Maintenance Challenges Research Articles

Implementasi Sistem Monitoring Tanaman Hidroponik Menggunakan Metode Fuzzy Sugeno

Farming using hydroponic systems has emerged as an effective alternative to address the problem of limited agricultural land. However, hydroponic farming requires strict maintenance and control. In light of these challenges, the development of monitoring technology is necessary to assist in monitoring and controlling hydroponic systems. The development of a monitoring system is carried out to address the challenges of maintenance and control in hydroponic farming through the development of effective and accurate monitoring technology, thus enhancing the overall productivity and success of hydroponic plant cultivation. This study aims to develop a monitoring system using Arduino Mega 2560, Wemos D1 R2, E201 sensor, DHT22 sensor, HC-SR04 sensor, and BH1750 sensor. In this research, the fuzzy logic method was applied to generate outputs for the water pump actuator, pH up pump, and pH down pump. The HC-SR04 and E201 sensor data served as fuzzy inputs, while the Arduino Mega 2560 functioned as the fuzzy data processor, and the Wemos D1 R2 was responsible for sending data to the server via the internet. The data successfully stored in the server's database would be displayed on the monitoring dashboard. The HC-SR04 sensor testing results showed an average %error of 0% and an accuracy of 100%. The E201 sensor exhibited an average %error of 0.07% and an accuracy of 99.33%. The DHT22 sensor had an average %error of 0.05% and an accuracy of 99.49% for temperature data, while for humidity data, it had an average %error of 2.58% and an accuracy of 75.23%. The BH1750 sensor had an average %error of 0.13% and an accuracy of 99.66%. The results of this study demonstrate that the hydroponic plant monitoring system using the described devices and methods can be implemented successfully.

An Optimal Opportunistic Maintenance Planning Integrating Discrete- and Continuous-State Information

Information-driven group maintenance is crucial to enhance the operational availability and profitability of diverse industrial systems. Existing group maintenance models have primarily concentrated on a single health criterion upon maintenance implementation, where the fusion of multiple health criteria is rarely reported. However, this is not aligned with actual maintenance planning of multi-component systems on many occasions, where multi-source health information can be integrated to support robust decision making. Additionally, how to improve maintenance effectiveness through a scientific union of both scheduled and unscheduled maintenance remains a challenge in group maintenance. This study addresses these research gaps by devising an innovative multiple-information-driven group replacement policy for serial systems. In contrast to existing studies, both discrete-state information (hidden defect) and continuous degradation information are employed for group maintenance planning, and scheduled postponed maintenance and unscheduled opportunistic maintenance are dynamically integrated for the first time to mitigate downtime loss. To be specific, inspections are equally spaced to reveal system health states, followed by the multi-level replacement implemented when either (a) the degradation of the continuously degrading unit reaches a specified threshold, or (b) the age of the multi-state unit since the defect’s identification reaches a pre-set age (delayed replacement). Such scheduling further enables the implementation of multi-source opportunistic replacement to alleviate downtime. The Semi-Markov Decision Process (SMDP) is utilized for the collaborative optimization of continuous- and discrete-state thresholds, so as to minimize the operational costs. Numerical experiments conducted on the critical structure of circulating pumps verify the model’s applicability.

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Offshore Wind Power Systems (OWPS) offer great energy and environmental advantages, but also pose significant Operation and Maintenance (O&M) challenges. In this survey, we analyze these challenges and propose some optimization strategies and technologies for OWPS comprehensively. The existing literature review mainly focuses on a certain field of offshore wind power O&M, but lacks a comprehensive introduction to offshore wind power. We consider the energy efficiency, reliability, safety, and economy of OWPS from various aspects, such as offshore wind and wave energy utilization, offshore wind turbine components, and wind power operation parameters, and compare them with onshore wind power systems. We suggest that OWPS can benefit from advanced design optimization, digital twin, monitoring and forecasting, fault diagnosis, and other technologies to enhance their O&M performance. This paper aims to provide theoretical guidance and practical reference for the technological innovation and sustainable development of OWPS.

