Real-time Typing Research Articles

Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.

The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.

Virtual cavity probe for the real-time identification of cavity burst-noise type in superconducting radio-frequency systems

Burst-noise events are primary trip sources at the China Accelerator Facility for superheavy Elements (CAFE2), characterized by a rapid burst noise in the cavity pick-up signal categorizable into three distinct types: flashover, electronic quench (E-quench), and partial E-quench. Herein, we design an algorithm identifying the burst-noise event types in real time to realize a real-time discrimination of the three types of burst-noise events. This algorithm is based on a virtual cavity probe constructed with the forward and reflected signals of the cavity and integrated into a field-programmable gate array (FPGA). Moreover, we introduce an innovative method for calibrating the transmission delay in channels. This FPGA-based low-level radio-frequency algorithm identifies the burst-noise event type in real time. Its effectiveness has been validated in the CAFE2 facility, offering valuable data support for future advancements in machine learning-based fault classification and dark-current characterization.

Intra-lineage microevolution of Wolbachia leads to the emergence of new cytoplasmic incompatibility patterns.

Mosquitoes of the Culex pipiens complex are worldwide vectors of arbovirus, filarial nematodes, and avian malaria agents. In these hosts, the endosymbiotic bacteria Wolbachia induce cytoplasmic incompatibility (CI), i.e., reduced embryo viability in so-called incompatible crosses. Wolbachia infecting Culex pipiens (wPip) cause CI patterns of unparalleled complexity, associated with the amplification and diversification of cidA and cidB genes, with up to 6 different gene copies described in a single wPip genome. In wPip, CI is thought to function as a toxin-antidote (TA) system where compatibility relies on having the right antidotes (CidA) in the female to bind and neutralize the male's toxins (CidB). By repeating crosses between Culex isofemale lines over a 17 years period, we documented the emergence of a new compatibility type in real time and linked it to a change in cid genes genotype. We showed that loss of specific cidA gene copies in some wPip genomes results in a loss of compatibility. More precisely, we found that this lost antidote had an original sequence at its binding interface, corresponding to the original sequence at the toxin's binding interface. We showed that these original cid variants are recombinant, supporting a role for recombination rather than point mutations in rapid CI evolution. These results strongly support the TA model in natura, adding to all previous data acquired with transgenes expression.

Real-time Cyber-Physical Security Solution Leveraging an Integrated Learning-Based Approach

Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore, accurate and real-time attack detection and identification are essential to ensure information security and reliability of CPS. In this paper, we propose a novel integrated learning method that accurately detects an attack of a CPS system and then identifies the attack type in real time. Specifically, we consider a One-Class Support Vector Machine (OCSVM) model that only relies on the data from the normal state for training to achieve a real-time and effective detection of a CPS system state (i.e., normal or under-attack). If the system is detected to be under-attack, we then develop a Pairwise Self-supervised Long Short-Term Memory (PSLSTM) approach to identify the attack type, which aims to accurately distinguish the known attack types and discover unknown new attacks. Lastly, experimental results show the proposed method achieves promising performances compared with conventional and state-of-the-art learning-based benchmarks.

An activity-based functional test for identifying homologous recombination deficiencies across cancer types in real time

Homologous recombination (HR)-mediated DNA repair is a prerequisite for maintaining genome stability. Cancer cells displaying HR deficiency (HRD) are selectively eliminated by poly(ADP-ribose) polymerase inhibitors (PARPis). To date, sequencing of HR-associated genes and analyzing genome instability have been used as clinical predictions for PARPi therapy. However, these genetic tests cannot reflect dynamic changes in the HR status. Here, we have developed a virus- and activity-based functional assay to quantify real-time HR activity directly. Instead of focusing on a few HR-associated genes, our functional assay detects endpoint HR activity and establishes an activity threshold for identifying HRD across cancer types, validated by PARPi sensitivity and BRCA status. Notably, this fluorescence-based assay can be applied to primary ovarian cancer cells from patients to reflect their level of HRD, which is associated with survival benefits. Thus, our work provides a functional test to predict the response of primary cancer cells to PARPis.

