absorbent PDMS, and the optimal structures for tuning the infrared emissivity are revealed to be 6 μm in diameter and 100 nm in height. Based on this design, we prepare samples with periods of 7 and 7.9 μm and experimentally demonstrate that a change in the period can cause a change in the emissivity and thus tunability in thermal control performance. The temperature difference between the two samples reaches 44.1 °C at a heating power of 0.28 W/cm2 for both samples. Furthermore, we construct a stretching platform that enables in situ mechanical stretching to realize dynamic changes in emissivity. The integral infrared emissivity of the sample increases from 0.32 to 0.5 at a biaxial tensile strain of 13%, achieving a 56% modulation rate of the integral infrared emissivity. The material is expected to enable dynamic thermal management.

Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity

<p>The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.</p>

Machine Learning-Driven Reconfigurable EONs with Neuromorphic Computing for Network Slicing and On-Demand Service Provisioning: A Review and Survey

Abstract: This paper delves into the substantial potential of integrating advanced technologies within Reconfigurable Elastic Optical Networks (REONs). By leveraging machine learning and neuromorphic computing, these networks can significantly enhance performance, scalability, and efficiency. Machine learning models facilitate dynamic resource management, allowing for the on-demand reconfiguration of optical networks to improve service provisioning and maintain high Quality of Service (QoS). Neuromorphic processors further boost network-slicing capabilities, optimizing bandwidth management and enabling the creation of customized virtual networks. Additionally, the incorporation of neuromorphic computing into REONs contributes to substantial energy savings, a critical factor for sustainable network operations. Techniques such as differential privacy and secure multi-party computation effectively address security and privacy challenges within optical networks. Future research should focus on developing scalable architectures, formulating energy-efficient algorithms, and designing solutions tailored to specific applications to maximize the potential of REONs. In summary, the integration of advanced computing techniques within REONs promises to revolutionize network management and service delivery, equipping future networks to deliver exceptional performance, scalability, and adaptability, thus meeting the evolving demands of modern communication environments.

Effective dynamic energy management algorithm for grid-interactive microgrid with hybrid energy storage system

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink.

Changing the focus: The need for cross-scale dynamic management in the Southern Ocean and implications for holistic conservation of Antarctic marine living resources

The Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR) establishes the regulatory framework for fisheries management and the establishment of Marine Protected Areas (MPA) in the Southern Ocean. Identifying foraging areas regularly used by penguins provides valuable information for the small-scale management of the krill fishery and the design of MPAs, currently under discussion in CCAMLR. We used geolocators to identify the areas used by Adélie penguins from Ardley Island (South Shetlands Islands) during the non-breeding season and Stable Isotope Analysis (SIA) (δ13 C and δ15 N) on feathers to study the trophic niche. Adélie penguins depended on resources available in CCAMLR subareas 48.1 and 48.5 during the post-breeding and molting stage, the subarea 48.5 during the post-molting stage and subareas 48.1, 48.2 and 48.5 during the pre-breeding stage, critical moments of the species’ annual cycle. The Domain 1, Weddell Sea and SOISS MPA proposal areas were extensively used. SIA results show the use of coastal and off-shore foraging areas in 2022, but principally off-shore areas in 2021. Adélie penguins dependence on resources from different subareas in different times of the year emphasizes the need of incorporating the temporal dimension of resources utilization when designing conservation measures in the Southern Ocean. With an increasing pressure on Adélie penguin colonies in the western Antarctic Peninsula, its long-term protection might depend on both 1) well designed and managed MPAs in the Domain 1 and the Weddell Sea, and 2) small-scale fisheries management that simultaneously consider activities undertaken in different small-scale management units.

Transition towards net zero emissions: Integration of a PV/T system with a hydroelectric generator and the impact of demand-side management

With the aim of diversifying different energy sources and achieving net zero emissions, hybrid renewable energy sources (HRES) represent the future of the world. However, several HRES simulation software do not integrate the Photovoltaic/thermal (PV/T) system. This article designs an optimal design model for a tri-hybrid Photovoltaic/thermal/hydroelectric (PV/T/H) system for a rural locality in the North Cameroon region. The two Demand Side Management (DSM) strategies used reveal that the DSM strategy significantly reduced the energy cost by 59 % and the emission by CO2 22 % compared to the No-DSM mode. Although the use of battery storage (BSS) is used in both cases, the optimal solutions obtained thanks to the multi-objective optimization method implemented on Matlab led to 418 PV/T panels, 2 MH generators, 2 diesel generators (DG) and 217 PV/T panels and 1 DG for DSM and No-DSM mode respectively. This study is a demonstration of the effect of dynamic tariffs and active demand management technologies on PV/T/H modeling and optimization. It also reveals the need to hybridize PV/T with other energy systems to increase performance and achieve net zero emissions.

