Primary Sensor Research Articles

Design of a Mobile Industrial Robot Platform based on Fixed Cameras

This study presents a design for an autonomous mobile robot system that utilizes environmental cameras for navigation and obstacle avoidance. Operating within a known environment, the robot employs these cameras to facilitate path planning and to circumvent obstacles. The hardware setup includes fixed cameras strategically placed in the environment to pinpoint the robot’s location and to identify obstacles amidst varying conditions. A computer system is essential for processing the data captured by the cameras, and a wireless transmission system communicates operational instructions to the robot.For navigation, the study explores various algorithms for path planning, selecting the most efficient one through comparative analysis. Once the path is established, the robot’s instructions are computed under the designed control rate, employing the vehicle’s PD control method. This method is informed by the external camera’s simulation of the vehicle, ensuring accurate and responsive navigation.The project’s goal is to create a mobile robot platform that leverages an external camera as its primary sensor. This design promises efficient autonomous navigation and the capability to avoid static obstacles. The robot’s design is conceptualized using SolidWorks and brought to life through implementation in the Python and MATLAB programming languages, demonstrating the system’s practicality and adaptability in real-world scenarios. Additionally, the system’s design considers ease of integration and scalability to accommodate future technological advancements and new application requirements.

CO2 response screen in grass Brachypodium reveals the key role of a MAP kinase in CO2-triggered stomatal closure.

Plants respond to increased CO2 concentrations through stomatal closure, which can contribute to increased water use efficiency. Grasses display faster stomatal responses than eudicots due to dumbbell-shaped guard cells flanked by subsidiary cells working in opposition. However, forward genetic screening for stomatal CO2 signal transduction mutants in grasses has yet to be reported. The grass model Brachypodium distachyon is closely related to agronomically important cereal crops, sharing largely collinear genomes. To gain insights into CO2 control mechanisms of stomatal movements in grasses, we developed an unbiased forward genetic screen with an EMS-mutagenized B. distachyon M5 generation population using infrared imaging to identify plants with altered leaf temperatures at elevated CO2. Among isolated mutants, a "chill1" mutant exhibited cooler leaf temperatures than wild-type Bd21-3 parent control plants after exposure to increased CO2. chill1 plants showed strongly impaired high CO2-induced stomatal closure despite retaining a robust abscisic acid-induced stomatal closing response. Through bulked segregant whole-genome sequencing analyses followed by analyses of further backcrossed F4 generation plants and generation and characterization of sodium azide and CRISPR-cas9 mutants, chill1 was mapped to a protein kinase, Mitogen-Activated Protein Kinase 5 (BdMPK5). The chill1 mutation impaired BdMPK5 protein-mediated CO2/HCO3- sensing together with the High Temperature 1 (HT1) Raf-like kinase in vitro. Furthermore, AlphaFold2-directed structural modeling predicted that the identified BdMPK5-D90N chill1 mutant residue is located at the interface of BdMPK5 with the BdHT1 Raf-like kinase. BdMPK5 is a key signaling component that mediates CO2-induced stomatal movements and is proposed to function as a component of the primary CO2 sensor in grasses.

The Accelerator and Brake Modulatory System of Carotid Body Sensitivity

The carotid body (CB) is the primary sensor of arterial gases in the human body that through reflex pathways maintains cardiovascular homeostasis. It is known that CB chemosensitivity increases upon repeated stimulation as well as disease states; however, the molecular basis for this remains elusive. Objectives: To investigate the effect of excitatory and inhibitory amino acids in mediating CB afferent activity. Hypothesis: We hypothesised that glutamate and γ-aminobutyric acid (GABA) act to modulate CB sensitivity. Methods: Electrophysiological recordings of carotid sinus nerve (CSN) afferent discharge were made from an ex vivo arterially perfused common carotid artery bifurcation preparation. Effects of agents on CB sensitivity were assessed from responses evoked by cyanide (CN−) (1.23 μmol bolus). Results: The CSN response to CN− consisted of a rapid high amplitude discharge that was quenched abruptly, leading to a low amplitude tail. Glutamate administration augmented the CSN response to CN− by 2-fold compared to baseline ( p=0.0005). In contrast, GABA administration suppressed the CN− response by 2-fold ( p = 3.71 x 10−5). An N-methyl-D-aspartic acid (NMDA) receptor antagonist (MK801; 100 μM) increased the response amplitude resulting in one single high amplitude discharge (160% increase from baseline), abolishing the low amplitude tail. Such an effect was also observed following GABAA receptor blockade with bicuculline (500 μM), when an 80% increase was observed. The CN− response was blocked by PPADS (a P2X receptor antagonist; 100 μM) (242 ± 154 vs 4.82 ± 2.23 % change from baseline; p=0.03), suggesting it to be solely mediated by purinergic transmission. Summary of the data: We propose that CN-evoked excitation of CB chemosensory cells leads to the co-release of ATP and glutamate. ATP activates directly P2X receptors on petrosal chemosensory afferents, while glutamate acting via NMDA receptors on chemosensory cells leads to activity-induced potentiation of ATP release and co-release of GABA to quench ATP-mediated CSN discharge in a time-controlled manner. This is supported by our finding that blockade of either excitatory (NMDAR) or inhibitory (GABAA) receptors both heighten the CN− evoked CB response. A statement of the conclusions: Our results suggest an intrinsic ‘accelerator and brake’ paracrine mechanism within the CB involving glutamate and GABA controlling CB afferent sensitivity. Research was supported by the Health Research Council of New Zealand and the Sidney Taylor Trust. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

