Deployment Time Research Articles

Radio-Frequency Identifier Devices (RFIDs): Our Experience With Wireless Localisation in Non-palpable Breast Masses at a UK Tertiary Breast Imaging Unit.

The aim of this report is to evaluate the impact of the percutaneous ultrasound-guided placement of wireless radio-frequency identifier devices (RFIDs; Hologic LOCalizer, Marlborough, Massachusett) and its impact in our practice of preoperative localisation of biopsy-proven breast cancers, post-vacuum assisted biopsy-site hematoma, and lymph nodes for targeted dissection pre-operatively.A single institutional retrospective analysis of RFID usage for preoperative localisation in screening and symptomatic patients with non-palpable biopsy-proven breast carcinoma was reviewed from the radiology information system at our tertiary breast imaging unit. Its impact on the radiological and surgical team practice was reviewed, including the number of appointments, the interval between scheduling image-guided localisation and intraoperative localisation, procedure failure, average deployment, and surgical time. Feedback from surgeons and pathologists practice was also taken into consideration.Fifty-nine RFID clips were placed for wireless localisation of breast cancers, lymph nodes, and post-vacuum-assisted biopsy hematoma over nine months. Seventy-three per cent (73%; n=43/59) of RFID devices were placed in biopsy-proven carcinomas under direct ultrasound guidance. The learning curve was small, as the delivery system was similar to the commonly used localisation clips.The pilot process involved RFID with radioisotope injections for the breast mass for the initial 28% (n=12 /43) cases, which were gradually transitioned into RFID only. Radioisotope was used for sentinel node purposes if required. For targeted node dissection, 3% (n=2/59) patients received RFID for biopsy-proven metastatic node localisation, with one of two with adjunct radioisotope injection. Post-vacuum-assisted biopsy (VAB) hematoma was localised in 24% (n=14/59) cases, four of which received adjunct radioisotope in the pilot phase.The average procedure time for RFID deployment was five minutes. The average time for surgery was 20 minutes. 1.6% (n=1/59) incidence of RFID slipping from the surface of the site through surgical exposure attributed to the superficial and immediate pre-operative placement of the RFID. This was salvageable with adjunct radioisotope injection within the pilot phase. There were no incidences of repeat localisation or repeat exploration surgeries.Planned pre-operative localisation with RFID allows for better planning and less pressured service delivery and a success rate of 98-99%. This ultimately avoids lost theatre time and patient demotivation. Surgeons have reported excellent intra-operative detectability in their approach. There has been no difficulty in the detection of the RFID within the surgical cavity despite hematoma. RFID localisers are expensive compared to our usual practice of radioisotope injection but this can be recuperated through uncoupled tariffs like gain in slots of one-stop clinics, flexibility for placement, and avoiding lost theatre time, as these can be placed up to 30 days before surgery.

One-year results with a low-profile endograft in subjects with thoracic aortic aneurysm and ulcer pathologies

ObjectiveThe study objective was to evaluate the safety and effectiveness of the second-generation, low-profile RelayPro (Terumo Aortic) thoracic endograft for the treatment of descending thoracic aortic aneurysm or penetrating atherosclerotic ulcer. MethodA prospective, international, nonblinded, nonrandomized, pivotal trial analyzed a primary safety end point of major adverse events at 30 days (death, myocardial infarction, stroke, renal/respiratory failure, paralysis, bowel ischemia, procedural blood loss) and a primary effectiveness end point of treatment success at 1 year (technical success, patency, absence of aneurysm rupture, type I/III endoleaks, stent fractures, reinterventions, aneurysm expansion, and migration) compared with performance goals from the previous generation Relay pivotal study. The study was conducted in 36 centers in the United States and Japan and enrolled participants between 2017 and 2019. ResultsThe study population of 110 patients had a median (interquartile range) age of 76 (70-81) years, 69 (62.7%) were male, and 43 (39.1%) were Asian. Patients were treated for 76 fusiform aneurysms (69%), 24 saccular aneurysms (22%), and 10 penetrating atherosclerotic ulcers (9%). Most patients (82.7%) were treated with a non-bare stent configuration. Technical success was 100%. The median (interquartile range) procedure time was 91 (64-131) minutes, and the deployment time was 16 (10-25) minutes. A total of 50 patients (73.5%) in the US cohort had percutaneous access, whereas centers in Japan used only surgical cutdown. The 30-day composite major adverse events rate was 6.4% (95% upper confidence interval, 11.6%; P = .0002): 2 strokes, 2 procedural blood losses greater than 1000 mL requiring transfusion, 2 paralysis events, and 1 renal failure. Primary effectiveness was 89.2% (lower 95% confidence interval, 81.8%; P = .0185). Nine subjects experienced 11 events (1 aneurysm expansion, 6 secondary interventions, and 4 type I endoleaks). There was no loss of stent-graft patency, no rupture, no fractures, and no migration. ConclusionsThe low-profile RelayPro thoracic endograft met the study primary end points and demonstrated satisfactory 30-day safety and 1-year effectiveness for the treatment of patients with aneurysms of the descending thoracic aorta or penetrating atherosclerotic ulcers. Follow-up is ongoing to evaluate longer-term outcomes and durability.

