Model Of Region Research Articles

Investigating forced convection in porous media-filled cavities for designing supplementary passive cooling systems

This study aims to propose innovative passive cooling methods within the reservoir section of a closed-loop liquid cooling system. The techniques employed in this study include using a cold wall, a porous medium, varying inlet positions, and nozzles of different sizes and shapes. The effects of parameters associated with these techniques on outlet temperature reduction, pressure drop, and average Nusselt number (Nu¯) have been analyzed in detail. The reservoir section has been numerically modeled as a two-dimensional (2D) and a three-dimensional (3D) cavity. The numerical model implements mass, momentum, energy, and conservation equations for the Darcy model in the porous region. A fluid flow with Re=50 with a laminar generalized porous media modeling approach employing Brinkman’s and Forchheimer’s terms has been used. The optimized inlet position with respect to maximum cooling (55.60 K) and moderate pressure drop (2.30kPa) for the 2D cavity has been identified in this work for a given Darcy number and porosity. Results show that decreasing porosity improves convective heat transfer efficiency and increases pressure drop. Similarly, varying Darcy’s number affects heat transfer, with lower Da values resulting in reduced outlet temperature reduction and lower Nu¯, with higher pressure drop. A similar analysis was conducted for the 3D reservoir. The use of a converging nozzle can lead to an increase in cooling by 61.61% and 3.44% for the 2D and 3D reservoirs, respectively.

Evaluating the dynamics of human-water symbiosis using the water-human harmony model in the fringe region of the Chhotanagpur Plateau, Eastern India

Evaluating the dynamics of human-water symbiosis using the water-human harmony model in the fringe region of the Chhotanagpur Plateau, Eastern India

Dataset-level color augmentation and multi-scale exploration methods for polyp segmentation

Automatic segmentation of polyps from colonoscopy images plays a critical role in early screening and treatment of colorectal cancer. Although deep learning methods have made significant progress, precise polyp segmentation faces two challenges: (1) the imbalance of color appearances in the limited training dataset hinders the generalization of the model, and (2) polyps have the diverse scales, locations and shapes with blurred boundary. To address the issues, Dataset-Level Color Augmentation (DLCA) and Convolutional Multi-scale Attention Module (CMAM) are proposed. DLCA employs the dataset-level color knowledge and generate new color appearances, to avoid the model learning false associations with colors. Meanwhile, CMAM simultaneously explores the region and boundary clues and model multi-scale context, which improves the accuracy of polyp localization and fine-grained segmentation. We conduct comprehensive experiments and compare our network with state-of-the-art methods. The proposed model is superior on multiple polyp datasets, especially on ETIS, where mDice and mIoU reach 0.839 and 0.766. Furthermore, it is validated that DLCA can be widely applied to most polyp segmentation methods, and CMAM is practical and plug-and-play. Finally, the model demonstrates promising generalization to breast ultrasound and skin lesion segmentation.

Charmonia in an unquenched quark model

In this work, we study the charmonium spectrum within an unquenched quark model including coupled-channel effects. In couple-channel calculations, we include all of the opened charmed meson channels with the once-subtracted method, meanwhile we adopt a suppressed factor to soften the hard vertices given by the P03 model in the high momentum region. We obtain a good description of both the masses and widths for the well-established states in the charmonium spectrum. Furthermore, we give predictions for the higher S-, P-, and D-wave charmonium states up to mass region of ∼5.0 GeV. The magnitude of mass shifts due to the coupled-channel effects is estimated to be about 10s MeV. Although many decay channels are opened for the higher charmonium states, they are relatively narrow states. Their widths scatter in the range of ∼10s–100 MeV. Many charmoniumlike states, such as χc1(4274), χc0(3915), χc0(4500), χc0(4700), X(4160), X(4350), Y(4500), and ψ(4660)/Y(4710), can be accommodated by the charmonium spectrum when the unquenched coupled-channel effects are carefully considered. Published by the American Physical Society 2024

A winter common wheat variety model for the Central NON-Blackearth region

A winter common wheat variety model for the Central NON-Blackearth region

Experience in Team Multidisciplinary Training of an Ambulance Station Using a Physical Model of the City

The most difficult part of training young professionals is the skills of decision-making, communication, and role allocation. To develop these skills, Kursk State Medical University conducts business games simulating the operation of an ambulance and disaster medicine station. Starting with oral exercises and board games, the authors aim to diversify the mechanics of the game and increase engagement. The result was a model of an entire region with a city, villages, enterprises and natural areas, where participants physically move by special transport and fully provide the entire pre-hospital stage in normal and emergency conditions.