Investigation of weather conditions on the output power of various photovoltaic systems

The quantity of solar radiation received by photovoltaic panel surfaces and their efficiency are influenced by environmental factors, including dust buildup and weather changes. This study presents an experimental analysis to determine how dust and rain affected the output of photovoltaic power for five different types and orientations of solar module systems of 5 kW each. All systems are installed at Applied Science Private University in Jordan, and attached to sunny portal system to monitor the PV output power for each type in real time. After one–four weeks of exposure, the effects of dust buildup and weather conditions on the power output of the module systems were examined. The results showed that four weeks of dust buildup resulted in 16.7%, 28%, 16.6%, 26.7%, and 21% reduction in PV power production for Monocrystalline (c-Si)/South, Monocrystalline (c-Si) East/West, Polycrystalline (p-Si)/South, Polycrystalline (p-Si)/East/West, and Thin-film (a-Si, microcrystalline)/South system respectively. In addition, a 20% average decrease and fluctuation in PV output power under wet conditions is observed. Moreover, the results showed that the power sold to the grid would be lower than predicted by 21% after one month of dust accumulation, which should be included in energy management planning.

A rapid segmentation method for weed based on CDM and ExG index

Precision spraying technology is promising for effective weed control application in agriculture, which is dependent on the perception of weed information. However, existing weed segmentation algorithms have significant limitations in the accurate segmentation of all weed types in real time. In this study, a rapid segmentation method for weed was proposed following the crop detection model (CDM) and the Excess Green Index (ExG). The CDM was introduced, such that the normal convolution in the YOLO-V4 Tiny backbone network was replaced with a depthwise separable convolution to increase the receptive field of the network while reducing the number of parameters. Moreover, the CDM incorporated a SPP structure in YOLO-V4 Tiny to reduce the effect of different scale targets on detection results. After training, the AP value of the CDM was increased to 94.83%, with an inference time of 11.13 ms per image. Subsequently, the CDM was combined with the ExG index and the optimized Otsu to segment the weeds from the maize field accurately and rapidly. As revealed by the experimental results, the proposed algorithm can segment weeds in real time and accurately with a precision of 92.50%, an IoU of 76.14%, as well as an accuracy (Acc) of 98.10%. The segmentation time per image reached 15.40 ms. Lastly, two deployment methods were proposed to make the algorithm conform to different field spraying operation requirements. In brief, the proposed method provides a reliable and efficient solution for weed segmentation in agricultural fields.

Design of a Mobile App to Monitor and Control in Real Time Type 2 Diabetes Mellitus in Peru

Diabetes in the world is a big problem. In addition, in Peru there are high cases of diabetes mellitus type 2, this happens due to insulin resistance, which causes damage to muscle, liver. In addition to the fact that the fat cells do not generate insulin correctly is because the body needs to generate more insulin so that glucose can enter the cells. Due to the problem that exists, a solution to the problem was sought with the objective of developing the design of a mobile application to monitor in real time, in such a way that it helps patients with diabetes to have a good monitoring plan. The methodology developed in the research is Design Thinking as it fits perfectly to the solution of the problems of society which was successfully developed using the phases of empathizing, defining, ideating, prototyping and finally testing. As a result, the design of the mobile application to monitor and control in real time patients with diabetes mellitus type 2 in Peru was obtained. In addition, obtained data on diabetes in Peru verified by the Ministry of Health, such as the results of registered cases of diabetes in Peru 2022, type of diabetes that develop in Peru on a large scale, type of diabetes in men and women. The complications that arise in the progression of the disease in addition to the cases of records according to sex and period of the year 2020 to 2022. Also, the cases presented diabetes throughout Peru therefore a survey was conducted where professors of the Universidad de Ciencias y Humanidades were conducted to qualify the prototypes for development which returned favorable results for implementation.