A comprehensive review on deploying robotics application in telecom network tower's field maintenance: Challenges with current practices and feasibility analysis for robotics implementation

AbstractThis survey article highlights the difficulties in the field maintenance of telecommunication towers. It critically analyses the main features of the deployment of robots to maintain telecommunication towers. The growing demand for mobile connectivity poses the need for more towers, and the subsequent problem of network maintenance becomes more critical. Most tower maintenance is required work at height; therefore, height‐related risks are more frequent. A rigorous review is conducted, and the growth of the telecommunications network and key on‐site maintenance challenges are analyzed. Despite numerous challenges, these towers are maintained manually by riggers (certified climbers) worldwide. It raises the question, Is it possible to implement automation by robots for the maintenance of telecommunications towers? The feasibility analysis to deploy the robots is conducted systematically. To access the tower through a robot, detailed information on the type of towers, the climbing arrangements available on the existing towers, and the necessary operations to be carried out at the height is collected. A critical analysis of the climbing robots currently available in the literature, their grasping technology, and control algorithms is performed. The opinion of experts in the telecommunication industry is very helpful in identifying the requirements of robotic systems. The design attributes especially needed for the climbing robot, and the execution of the maintenance in height are highlighted. Due justification is given for deploying robots for field maintenance of telecom towers. The recommended methodology for designing an automation system helps research in the field of maintenance of telecom towers through robots, which could bring a remarkable solution to the telecom sector.

Implementing green facades: A step towards sustainable smart buildings

As cities around the globe strive towards becoming “smart” and sustainable, the integration of green facades into smart building design has emerged as a viable solution to address several environmental challenges. This paper conducts a comprehensive review of current research and applications in green facades, analyzing their role in enhancing energy efficiency, improving indoor air quality, and contributing to overall building sustainability. The impact of green facades on reducing energy consumption, through natural insulation and shading, is explored in-depth, alongside their role in air purification and enhancing the aesthetic appeal of urban environments. Despite the potential of green facades, several gaps between theory and practice exist, including challenges in implementation, maintenance, and cost-effectiveness. This paper identifies these barriers and provides a robust discussion of potential solutions, which include technological innovations, policy support, and public awareness initiatives. Ultimately, the integration of green facades in smart buildings presents a significant step forward in creating sustainable urban environments, necessitating further research and action in this realm.

Cross domain matching for semantic point cloud segmentation based on image segmentation and geometric reasoning

Many infrastructure assets in transportation such as roads and bridges represent challenges for inspection and maintenance due to advanced age, structural deficiencies and modifications. Concepts such as Building Information Modelling (BIM) aim to alleviate the problem of health monitoring and asset management by providing digital building models constructed from survey data to all stakeholders. Ageing and oftentimes poorly-documented infrastructure objects such as bridges in particular benefit from a continuous integration of changes to form a digital twin which reflects the asset’s as-is state. However, the process of reconstructing geometric–semantic models from survey data is a manual and labour-intensive process and makes continuously updating the models a difficult task. To automate this process, a cross-domain approach using an artificial neural network is presented which performs semantic segmentation in the image domain and transfers the results over to the point cloud. For the following fine segmentation, geometric knowledge in the 3D domain is used for post-processing and filtering via geometric reasoning. Using this method, a 3D semantic segmentation is achieved which does not require any 3D point cloud training data and only a low amount of image training data.

THE POTENTIAL OF REVERSE ENGINEERING AND DIGITAL FABRICATION FOR THE REPAIR OF HIGH-TECH ARCHITECTURE

Abstract. Novel façade constructions and innovative load-bearing structures of the second half of the 20th century pose new challenges for maintenance and repair. The use of advanced technology, especially in High-Tech architecture, calls for the development of specific repair methods adapted to specific building materials and construction techniques. The conservation of these aging facades, often composed of custom-made metal components, requires the production of intricate spare parts to avoid unduly replacements. Digital fabrication presents a possible solution as it allows for the resource-efficient production of complex and non-planar geometries in small numbers while remaining cost-effective. The paper explores the use of photogrammetry and laser scanning to reverse engineer spare parts, as original plans are often missing or incomplete. A complete workflow for the fabrication of bespoke metal components is described using two examples of Swiss High-Tech architecture featuring challenging structural nodes and planar aluminum panels. Finally, the feasibility of remanufacturing spare parts is demonstrated with 3d-printed models.

