Time Stamp Research Articles

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks.

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks.

Event driven high speed data acquisition with IEEE 1588 synchronization for long pulse operations of Indian Test Facility for ITER DNB

The Indian Test Facility (INTF) is a negative Hydrogen ion based 100 kV, 60A, 5 Hz modulated NBI system having duty cycle of 3 s ON time to 20 s OFF time. Prime objective of the facility is to characterize ITER Diagnostic Neutral Beam (DNB) with full specifications, prior to shipment and installation in ITER. In order to fully characterize ITER DNB, a matured Data Acquisition and Control system (DACS) is needed which will be responsible for supervisory control of the facility and for acquiring and monitoring signals at run time. Apart from conventional DACS, high speed data acquisition is needed for diagnostics and other signals with sampling rate requirements of 100 MS/s. For acquiring data from such systems it is imperative that the data be acquired only for certain duration on the occurrence of the event. Such acquisition systems are classified as event driven data acquisition system and are widely used in fusion engineering community.Development and implementation of high speed event driven data acquisition systems pose challenges in terms of data buffering, data streaming and synchronization. The precise timing of the event occurrence with respect to the main pulse should also be known for which a timing and synchronization system is needed. To fulfill this purpose, IEEE 1588 protocol offers a network based method of synchronizing multiple nodes based on Ethernet with sub microsecond accuracy. The present work describes the development of an event driven high speed data acquisition in synchronization with IEEE 1588 based timing network. The work proposes the time stamping of the event from synchronized network integrated with the event acquisition platform. The developed system has been tested for pulse durations longer than 3600 s. The performance of event data acquisition system has been thoroughly characterized and improved by applying various software optimizations in LabVIEW. The present work although intended to be used for INTF can be used for any such facility in fusion or other areas.The paper provides results of IEEE 1588 based timing network in various configurations and event data acquisition system developed on high speed FPGA controlled digitizer board with prototype results.

Gupta-Yantra : Spying Robot for Defence

Recently, with rising public security awareness, the demand for an efficient surveillance system has become highly significant. But, several monitoring systems developed have certain drawbacks limiting their applications. Therefore, a refined surveillance system has been proposed which could be utilized in many fields, especially in border areas for intrusion detection, to reduce the crime rates and to serve tremendous purposes. The project focuses on building a spying robot for maneuvering and monitoring in perilous environments. The module includes Raspberry Pi interfaced with DC motors, GPS, Pi camera, Temperature sensor, Fire sensor and ultrasonic. Meanwhile, the motion of the robot could be controlled by a web application through Wi-Fi connectivity. This robot is programmed to stream the live data over a virtual network and update the captured images along with the time stamp and location at certain instances to the cloud. Hence, it can transmit real-time videos and pictures with temperature measured.

A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment

It is a known fact that a comfortable residential space or workspace is crucial to productivity and well-being. In smart home environments, sensors can be used to sense environment and occupant data and actuators can be controlled accordingly for maximizing thermal comfort. However, keeping in view the growing demand of energy and scarcity of energy generation sources, it is mandatory to optimize the energy usage complementary to optimize thermal comfort. In order for the developed method to be interoperable and heterogeneity aware across different edge device vendors and for ease of hardware connectivity in smart homes, a REST API based framework is developed. Sensors mounted with IoT devices collect data from its respective smart home environment and store the data in its local database. An edge server is used to control the connectivity among these IoT devices and to initiate the decentralized federated learning mechanism. Each IoT device train local deep learning models for predicting energy usage, indoor temperature, indoor humidity for maintaining a comfortable environment state using minimal energy. Models from each IoT devices are aggregated at each IoT device iteratively to make federated learning independent of central server. Global deep learning model is iteratively trained for each prediction requirement. Later, these models are used to predict indoor temperature, humidity and energy consumption for time stamp (t+1), keeping in view environmental and occupant related sensed data at time stamp (t). Optimization of energy consumption and thermal comfort is mathematically modeled and based on the predictions of DL models, environment is controlled by handling actuators using a particle swarm optimization (PSO) mechanism. Relevant constraints are incorporated in PSO for limiting the positional variations in particles for efficient learning. The method continually learns and adapts to changes in the surroundings, providing real-time monitoring and control of thermal comfort to the end users. To validate our approach, data is collected and experiments are conducted in a real-time smart home’s environment. The results are compared with those obtained without using the proposed predictive optimization mechanism. The study demonstrates the effectiveness of proposed predictive optimization mechanism in providing a comfortable and productive smart home or workspace environment utilizing 38% less energy as compared to competitor.

