Requirements Of Different Types Research Articles

Evaluation and regulation methods for the utilization of Type-II oil reservoirs using ASP flooding

Abstract Existing methods for calculating oil reservoir utilization in chemical flooding rely on the instantaneous water absorption profile, which fails to provide an accurate description of the actual utilization conditions of the reservoir, making it imperative to find a method that accurately reflects the reservoir utilization. Type-II oil reservoirs often exhibit significant vertical heterogeneity. During chemical flooding, the impacts of high-permeability layer utilization, low-permeability layer utilization, and the simultaneous utilization of both layers on recovery are to be explored. Therefore, it is important to investigate the recovery requirements for different types of reservoirs. This study explores the solutions for improved reservoir utilization as well as the evaluation and regulation methods for the utilization of secondary oil reservoirs using the Alkaline-Surfactant-Polymer (ASP) flooding, which is of great significance for the adjustment of ASP flooding, injection, and production of secondary oil reservoirs.

Dynamic Resource Allocation and Offloading Optimization for Network Slicing in B5G Multi-tier Multi-tenant Systems

For differentiating and customizing different types of flows guaranteeing individual 5QI QoS requirements, 5G and Beyond 5G (B5G) first specify several key technologies, e.g., 1) virtualizing radio resources, network functions and network servers, 2) network slicing, 3) Service Function Chaining (SFC) and flow steering, etc. Furthermore, for reducing E2E delay and path/link traffic congestion for diverse flowing while accessing cloud computing, Multi-access Edge Computing (MEC) is specified in 5G/B5G ETSI. Although several extensively related studies discussed network slicing, SFC and MEC, they seldom consider both resource allocation and traffic offloading in a tenant efficiently, simultaneously. Thus, for efficiently addressing above critical issues, three motivations are proposed, including 1) to dynamically allocate resource for B5G with multi-tier multi-tenant networking, 2) to propose adaptively vertical and horizontal offloading to the computing node for diverse types of flows, and 3) to minimize the blocking rate while guaranteeing the delay constraint. Two novel efficient algorithms are proposed: Fast Latency Decrease Resource Allocation (FLDRA) and Minimum Cost Resource Allocation (MCRA). These two proposed algorithms achieve dynamic E2E resource allocation and optimal vertical and horizontal offloading to the computing node while guaranteeing the E2E latency and 5QI QoS requirements for different types of flows. Numerical results demonstrate that FLDRA minimizes resource allocation while MCRA balances the loading of resource availability. The proposed algorithms of FLDRA and MCRA significantly outperform the compared approaches in blocking rates. Moreover, the proposed MCRA algorithm yields the highest resource utilization, the least network delay, etc.

Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief

Earthquake disasters are usually very destructive and pose a great threat to human life and property. Based on the relatively mature technology of unmanned aerial vehicles (UAVs) and their high flexibility, these devices are widely used for information collection and processing in post-disaster relief operations. However, UAVs are limited by their battery capacity, which makes it hard for them to perform both large-scale information gathering and data processing at the same time. Nowadays, smartphones (SPs), which have become portable devices for people, have the characteristics of strong computing power, rich communication means and wide distribution. Therefore, in this study, we developed SPs to assist UAVs in computation incentive-based task execution. To balance the cost of UAVs and the execution utility of SPs during the task execution process, a multi-objective optimization problem was established, and the Multi-Objective Mutation-Immune Bat (MOMIB) algorithm was developed to optimize the proposed problem. Additionally, considering the diversity of tasks in real-world scenarios, Quality of Service (QoS) coefficients were introduced to ensure the performance requirements of different types of tasks. A large number of simulation experiments show that the task-offloading scheme that we propose is effective.