New approach in assembly, disassembly and maintenance processes I4.0 by displaying in augmented reality environment

In the current ever-accelerating time of deployment of new production systems associated by various manipulating, technological and maintenance procedures, there is a demand from the point of view of the industrial sphere for shortening these operations and efficiency of the selected processes. It is the ability to visualize these processes using modern augmented reality (AR) technology that is the way to achieve the desired aspects. Visualization via smartphones, tablets, headsets in the AR environment allows users to provide comprehensive information with the exact procedure and information about the required tasks (assembly, disassembly, service operations, maintenance, wiring diagrams, etc.). The use of AR technology, also in view of the ongoing pandemic situation Covid 19, makes possible to involve less qualified operators in the industrial sphere. Deployment of the less skilled operators in the production sphere, using AR technology, can make up for the shortage of skilled labour that is currently lacking in every industry. In this way, it is also possible to keep the industrial sector in good condition without undesirable factors such as production shutdowns, long service operations, and poorly trained service personnel.

Исследование прочностных характеристик долговременных быстровозводимых геодезических купольных укрытий для нужд аэромобильных группировок МЧС

PURPOSE. The article deals with examples of eliminating natural and man-made disasters with the involvement of means and forces of EMERCOM of Russia. The authors note the need to search for alternative options for structural layouts of long-term shelters to ensure the possibility of accommodating people during emergency situations elimination. A functional analysis of the requirements for the design of long-term shelters is carried out. The results of an interdisciplinary study of the two types of structural layouts of geodesic dome shelters of increased capacity are presented. METHODS. On the basis of calculations by the method of strength finite element analysis, the authors have obtained the dependencies of stress and strain state of separate structure elements on the choice of the type and characteristics of the structural layout and conducted an integrated assessment of functional, strength and technological characteristics. FINDINGS. Applying the integrated approach, the design and technological solutions considered in the article will allow engineering new, more comfortable and technically advanced long-lasting and rapidly deployable dome shelters due to: – reducing the deployment time; – increasing mobility; – simplifying the deployment process thanks to the unification of structural elements; – simplifying the implementation of correct assembly visual control during the structure erection; – increasing safe operation life by reducing the maximum stress and strain values for especially critical (core support) parts of the structure; – simplifying the structure technical condition control while in service. These factors are especially relevant to solving the problems of accommodating large groups of people with pressing need for long-term housing in emergency situations. In the future, with the help of the approach described, it is possible to increase the operating efficiency of airmobile groups of EMERCOM while eliminating the consequences of emergency situations. RESEARCH APPLICATION FIELD. The use of the results obtained will make it possible to adequately choose the type of geodesic dome structural layout for creating long-term shelters in order to ensure operating efficiency of airmobile groups of EMERCOM while eliminating the consequences of emergencies. CONCLUSIONS. The comprehensive comparison of the two types of structural layouts in terms of strength, technological and functional criteria has demonstrated the undeniable advantages of the second type (a hemisphere based on a cylinder) compared to the first one (3/4 of a sphere segment).