Municipal street maintenance challenges and management practices in Sweden

The municipal street network acts as a multifunctional asset by providing people, vehicles and public services with a well-functioning infrastructure. To keep it in good condition, optimal maintenance measures are required which would result in an efficient use of taxpayers’ money. This paper investigates the street network deterioration processes and the management practices that the municipal administrations have applied in Sweden. The study is based on a survey with Swedish municipalities using questionnaires and complementary interviews. The answers provide insight into a wide range of common pavement distresses and deterioration factors, along with pavement management practices. The study identifies that potholes, surface unevenness and alligator cracking are the most cited challenges, while pavement ageing, heavy traffic and patches are the most noted causes. Similarly, the cold climate and population density are influential factors in pavement deterioration. Allocation of the maintenance and rehabilitation and reconstruction budget is higher in the northern part of the country as well as in densely populated municipalities. Condition data collection and use of commercial Pavement Management Systems (PMS) are limited. Addressing the challenges effectively may be possible through the enhancement of the budget, feasible/clear guidelines from municipal councils/politicians, and reducing the gap between street network administrations and utility service providers.

PFCE2: A versatile parameter-free calibration enhancement framework for near-infrared spectroscopy

Near-infrared (NIR) spectroscopy is a widely used technique for chemical analysis, but it has faced challenges of calibration transfer, maintenance, and enhancement among different instruments and conditions. The parameter-free calibration enhancement (PFCE) framework was developed to address these challenges with non-supervised (NS), semi-supervised (SS), and full-supervised (FS) methods. This study presented PFCE2, an updated version of the PFCE framework that incorporates two new constraints and a new method to improve the robustness and efficiency of calibration enhancement. First, normalized L2 and L1 constraints were introduced to replace the correlation coefficient (Corr) constraint used in the original PFCE. These constraints preserve the parameter-free feature of PFCE and impose smoothness or sparsity on the model coefficients. Second, multitask PFCE (MT-PFCE) was proposed within the framework to address the calibration enhancement among multiple instruments, enabling the framework to be versatile for all possible calibration transfer situations. Demonstrations conducted on three NIR datasets of tablets, plant leaves, and corn showed that the PFCE methods with the new L2 and L1 constraints can result in more accurate and robust predictions than the Corr constraint, especially when the standard sample size is small. Moreover, MT-PFCE could refine all models in the involved scenarios at once, leading to significant enhancement in model performance, compared to the original PFCE method with the same data requirements. Finally, the applicable situations of the PFCE framework and other analogous calibration transfer methods were summarized, facilitating users to choose suitable methods for their application. The source codes written in both MATLAB and Python are available at https://github.com/JinZhangLab/PFCE and https://pypi.org/project/pynir/, respectively.

Smart all-time vision: The battery-free video communication for urban administration and law enforcement

The Chinese government is dedicated to enhancing the level of informatization in administrative law enforcement to ensure fairness and increase credibility. Currently, law enforcement has exposed such problems as insufficient force, difficulties in collecting evidence, and low evidential power. These problems contradict the principles of rule of law and standardization. Many local governments have implemented advanced information technologies for urban administration and law enforcement, including big data, artificial intelligence, and IoT. Among these technologies, IoT is the core communication infrastructure for innovative law enforcement platforms. However, traditional video communications rely on batteries or dedicated power sources, leading to maintenance challenges and high power consumption. In this paper, we propose AT-Eye, a new video communication paradigm for all-time law enforcement video monitoring, which is battery-free and high-throughput. The key enabler of AT-Eye is ambient backscatter technology, which enables energy harvesting and video communication simultaneously. Unlike previous methods, our proposal introduces a novel CCK-based modulation for tag cameras and achieves 4-way concurrency. The modulation is simple because it uses phase difference and it is energy-efficient because generating square waves is adequate. Moreover, to ensure full compatibility with COTS WiFi, we modulate the physical service data unit. Therefore, data recovery is possible using only commercial NICs. We conduct comprehensive experiments to examine our proposal and experiment results show that AT-Eye achieves a throughput of 10.8 Mbps with COTS radios. We also simulate a 16-way battery-free tag camera system, demonstrating AT-Eye's feasibility of high-definition video communication.