Design of multi-channel electronic prototype for 101Sn energy level lifetime measurement

In this study, a 32-channel front-end data acquisition unit (DAQU) with high temporal resolution and count rate was developed at the Heavy Ion Research Facility in Lanzhou (HIRFL), China, to measure the lifetime of 101Sn on the Spectrometer for Heavy Atoms and Nuclear Structure. The DAQU data analysis module uses a field programmable gate array (FPGA) to implement and time stamp extraction through an FPGA-based tapped delay line (TDL) time-to-digital converter. The energy measurements were implemented using a trapezoidal algorithm and a sliding baseline averaging method. The DAQU was tested and calibrated using a pulse generator and cosmic rays. The results show that the system had a dead time of less than 500 ns, a dynamic range of “0” to “500” mV, and an energy resolution of better than 4.8‰ (full width at the half-maximum, FWHM). In addition, the single-channel temporal resolution under multi-channel settings surpasses 111.2 ps (FWHM), satisfying the experiment's requirements for the electronics system.

Towards complex dynamic physics system simulation with graph neural ordinary equations

The great learning ability of deep learning facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour both in academia and industry. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting particles and varying temporal dependencies between particle system states in different time stamps, which dominate particles’ interacting behavior and the physical systems’ evolution patterns. Existing learning based methods fail to fully account for the complexities, making them unable to yield satisfactory simulations. To better comprehend the complex physical laws, we propose a novel model – Graph Networks with Spatial–Temporal neural Ordinary Differential Equations (GNSTODE) – that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework. Through training with real-world particle–particle interaction observations, GNSTODE can simulate any possible particle systems with high precisions. We empirically evaluate GNSTODE’s simulation performance on two real-world particle systems, Gravity and Coulomb, with varying levels of spatial and temporal dependencies. The results show that GNSTODE yields better simulations than state-of-the-art methods, showing that GNSTODE can serve as an effective tool for particle simulation in real-world applications. Our code is made available at https://github.com/Guangsi-Shi/AI-for-physics-GNSTODE.

LVI-Fusion: A Robust Lidar-Visual-Inertial SLAM Scheme

With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness and accuracy. In this study, a localization and mapping scheme named LVI-fusion based on multi-sensor fusion of camera, lidar and IMU is proposed. Different sensors have different data acquisition frequencies. To solve the problem of time inconsistency in heterogeneous sensor data tight coupling, the time alignment module is used to align the time stamp between the lidar, camera and IMU. The image segmentation algorithm is used to segment the dynamic target of the image and extract the static key points. At the same time, the optical flow tracking based on the static key points are carried out and a robust feature point depth recovery model is proposed to realize the robust estimation of feature point depth. Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness.

Technical note: Flagging inconsistencies in flux tower data

Abstract. Global collections of synthesized flux tower data such as FLUXNET have accelerated scientific progress beyond the eddy covariance community. However, remaining data issues in FLUXNET data pose challenges for users, particularly for multi-site synthesis and modelling activities. Here, we present complementary consistency flags (C2Fs) for flux tower data, which rely on multiple indications of inconsistency among variables, along with a methodology to detect discontinuities in time series. The C2F relates to carbon and energy fluxes, as well as to core meteorological variables, and consists of the following: (1) flags for daily data values, (2) flags for entire-site variables, and (3) flags at time stamps that mark large discontinuities in the time series. The flagging is primarily based on combining outlier scores from a set of predefined relationships among variables. The methodology to detect break points in the time series is based on a non-parametric test for the difference in distributions of model residuals. Applying C2F to the FLUXNET 2015 dataset reveals the following: (1) among the considered variables, gross primary productivity and ecosystem respiration data were flagged most frequently, in particular during rain pulses under dry and hot conditions. This information is useful for modelling and analysing ecohydrological responses. (2) There are elevated flagging frequencies for radiation variables (shortwave, photosynthetically active, and net). This information can improve the interpretation and modelling of ecosystem fluxes with respect to issues in the driver. (3) The majority of long-term sites show temporal discontinuities in the time series of latent energy, net ecosystem exchange, and radiation variables. This should be useful for carefully assessing the results in terms of interannual variations in and trends of ecosystem fluxes. The C2F methodology is flexible for customizing and allows for varying the desired strictness of consistency. We discuss the limitations of the approach that can present starting points for future developments.