Resonant multiscalar production in the generic complex singlet model in the multi-TeV region

We develop benchmarks for resonant discalar production in the generic complex singlet scalar extension of the Standard Model (SM) with no additional symmetries, which contains two new scalars. These benchmarks maximize discalar resonant production modes at future pp colliders: pp→h2→h1h1, pp→h2→h1h3, and pp→h2→h3h3, where h1 is the observed SM-like Higgs boson and h2,3 are new scalars. The decays h2→h1h3 and h2→h3h3 may be the only way to discover h3, leading to a discovery of two new scalars at once. Current LHC and projected future collider (HL-LHC, FCC-ee+HL-LHC, ILC500+HL-LHC) constraints on this model are used to produce benchmarks at the HL-LHC for h2 masses between 250 GeV and 1 TeV and a future pp collider (FCC-hh) for h2 masses between 250 GeV and 12 TeV. We update the current LHC bounds on the singlet-Higgs boson mixing angle for these benchmarks. As the mass of h2 approaches the multi-TeV region, certain limiting behaviors of the maximum rates are uncovered due to theoretical constraints on the parameters. These limits, which can be derived analytically, are BR(h2→h1h1)→0.25, BR(h2→h3h3)→0.5, and BR(h2→h1h3)→0. It can also be shown that the maximum rates of pp→h2→h1h1 and pp→h2→h3h3 approach the same value. Hence, all three h2→hihj decays are promising discovery modes for h2 masses at and below O(1 TeV), while above O(1 TeV) the decays h2→h1h1 and h2→h3h3 are more encouraging. We choose benchmark masses for h3 to produce a large range of decay signatures including multi-b, multivector boson, and multi-SM-like Higgs production. As we will show, the behavior of the maximum rates leads to the surprising conclusion that in the multi-TeV region this model may be discovered in the Higgs quartet production mode via h2→h3h3→4h1 decays before Higgs triple production is observed. The maximum di- and four Higgs production rates are similar in the multi-TeV range. Published by the American Physical Society 2024

Leveraging Crowdsourcing for Mapping Mobility Restrictions in Data-Limited Regions

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure.

Coordinated referral and scheduling decisions for specialized healthcare services

This paper studies the coordination of referral and scheduling policies where access to specialized medical services, such as surgical procedures, imaging or other consultations, is managed through a centralized intake system. In this increasingly common mode of operation, a central body makes decisions regarding the allocation of patients between a set of providers, followed by the provider taking control of the scheduling and delivery of the required service. To study the coordination of the allocation and scheduling policies we present a sequential optimization model where both referrer and provider attempt to maximize the proportion of a heterogeneous set of patients receiving treatment within a targeted wait time. Motivated by observations of performance thresholds in queueing systems, we introduce an achievable region model which permits study of providers who vary in ability to coordinate scheduling different patient streams. We characterize optimal referral and scheduling policies for a range of demand patterns and operational abilities. We find that the optimal referral policy can result in large discrepancies in outcomes for different patient types but that the discrepancy in outcomes is muted when operational abilities are higher. In situations where operational abilities are low, it is optimal for the providers to prioritize patients by stream. Low capacity for both providers results in an optimal referral and scheduling policy where certain patients streams are deprioritized and with certainty will not be seen within their wait time targets. We also study the cost of enforcing parity between patient classes and find that the cost is reduced when providers have higher operational abilities. This paper illustrates how operational constraints lead to incentives to prioritize different patient streams. In particular, we show that when operations are challenging, a more hands-on design is required to ensure clinical intake systems maintain equitable access to the service.

