Rational Deployment Research Articles

Optimization of The Development Plan for Ultra-low Permeability And Weak Volatility Reservoir

Abstract Block F1 of F oil field belongs to ultra-low permeability volatility reservoir. Compared with similar reservoirs at home and abroad, the reservoir property of this block is poor and the permeability is only 1.4 mD. There is no development experience for reference. It is urgent to explore the effective utilization technology of volatility reserves in F oil field. In view of the problems that the preliminary research results of F1 block cannot meet the development needs, the reservoir research is not deep, and the development rules are not clear, Through fine structural interpretation and reservoir prediction, the geological characteristics of the oil reservoir have been clarified; We used multiple indicators to evaluate the initial production capacity of the block, And clarified the initial development rules of the weak volatility reservoir. Perfect the dynamic analysis and production capacity evaluation technology of weak volatility reservoir. By using the reservoir engineering method, we optimized the reasonable working system and the development plan. So we expanded the potential of block development, adjustment and external expansion and optimized the development plan, then we constructed the rational well network deployment technology of weak volatility reservoirs which can provide guidance for the effective development of the F1 block. The results can provide reference for the development of other ultra- low permeability weak volatility reservoirs.

Transgenic early japonica rice: Integration and expression characterization of stem borer resistance Bt gene

BackgroundTransgenic insect-resistant rice offers an environmentally friendly approach to mitigate yield losses caused by lepidopteran pests, such as stem borers. Bt (Bacillus thuringiensis) genes encode insecticidal proteins and are widely used to confer insect resistance to genetically modified crops. This study investigated the integration, inheritance, and expression characteristics of codon-optimised synthetic Bt genes, cry1C* and cry2A*, in transgenic early japonica rice lines. MethodsThe early japonica rice cultivar, Songgeng 9 (Oryza sativa), was transformed with cry1C* or cry2A*, which are driven by the ubi promoter via Agrobacterium tumefaciens-mediated transformation. Molecular analyses, including quantitative PCR (qPCR), enzyme-linked immunosorbent assay (ELISA), and Southern blot analysis were performed to confirm transgene integration, inheritance, transcriptional levels, and protein expression patterns across different tissues and developmental stages. ResultsStable transgenic early japonica lines exhibiting single-copy transgene integration were established. Transcriptional analysis revealed variations in Bt gene expression among lines, tissues, and growth stages, with higher expression levels observed in leaves than in other organs. Notably, cry2A* exhibited consistently higher mRNA and protein levels than cry1C* across all examined tissues and developmental time points. Bt protein accumulation followed the trend of leaves > stem sheaths > young panicles > brown rice, with peak expression during the filling stage in the vegetative tissues. ConclusionsSynthetic cry2A* displayed markedly elevated transcription and translation compared to cry1C* in the transgenic early japonica rice lines examined. Distinct spatiotemporal patterns of Bt gene expression were elucidated, providing insights into the potential insect resistance conferred by these genes in rice. These findings will contribute to the development of insect-resistant japonica rice varieties and facilitate the rational deployment of Bt crops.

Early detection of dementia with default-mode network effective connectivity

Altered functional connectivity precedes structural brain changes and symptoms in dementia. Alzheimer’s disease is the largest contributor to dementia at the population level, and disrupts functional connectivity in the brain’s default-mode network (DMN). We investigated whether a neurobiological model of DMN effective connectivity could predict a future dementia diagnosis at the single-participant level. We applied spectral dynamic causal modeling to resting-state functional magnetic resonance imaging data in a nested case–control group from the UK Biobank, including 81 undiagnosed individuals who developed dementia up to nine years after imaging, and 1,030 matched controls. Dysconnectivity predicted both future dementia incidence (AUC = 0.82) and time to diagnosis (R = 0.53), outperforming models based on brain structure and functional connectivity. We also evaluated associations between DMN dysconnectivity and major risk factors for dementia, revealing strong relationships with polygenic risk for Alzheimer’s disease and social isolation. Neurobiological models of effective connectivity may facilitate early detection of dementia at population level, supporting rational deployment of targeted dementia-prevention strategies.

Application Strategies of Automatic Addressable Single-Lamp Control Technology in Tunnel Lighting

The article mainly studies the application strategy of automatic addressable single-lamp control technology in tunnel lighting. It encompasses an introduction to this technology, an analysis of the tunnel lighting system using automatic addressable single-lamp control technology, and outlines the main development direction for this technology in modern tunnel lighting. The aim is to offer insights that can inform the rational deployment of this technology, thereby enhancing the lighting control effectiveness in modern tunnels and meeting their specific lighting requirements more effectively.