Global Opposition Learning and Diversity ENhancement based Differential Evolution with exponential crossover for numerical optimization

Since the inception of Differential Evolution (DE), the vast majority of studies on it indicate that exponential crossover does not solve the real-parameter optimization in continuous spaces very well. However, we find that once the appropriate crossover rate CR and its corresponding parameter control are found, the DE variants with exponential crossover can achieve comparable performance with the ones employing binomial crossover. In this paper, a new DE algorithm, called Global Opposition Learning and Diversity ENhancement based DE with exponential crossover (GOLDEN-DE), is proposed to fill the gap in this field. The GOLDEN-DE algorithm has the following highlights: First, a novel global opposition learning mechanism is proposed in our GOLDEN-DE algorithm; Second, a Novel Parameter Control, namely NPC technique, with fitness-independent characteristic is incorporated in the GOLDEN-DE algorithm. Third, a novel population Diversity ENhancement (DEN) mechanism is proposed, and individuals in the stagnation status can be re-calculated in order to enhance population diversity. Fourth, a time stamp mechanism for handling too old inferior solutions in the external archive is also put forward. We evaluate the GOLDEN-DE algorithm for real-parameter single-objective global optimization on 88 benchmarks from the three universal test sets, including CEC2013, CEC2014, and CEC2017 test suites, and experiment results show that our GOLDEN-DE algorithm is competitive with several state-of-the-art DE variants.

Comparison of periprocedural and procedure room times between moderate sedation and monitored anesthesia care in interventional radiology.

In recent years, there has been increased utilization of monitored anesthesia care (MAC) in interventional radiology (IR) departments. The purpose of this study was to compare pre-procedure bed, procedure room, and post-procedure bed times for IR procedures performed with either nurse-administered moderate sedation (MOSED) or MAC. An institutional review board-approved single institution retrospective review of IR procedures between January 2010 and September 2022 was performed. Procedures performed with general anesthesia or local anesthetic only, missing time stamps, or where <50 cases were performed for both MAC and MOSED were excluded from the study. Pre-procedure bed, procedure room, post-procedure bed, and total IR encounter times were compared between MAC and MOSED using the t-test. The effect size was estimated using Cohen's d statistic. 97,480 cases spanning 69 procedure codes were examined. Mean time in pre-procedure bed was 27 min longer for MAC procedures (69 vs. 42 min, P < 0.001, d = 0.95). Mean procedure room time was 11 min shorter for MAC (60 vs. 71 min, P < 0.001, d = 0.48), and mean time in post-procedure bed was 10 min longer for MAC (102 vs. 92 min, P < 0.001, d = 0.22). Total IR encounter times were on average 27 min longer for MAC cases (231 vs. 204 min, P < 0.001, d = 0.41). MAC improves the utilization of IR procedure rooms, but at the cost of increased patient time in the pre- and post-procedure areas.

PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python

AbstractThe International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum useable ionospheric frequency foF2 and the propagation factor M(3,000)F2. At the U.S. Naval Research Laboratory, a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package.

AstroPix4 — a novel HV-CMOS sensor developed for space based experiments

For the proposed space based gamma-ray observatory All-sky Medium-Energy Gamma-ray Observatory eXplorer (AMEGO-X), a silicon tracker based on a novel High Voltage-CMOS (HV-CMOS) sensor called AstroPix, is currently being developed. Preliminary measurements with the first full reticle prototype AstroPix3 show that the power target of 1.5 mW/cm2 can currently not be reached due to the digital consumption of 3.08 mW/cm2, while the analog power consumption of 1.04 mW/cm2 and a break down voltage of over 350 V look promising. Based on these results, the design changes in AstroPix4, submitted in May 2023, are presented, containing changes to the time stamp generation and readout architecture. A digital power consumption below 0.25 mW/cm2 is expected by removing the fast 200 MHz clock used to measure the time-over-threshold (ToT) and an LVDS receiver. A maximum resolution of 3.125 ns for time-of-arrival (ToA) and ToT is reached by adding per-pixel Flash-Time-to-Digital Converter (TDCs) controlled by a global delay-locked loop (DLL).

Optimizing time resolution and power consumption in a current-mode circuit for SiPMs

Several applications that employ SiPMs require high time precision readout electronics. This work presents a study for the optimization of timing resolution of readout electronics for SiPMs focused on the effect of sensor area, transistor scaling and power consumption on electronic jitter. The design of the most critical stages are presented, specially the front-end input stage in current-mode. The performance of three different technologies (180, 130 and 65 nm) are studied. 65 nm is the best option to obtain good timing resolution with less power consumption. Dividing the sensor into smaller segments improves the Single Photon Electronics Jitter (SPEJ), but does not translate into a better Coincidence Time Resolution (CTR) when keeping the power per unit area constant, performing analog summation or employing an averaging algorithm of the time stamps for small LSO:Ce:%0.2Ca scintillator crystal.