Hydrological simulation using the SWAT model in a semi-arid region in the southern part of Zacatecas, Mexico

Hydrological simulation using the SWAT model in a semi-arid region in the southern part of Zacatecas, Mexico

Formation of Methane Hazards During Underground Coal Production in the Longwall Area Ventilated by System Y

Abstract The article addresses a critical and timely issue: improving safety in underground coal mining. The primary objective of the paper was to develop a research methodology based on modelling studies to identify and assess the state of methane hazards during mining operations. To achieve this, structural modelling of the physical and chemical phenomena occurring in mining regions was conducted using Computational Fluid Dynamics. The core research was performed using the finite volume method on a real longwall exploitation site ventilated by a Y-system. This approach enabled the determination of methane and oxygen concentration distributions in the mining region and goafs, treated as a porous and permeable medium. Based on these findings, potential fire and/or methane explosion hazard zones were identified in the goaf. The model test results underwent a validation process, comparing them with actual measurements. The determined errors were within an acceptable range, confirming the accuracy of the developed model of the mining region and the phenomena within it. Furthermore, the model was used to predict the locations of zones at risk of fire and/or methane explosion in the goafs, particularly in areas with potentially increased gas emissions. The results clearly demonstrate the significant potential of using model studies to diagnose and forecast methane hazards in underground mining operations. Identifying these potential danger zones allows for the implementation of preventive measures to reduce the likelihood of dangerous incidents.

Abstract P215: The reninness score: integrative analysis of multi-omic data to define renin cell identity.

Renin cell (RC) descendants regain the endocrine renin phenotype to overcome homeostatic threats, a process known as recruitment. The determinants that control the identity, fate, and recruitment of RCs are not well understood. We aim to 1) define the chromatin pattern that determines RC identity; and 2) develop a computational tool to score samples by similarity to that pattern. We compared open chromatin regions (ATAC-seq) between non-renin-expressing (n = 184) and renin-expressing samples (n = 19) from three different sources and stimulation states: 1) primary RCs in basal state (WT); 2) primary RCs chronically stimulated to produce renin (Recruited); and 3) constitutively renin-expressing tumoral cells. Differential analyses between the three types of RCs and the non-RCs revealed 1,525 unique open chromatin regions shared in at least two RC groups, including regions associated with RC-specific genes: Ren1 and Akr1b7. Increased accessibility regions in RC groups are cell type specific and enriched in renin-related GO terms ( e.g., cAMP and Notch signaling pathway). The bZip family ( e.g. , Atf3, p < 10 -2147 ) was the most enriched motifs in tumoral cells, and MEF2 family ( e.g. , MEF2a, p < 10 -91 ) in primary cells. We did not find a unique set of TFs specific to the Recruited group, indicating that renin recruitment may not be regulated by a recruitment-specific factor family, but could instead be driven by post-translational modification, differential co-factors, or epigenetic modifications. Next, we used a machine-learning approach to calculate a "reninness" score by training a genomic region model with renin and non-renin bulkATAC-seq data. We identified a cluster of regions (n = 112,992) around the trained renin label, which included 89% (21,820 of 24,612) of the regions with increased accessibility from the differential analyses. We calculated the reninness score based on the distance between the trained label and the predicted bulkATA sample embeddings using the trained model. Homogeneous WT RCs had the highest score, followed by renin-expressing tumoral and primary cells. Non-RCs had the lowest scores. We also adapted bulkATAC region-embedding models to pseudo-bulk ATAC- and scATAC-seq data. The observed reninness scores show that the model can also capture differences in renin recruitment potential. In conclusion, we identified the chromatin configurations defining the identity and recruitment of RCs and developed a computational score to quantify it.

The Beneficial Role of Silicon Valley's Technological Innovations and Venture Capital in Strengthening Global Financial Markets

This research explores the substantial influence of Silicon Valley's technology environment on worldwide financial markets. It looks at how Silicon Valley's technology developments and venture capital improve market efficiency, boost economic growth, and help to stabilize world financial institutions. The results highlight the importance of Silicon Valley's venture capital firms in not just providing vital financing but also in strategically selecting and growing entrepreneurs to include into a larger economic network. The increase in productivity in different industries is thanks to the groundbreaking advancements in artificial intelligence and microprocessor technology in Silicon Valley. These progressions have not just improved operational effectiveness greatly but also encouraged the emergence of fresh markets. Indoor air quality challenges emphasize the need for innovation in building design. (Ashayeri, M., Piri, S., & Abbasabadi, N, 2024). Dimensionality reduction in multimodal deep learning enhances accuracy and reduces computational costs, critical for efficient AI applications. (M Bodaghi, M Hosseini, R Gottumukkala, 2024) Silicon Valley, a prime example of innovation, still showcases its significant impact on shaping present financial environments as it continues to prosper. Despite the fact that the total quantity of money invested in various countries throughout the globe is always growing, this is the case. The accomplishments of this area may serve as a model for other regions that are interested in promoting economic growth via the deployment of cutting-edge technology and creative investment methods. These places can learn from this region's successes.