Mechanical fabrication and evaluation of bioresorbable barbed sutures with different barb geometries

Bioresorbable polymeric sutures are gaining interest from surgeons and patients as they reduce surgical stress and trauma. This study involves two bioresorbable polymers, namely, catgut and poly(4-hyrdorxybutyrate) (P4HB) that are used widely in cosmetic procedures. P4HB barbed sutures are favorably used in rhytidectomy (micro-facelifts) procedures while catgut sutures are widely used for external wound closure after surgical interventions. This study involves the mechanical fabrication of catgut and P4HB barbed sutures and compares their mechanical and anchoring properties. Barbed sutures were fabricated with two different barb geometries namely, straight and curved barbs. The mechanical properties were evaluated via tensile testing, and the anchoring performance was studied by means of a suture-tissue pull-out protocol using porcine dermis tissue which was harvested from the medial dorsal site. The fabricated barbed sutures of both materials showed a similar trend compared to non-barbed sutures of decreases in failure stress, strain at failure, and work to rupture or toughness which was reduced by about 70%. At the same time there was a 15% increase in the initial modulus or stiffness of the barbed sutures. The pull-out force for the barbed sutures with straight barbs was similar for both P4HB (5.04 ± 0.8 N) and catgut (4.47 ± 3.8 N), and as expected, were higher than that of non-barbed sutures of the same size. It was also observed that barbed sutures with curved barbs also required a higher pull-out force than those sutures with straight barbs. It was concluded that by barbing sutures with different barb shapes and geometries, a range of barbed suture products could be fabricated, each meeting the closure requirements for different types of tissue and therefore being suitable for different surgical procedures.

Multifunctional hydrogels for wound healing

Abstract The process of wound healing is complex and dynamic. Given the unique nature of the skin, wound healing requires dressings that meet a wide range of functional requirements. Due to their unique structure and properties, hydrogel dressings provide ideal conditions for supporting the process of wound healing and can be enhanced with additional functions to meet the specific requirements of different types of wounds. Compared to traditional wound dressings, the newly developed hydrogel wound dressing offers several advantages, including its soft texture, ability to retain moisture, oxygen permeability, and comfort. With the advancement of medical technology, higher demands are being placed on the functionality of hydrogel dressings. Therefore, this paper offers a comprehensive overview of hydrogel wound dressings with various functions. These functions primarily include hemostatic hydrogel dressings, antibacterial hydrogel dressings, angiogenesis-promoting hydrogel dressings, and other functional hydrogel dressings. The paper also reviews the research and application of these dressings in recent years.

Research on fault diagnosis of industrial materials based on hybrid deep learning model

Abstract Bearing fault detection is becoming more and more important in industrial development, and deep learning image processing technology provides a new solution for this. In this study, ResNet50 is used to replace VGG-16 as the feature extraction network of Faster R-CNN, and feature pyramid network (FPN) and parallel attention module (PAM) are introduced to achieve higher detection accuracy and speed. The experimental validation was conducted with the Case Western Reserve University bearing dataset using a three-fold cross-validation and compared with Yolov5, FPN, and the original Faster R-CNN model. The experimental results show that the accuracy of the proposed bearing image fault detection method is 78.6%, the accuracy is 77.4%, and the recall rate is 76.9%, which can locate and identify bearing faults more accurately. Future work could focus on further optimizing the model structure to enhance detection performance, strengthening the model’s generalization ability to meet the detection requirements of different types of bearing faults.

Metrology for Indoor Radon Measurements and Requirements for Different Types of Devices.

Indoor radon measurements have been conducted in many countries worldwide for several decades. However, to date, there is a lack of a globally harmonized measurement standard. Furthermore, measurement protocols in the US (short-term tests for 2-7 days) and European Union countries (long-term tests for at least 2 months) differ significantly, and their metrological support is underdeveloped, as clear mathematical algorithms (criteria) and QA/QC procedures considering fundamental ISO/IEC concepts such as "measurement uncertainty" and "conformity assessment" are still absent. In this context, for many years, the authors have been advancing and refining the theory of metrological support for standardizing indoor radon measurements based on a rational criterion for conformity assessment within the ISO/IEC concepts. The rational criterion takes into account the main uncertainties arising from temporal variations in indoor radon and instrumental errors, enabling the utilization of both short- and long-term measurements while ensuring specified reliability in decision making (typically no less than 95%). The paper presents improved mathematical algorithms for determining both temporal and instrumental uncertainties. Additionally, within the framework of the rational criterion, unified metrological requirements are formulated for various methods and devices employed in indoor radon measurements.