Common Mental Disorders in Smart City Settings and Use of Multimodal Medical Sensor Fusion to Detect Them

Cities have undergone numerous permanent transformations at times of severe disruption. The Lisbon earthquake of 1755, for example, sparked the development of seismic construction rules. In 1848, when cholera spread through London, the first health law in the United Kingdom was passed. The Chicago fire of 1871 led to stricter building rules, which led to taller skyscrapers that were less likely to catch fire. Along similar lines, the COVID-19 epidemic may have a lasting effect, having pushed the global shift towards greener, more digital, and more inclusive cities. The pandemic highlighted the significance of smart/remote healthcare. Specifically, the elderly delayed seeking medical help for fear of contracting the infection. As a result, remote medical services were seen as a key way to keep healthcare services running smoothly. When it comes to both human and environmental health, cities play a critical role. By concentrating people and resources in a single location, the urban environment generates both health risks and opportunities to improve health. In this manuscript, we have identified the most common mental disorders and their prevalence rates in cities. We have also identified the factors that contribute to the development of mental health issues in urban spaces. Through careful analysis, we have found that multimodal feature fusion is the best method for measuring and analysing multiple signal types in real time. However, when utilizing multimodal signals, the most important issue is how we might combine them; this is an area of burgeoning research interest. To this end, we have highlighted ways to combine multimodal features for detecting and predicting mental issues such as anxiety, mood state recognition, suicidal tendencies, and substance abuse.

A SMART DATA APPROACH TO ANALYZE VEHICLE FLOWS

Abstract. In the logic of Smart Cities it is of fundamental importance to analyze the traffic situation through dedicated sensors and networks. According to this approach and through the potential of smart data is based this study. Improve prediction of traffic patterns by analyzing and counting vehicles in a virtualized scene in real time. In the past, the technique of hardware inductive coils was used that were dropped in the asphalt to exploit the principle of magnetic induction in order to verify the transit of vehicles. This technique is not able to classify vehicles or estimate their speed, unless using multiple inductive coils. The proposed system provides for the virtualization of an area of interest which requires a selection and mapping of the areas where the control areas are to be included. The “image detection” techniques allow us to classify the vehicles in transit. With the techniques of “machine learning” can to able to verify the flow, count the vehicles present in the scene and classify them by vehicle type in real time. The vehicle counting and classification data available in the cloud platform allow to model and update the main nodes of the network in order to improve the prediction and estimates of the best routes of the road network according to the degree of saturation of the flows and the length of the line of the graph. The model can also indicate additional information of an environmental nature in an ITS system present in the cloud.

Implementation of a Novel Tool to Collect Milk Feeding Data on Infants in Primary Care Clinics.

We aimed to capture milk feeding type in real time in a racially and socioeconomically diverse population. An electronic tool to assess milk feeding type at every medical visit for children aged 0 to 2 years was designed and incorporated into nursing workflows. The Milk Box tool was successfully added to the electronic clinical workspace of a large health system. There were eight clinics, with diverse characteristics, which incorporated the use of the Milk Box tool over 12 months. Time to 50% uptake of Milk Box varied from 3 to 5 months. Time to >80% uptake varied from 6 to 8 months. Our results show that Milk Box can be quickly incorporated into a clinical workflow when the team is given appropriate training and support. The tool also allows a primary care practice to study local breast milk consumption trends and to provide both individualized and system-level lactation support.

COVID-19 Outbreak: Detecting face mask types in real time

While the COVID-19 outbreak poses several major hazards to the world, it also serves as a reminder that we must take care to prevent the virus from spreading. Wearing a mask is one of the most effective non-pharmaceutical strategies for preventing the spread of infectious diseases. Therefore, to aid in the prevention of a public epidemic, an automatic real-time mask recognition and categorization solution is urgently required. The efficiency of facemasks has been called into question, owing to poor mask selection. N95 masks must be worn in jobs where there is a high danger of contracting the virus. Surgical, DIY, and N95 masks all have varied degrees of effectiveness. To safeguard public safety, we created a deep learning model that can classify different types of masks as well as determine if there is no mask in real-time video or images. Our framework consists of three main phases: Face detection using object detection API, application of face mask classifier and classification of masks into four different categories. In this study, we show that SSD MobileNet V2 outperforms both SSD MobileNet V1 and SSD Resnet50 V1 in terms of sensitivity and precision.

Real Time Detection Of Waste Type Using Single Shot Multibox Detector

The lack of human initiative to manage their own wastes is one of many reasons why waste management in residential area is not optimal. A system to detect waste type in real time is a necessity to support the waste management process to be faster and optimal. This research propose the waste type detection systems using 2 types of Single Shot Multibox Detector models, SSD300 and SSD512. Both models were compared based on the accuracy and speed of detection on TACO dataset dan Waste Classification Data. SSD512 achieves a better accuracy of 0.63 mAP compared to the accuracy of SSD300, which is 0.57 mAP. Both models can also be said to be real time, with the SSD300's detection speed being faster at 51 fps compared to the SSD512's detection speed at 28 fps.