Deciphering the transfer of hydroclimate signals to tree-ring δ18O using a proxy system model in East Asia's Meiyu region

The stable oxygen isotope ratios of whole tree-ring α-cellulose (δ18OWR) have been interpreted as an indicator of early summer hydroclimate in the Meiyu region of East Asia. However, the underlying physical mechanism often remains unclear. Here we provide a mechanistic understanding through intra-annual tree-ring oxygen isotope analysis and process-based δ18OWR modelling over the period 1979–2006. The selected tree species for analysis is Pinus taiwanensis, whose δ18OWR exhibit the strongest linear relationship with relative humidity (RH) in June. The results indicated that the June RH signal is predominantly contained in tree-ring earlywood rather than latewood. The strong response of δ18OWR to June RH is not due to the legacy effect. Using the proxy system model (PSM) of δ18OWR, we obtained a modeled δ18OWR time series that is significantly positively correlated with the measured δ18OWR time series. The modeled and measured δ18OWR series show similar relationships with monthly RH. Sensitivity experiments with PSM revealed that the June RH signal is originated from the oxygen isotopes of source water and leaf water. Rapid cellulose formation in June plays a role in enhancing the June RH signal. Our study demonstrates how δ18OWR record early summer hydroclimate signals from a process perspective, and that the PSM is effective in modelling the interannual δ18OWR variability in the Meiyu region.

Systematic Study of Battery Electric Vehicle Ownership in England

Battery electric vehicles (BEVs) form a big part of the UK’s agenda for decarbonizing road transport. Although there have been various policies such as purchase grants and vehicle tax exemptions, the BEV penetration rate in the UK was only 2.5% in September 2023. Most research analyzing electric vehicle (EV) ownership in the UK is either based on stated preference survey data or includes plug-in hybrid vehicles in the analysis because of the sparsity of BEV ownership data; there is limited research based on household revealed preference (RP) data. This paper develops a BEV ownership model using RP household-level data from England to discover influential factors, to validate the findings in the literature, or both. Specifically, this paper uses the subset of the UK National Travel Survey (NTS) special license data to estimate a series of binary logit models of BEV ownership as a function of several sociodemographic, regional, and temporal factors, and discusses the related policy implications. Household income, multivehicle ownership (resulting from range anxiety), and overnight parking on street (resulting from insufficient public charging infrastructure) are influential factors found in this study that align with previous studies. On the other hand, households with a mortgage loan, geographical attributes (such as population density), and household composition (e.g., number of adults and children) are new factors identified in this study. We also present a future BEV ownership prediction model for regions of England which clearly suggests that improving public charging infrastructure, especially in the north, is required to achieve widespread growth in BEV ownership.

Ongoing and Fossil Large-scale Outflows Detected in a High-redshift Radio Galaxy: [C ii] Observations of TN J0924-2201 at z = 5.174

We present Atacama Large Millimeter/submillimeter Array observations of the [C ii] 158 μm line and the underlying continuum emission of TN J0924−2201, which is one of the most distant known radio galaxies at z > 5. The [C ii] line and 1 mm continuum emission are detected at the host galaxy. The systemic redshift derived from the [C ii] line is z [C II] = 5.1736 ± 0.0002, indicating that the Lyα line is redshifted by a velocity of 1035 ± 10 km s−1, marking the largest velocity offset between the [C ii] and Lyα lines recorded at z > 5 to date. In the central region of the host galaxy, we identify a redshifted substructure of [C ii] with a velocity of 702 ± 17 km s−1, which is close to the C iv line with a velocity of 500 ± 10 km s−1. The position and the velocity offsets align with a model of an outflowing shell structure, consistent with the large velocity offset of Lyα. The nondetection of [C ii] and dust emission from the three CO(1–0)-detected companions indicates their different nature compared to dwarf galaxies, based on the photodissociation region model. Given their large velocity of ∼1500 km s−1, outflowing molecular clouds induced by the active galactic nucleus are the most plausible interpretation, and they may exceed the escape velocity of a 1013 M ⊙ halo. These results suggest that TN J0924−2201, with ongoing and fossil large-scale outflows, is in a distinctive phase of removing molecular gas from a central massive galaxy in an overdense region in the early Universe. A dusty H i absorber at the host galaxy is an alternative interpretation.