Developing a strategic viability-fit model of mobile technology adoption in hotels

开发一项酒店移动科技应用的战略可行性模型研究目的尽管酒店中的移动技术 (MT) 具有预期的明显行业优势, 它们的采用速度比预期的要慢。本研究旨在确定酒店经理在其行业中采用 MT 时面临的问题和挑战, 以便我们可以确定采用和管理 MT 的战略位置, 并为酒店管理实践建立参考框架。研究设计/方法/途径：作者与管理人员和豪华酒店的高管进行了一系列焦点小组访谈 (FGI) 。 FGI 以半结构化的形式分组进行, 询问关于他们酒店采用 MT 的问题以及他们面临的挑战。 FGI 的文字记录使用扎根理论方法进行分析。研究发现：FGI 脚本的开放和轴向编码揭示了 15 个潜在挑战类别酒店的 MT。随后的选择性编码揭示了两个潜在的维度：可行性, 匹配度。有了这两个基本维度, 就可以开发出采用 MT 的战略模型。这模型确定了酒店中的四种 MT 采用策略：观察者、实验者、探索者和领导者。研究原创性/价值：这里开发和展示的模型可以帮助分析酒店在采用MT的战略定位采用。根据定位分析结果, 酒店经理可以适当地决定资源调动优先事项和部署时间。酒店; 战略; 热情好客; 科技; 采用; 焦点小组访谈; 可行性; 匹配度

Unravelling the role of membrane pore size in polar organic chemical integrative samplers (POCIS) to broaden the polarity range of sampled analytes.

Polar organic chemical integrative samplers (POCIS) are widely used in their standard configuration for sampling contaminants in water bodies. A wider polyethersulfone (PES) membrane pore size was employed in POCIS exposed in a static calibration experiment to investigate the uptake of 21 emerging contaminants ranging from hydrophilic (perfluoroalkyl compounds, xanthines, an artificial sweetener) to more hydrophobic compounds (pharmaceuticals, oestrogens, UV filters). Compared to standard POCIS with 0.1-µm pore size PES membranes, the POCIS with 5-µm pore size PES membranes did not increase sampling rates for compounds of relatively low and mid-hydrophobicity. However, the uptake of more hydrophobic and anionic compounds, which either poorly diffuse through or are retained within the standard 0.1-µm PES membrane, showed a marked increase. This led to the first ever recorded sampling rates for triclosan (0.249 L day-1) and two UV filters (0.075-0.123 L day-1). Based on these results, more attention should be placed on the choice of the appropriate membrane for each POCIS application. The most suitable configuration depends on the studied compound physico-chemical characteristics-such as the polarity and the compound membrane-to-sorbent partitioning coefficient-but also on the site conditions (deployment time, fouling, flow variations, et.).

Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing.

High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-time performance. Owing to the large amount of data obtained by sensors, it has been proven that deep learning, as a powerful data-driven approach, can perform effectively in the field of track detection. However, it is difficult and expensive to obtain labeled data from railways during operation. In this study, we used a segment of a high-speed railway track as the experimental object and deployed a distributed optical fiber acoustic system (DAS). We propose a track detection method that innovatively leverages semi-supervised deep learning based on image recognition, with a particular pre-processing for the dataset and a greedy algorithm for the selection of hyper-parameters. The superiority of the method was verified in both experiments and actual applications.

Use of diffusive gradients in thin films (DGT) to measure potentially bioavailable metals in southeastern USA blackwater streams

We used diffusive gradients in thin films (DGT) to measure potentially bioavailable metals in coastal plain streams in the southeastern USA that exhibited strong to moderate blackwater characteristics. Metals were partitioned into particulate metals, DGT-inert metals (i.e., colloidal and refractory organic complexes not accumulated by DGT), and DGT-labile metals (i.e., free metal ions, small inorganic complexes, and labile organic complexes). We also examined the influence of different DGT deployment times using data collected from the field and a follow-up laboratory study. The DGT-measured fraction of dissolved metals in the streams was 15% for Cd, 21% for Zn, 33% for Cu, 37% for Pb, and 98% for Mn. Metals bound to particulates predominated only for Pb. Most of the Cd, Pb, Zn, and Cu were associated with colloids, refractory organic complexes, or particles. Relatively small amounts were in free ion or labile complexes likely to be bioavailable through respiratory surfaces. Modeled concentrations of free and inorganically bound Cu and Pb were lower than the DGT fraction indicating that DGT accumulated some organically bound Cu and Pb that might not have been bioavailable. DGT-exposure times in excess of 5days may have contributed to the accumulation of partly labile organic-metal complexes and were associated with substantial biofouling that caused metal uptake by DGT to depart from linearity.