CompactNet: A Light-Weight Deep Learning Framework for Smart Intrusive Load Monitoring

Energy load monitoring via smart plugs or smart sockets has become more and more popular. Various studies have been undertaken to monitor energy consumption of household appliances and analyze the collected power data to obtain useful insights on consumers’ behaviors. The main challenge in load monitoring is to automatically recognize appliances in real time since the existing energy disaggregation process is time-consuming and labour-intensive. Although several deep learning models can achieve high accuracy on appliance classification, they usually consume large memory, hence not suitable for resources-constrained IoT devices. To resolve the issue, we demonstrate in this paper, for the first time, a novel framework named Smart Intrusive Load Monitoring based on a compact network (CompactNet), which is able to determine appliance types in real time. Specifically, our method distills the knowledge of an ensemble of large deep networks to a much more compact network. Our CompactNet accurately classifies various types of appliances, but its size is reduced by approximately eight times, making it possible to be deployed on edge IoT sensors for appliance recognition.

Toward optical spectroscopy-guided lung biopsy: Demonstration of tissue-type classification.

The diagnostic yield of standard tissue-sampling modalities of suspected lung cancers, whether by bronchoscopy or interventional radiology, can be nonoptimal, varying with the size and location of lesions. What is needed is an insitu sensor, integrated in the biopsy tool, to objectively distinguish among tissue types in real time, not to replace biopsy with an optical diagnostic, but to verify that the sampling tool is properly located within the target lesion. We investigated the feasibility of elastic scattering spectroscopy (ESS), coupled with machine learning, to distinguish lung lesions from the various nearby tissue types, in a study with freshly-excised lung tissues from surgical resections. Optical spectra were recorded with an ESS fiberoptic probe in different areas of the resected pulmonary tissues, including benign-margin tissue sites as well as the periphery and core of the lesion. An artificial-intelligence model was used to analyze, retrospectively, 2032 measurements from excised tissues of 35 patients. With high accuracy, ESS was able to distinguish alveolar tissue from bronchi, alveolar tissue from lesions, and bronchi from lesions. This ex vivo study indicates promise for ESS fiberoptic probes to be integrated with surgical intervention tools, to improve reliability of pulmonary lesion targeting.

Laser Beam Pointing Stabilization Control through Disturbance Classification

In laser systems, beam pointing usually drifts as a consequence of various disturbances, e.g., inherent drift, airflow, transmission medium variation, mechanical vibration, and elastic deformation. In this paper, we develop a laser beam pointing control system with Fast Steering Mirrors (FSMs) and Position Sensitive Devices (PSDs), which is capable of stabilizing both the position and angle of a laser beam. Specifically, using the ABCD matrix, we analyze the kinematic model governing the relationship between the rotation angles of two FSMs and the four degree-of-freedom (DOF) beam vector. Then, we design a Jacobian matrix feedback controller, which can be conveniently calibrated. Since disturbances vary significantly in terms of inconsistent physical characteristics and temporal patterns, great challenges are imposed to control strategies. In order to improve beam pointing control performance under a variety of disturbances, we propose a data-driven disturbance classification method by using a Recurrent Neural Network (RNN). The trained RNN model can classify the disturbance type in real time, and the corresponding type can be subsequently used to select suitable control parameters. This approach can realize the universality of the beam stabilization pointing system under various disturbances. Experiments on beam pointing control under several typical external disturbances are carried out to verify the effectiveness of the proposed control system.