Generation of Customized Bone Implants from CT Scans Using FEA and AM.

Additive manufacturing (AM) allows the creation of customized designs for various medical devices, such as implants, casts, and splints. Amongst other AM technologies, fused filament fabrication (FFF) facilitates the production of intricate geometries that are often unattainable through conventional methods like subtractive manufacturing. This study aimed to develop a methodology for substituting a pathological talus bone with a personalized one created using additive manufacturing. The process involved generating a numerical parametric solid model of the specific anatomical region using computed tomography (CT) scans of the corresponding healthy organ from the patient. The healthy talus served as a mirrored template to replace the defective one. Structural simulation of the model through finite element analysis (FEA) helped compare and select different materials to identify the most suitable one for the replacement bone. The implant was then produced using FFF technology. The developed procedure yielded commendable results. The models maintained high geometric accuracy, while significantly reducing the computational time. PEEK emerged as the optimal material for bone replacement among the considered options and several specimens of talus were successfully printed.

Barriers, Challenges, and Opportunities in the Adoption of the Circular Economy in Mexico: An Analysis through Social Perception

This study explores the transition toward sustainable economic models through the circular economy (CE) in Mexico. Utilizing a mixed-methods approach, this research incorporates a comprehensive literature review and analyzes responses from 42 stakeholders, gathered through surveys and focus groups. These stakeholders comprise a diverse group including PhD students, professors, researchers, industry professionals in sustainability and the environment, and government advisors and coordinators from the Mexican Secretary of Environment. This representative sample provides a broad perspective on the barriers, opportunities, and societal perceptions regarding CE. The findings reveal significant challenges such as economic barriers, regulatory inadequacies, and a lack of awareness and education, all of which hinder the adoption of CE practices. Despite these challenges, there is a generally optimistic view among stakeholders about CE’s potential to positively impact societal needs, suggesting robust opportunities for innovation and policy enhancement to foster sustainable development. Key recommendations include intensifying educational programs to elevate public understanding and engagement, formulating supportive policies that facilitate CE adoption, and promoting intersectoral collaboration to leverage collective expertise and resources. Additionally, the research underscores the necessity of integrating CE principles into urban planning and policy frameworks to effectively address specific local challenges such as waste management, pollution, and urban sprawl. By providing a detailed analysis of the current state and potential of CE in Mexico, this paper contributes valuable insights to the global discourse on sustainability. It proposes strategic actions to overcome existing hurdles and capitalize on opportunities within the CE framework, charting a path forward for Mexico and serving as a model for other regions facing similar sustainability challenges

QFD-based optimization model for mitigating sustainable supply chain management adoption challenges for Bangladeshi RMG industries

In response to heightened pressures from regulatory mandates, global competition, and evolving customer expectations, industries worldwide are compelled to prioritize environmental initiatives, often at the expense of economic considerations. The research gap addressed in this study is the lack of a comprehensive, data-driven optimization model for effectively mitigating sustainable supply chain management adoption challenges specific to the Bangladeshi Readymade Garments (RMG) industry. While previous studies often relied on single techniques, this research proposes a novel AHP integrated QFD-based MILP optimization model. This innovative approach empowers Bangladeshi RMG industries to make data-driven decisions for prioritizing sustainability challenges and selecting cost-effective mitigation strategies to promote the integration of sustainability initiatives within the sector. The study identifies and prioritizes 25 sustainable supply chain management adoption challenges and proposes 16 mitigation strategies. The model emphasizes the critical interplay between sustainability performance and implementation costs, achieving a sustainability performance score of 0.4511 while effectively implementing 12 out of 16 strategies within the expected budget. The optimal solution incorporates green strategies, technology integration, and aspects of Industry 5.0, demonstrating a holistic approach to sustainable supply chain management. The findings are crucial for Bangladeshi RMG industries aiming for global market competitiveness and contribute significantly to the academic field by introducing a robust, data-driven decisions for sustainable supply chain optimization. The implications extend beyond the RMG sector, offering a replicable model for other industries and regions facing similar sustainability challenges.

Detection of Oil Spill in SAR Image Using an Improved DeepLabV3.

Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model.