Design of advanced steels by integrated computational materials engineering

AbstractThe integrated computational materials engineering (ICME) has achieved great success in accelerating the rational design and deployment of new materials. It is a new route of designing new materials and processes and highlighted by Materials Genome Initiative/Engineering that stresses the high‐throughput computation in addition to high‐throughput experimentation and materials informatics. This article presents a brief review on the basic theories and multi‐scale computational tools of ICME to design advanced steel grades, including the first‐principles calculations, the CALPHAD method (i.e., computational thermodynamics) fueled by dedicated databases, diffusion and phase‐field simulations, as well as finite analysis methods and machine learning. In the ICME scheme to deal with steels, the CALPHAD method is considered as the core to readily consider multi‐component systems and integrated to link the microscopic simulations (such as diffusion and phase field method to predict microstructure evolutions in response to external conditions) and macroscopic finite analysis method to deal with mechanical properties. Two applications are also presented to address the new routes to carry out materials design, especially for advanced steels.

Layout optimization scheme for coexistence of multiple coverage capabilities in base stationsBased on genetic algorithm

With the development of 5G technology, communication networks have become increasingly complex, leading to a continuous growth in the number of base stations. While this has resulted in faster speeds, lower latency, and greater capacity, it has also presented a pressing issue: weak coverage points, areas with weak signal radiation from base stations. To address this challenge, this paper proposes a layout optimization scheme for the coexistence of base stations with multiple coverage capabilities, based on a genetic algorithm. The scheme considers the rational deployment of various types of base stations (represented as macro base stations and micro base stations in the paper) to improve the situation of weak coverage points. The specific steps of the optimization layout scheme include initializing existing base station coverage, calculating distances to weak coverage points, selecting new base station locations, updating data, and analyzing calculations. The final output includes the coordinates and types of new base stations needed. Through model simulation, the proposed optimization layout scheme can consider cost, coverage effectiveness, and threshold constraints while accommodating various requirements. Additionally, the scheme is highly applicable and can be integrated with computer analysis software. Finally, a dialectical analysis of the model is conducted, recognizing the flexibility and practicality advantages of the layout optimization scheme based on genetic algorithms, while acknowledging potential limitations such as overly idealistic assumptions.

Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning

The safety of battery packs is greatly affected by individual abnormal cells. However, it is challenging to diagnose abnormal aging batteries in the early stages due to the low abnormality rate and imperceptible initial performance deviations. This paper proposes a feature engineering and deep learning (DL)-based method for abnormal aging prognosis and end-of-life (EOL) prediction. The mathematical model of dimensionless indicators (DIs) is applied to partial voltage-capacity (V-Q) of one cycle to construct the optimal DIs with high sensitivity to abnormal aging. Taking the optimal DIs and partial V-Q as inputs, an abnormal aging prognosis model is established, and an ablation study is designed to verify the necessity of the selected DIs. Finally, the gated recurrent unit (GRU) is utilized to establish EOL prediction models for normal and abnormal degradation batteries, respectively. The proposed method is verified by a Lithium Cobalt Oxide (LiCoO2) battery dataset. The results indicate that the 100% prognosis of abnormal degradation batteries is achieved and the EOL prediction accuracy is less than 3.7%. This work highlights the rapid abnormal battery detection using data of one cycle without excessive battery testing, which contributes to the rational deployment of batteries and reduces the probability of failures during operation.

Antimicrobial resistance markers distribution in Staphylococcus aureus from Nsukka, Nigeria

BackgroundMultidrug resistance in Staphylococcus aureus continues to influence treatment complications in clinical settings globally. Multidrug-resistant-S. aureus (MDR-SA) is often genetically driven by resistance markers transferable in pathogenic strains. This study aimed to determine the distribution of resistance markers in clinical isolates of S. aureus in Nsukka, Nigeria.MethodsA total of 154 clinical samples were cultured on mannitol salt agar. Isolates were characterized using conventional cultural techniques and confirmed by PCR detection of S. aureus-specific nuc gene. Antibiotic resistance profiles of the isolates were determined against selected antibiotics using the disk-diffusion method, while screening for antibiotic resistance genes (Mec A, Erm A, Erm B, Erm C, Van A, and Van B) was by PCR.ResultsA total of 98 isolates were identified as S. aureus by conventional methods. Of these, 70 (71.43%) were confirmed by PCR. Phenotypically, the isolates exhibited high degrees of resistance to oxacillin (95.72%), erythromycin (81.63%), and ertapenem (78.57%) and 75.51% and 47.30% against methicillin and vancomycin, respectively. Multiple antibiotic resistance indexes of the isolates ranged from 0.3 to 1, and the most prevalent pattern of resistance was oxacillin-ertapenem-vancomycin-erythromycin-azithromycin-clarithromycin-ciprofloxacin- cefoxitin-amoxicillin-clavulanic acid. PCR screening confirmed the existence of various antibiotic resistance makers among the strains, with the most common resistance genes found in the isolates being Mec A (32.14%), Van A (21.43%), Van B (10.71%), Erm B (10.71%), and Erm C (17.86%). None possessed the Erm A gene.ConclusionThe study supports the need for necessary action, including rational drug use, continuous surveillance, and deployment of adequate preventive and curative policies and actions.

Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.

National Ambulance Services in India: A Narrative Review

Background: A strong and effective emergency referral transport network is a prerequisite for a country's assured emergency ecosystem. Ambulances have grown exponentially since the start of National Ambulance Services under the National Health Mission (NHM) a decade ago. This paper aims to understand the referral systems, strategies and the progress and undertaken by the Government of India to upgrade and strengthen the referral transport system. Methods: We conducted a narrative review and secondary data analysis on referral transportation systems from government portals. Key Interviews were held with key stakeholders’ expertise in health systems. Results: The number of ambulances increased by 57.5 % from 2012 to 2022. Hitherto, 32.2 % of Advanced life support (ALS) ambulances have been shorted against population norms. Although the accessibility of referral services has improved, there is a significant gap in delivering timely in-transit service. Conclusions: The existing referral system requires the rational deployment of well-equipped BLS & ALS ambulances as per population norms with adequately trained staff or EMTs (Emergency Medical Technicians). A systematic plan for monitoring and accountability to ensure timely arrival within the “golden hour” is the need of the hour.

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

Bridging clinic and wildlife care with AI-powered pan-species computational pathology

Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). In 18 other vertebrate species (mammalia = 11, reptilia = 4, aves = 2, and amphibia = 1), accuracy (range 0.57–0.94) is influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. Furthermore, a spatial immune score based on artificial intelligence and spatial statistics is associated with prognosis in canine melanoma and prostate tumours. A metric, named morphospace overlap, is developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on understanding of morphological conservation, which could vastly accelerate developments in veterinary medicine and comparative oncology.

Scientific and methodological apparatus for substantiating rational parameters and technologies for the deployment of temporary accommodation facilities for the affected population

The final article of this series presents the results of a scientific study on the development of the main scientific components that make up the scientific and methodological apparatus that allows to justify rational equipment compositions, placement schemes, deployment technologies and technical and economic assessments of temporary accommodation points of the affected population.

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.

Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a road map for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technologic revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy, and workflow efficiency, as well as emerging challenges and critical responsibilities, are discussed. Establishing and maintaining leadership in AI require a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, and referring providers, among other stakeholders, while protecting our patients and society. This strategic plan was prepared by the AI task force of the Society of Nuclear Medicine and Molecular Imaging.

Roadside infrastructure deployment scheme based on Internet of Vehicles information service demand

AbstractRoadside unit (RSU) plays an important role in communication relay and data processing in the Internet of Vehicles. The rational deployment of roadside units directly affects the operational efficiency and system robustness of the Internet of Vehicles. This article proposes an RSU deployment scheme based on vehicular transmission demand. First, the mobility model of vehicles is mined through system communication transmission requirements to determine the total delay of vehicle communication transmission tasks. The delay includes two parts, namely, the vehicle data transmission delay and the channel service delay. Second, the optimization objective of the model is established, and some parameters such as inhibition distance coefficient are defined as constraint conditions. Finally, a evolutionary feature‐based RSU deployment algorithm, ICA‐EFRDA, preprocessed by a greedy algorithm is proposed to implement RSU deployment. The deployment results show that the proposed scheme has lower delay and higher coverage time ratio than the EFRDA scheme in both urban and suburban road networks. Under limited cost constraints, the system decides to place a limited number of RSUs in low‐traffic density areas of the suburban road network to improve performance. When the traffic density is high, the increased placement of RSUs in the urban road network has a more obvious effect on improving the service capability of the system. Under different deployment cost constraints, ICA‐EFRDA outperforms the comparison schemes in terms of the contacts per trip, contact probability, and packet delivery ratio.