Research оf API architecture for client­server communication

API architectures are an important component of modern systems. They provide interaction between various programs and services, and also provide access to data and functionality to other systems. Equally important is the impact of APIs on business. There are several API trends that affect business: API usage expansion, API usage simplification, API security. These trends have a significant impact on business, they allow companies to: increase interaction with customers, collaborate with partners, optimize internal processes. However, choosing the right API architecture is a difficult task. There are many different API architectures, each with its own advantages and disadvantages. Also, different types of systems have different API architecture requirements. Possible problems can be a lack of sufficient documentation, which makes it difficult for developers to choose the right solution, also incompatibilities between different protocols, data formats, request and response methods, which can make it difficult to interact between different systems, and the cost of implementing an API architecture can be significant, which can be important factor for developers. Future prospects may include: creation of new architectures, expansion of API usage, API automation. This study is devoted to the study of API architectures for communication between the client and the server. The goal of the study is to determine which API architecture is best for a specific type of system with specific requirements. To achieve this research goal, an analysis of existing API architectures will be conducted. The analysis will include a study of the advantages and disadvantages of different architectures, as well as their compatibility with the requirements of different types of systems. The results of the study aim to consider the following API architectures: SOAP, REST, GraphQL, gRPC, WebSocket, WebHook, MQTT, and also help developers in choosing the optimal API architecture to meet the specific needs of their projects.

RESEARCH ON 3D MEASUREMENT TECHNOLOGY BASED ON MACHINE VISION

Measurement is an important means for humans to understand and transform the world, and it is the technological foundation for breaking through the forefront of science and solving major problems in economic and social development. The three-dimensional measurement system serves primary national needs, leads national economic development, and ensures national defense security. With the arrival of a new industrial revolution, major industrial countries worldwide have begun to accelerate the strategic deployment of intelligent manufacturing. As an essential component of the construction of smart factories and lighthouse factories, the 3D measurement system will play a vital role in deepening the implementation of the manufacturing power strategy and promoting the high-end, intelligent, and green manufacturing process. In recent years, optical 3D measurement technology represented by surface structured light has developed rapidly and has been widely applied in multiple material processing fields such as forging, casting, and sheet metal. To analyze the manufacturing accuracy of complex parts using surface structured light 3D measurement technology, it is necessary to first scan and reconstruct the overall 3D surface of the part. The reconstructed 3D point cloud data of the part surface should be roughly and precisely matched with the design model. Finally, based on this, data comparison and accuracy analysis should be carried out according to the actual detection requirements of different types of parts. In this process, the accuracy of 3D reconstruction of complex parts and the alignment accuracy between the reconstruction results and the design model directly determine the reliability and accuracy of the final part manufacturing and machining accuracy analysis. This article uses cases to illustrate the application of machine vision in 3D online measurement. The application of three-dimensional measurement technology in online quality inspection and grinding of blades can not only improve the measurement accuracy of online quality inspection of blades but also maintain the relative stability of robot grinding force and improve the grinding effect in robot grinding of aircraft engine blades. Applied to online quality inspection of train wheel hubs, three-dimensional measurement of train wheel adapters and high-speed train wheel size online inspection has been achieved.

Quick-look method and system for multi-domain space science experiment data

Space science experiment data contains huge application value. It is important to design methods and systems to have a quick look at the process of data processing. An integration framework is proposed in this paper. Several basic visual components are designed to support the quick look requirements of different types of data. The dynamic configuration mechanism of the visual view can effectively improve the scalability and user experience friendliness of the system. The method of the interactive layout of the front-end interface can make the visual effect of the page richer. It also supports the self-adaptation of the display screen with different resolutions. The dynamic registration mechanism for visual views ensures the synchronization of data display in multiple identical visual views. Finally, a quick-look system is implemented, which achieves the integration and quick look of multi-domain space science experiment data. The validity of the proposed system is verified by the practical application.