Time Stamp Camera Application as A Presence to Improve the Quality of Education

The research was conducted at Yadika Lubuklinggau High School, Musirawas Regency, South Sumatra Province, aiming to determine the planning, organization, and implementation of supervision to improve quality. This research was conducted with a qualitative approach and used a case study methodology. The data collection technique is carried out by; observation, interviews, and documentation. The focus of the research is supervisory management to improve quality, with a sub-focus; on planning supervision, organizing supervision, and implementing supervision to improve the quality of Yadika Lubuklinggau High School, Musirawas Regency, South Sumatra Province. The research novelty is the time stamp camera application for attendance at Yadika Lubuklinggau High School. This research produces a supervision model at Yadika Lubuklinggau High School, where planning, organizing, and implementing supervision is carried out in predetermined stages and adds feedback from teachers as actors in the field so that the overall results provide an increase in the quality of Yadika Lubuklinggau High School as indicated by accreditation. A, student achievement at local, regional, and national levels.

Algorithmic Foundation of Federated Learning with Sequential Data

The current analysis of federated optimization algorithms for training deep neural networks assumes that the data is non-sequential (e.g., images), which incurs a smooth loss objective. In contrast, edge devices generate lots of sequential data every day, where these sequences exhibit significant sequential correlation at different time stamps (e.g., text messages). In order to learn from such sequential data, people typically use a class of neural networks that is inherently nonsmooth, with a potentially unbounded smoothness parameter. Examples include recurrent neural networks, long-short-term memory networks, and transformers. It remains unclear how to design provably efficient algorithms for training these neural networks to learn from sequential data. My goal is to lay the algorithmic foundation of federated learning with sequential data, which contributes novel algorithms for learning from a range of real-world sequential data (e.g., natural language, electronic health record, transportation, time series, etc.) using state-of-the-art deep neural networks. In this talk, I will first motivate the problem by showing that the transformer, which is widely used for sequential data learning, has an unbounded smooth landscape. Then, I will introduce provably efficient federated deep learning algorithms in the presence of unbounded smoothness. In particular, I will introduce a few efficient algorithms for various settings of federated learning, including homogeneous data, heterogeneous data, and partial client participation. The main result is twofold. First, we show that the designed algorithms provably small computational and communication complexities. Second, we establish fundamental hardness results in the unbounded smoothness setting. Ultimately, I will discuss the future challenges of extending our research framework from small-scale neural networks to large language models.

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

Tracing passive traveling surge-based fault management control scheme in unearthed distribution systems

In this paper, a novel enhanced centralized fault management technique is proposed for smart unearthed distribution systems based on tracing passive traveling waves. The main objective of the proposed scheme is to efficiently identify the faulted section among multiple short-length sections within the distribution system. This is accomplished by installing synchronized surge detectors at both the lateral panel substations and the ends of branches. The identification process relies on analyzing the amplitudes and time stamps of the first arrival waves of the voltages. The distribution system is divided into zones based on the number of lateral panel substations, and the proposed approach encompasses three essential steps: determining the faulted zone, identifying the faulted path within the determined zone, and precisely pinpointing the faulted section. The faulted zone is determined by comparing the energies of the first arrival waves across all lateral panel substations. To identify the faulted path and section, two criteria and a voting system are proposed. The strength of the proposed scheme lies in its ability to cover all possible fault scenarios at various locations. Extensive tests are conducted using the detailed simulation of the IEEE 33-bus system with the PSCAD program. The mathematical core of the proposed approach is implemented using MATLAB. The results obtained from these simulations confirm the high reliability of the proposed fault management scheme, making it a viable solution for implementation in distribution systems.

Differing Approaches to Pain Management for Intrauterine Device Insertion and Maintenance: A Scoping Review.