Terrain Analytics for Precision Agriculture with Automated Vehicle Sensors and Data Fusion

Precision agriculture aims to use minimal inputs to generate maximal yields by managing the plant and its environment at a discrete instead of a field level. This new farming methodology requires localized field data including topological terrain attributes, which influence irrigation, field moisture, nutrient runoff, soil compaction, and traction and stability for traversing agriculture machines. Existing research studies have used different sensors, such as distance sensors and cameras, to collect topological information, which may be constrained by energy cost, performance, price, etc. This study proposed a low-cost method to perform farmland topological analytics using sensor implementation and data processing. Inertial measurement unit sensors, which are widely used in automated vehicle study, and a camera are set up on a robot vehicle. Then experiments are conducted under indoor simulated environments that include five common topographies that would be encountered on farms, combined with validation experiments in a real-world field. A data fusion approach was developed and implemented to track robot vehicle movements, monitor the surrounding environment, and finally recognize the topography type in real time. The resulting method was able to clearly recognize topography changes. This low-cost and easy-mount method will be able to augment and calibrate existing mapping algorithms with multidimensional information. Practically, it can also achieve immediate improvement for the operation and path planning of large agricultural machines.

Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data

Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and companionship type and (2) propose and validate novel geosocial features of smartphone-based Bluetooth and GPS data for predicting loneliness and companionship type in real time. We base our features on intuitions characterizing the quantity and spatiotemporal predictability of an individual's Bluetooth encounters and GPS location clusters to capture personal significance of social exposure scenarios conditional on their temporal distribution and geographic patterns. We examine our features' statistical correlation with momentary loneliness through regression analyses and evaluate their predictive power using a sliding window prediction procedure. Our features achieved significant performance improvement compared to baseline for predicting both momentary loneliness and companionship type, with the effect stronger for the loneliness prediction task. As such we recommend incorporation and further evaluation of our geosocial features proposed in this study in future mental health sensing and context-aware computing applications.

Enhanced Efficacy of Simultaneous PD-1 and PD-L1 Immune Checkpoint Blockade in High-Grade Serous Ovarian Cancer.

Immune therapies have had limited efficacy in high-grade serous ovarian cancer (HGSC), as the cellular targets and mechanism(s) of action of these agents in HGSC are unknown. Here we performed immune functional and single-cell RNA sequencing transcriptional profiling on novel HGSC organoid/immune cell co-cultures treated with a unique bispecific anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibody compared with monospecific anti-PD-1 or anti-PD-L1 controls. Comparing the functions of these agents across all immune cell types in real time identified key immune checkpoint blockade (ICB) targets that have eluded currently available monospecific therapies. The bispecific antibody induced superior cellular state changes in both T and natural killer (NK) cells. It uniquely induced NK cells to transition from inert to more active and cytotoxic phenotypes, implicating NK cells as a key missing component of the current ICB-induced immune response in HGSC. It also induced a subset of CD8 T cells to transition from naïve to more active and cytotoxic progenitor-exhausted phenotypes post-treatment, revealing the small, previously uncharacterized population of CD8 T cells responding to ICB in HGSC. These state changes were driven partially through bispecific antibody-induced downregulation of the bromodomain-containing protein BRD1. Small-molecule inhibition of BRD1 induced similar state changes in vitro and demonstrated efficacy in vivo, validating the co-culture results. Our results demonstrate that state changes in both NK and a subset of T cells may be critical in inducing an effective anti-tumor immune response and suggest that immune therapies able to induce such cellular state changes, such as BRD1 inhibitors, may have increased efficacy in HGSC. SIGNIFICANCE: This study indicates that increased efficacy of immune therapies in ovarian cancer is driven by state changes of NK and small subsets of CD8 T cells into active and cytotoxic states.

Representación en tiempo real de señales de radar empleando Odroid XU4

El sistema de representación en los radares modernos se realiza a partir de la obtención de señales digitales. En el caso de los radares de seguimiento la actualización de la representación se realiza en el orden de las décimas de milisegundos. Para lograr la actualización del display en tiempo real el proceso de adquisición, procesamiento de la señal y representación tiene que cumplir con la exigencia de tiempo de exploración del radar. El objetivo del trabajo es lograr la visualización de la información para un radar en tiempo real empleando la placa Odroid XU4. Se utilizó el método de programación paralela, mediante la creación de hilos en el Entorno de Desarrollo Integrado Qt Creator y el empleo del patrón de programación paralela segmentación de cauce. Esto permitió el uso eficiente de los recursos de cómputo, obteniéndose una disminución del tiempo de ejecución y una mayor aceleración con respecto a la variante secuencial.