Spherical fuzzy bipartite graph based QFD methodology (SFBG-QFD): Assistive products design application

Assistive product (AP) aims to provide supplementary treatment for patients' physical function and daily activities to improve their functional status. One challenge in the AP design is prioritizing design requirements (DRs) based on patient requirements (PRs) involving diverse and variable interaction behaviors. This work developed and applied a quality function deployment method based on the spherical fuzzy bipartite graph (SFBG-QFD) to comprehensively consider the dependency relationship between PRs and DRs during AP design. Specifically, a spherical fuzzy bipartite graph is constructed in the PR-DR relationship matrix. The projection algorithm generates the one-mode spherical fuzzy graphs of PRs and DRs, reflecting the internal relationship of PR-PR and DR-DR pairs. Subsequently, the interaction influence of PRs and DRs is defined as an additional criterion for the QFD method. Through the SFBG-QFD method, designers can better consider the uncertainty of interaction between different types of requirements and make more reliable decisions. The effectiveness of the proposed method is verified through the AP design for pneumoconiosis patients, and the stability and potential of the proposed method are emphasized through comparative studies.

Framework for Virtualized Network Functions (VNFs) in Cloud of Things Based on Network Traffic Services

The cloud of things (CoT), which combines the Internet of Things (IoT) and cloud computing, may offer Virtualized Network Functions (VNFs) for IoT devices on a dynamic basis based on service-specific requirements. Although the provisioning of VNFs in CoT is described as an online decision-making problem, most widely used techniques primarily focus on defining the environment using simple models in order to discover the optimum solution. This leads to inefficient and coarse-grained provisioning since the Quality of Service (QoS) requirements for different types of CoT services are not considered, and important historical experience on how to provide for the best long-term benefits is disregarded. This paper suggests a methodology for providing VNFs intelligently in order to schedule adaptive CoT resources in line with the detection of traffic from diverse network services. The system makes decisions based on Deep Reinforcement Learning (DRL) based models that take into account the complexity of network configurations and traffic changes. To obtain stable performance in this model, a special surrogate objective function and a policy gradient DRL method known as Policy Optimisation using Kronecker-Factored Trust Region (POKTR) are utilised. The assertion that our strategy improves CoT QoS through real-time VNF provisioning is supported by experimental results. The POKTR algorithm-based DRL-based model maximises throughput while minimising network congestion compared to earlier DRL algorithms.

A wearable gloved sensor based on fluorescent Ag nanoparticles and europium complexes for visualized assessment of tetracycline in food samples.

The abuse of tetracycline antibiotics leads to accumulating residues in the human body, seriously affecting human health. Establishing a sensitive, efficient, and reliable method for qualitative and quantitative detection of tetracycline (TC) is necessary. This study integrated silver nanoclusters and europium-based materials into the same nano-detection system to construct a visual and rapid TC sensor with rich fluorescence color changes. The nanosensor has the advantages of a low detection limit (10.5nM), high detection sensitivity, fast response, and wide linear range (0-30μM), which can meet the analysis requirements of different types of food samples. In addition, portable devices based on paper and gloves were designed. Through the smartphone's chromaticity acquisition and calculation analysis application (APP), the real-time rapid visual intelligent analysis of TC in the sample can be realized, which guides the intelligent application of multicolor fluorescent nanosensors.

A Cross-Layer Media Access Control Protocol for WBANs

Wireless body area network (WBAN) is an emerging comprehensive technology that can deeply integrate with e-health and smart sports. As a wearable network, improving the quality of network service and user experience is crucial. Due to the miniaturized design of sensors, their available energy from batteries is limited, making the extension of their lifetime a key research challenge. Existing studies have proposed methods to improve energy efficiency, but there are still limitations in addressing dynamic adaptive aspects of differential energy distribution and channel conditions. In order to further extend the lifetime of sensor nodes and networks while ensuring quality of service, and to provide a reliable transmission mechanism for heterogeneous application data, this paper presents a cross-layer optimized MAC protocol mechanism. The protocol takes into account the transmission requirements of different types of data and redesigns the superframe. To improve the lifetime of nodes, we propose an energy-adaptive adjustment mechanism considering the channel conditions. At the same time, a cooperative transmission mechanism is proposed to further enhance network lifetime. In experiments conducted on two typical networks, compared to IEEE 802.15.6, the power adjustment scheme improves the network lifetime by 2.8 to 3.7 times, and the cooperative mechanism between nodes further increases the network lifetime by 17% to 44%. Our proposed scheme effectively extends the network lifetime while ensuring quality of service, avoiding frequent battery resets for users, and effectively improving the user experience quality.