Evaluating drivers and predictability of catch composition in a highly mixed trawl fishery using stacked and joint species distribution models

Evaluating drivers and the predictability of catch is valuable for the management of mixed fisheries. Drivers can represent or help to identify levers for management and predictable catch compositions are a key component of simulation tools and dynamic management strategies. But modelling mixed fisheries can be challenging due to the large number of taxa, and analysis typically focuses on a few key species or highly aggregated taxa.Here we employ seven types of stacked and joint species distribution models to explore the drivers and predictability of trawl-level catches in an ocean prawn trawl fishery, in New South Wales, Australia. Catch data was sourced from an observer program, with 130 taxa able to be modelled. The main drivers of catch composition were latitude, depth, and seasonality represented here by water temperature. Water column mixing, lunar illumination, and fishing effort were also important for some taxa. Up to 60–80 taxa were predicted with good predictive skill (AUC>0.8, >35 % decline in mean absolute error relative to an intercept-only model), and an additional 40–60 taxa were predicted with lower but still useful predictive skill (AUC>0.7, 25–35 % decline in error). However, the level of predictive skill varied considerably among model type.The best framework for prediction was stacked random forests using a hurdle modelling approach, followed by a spatial joint species distribution model. Our results show that predictive models at a fine spatial-temporal and taxonomic resolutions can be a viable information tool for highly mixed fisheries, but these tools ultimately need to be tested against specific management objectives and performance metrics, such as spatial closures and bycatch:target catch ratios.

Joint wireless energy transmission and dynamic power management for full-duplex relay system

The rapid deployment of fifth-generation (5G) mobile communication systems demands sustainable power solutions. This paper introduces a novel joint design for wireless energy transmission and dynamic power management in full-duplex relay systems, aiming to ensure a continuous power supply for relay nodes. Our approach integrates energy harvesting from RF signals and dynamic battery management, focusing on optimizing throughput via energy allocation and precoding design. Simulation results demonstrate that our scheme significantly outperforms conventional non-battery half-duplex schemes in system throughput. Notably, we observe performance increments in throughput compared to all mentioned traditional methods, highlighting the efficacy of our design in enhancing 5G network sustainability and efficiency. This innovative approach presents a promising solution for effective power management in next-generation mobile networks.

Dynamic management of ground thermal response uncertainty through temporal analysis of parameter sensitivity

Accurately estimating the dimensionless ground thermal response factor (g-function) is critical for the design and operation of geothermal energy systems. This process, however, is fraught with uncertainties stemming from the difficulty in controlling on-site geometric parameters and estimating thermal properties through field experiments. The relative contributions of these parameters to response uncertainty change temporally as the response progresses. Thus, understanding this temporal sensitivity variation is crucial for effective response uncertainty management. To address this, we conducted transient global sensitivity analyses. Regardless of borefield setups, ground thermal properties were key factors in thermal response uncertainty. However, their influence decreased from the late-mid term as borehole radius and borehole distance became more significant in single-borehole and multi-borehole setups, respectively. This time-varying relative sensitivity suggests that prioritizing parameters to reduce response uncertainty depends on the temporal characteristics of the ground load. To demonstrate this, we systematically halved the uncertainty for each parameter and examined the resultant change in the fluid temperature uncertainty for a dynamic ground load scenario. Notably, prioritizing parameters with high early-response sensitivity significantly reduced fluid temperature uncertainty. These findings highlight the need for dynamic uncertainty management strategies in geothermal system design and operation. For dynamic load profiles utilizing the ground as a heat source or sink, prioritizing parameters with high sensitivity in the early- to mid-term is crucial. Conversely, for long-term storage applications, focusing on parameters with high sensitivity in the mid- to long-term response becomes essential.