Design and Analysis of Dynamic Block-setup Reservation Algorithm for 5G Network Slicing

In 5G, network functions can be scaled out/in dynamically to adjust the capacity for network slices. The scale-out/-in procedure, namely autoscaling, enhances performance by scaling out instances and reduces operational costs by scaling in instances. However, the autoscaling problems in 5G networks are different from those in traditional cloud computing. The 5G network functions must be considered the simultaneous deployment of multiple instances; moreover, the deployment of 5G network functions is more frequent than that of traditional cloud computing. Both the number and timing of deployment will substantially affect the cost-effectiveness of the system. In this paper, we first identify the autoscaling issues specifically based on the 3GPP standards. We develop a low-complexity analytical queuing model to formulate the problem and quantify a set of performance metrics with closed-form solutions. The proposed analytical model and closed-form solutions are cross-validated by extensive simulations. The analytical model offers design insights and theoretical guidelines, helping us study the effectiveness of reservations. We proposed a dynamic block-setup reservation algorithm (DBRA) to find the optimal reserved number and threshold value of network slices. Therefore, mobile operators can balance the system's cost-effectiveness without large-scaled testing and real deployment, saving cost on time and money.

Synthesizing Decentralized Controllers With Graph Neural Networks and Imitation Learning

Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controllers are readily available, they face limitations in terms of scalability and implementation, as they do not respect the distributed information structure imposed by the network system of agents. Given the difficulties in finding optimal decentralized controllers, we propose a novel framework using graph neural networks (GNNs) to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn</i> these controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. We show that GNNs learn appropriate decentralized controllers by means of imitation learning, leverage their permutation invariance properties to successfully scale to larger teams and transfer to unseen scenarios at deployment time. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.

Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products

Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the simulation aspects of DT and the data and design infrastructures are discussed. Referring to the simulation and theoretical factors of DTs, high-throughput simulation and automation and artificial intelligence-assisted multiscale atomistic modeling are detailed through several cases studies. With respect to data and data mining technologies, entropy and its application for attribute selection in decision trees are discussed to emphasize knowledge-based modeling, simulation and data analysis in machine learning coherently. Guided by the perspectives and case studies of the digital thread, we present our recent work on the design, manufacturing and product service via big data-assisted DTs for smart design and manufacturing by integrating some of these advanced concepts and technologies. It is believed that big data-assisted DTs for smart design and manufacturing effectively support better products with the application of novel materials by reducing the time and cost of materials design and deployment.

Variability of Mammographic Imaging Surveillance in Breast Cancer Survivors.

Variability of Mammographic Imaging Surveillance in Breast Cancer Survivors.

Improving Performance of C-V2X Sidelink by Interference Prediction and Multi-Interval Extension

Cellular Vehicle-to-Everything (C-V2X), as a next-generation V2X communication technique, has attracted much attention recently. Especially, SideLink (SL) of C-V2X in the mode 4 is a promising technique for disseminating various information in a non-line-of-sight area via direct inter-vehicle communication without requiring base stations. In order to better prevent accidents, it is important to improve the reliability and adaptability of SL. However, the Semi-Persistent Scheduling (SPS) method has a limited performance at short distances due to transmission collisions, and cannot well support applications with different transmission intervals. To solve these problems, in this paper, we propose a new packet collision avoidance method, Interference Prediction and Multi-Interval extension (IPMI), for the C-V2X SL mode 4, based on directly predicting the interference vehicles (defined as vehicles in the overlapping communication range of two vehicles using the same resource, and susceptible to packet collisions at reception), without causing extra overhead. Specifically, the proposed method selects for each vehicle a resource that has (i) a minimal number of interference vehicles and (ii) a maximal inter-vehicle distance, to reduce packet collisions. It is further extended to support applications with different transmission intervals. Simulation results confirm that compared with the conventional methods, the proposed method can achieve higher reliability and still have a promising performance even in times of partial deployment.