Effect of protein concentration on thermal aggregation and Ca2+-induced gelation of soymilk protein

The concentration of protein in soymilk is a key element in the production of soy products. In this study, the thermal aggregation behavior of soymilk proteins and the properties of aggregates formed at different concentrations (33 mg/g-56 mg/g) were analyzed. Results showed that the soymilk proteins formed two kinds of aggregates which demonstrated different properties at particular concentrations. In the low concentration range (<40 mg/g), rod-like aggregates were formed. The content and size of the aggregates increased with growing protein concentration, but the morphology didn't change. Additionally, the surface hydrophobic groups, sulfhydryl groups(-SH) and electrostatic charge of the protein aggregates exposed to the outside also increased, which drastically (20%) reduced the activation energy of the Ca2+-induced gelation and led to the cross-linkage of protein aggregates to form a dense, cluster-like network structure, which facilitated the formation of gels with high levels of hardness and increased water holding capacity (WHC). At protein concentrations greater than 40 mg/g, however, the protein subunits formed aggregates, which also combined with each other along an axial direction through the hydrophobic groups and -SH on the surface, thereby forming linear or even micro-network agglomerates. In this way, the surface hydrophobicity(S0) and -SH content decreased linearly with the rising protein concentration, while the surface charge density increased, the activation energy required for soymilk gelation increased, and the protein aggregates crosslinked to form a coarse network structure with large voids. Hence, the hardness, elasticity, toughness and WHC of the gel decreased with the increase in protein concentration. This study reveals the mechanism by which protein concentration changes the gel-forming ability of soymilk, and have important theoretical reference for enterprises to select the appropriate protein concentration for soymilk preparation, or to optimize the “concentration dilution method” to meet the processing requirements of different types of soy products.

Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models

The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and, in particular, a shift toward Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies and suffer from a lack of structure that hinders their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP), in general, and Large Language Models (LLMs), in particular, can serve as an enabler for the conversion of NL requirements into machine-readable requirements. Doing so is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach consists of an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs are utilized. Two of these models are fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERTs), specifically, aeroBERT-NER and aeroBERT-Classifier, which are trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, is utilized to identify sentence chunks present in NL requirements. All three language models are utilized together to achieve the standardization of requirements. The effectiveness of the methodologies is demonstrated through the semi-automated creation of boilerplates for requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs).

Machine learning for accelerating screening in evidence reviews

AbstractEvidence reviews are important for informing decision‐making and primary research, but they can be time‐consuming and costly. With the advent of artificial intelligence, including machine learning, there is an opportunity to accelerate the review process at many stages, with study screening identified as a prime candidate for assistance. Despite the availability of a large number of tools promising to assist with study screening, these are not consistently used in practice and there is skepticism about their application. Single‐arm evaluations suggest the potential for tools to reduce screening burden. However, their integration into practice may need further investigation through evaluations of outcomes such as overall resource use and impact on review findings and recommendations. Because the literature lacks comparative studies, it is not currently possible to determine their relative accuracy. In this commentary, we outline the published research and discuss options for incorporating tools into the review workflow, considering the needs and requirements of different types of review.