Integrating preservation into librarian workflows

The core mission of libraries is to ensure perpetual access to the record of knowledge. As a review of the NASIG webinar (formerly North American Serials Interest Group), ‘Integrating Preservation into Librarian Workflows’, by Jill Emery and Sunshine Carter, this article examines working models constructed to sustain perpetual access for their institutional communities. In reflecting on these data-intensive practices, both presenters now recognize that previously impactful collection development business decisions were being made in the dark. Reviewing the webinar also reveals that this issue of preservation access has two critically distinct aspects, which should not be conflated as interchangeable. One is concerned with long-term preservation and the other addresses a library’s ability to provide post-cancellation access to its user community, given budgetary or physical space constraints. The following is an analysis of how effective the processes explored in the webinar are in addressing both post-cancellation access and long-term perpetual access goals. Based on a 2018 NASIG survey, results indicated that many organizations in scholarly communications lacked preservation policies. In June 2022, as a result of the survey, NASIG released the model digital preservation policy as a template to guide consequential and explicit decision-making by addressing issues including scope, roles, responsibilities, tools and techniques. These policy issues are important for librarians to understand before negotiating content licenses, in sustaining long-term discovery and access, and when developing collaborative access frameworks to address collection development and maintenance challenges.

RPA engages telomeric G-quadruplexes more effectively than CST.

G-quadruplexes (G4s) are a set of stable secondary structures that form within guanine-rich regions of single-stranded nucleic acids that pose challenges for DNA maintenance. The G-rich DNA sequence at telomeres has a propensity to form G4s of various topologies. The human protein complexes Replication Protein A (RPA) and CTC1-STN1-TEN1 (CST) are implicated in managing G4s at telomeres, leading to DNA unfolding and allowing telomere replication to proceed. Here, we use fluorescence anisotropy equilibrium binding measurements to determine the ability of these proteins to bind various telomeric G4s. We find that the ability of CST to specifically bind G-rich ssDNA is substantially inhibited by the presence of G4s. In contrast, RPA tightly binds telomeric G4s, showing negligible changes in affinity for G4 structure compared to linear ssDNAs. Using a mutagenesis strategy, we found that RPA DNA-binding domains work together for G4 binding, and simultaneous disruption of these domains reduces the affinity of RPA for G4 ssDNA. The relative inability of CST to disrupt G4s, combined with the greater cellular abundance of RPA, suggests that RPA could act as a primary protein complex responsible for resolving G4s at telomeres.

Challenges in Agile Software Maintenance for Local and Global Development: An Empirical Assessment

Agile methods have gained wide popularity recently due to their characteristics in software development. Despite the success of agile methods in the software maintenance process, several challenges have been reported. In this study, we investigate the challenges that measure the impact of agile methods in software maintenance in terms of quality factors. A survey was conducted to collect data from agile practitioners to establish their opinions about existing challenges. As a result of the statistical analysis of the data from the survey, it has been observed that there are moderately effective challenges in manageability, scalability, communication, collaboration, and transparency. Further research is required to validate software maintenance challenges in agile methods.

Effects of recreational SCUBA diving practiced once a week on neurohormonal response and myokines-mediated communication between muscles and the brain.