Intrauterine devices (IUDs) are considered a reliable contraceptive option for women,but they can come with side effects. There is a disconnect in standard guidelines for IUD insertion within and without the U.S. The objective of this review was to address a gap in the literature regarding official procedures for pain management during IUD implantation.This scoping review was initiated using keywords to extract relevant articles from multiple databases: U.S. National Library of Medicine National Institutes of Health (PubMed), MEDLINE (Ovid), and Excerpta Medica dataBASE (EMBASE, Ovid). Initially, 457 articles were identified and after a rigorous screening and selection process, 37 articles were chosen to be further assessed to ascertain if they met the study's inclusion criteria. Those 37 articles were further evaluated fully to check for relevancy. From that process, 19 articles were chosen for the review, and all passed quality assessment evaluations using the JB Appraisal Tools. To best address the research question, the data from the 19 articles were divided into three categories: 1) circumstantial factors, 2) non-pharmacological methods, and 3) pharmacological methods. Circumstantially, women with previous vaginal deliveries experienced the lowest pain during the procedure, and nulligravid (never pregnant) women experienced the most pain. Lower pain scores were reported by lactating women compared to non-lactating. Black women experienced the most anticipated pain compared to other races. Regarding non-pharmacological methods, different insertion techniques, tools, and the use of a cold compress were found to not affect the level of pain during IUD insertion. Lastly, it was shown that pharmacological methods such as lidocaine gel, lidocaine paracervical block, and lidocaine combined with either diclofenac or prilocaine decreased pain scores at different time stamps of the procedure. Also, oral ketorolac and a vaginal combination of misoprostol and dinoprostone helped reduce pain.Findings from this scoping review revealed a lack of uniformity across practices when performing IUD insertions, possibly due to differences in procedures across circumstantial factors, non-pharmacological methods, and pharmacological methods. More research is needed to investigate the intricacies of pain with IUD insertion. Moving forward, especially following a potential increase in the use of IUDs after the reversal of Roe v. Wade, establishing this gap may lead to a more refined standardized protocol to mitigate pain with IUD insertions.

‘View from the window’: On time, politics and domestics during the coronavirus disease 2019 pandemic

The present article aims to contribute to theorisations of the politics of time through a detailed engagement with techno-aesthetic strategies of temporal synchronisation and heterogeneisation of everyday rhythms during the coronavirus disease 2019 pandemic. The exploration focuses on practices of mediatisation of domestic spaces, in particular by examining photographs posted on the Bulgarian-language ‘View from the Window’ Facebook group. The group was founded during the first coronavirus lockdown in April 2020 and its members used it to share images taken from their private homes onto exterior spaces. Theoretically, I build on Henri Lefebvre’s rhythmanalytical project, from which I derive an understanding of the polyrhythmic and multi-scalar character of the everyday, while engaging writings from visual cultures, performance studies, architecture and media theory. The article looks closely at two formal aesthetic strategies that established themselves for posting on the group: The visual strategy of framing and the use of time stamps in image captions. On the one hand, I show how these media practices are engaged in synchronising views with each other; I argue that they contribute to the creation of an equivalence between views while obfuscating their underlying socio-material conditions, which tend to remain outside of the photographic frame. On the other hand, I also demonstrate that these practices can be equally generative of asynchronicity and dissonance between views, bringing to the fore a spatio-temporal heterogeneity and the articulation of stark socio-political differences. The article thus contributes an analysis of the interplay between time, politics and domestics through the conceptual prism of an engagement with aesthetic practices of mediation.

An evaluation of the recommendations for primary nutrition research addressing noncommunicable disease using the EPICOT+ framework: A cross‐sectional descriptive meta‐research study of Cochrane nutrition systematic reviews

AbstractBackgroundNoncommunicable diseases (NCDs) are a global health problem and many risk factors associated with the development of NCDs are related to nutrition.AimsThis study aimed to describe and summarize the research recommendations for primary research made in Cochrane nutrition systematic reviews (SRs) addressing NCDs, according to the evidence, population, intervention, comparison, outcome, time stamp (date of search), study design, time frame (length of follow‐up) and burden of disease (EPICOT+) framework.Materials &amp; MethodsWe screened a database of Cochrane nutrition SRs (n = 692) in March 2021 to identify those SRs addressing the four main NCDs (cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes) and their nutrition‐related risk factors (obesity, unhealthy diets). The “implications for research” sections of included SRs (and the “search methods” sections for the time stamp item) were analyzed using EPICOT+.ResultsWe included 150/692 SRs; most addressed cardiovascular diseases (38.6%), and cancer (17.3%). The EPICOT+ items with the most reported research recommendations were time stamp (98.7%), intervention (94.7%), study design (89.3%), and outcomes (86%), and the least reported were the time frame (52%), comparison (30%), and burden of disease (8%). Most SRs recommended more studies overall (98.7%), assessing specific interventions (93.3%) (e.g., specific foods/food groups and diets/dietary patterns, micronutrient/complementary supplements, nutrition education techniques, and policies/programs) and a range of clinical and patient‐related outcomes (84.7%). Recommendations related to study design and quality included the need for more randomized (72.7%), better quality (55.3%), larger (44.7%), and better reported (26%) studies with a long‐term duration of follow‐up (50.7%).DiscussionOur findings show that research recommendations reported in Cochrane nutrition SRs addressing the four major NCDs and their nutrition‐related risk factors largely followed the EPICOT+ framework. For example, items that could be improved upon were the comparison and time frame items.ConclusionThese recommendations could contribute more significantly to planning future primary studies addressing important evidence gaps and limitations in the current evidence base.