A dynamic priority bus lane strategy in heterogeneous traffic environments

Allowing connected and automated vehicles (CAVs) to drive in bus lanes can enhance the utilization efficiency of exclusive bus lanes. However, this could also lead to a certain degree of heightened bus operating delays. In a connected and automated environment, vehicle-to-vehicle (V2V) communication is essential for implementing dynamic management strategies for bus lanes. This study focuses on urban arterial roads as the research scenario. Based on the characteristics of the car-following and lane-changing behaviors of human-driven vehicles (HDVs) and CAVs, a simulation platform for heterogeneous traffic flow is established. Four strategies for bus lane control are proposed: exclusive bus lanes (EBLs), bus and CAV mixed lanes (BCMLs), dynamic bus lanes (DBLs), and dynamic priority bus lanes (DPBLs). Through simulation experiments, an analysis is conducted to compare traffic operation characteristics under different bus lane strategies, assess the advantages of these strategies, and determine the traffic density conditions for their implementation. The results indicate that in comparison to the EBL strategy, the other strategies enhance traffic flows but result in different levels of travel delays for buses. Compared to the DBL and BCML strategies, the DPBL strategy is applicable under a broader range of traffic density conditions. Additionally, sensitivity analysis revealed that the effects of the V2V communication distance and bus flows on the applicability of DPBL strategies are relatively minor. These results provide a theoretical basis and practical guidance for future bus lane management.

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.

DELTA: Memory-Efficient Training via Dynamic Fine-Grained Recomputation and Swapping

To accommodate the increasingly large-scale models within limited-capacity GPU memory, various coarse-grained techniques, such as recomputation and swapping, have been proposed to optimize memory usage. However, these methods have encountered limitations, either in terms of inefficient memory reduction or diminished training performance. In response to this, our paper introduces DELTA, an innovative approach for memory-efficient large-scale model training that combines fine-grained memory optimization and prefetching technology to reduce memory usage while maintaining high training throughput concurrently. Initially, we formulate the problem of memory-throughput joint optimization as an easy-solving 0/1 Knapsack problem. Leveraging this formalization, we use an improving polynomial complexity heuristic algorithm to address the problem effectively. Furthermore, we introduce a novel bidirectional prefetching technology into dynamic memory management, which significantly accelerates the model training when compared to relying solely on recomputation or swapping. Finally, DELTA offers users an automated training execution library, eliminating the need for manual configuration or specialized expertise. Experimental results demonstrate the effectiveness of DELTA in reducing GPU memory consumption. Compared to state-of-the-art methods, DELTA achieves substantial memory savings ranging from 40% to 72%, while maintaining comparable convergence performance for various models, including ResNet-50, ResNet-101, and BERT-Large. Notably, DELTA enables the training of GPT2-Large and GPT2-XL with batch sizes increased by 5.5 × and 6 ×, respectively, showcasing its versatility and practicality in enabling large-scale model training on GPU hardware.

The use of non-invasive tomographic imaging to monitor lung condition

The development of ultrasound tomography (UST) aimed to significantly enhance the precision and safety of non-invasive UST imaging, particularly for studying crystallization processes in biological tissues. The initiative sought to address the limitations of previous versions by incorporating advanced technological upgrades and providing a more user-friendly interface. The revised system comprises eight four-channel measurement cards interconnected via a high-speed FD CAN bus capable of 8 MBPS data transfer. Each card features a dedicated square wave generator, band-pass filters tailored to specific ultrasonic transducer frequencies, and a sophisticated signal envelope processing unit. The core processing unit is built around a 32-bit STM32G474RE microcontroller, ensuring robust data handling and image reconstruction capabilities. The UST device presented demonstrates improved image clarity and reduced noise interference. The measurement card redesign has achieved a sampling rate of 4 MBPS per channel and includes a two-stage amplification for dynamic range management. Upgraded power components, comprehensive shielding, and the integration of advanced analog switches have led to an enhanced signal-to-noise ratio, pivotal for high-resolution imaging. The advancements presented in the UST device mark a noteworthy progression in ultrasound imaging technology, extending beyond traditional applications. High-speed data collection, precise signal processing, and user-centered design have all come together to make a system that can image crystallization processes more accurately and accurately over and over again.