The TIP60-ATM axis regulates replication fork stability in BRCA-deficient cells

Maintenance of replication fork stability is essential for genome preservation. Stalled replication forks can be reversed by translocases such as SMARCAL1, and unless protected through the activity of the BRCA pathway, are subsequently subjected to nucleolytic degradation. The ATM and ATR kinases are master regulators of the DNA damage response. ATM activation upon DNA damage is mediated by the acetyltransferase TIP60. Here, we show that the TIP60-ATM pathway promotes replication fork reversal by recruiting SMARCAL1 to stalled forks. This enables fork degradation in BRCA-deficient cells. We also show that this ATM activity is not shared by ATR. Moreover, we performed a series of genome-wide CRISPR knockout genetic screens to identify genetic determinants of the cellular sensitivity to ATM inhibition in wildtype and BRCA2-knockout cells, and validated the top hits from multiple screens. We provide a valuable list of common genes which regulate the response to multiple ATM inhibitors. Importantly, we identify a differential response of wildtype and BRCA2-deficient cells to these inhibitors. In BRCA2-knockout cells, DNA repair genes (including RAD17, MDC1, and USP28) were essential for survival upon ATM inhibitor treatment, which was not the case in wild-type cells. These findings may eventually help guide the way for rational deployment of ATM inhibitors in the clinic.

Nanophysical Antimicrobial Strategies: A Rational Deployment of Nanomaterials and Physical Stimulations in Combating Bacterial Infections.

The emergence of bacterial resistance due to the evolution of microbes under antibiotic selection pressure, and their ability to form biofilm, has necessitated the development of alternative antimicrobial therapeutics. Physical stimulation, as a powerful antimicrobial method to disrupt microbial structure, has been widely used in food and industrial sterilization. With advances in nanotechnology, nanophysical antimicrobial strategies (NPAS) have provided unprecedented opportunities to treat antibiotic‐resistant infections, via a combination of nanomaterials and physical stimulations. In this review, NPAS are categorized according to the modes of their physical stimulation, which include mechanical, optical, magnetic, acoustic, and electrical signals. The biomedical applications of NPAS in combating bacterial infections are systematically introduced, with a focus on their design and antimicrobial mechanisms. Current challenges and further perspectives of NPAS in the clinical treatment of bacterial infections are also summarized and discussed to highlight their potential use in clinical settings. The authors hope that this review will attract more researchers to further advance the promising field of NPAS, and provide new insights for designing powerful strategies to combat bacterial resistance.

The RppC-AvrRppC NLR-effector interaction mediates the resistance to southern corn rust in maize

Southern corn rust (SCR), caused by the fungal pathogen Puccinia polysora, is a major threat to maize production worldwide. Efficient breeding and deployment of resistant hybrids are key to achieving durable control of SCR. Here, we report the molecular cloning and characterization of RppC, which encodes an NLR-type immune receptor and is responsible for a major SCR resistance quantitative trait locus. Furthermore, we identified the corresponding avirulence effector, AvrRppC, which is secreted by P. polysora and triggers RppC-mediated resistance. Allelic variation of AvrRppC directly determines the effectiveness of RppC-mediated resistance, indicating that monitoring of AvrRppC variants in the field can guide the rational deployment of RppC-containing hybrids in maize production. Currently, RppC is the most frequently deployed SCR resistance gene in China, and a better understanding of its mode of action is critical for extending its durability.

Spatial Distribution Characteristics and Accessibility Analysis of Characteristic Towns in Guangdong Province Based on Ripley’s K Function

Characteristic towns are an essential focus for developing urbanization and implementing rural revitalization in China. Studying the spatial distribution characteristics and influencing factors are of great significance for the scientific and rational cultivation and deployment of characteristic towns and promotion of the integrated development of urban and rural areas. The spatial distribution characteristics and accessibility of 142 characteristic towns in Guangdong Province were investigated using Ripley's K function and kernel density estimation. The results show that (1) the characteristic towns in Guangdong Province present the overall spatial distribution characteristics of “the Pearl River Delta region” and the local distribution characteristics of multicore diffusion in “eastern Guangdong” and “western Guangdong”; (2) the spatial distribution of industrial development, cultural tourism type, innovative and creative characteristic towns shows significant agglomeration, whereas the agglomeration characteristics of agricultural service‐oriented and commercial and trade circulation‐oriented characteristic towns are not significant; (3) population, economy, resources, and location are the main factors affecting the spatial distribution of characteristic towns in Guangdong Province; (4) the overall spatial accessibility of characteristic towns in Guangdong Province is relatively good. However, the accessibility of characteristic towns of industrial development is better than that of other types of characteristic towns.

Mechanomaterials: A Rational Deployment of Forces and Geometries in Programming Functional Materials (Adv. Mater. 46/2021)

Mechanomaterials In article number 2007977, Huajian Gao, Xiaodong Chen, and co-workers profile an emerging field coined as “mechanomaterials”, which emphasizes the proactive and rational manipulation of forces and geometries to dig for novel functional materials on multiple scales, ranging from atomic, molecular, nanoscale, and microscale levels, while their constituents usually remain identical.