During physical activity, activation of muscular, endocrine, and nervous systems, results in intensive crosstalk between muscles and other organs, which enables response to physiological stress. In SCUBA diving, extreme environmental conditions represent an additional challenge for homeostasis maintenance, but underlying mechanisms are largely unknown. We aimed to contribute to the understanding of neurohormonal response and muscle-brain crosstalk by measuring the concentrations of the selected hormones secreted by the pituitary-target organ axis and myokines involved in the muscle-brain endocrine loop in recreational SCUBA (rSCUBA) divers. Fourteen male divers performed five open-water recreational dives (one per week, depth of 20-30 m, lasting 30 min, between 9 and 10 am), after a winter non-diving period of 5 months. Blood samples were collected immediately before and after the first, third, and fifth dives. Adrenocorticotropic hormone (ACTH), cortisol, thyroid-stimulating hormone (TSH), free thyroxine (fT4), prolactin, total testosterone, growth hormone (GH), insulin-like growth factor-1 (IGF-1), irisin, brain-derived neurotrophic factor (BDNF), S100B, glial fibrillary acidic protein (GFAP), and neuron-specific enolase (NSE) were measured using commercially available immunoassays. Cortisol and ACTH levels decreased after every dive, while total testosterone decreased only after the first dive. No significant changes in post-dive values, as well as the cumulative effect on any other measured hormone, were observed. Although irisin and BDNF levels decreased after the first and third dives, the fifth dive caused a significant increase in both myokines. Changes in IGF-1 levels were not observed. All three dives caused a significant increase in S100B levels. A statistically significant decrease in GFAP concentration was observed after every dive, while NSE pre-dive concentration declined over the studied period. The cumulative effect on myokine levels was reflected in a continuous decline in irisin and BDNF pre-dive levels throughout the studied period, but an increasing trend after the fifth dive was observed. Observed changes in myokines and hormone levels point to a specific response to rSCUBA practiced once a week, most likely due to extreme environmental conditions. Further studies on communication between muscles and other organ systems, particularly on the muscle-brain endocrine loop, are required for a deeper understanding of the adaptation mechanisms to this kind of physiological stress.

Suboptimal Comments in Java Projects: From Independent Comment Changes to Commenting Practices

High-quality source code comments are valuable for software development and maintenance, however, code often contains low-quality comments or lacks them altogether. We name such source code comments as suboptimal comments. Such suboptimal comments create challenges in code comprehension and maintenance. Despite substantial research on low-quality source code comments, empirical knowledge about commenting practices that produce suboptimal comments and reasons that lead to suboptimal comments are lacking. We help bridge this knowledge gap by investigating (1) independent comment changes (ICCs) —comment changes committed independently of code changes—which likely address suboptimal comments, (2) commenting guidelines, and (3) comment-checking tools and comment-generating tools, which are often employed to help commenting practice—especially to prevent suboptimal comments. We collect 24M+ comment changes from 4,392 open-source GitHub Java repositories and find that ICCs widely exist. The ICC ratio —proportion of ICCs among all comment changes—is ~15.5%, with 98.7% of the repositories having ICC. Our thematic analysis of 3,533 randomly sampled ICCs provides a three-dimensional taxonomy for what is changed (four comment categories and 13 subcategories), how it changed (six commenting activity categories), and what factors are associated with the change (three factors). We investigate 600 repositories to understand the prevalence, content, impact, and violations of commenting guidelines. We find that only 15.5% of the 600 sampled repositories have any commenting guidelines. We provide the first taxonomy for elements in commenting guidelines: where and what to comment are particularly important. The repositories without such guidelines have a statistically significantly higher ICC ratio, indicating the negative impact of the lack of commenting guidelines. However, commenting guidelines are not strictly followed: 85.5% of checked repositories have violations. We also systematically study how developers use two kinds of tools, comment-checking tools and comment-generating tools, in the 4,392 repositories. We find that the use of Javadoc tool is negatively correlated with the ICC ratio, while the use of Checkstyle has no statistically significant correlation; the use of comment-generating tools leads to a higher ICC ratio. To conclude, we reveal issues and challenges in current commenting practice, which help understand how suboptimal comments are introduced. We propose potential research directions on comment location prediction, comment generation, and comment quality assessment; suggest how developers can formulate commenting guidelines and enforce rules with tools; and recommend how to enhance current comment-checking and comment-generating tools.

Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast

Abstract. Turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

Mobile augmented reality-based visualization framework for lifecycle O&M support of urban underground pipe networks

The structural deterioration of urban underground pipe networks is the accumulated combined effects of physical, environmental and operational factors during the whole lifecycle. Accurate on-site locating and mapping of pipes and their conditions has been a great challenge for construction, maintenance and emergency response in a dynamic manner. Existing approaches, mostly based on design-centric and static visualization, are unsuitable for implementation within the operation and maintenance (O&M) phase because they are performed in a completely virtual environment and are unable to interact with buried assets on site. An integrated hyperrealistic visualization framework based on mobile augmented reality (MAR) is developed to support various real-life project tasks for O&M support of urban underground pipe networks. The proposed approach consists of three parts: a cloud-based data management platform, a 3D visualization development platform, and a location-based registration method for MAR. A cloud–edge–mobile layer is proposed to manage, analyze, process and visualize multisource data, especially for dynamic data. Field experiments on a real-life urban gas pipe network were carried out to investigate the feasibility of the proposed framework to achieve informed O&M support. The results demonstrate that (1) a smartphone linked to a Lite RTK device allows for both accurate locating and easy operation; (2) both the 3D virtual digital model and the multisource data visually superimpose onto the realistic environment, enabling on-site human–machine interaction in a hyperrealistic manner; and (3) MAR-based immersive visualization contributes to avoiding excavation damage, improving O&M efficiency and assisting emergency responders to make timely and better–informed decisions based on multisource information fusion.

Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications.

Technology plays a significant role in our daily lives as real-time applications and services such as video surveillance systems and the Internet of Things (IoT) are rapidly developing. With the introduction of fog computing, a large amount of processing has been done by fog devices for IoT applications. However, a fog device's reliability may be affected by insufficient resources at fog nodes, which may fail to process the IoT applications. There are obvious maintenance challenges associated with many read-write operations and hazardous edge environments. To increase reliability, scalable fault-predictive proactive methods are needed that predict the failure of inadequate resources of fog devices. In this paper, a Recurrent Neural Network (RNN)-based method to predict proactive faults in the event of insufficient resources in fog devices based on a conceptual Long Short-Term Memory (LSTM) and novel Computation Memory and Power (CRP) rule-based network policy is proposed. To identify the precise cause of failure due to inadequate resources, the proposed CRP is built upon the LSTM network. As part of the conceptual framework proposed, fault detectors and fault monitors prevent the outage of fog nodes while providing services to IoT applications. The results show that the LSTM along with the CRP network policy method achieves a prediction accuracy of 95.16% on the training data and a 98.69% accuracy on the testing data, which significantly outperforms the performance of existing machine learning and deep learning techniques. Furthermore, the presented method predicts proactive faults with a normalized root mean square error of 0.017, providing an accurate prediction of fog node failure. The proposed framework experiments show a significant improvement in the prediction of inaccurate resources of fog nodes by having a minimum delay, low processing time, improved accuracy, and the failure rate of prediction was faster in comparison to traditional LSTM, Support Vector Machines (SVM), and Logistic Regression.

Exploring the Use of a Fit-for-Purpose Surgical Headlight in Sub-Saharan Africa: Mixed Methods Study.

High-quality surgical lighting is often lacking in low-resource settings. Commercial surgical headlights are unavailable due to high cost and supply and maintenance challenges. We aimed to understand user needs of a surgical headlight for low-resource settings by evaluating a preselected robust but relatively inexpensive headlight and lighting conditions. We observed headlight use by ten surgeons in Ethiopia and six in Liberia. All surgeons completed surveys about their lighting environment and experience using headlight, and were subsequently interviewed. Twelve surgeons completed logbooks on headlight use. We distributed headlights to 48 additional surgeons, and all surgeons were surveyed for feedback. In Ethiopia, five surgeons ranked operating room light quality as poor or very poor; seven delayed or cancelled operations within the last year and five described intraoperative complications due to poor lighting. In Liberia, lighting was rated as "good", however fieldnotes, and interviews noted generator fuel-rationing, and poor lighting conditions. In both countries, the headlight was considered extremely useful. Surgeons recommended nine improvements, including comfort, durability, affordability and availability of multiple rechargeable batteries. Thematic analysis identified factors influencing headlight use, specifications and feedback, and infrastructure challenges. Lighting in surveyed operating rooms was poor. Although conditions and need for the headlights differed between Ethiopia and Liberia, headlights were considered highly useful. However, discomfort was a major limiting factor for ongoing use, and the hardest to objectively characterise for specification and engineering purposes. Specific needs for surgical headlights include comfort and durability. Refinement of a fit-for-purpose surgical headlight is ongoing.

An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior

The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices’ retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.