Hospital management and challenges during COVID-19 outbreaks: lessons from a level 1 hospital in the southeast of Iran-case study

BackgroundThe COVID-19 pandemic has presented unparalleled challenges to hospitals globally, particularly those in resource-limited settings. This case study elucidates the experiences and lessons from managing a 40-bed public hospital in Rask, Iran, during the pandemic.MethodsData were gathered through focus group discussions with the management team and key informants, supplemented by document analysis. Thematic analysis was employed to discern key themes and subthemes related to the challenges encountered and solutions implemented by the hospital.ResultsThe study underscores the necessity of a dynamic management team, effective external stakeholder communication, staff involvement in decision-making, and establishing connections with pre-hospital care units. It also emphasizes the need for adaptable protocols, comprehensive staff training, and infrastructure enhancements. Despite these strategies, the hospital grappled with unresolved issues such as inadequate disaster planning, ethical dilemmas, and poor inter-hospital coordination.ConclusionThis case study offers insights into managing a small hospital in a resource-limited setting during a health crisis, highlighting the importance of effective leadership, staff support, infrastructural flexibility, and community engagement. The lessons gleaned can guide the development of context-specific strategies to bolster the resilience and preparedness of similar hospitals. Further research is warranted to assess the long-term impacts of the pandemic on these hospitals and to explore the perspectives of frontline staff, patients, and families for a more comprehensive understanding of effective hospital management during health disasters.

Severe Acute Pancreatitis: Management and Prognosis Experience of the Intensive Care Unit at Moulay Ismail Military Hospital Meknes, Morocco

Introduction: Severe acute pancreatitis is considered "a true ICU disease" as all organs can be affected either initially or secondarily. The overall mortality rate for acute pancreatitis of all forms is 4-10%, which can reach 30% in severe forms. The aim of our study was to reveal and describe the prognostic, epidemiological, clinical, paraclinical, and evolutionary factors of severe acute pancreatitis. Patients and Methods: This retrospective study focused on cases of severe acute pancreatitis admitted to the intensive care unit of Moulay Ismail Military Hospital in Meknes, Morocco, between January 2017 and September 2018, involving 23 patients. The study detailed epidemiological, clinical, biological, radiological, and evolutionary aspects. Results: 23 cases of severe acute pancreatitis were recorded, with 68% women and 32% men, with an average age of 62 years. Biliary origin was the most common cause of severe acute pancreatitis (69%). The diagnosis was established based on a suggestive clinical picture associated with lipase levels >3x normal. Most patients presented to the hospital within 12 to 24 hours of symptom onset. Abdominal CT revealed a predominance of Balthazar stage D and E acute pancreatitis in 77% of cases. The severe acute pancreatitis rate among all hospitalized patients during our study period was 18%. The average length of stay in the ICU was 5 days (range 2-13 days). The outcome was marked by a 34% mortality rate. Conclusion: According to our study, the mortality rate for severe acute pancreatitis is 34%, highlighting the severity of this condition and the need for early and dynamic patient management.

A physics-guided automated machine learning approach for obtaining surface radiometric temperatures on sunny days based on UAV-derived images

Urban surface radiometric temperatures, approximate to the surface kinetic temperatures, are predominantly retrieved using satellites or unmanned aerial vehicles (UAVs) and exhibit pronounced spatiotemporal variations. Despite numerous methods ranging from empirical to physical models for obtaining urban microscale surface radiometric temperatures via UAVs, challenges remain given the limited physical significance and substantial professional barriers to method application. Against this background, this study introduces a novel and straightforward approach for acquiring spatially distributed radiometric temperatures on sunny days without understanding the complex radiative transfer process as well as acquiring low-altitude atmospheric parameters. An automated machine learning was employed to train a model capable of efficiently estimating radiometric temperatures. Training and testing datasets were created based on the urban radiative transfer equation, incorporating three independent variables: UAV-measured surface brightness temperature, broadband emissivity, and sky view factor, which collectively represent the diverse thermal environments across different surface characteristics and urban layouts during sunny transitional and summer seasons. The model's accuracy was subsequently confirmed through direct comparisons with radiometric temperatures retrieved from UAV-collected multimodal images and kinetic temperatures synchronously collected on the ground across four periods. The results indicate that AutoGluon achieved high accuracy (MAE: 0.04 K; RMSE: 0.06 K; R2: 0.99). Additional ground measurement validations further demonstrated the model's reliability, with absolute biases on sunlit surfaces maintained within 1.25 K. Given its capability for real-time, high-spatial-resolution mapping of radiometric temperatures (April test: 8.70 cm, July test: 6.89 cm) in urban microscales with considerable heterogeneity, such a method is envisioned to be an effective tool for the dynamic monitoring and management of thermal environments at the microscale level in urban settings.