Clinical assessment of HRD scoring in relation to PARPi response in terms of statistical guidance on reporting results for CDx tests evaluation.

e17597 Background: Homologous recombination deficiency (HRD) is a tumor characteristic that can be assessed via different biomarkers such as individual mutations in BRCA1 and BRCA2 genes, and/or other HRR genes and genomic instability. We assessed a combined HRD score in three measures; BRCAs mutations, HRR mutation (HRRm) status, and genomic instability scoring via a specific bioinformatic algorithm of the testing panel to establish clinical validation. Methods: We tested 157 ovarian cancer patients’ FFPE samples to define HRD score, BRCA1/2 mutation and other HRRm status, to camper the responses to PARP inhibitors. For the HRD scoring, all the samples that were previously tested with alternative HRD tests and the cases had an indication of PARPi also tested via HRDInfo (CE-IVD) Panel for clinical diagnostics in this study. The PPA, NPA and OPA have been measured according to the FDA regulations of overall agreement for a follow-on CDx to show clinical evidence. Results: Depending on the three levels of testing strategy, BRCA1-2 mutation status in relation to the clinical response for PARPi has the lowest positive percent agreement (PPA) with 63.41% while HRD score has the highest with 97.62% that is higher than the any alternative HRD tests. All the PPA, NPA and OPA measures showed in the Figure for BRCA1-2 status and Genomic Instability stand alone and all BRCA1-2 plus other HRR genes plus Genomic Instability Status (GIS) to define HRD scoring. Conclusions: Even though the testing rate of HRD tests are still low all around the world and limited to specific cancers, there is an increasing requirement for different types of cancers. However, most alternative HRD tests have high overall concordance but fail to reach FDA CDx test requirements. Thus, this study showing the clinical evidence in the treatment of PARPi response data together with high PPA, NPA and OPA measures of HRDInfo panel and its bioinformatics algorithm. [Table: see text]

Broadband omnidirectional piezoelectric–electromagnetic hybrid energy harvester for self-charged environmental and biometric sensing from human motion

Vibration induced by human motions features a wide frequency spectrum and spatiotemporal-variable directions. Yet, it is challenging to concurrently expand frequency bandwidth and achieve multidirectional capability for effectively human-induced biomechanical energy harvesting and biometrics sensing. In this paper, we report a piezoelectric–electromagnetic hybrid energy harvester with coupled vibrational stabilization and induced frequency-up rotational mechanisms for omnidirectional and broadband vibration energy harvesting. Experimental studies demonstrate the proposed hybrid energy harvester operates effectively over a wide excitation frequency range from 6.0 to 16.0 Hz with omnidirectional responses. The piezoelectric conversion unit can provide a maximum output power of 0.9 mW and the electromagnetic conversion unit’s maximum output power is 22.7 mW. The hybrid energy harvester satisfies the power requirements of different types of portable electronics and realizes various self-charged sensing electronics that are stimulated by human movement. Various types of human motion signals are detected by piezoelectric and electromagnetic units via machine learning techniques to realize self-powered motion monitoring. Compared with a single type of signal, the identification accuracy of the hybrid device is improved from 85.6% to 100%. The proposed energy harvester for self-powered sensing paves the way to green intelligent life such as health management, environmental monitoring and human-machine interaction.

Molecular Farming Strategy for the Rapid Production of Protein-Based Reagents for Use in Infectious Disease Diagnostics.

Recombinant proteins are a major breakthrough in biomedical research with a wide range of applications from diagnostics to therapeutics. Strategic construct design, consistent expression platforms, suitable upstream and downstream techniques are key considerations to produce commercially viable recombinant proteins. The recombinant antigenic protein production for use either as a diagnostic reagent or subunit vaccine formulations is usually carried out in prokaryotic or eukaryotic expression platforms. Microbial and mammalian systems dominate the biopharmaceutical industry for such applications. However, there is no universal expression system that can meet all the requirements for different types of proteins. The adoptability of any expression system is likely based on the quality and quantity of the proteins that can be produced from it. The huge demand of recombinant proteins for different applications requires an inexpensive production platform for rapid development. The molecular farming scientific community has been promoting the plant system for nearly three decades as a cost-effective alternative to produce high-quality proteins for research, diagnostic and therapeutic applications. Here, we discuss how plant biotechnology could offer solutions for the rapid and scalable production of protein antigens as low-cost diagnostic reagents for use in functional assays.