Self-Supervision &amp; Meta-Learning for One-Shot Unsupervised Cross-Domain Detection

Deep detection approaches are powerful in controlled conditions, but appear brittle and fail when source models are used off-the-shelf on unseen domains. Most of the existing works on domain adaptation simplify the setting and access jointly both a large source dataset and a sizable amount of target samples. However this scenario is unrealistic in many practical cases as when monitoring image feeds from social media: only a pretrained source model is available and every target image uploaded by the users belongs to a different domain not foreseen during training. We address this challenging setting by presenting an object detection algorithm able to exploit a pre-trained source model and perform unsupervised adaptation by using only one target sample seen at test time. Our multi-task architecture includes a self-supervised branch that we exploit to meta-train the whole model with single-sample cross-domain episodes, and prepare to the test condition. At deployment time the self-supervised task is iteratively solved on any incoming sample to one-shot adapt on it. We introduce a new dataset of social media image feeds and present a thorough benchmark with the most recent cross-domain detection methods showing the advantages of our approach.

Appropriate chamber deployment time for separate quantification of CH&lt;SUB&gt;4&lt;/SUB&gt; emissions via plant and ebullition from rice paddies using a modified closed-chamber method

Appropriate chamber deployment time for separate quantification of CH&lt;SUB&gt;4&lt;/SUB&gt; emissions via plant and ebullition from rice paddies using a modified closed-chamber method

Realization of the penetration rate for autonomous vehicles in multi-vehicle assignment models

Growing development in technologies that can lead to fully automated driving is at pace. This can result in an enormous change in traffic operations and network properties. However, there are uncertainties about the full deployment time of these autonomous vehicles on road networks. The transition period from vehicles with drivers to driverless will result in a mutual environment with an interaction between traditional (that is, manual) vehicles, automated vehicles and infrastructure. In this context, this research attempts to focus on the various factors of land use, user demographics and road network affecting the percentage of autonomous vehicles into the multi-vehicle assignment models and their subsequent impacts on the traffic network properties. This research aims to use a realistic approach to evaluate the percentage of autonomous vehicles to be injected into the traffic models via an indicator matrix and seven decision indices. A macroscopic traffic model is formulated for mixed traffic flow to which demand is assigned following a stochastic user equilibrium approach using the Frank Wolfe algorithm. The formulated model is applied to a real-world city network for a small part of the Italian city of Genoa. Results showed an effective improvement in traffic network properties with increment in capacities and flow speeds against the saturation grade for the given network.

Design, Implementation, and Deployment of Modular Battery Management System for IIoT-Based Applications

This paper proposes design and implementation of a battery management system (BMS) for the industrial internet of things (IIoT) enabled applications. The hardware and software development of this BMS is briefly presented in this paper. In terms of hardware development, the presented BMS have modular topology and has 1) high fault tolerance and 2) has exceptionally flexible deployment owing to its topology having multiple local management units (LMUs) connected to a central management unit (CMU). This hardware design approach aims to address the overall design efficiency and cost trade-off of BMS deployment. The hardware design efficiency is tested using actual deployment. In terms of fault tolerance using 1 – 3 LMUs at fault, the voltage monitoring accuracy is maintained for each LMUs. An average of 0.00017 V, 0.0008 V and 0.001 V voltage difference is yielded for 1, 2, and 3 modules at fault respectively. Additionally, core BMS sub-circuit is tested to verify hardware design efficiency such as the DC-to-DC converter which yielded 92.74%. Furthermore, the CMU is integrated with a wireless communication circuit that enables IIoT-based applications such as the emerging edge-based, and a plethora of intelligent deployment. In terms of software, the presented BMS aims to realize state-of-the-art processing through IIoT based approach. For software testing and verification, the BMS is deployed to an unmanned ground vehicle (UGV). The signal stability is tested for UGV based application at a 3500s deployment time whereas an average of 0.0010V voltage difference is yielded. This is verified using time markers which is further analyzed using software-based signal processing and acquisition simulation. Concisely, the proposed BMS aims to converge IIoT applications to its actual deployment. The proposed BMS is designed, implemented, and successfully deployed to test its viability both in the simulation platform and actual deployment.