Exploring digital transformation strategy to achieve SMEs resilience and antifragility: a systematic literature review

Nowadays, the business environment has become more dynamic, making survival issues more challenging for small and medium enterprises (SMEs). Academic literature proposes digital transformation as a facilitator for SMEs to generate resilience and antifragility to overcome this challenge. However, SMEs need appropriate strategies to be successful in their digital transformation journey. This study aims to construct a digital transformation strategy framework for SMEs to generate resilience and antifragility. We use systematic literature review (SLR) to capture critical knowledge from the published literature. The primary articles were analyzed using thematic analysis with Wolcott’s (1994) procedure to construct the framework based on the primary studies. We found the critical values SMEs need to achieve successful digital transformation, including dynamic capabilities, digital capability, digital inclusion, leadership orientation, learning and knowledge management, and collaboration. However, SMEs need a deeper level of learning, higher digital capability, flexibility, and agility to be antifragile. Furthermore, we found that dynamic capabilities are the leading theory used to describe and investigate how a firm generates successful transformation. Finally, this study proposes a conceptual framework for digital transformation strategies to generate SME resilience and antifragile by connecting theory and practical concepts. It also suggests future research agendas.

Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments.

A novel membrane-on-chip guides morphogenesis for the reconstruction of the intestinal crypt-villus axis

Reconstructing the microscale villous organisation and functionality of the small intestine is essential for developingin vitroplatforms tailored for absorption studies as well as for investigating intestinal morphogenesis in development and disease. However, the current fabrication techniques able to mimic the villus-crypt axis poses significant challenges in terms of reconstruction of the complex 3D microarchitecture. These challenges extend beyond mere structural intricacies to encompass the incorporation of diverse cell types and the management of intricate fluid dynamics within the system. Here, we introduce a novel microfluidic device calledIn-Crypts, which integrates a cell-instructive membrane aimed at inducing and guiding Caco-2 cells morphogenesis. Patterned topographical cues embossed onto the porous membrane induce the formation of a well-organized intestinal epithelium, characterized by proliferating crypt-like domains and differentiated villus-like regions. Notably, our cell-instructive porous membrane effectively sustains stem cells development, faithfully replicating the niche environment ofin vivointestinal crypts thus mirroring the cell biogeography observedin vivo. Moreover, by introducing dynamic fluid flow, we provide a faithful recapitulation of the native microenvironmental shear stress experienced by the intestinal epithelium. This stress plays a crucial role in influencing cell behaviour, differentiation, and overall functionality, thus offering a highly realistic model for studying intestinal physiology and pathology. The resulting intestinal epithelium exhibits significantly denser regions of mucus and microvilli, characteristic typically absent in static cultures, upregulating more than 1.5 of the amount expressed in the classical flattened configuration, enhanced epithelial cell differentiation and increased adsorptive surface area. Hence, the innovative design ofIn-Cryptsproves the critical role of employing a cell-instructive membrane in argument the physiological relevance of organs-on-chips. This aspect, among others, will contribute to a more comprehensive understanding of organism function, directly impacting drug discovery and development.

Marine Fish Movement: home range sizes for commercially relevant species

Estimates of home range sizes for marine fishes are essential for designing and assessing the effects of spatial wildlife conservation policies and management interventions. However, in situ studies of marine species movement are challenging and often expensive, resulting in a paucity of data on the home range size of the vast majority of marine fishes. Here, we develop a set of new datasets, which we have collectively named Marine Fish Movement, that synthesises published empirically evaluated home ranges reported for adult marine fishes that interact with fisheries and leverage these data to estimate home range sizes for unstudied species. The empirical data contain estimated home range sizes (km2) for 193 species across 63 family groups from 179 studies published between 1971 and 2022. We use a random forest regression model to estimate home range sizes (km2) for 664 fished marine species currently lacking home range estimates. Marine Fish Movement can inform spatial interventions including the design and management of marine protected areas and dynamic fisheries management to meet sustainability goals.