A novel vision-based weakly supervised framework for autonomous yield estimation in agricultural applications

Autonomous systems have been established as a ground-breaking technology in agriculture, particularly for resource optimization and labor savings. However, even those solutions that are limited to monitoring activities, such as yield estimation, rely on costly robotic platforms equipped with a series of range devices (e.g., LIDAR and GPS-RTK). Recently, vision-based strategies have gained considerable attention as a less expensive and more efficient alternative, capable to be on par with or even surpass approaches that benefit from range sensors. Nonetheless, they exploit deep learning methodologies, which require burdensome labeling procedures to perform training. To address these shortcomings, we present a novel approach that performs yield estimation requiring only a monocular camera and needs a limited amount of supervision information. It detects, locates and maps fruits and tree canopies to estimate the total yield of a specific crop. To keep the image labeling effort to a minimum, we propose a weakly-supervision paradigm that only requires a simple binary label encoding the presence or the absence of fruits in the training images. Our approach does not make any assumptions on the underlying platform, i.e., it can be used by collecting images either with a hand-held camera or with an autonomous robot. Therefore, we are able to considerably reduce the deployment time, the energy and the cost of the overall yield estimation system. At the same time, we keep the performance comparable to both vision-based fully supervised baselines (which require costly labeling operations) and classical systems that rely on more expensive and power-demanding sensors.

Safety and efficacy of the Mynx Control vascular closure device in peripheral arterial procedures: A prospective study.

This article aims to present a prospective study investigating the safety and efficacy of the Mynx Control extravascular closure device (Cordis Corporation, FL, USA), for femoral arterial closure in patients undergoing peripheral arterial procedures. Between January 2020 and February 2021, 100 Mynx Control devices were deployed in 91 consecutive patients (mean age: 67.5 ± 16.9years) who underwent peripheral arterial procedures. We used ultrasound and/or fluoroscopy during Mynx Control deployment and ultrasound post-procedure to detect complications. Femoral artery punctures included 62 (62%) antegrade and 38 (38%) retrograde punctures. The mean activated clotting time at time of device deployment was 221s. The primary endpoints were technical success, device failure, and complication rates up to 30days. A 5F vascular sheath was used in 43 cases (43%) (36 (36%) 6F and 21 (21%) 7F). The majority of our cases had antegrade access (62%). Overall technical success rate was 97% in both antegrade and retrograde cases. In total, there were 4 minor complications: 3 (3%) cases of pseudoaneurysm and 1 (1%) case of haematoma. No major complications were recorded post-procedure or 30days post index procedure. The Mynx Control vascular closure device is safe and effective in achieving haemostasis in patients undergoing antegrade and retrograde peripheral angioplasty procedures.

An effectiveness of artificial coral reefs in the restoration of marine living resources

Marine fisheries have an important role in the local social-economic development of Vietnamese coastal communities. However, commercial stocks have been collapsible during the past 20 years due to the pressure from large numbers of inshore fishing vessels and other industrial operations in the coastal areas. Artificial coral reefs (ACRs) have been broadly used to mitigate human impacts in coastal ecosystems, and enhance resource reproduction and fisheries. In this study we constructed 500 cylindrical-shaped ACRs and deployed them over 1.5 km2 in the waters of Quang Nam to provide an alternative habitat to recover natural resources. We monitored fish assemblages associated with ACRs using SCUBA diving methods and trammel nets during a 2-year period. Results indicated that both the number of species and individuals increased with time since ACRs deployment. Only 44 marine species were found in the absence of ACRs, increasing as much as 193 species after 1 year deployment, and 237 species after 2 years deployment. Catch per unit effort (CPUE) and weight per unit effort (WPUE) for all species caught by trammel nets that were used to survey, were also associated with increasing time of deployment. This study suggests that ACRs are viable to restore inshore natural resources.