The Integrated Stress Response Regulates Diurnal Rhythms in Liver Metabolism

Disruptions in biological rhythms are associated with the development of metabolic diseases. General control nonderepressible 2 (GCN2), a primary sensor of amino acid insuffciency and activator of the integrated stress response (ISR), plays an integral role in liver metabolism. The objective of this work was to examine the impact of feeding a low protein quality (LPQ) diet on diurnal rhythms in liver metabolism. We hypothesized that activation of the ISR by GCN2 maintains metabolic rhythms within the liver during LPQ feeding. To address our objective, wild type (WT) and whole body Gcn2 knockout (GCN2KO) mice were housed in total darkness and provided free access to either a Control or a LPQ diet (devoid of leucine) for 8-days and then killed at 2 circadian time (CT) points 12-hours apart (CT3, CT15). WT mice fed a Control diet showed oscillations in hepatic ISR activation that were lost in the livers of GCN2KO mice. Diurnal patterns in the liver transcriptome including genes regulating triglyceride metabolism were also dampened in GCN2KO mice. WT mice fed LPQ showed increased hepatic ISR activation and greater abundance of ISR target genes relative to Control only at CT15 (p<0.05 by two factor ANOVA). GCN2KO mice fed LPQ failed to show oscillations in ISR activation and instead displayed reductions in liver intracellular amino acids alongside significant upregulation of genes encoding gluconeogenesis and amino acid catabolism at CT15 (q<0.05, Log2FC>1). In conclusion, GCN2 is required for diurnal ISR activation and execution in the liver. LPQ feeding increases hepatic ISR activation in a time dependent manner. Sensing of LPQ by GCN2 is necessary for the maintenance of rhythms in liver macronutrient metabolism. NIH DK109714, Pilot award from the NJ IFNH Center for Human Nutrition, Exercise, and Metabolism (NExT). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

РАДІОВИМІРЮВАЛЬНИЙ ПЕРЕТВОРЮВАЧ ВОЛОГОСТІ ҐРУНТУ НА БАЗІ ПЕРВИННОГО СЕНСОРА YL-69

Soil moisture monitoring in agriculture is becoming an integral part of ensuring optimal conditions for plant growth and achieving high yields, as well as an important environmental aspect for preserving natural resources and ecosystems. The increasing popularity of wireless sensor networks for measuring soil moisture underscores the need for the development of modern technologies in this field. This enables a continuous flow of real-time data on soil conditions, which is crucial for effective agricultural management and ecological balance. The paper presents the development of a radio-based soil moisture transducer with a moisture-sensitive resistive element based on a transistor structure with negative resistance. The YL-69 resistive sensor was utilized as the primary soil moisture sensor. An analytical expression for the conversion function of the radio-based soil moisture transducer is determined. Computer simulation of the transducer's operation was conducted in the OrCAD environment, resulting in a family of current-voltage characteristics for the investigated radio-based transducer. A region with negative differential resistance is observed on the current-voltage characteristics. An experimental conversion function of the radio-based soil moisture transducer based on the YL-69 primary sensor was obtained. From the conversion function, it is evident that as soil moisture increases in the range from 0% to 50%, the frequency of the output signal of the radio-based soil moisture transducer decreases from 680 kHz to 452 kHz. The highest sensitivity value of the radio-based soil moisture transducer is achieved in the soil moisture measurement range from 20% to 45% and is 8.7 kHz/%. The operation of radio-based soil moisture transducers is based on the functional dependence of semiconductor device impedance on soil moisture, which opens up wide prospects for improving agricultural technologies and preserving the environment. This transducer can be used for measuring soil moisture in agriculture, which can be utilized to create automatic systems for monitoring soil moisture and watering plants.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Rack force fault tolerance estimation of steer-by-wire system under different resolver faults based on sensor flows

In the context of vehicle intelligence, steer-by-wire (SbW) has great potential for development and has become a current research hotspot. The key challenge for SbW systems is to obtain the nonlinear rack force generated by tire-road interaction, which is the source of the driver's real steering feel and the main disturbance of steering angle tracking control. Existing rack force estimators either are only suitable for non-aggressive steering or assume that the measured signals are reliable. This paper proposes a high-precision rack force estimation algorithm that is robust to primary sensor faults of a SbW system. Further, a rack force estimator based on the established high-order SbW mechanism model is designed, and a detailed stability proof and feedback gain selection guidance are given. The resolver sensor model is provided and analyzed, and a fault detection method based on discrete Fourier transform (DFT) and data fusion is developed, which has good real-time performance and high accuracy. The performance of rack force estimator was verified and compared by simulations and hardware-in-loop (HIL) experiments with various steering maneuvers and different resolver faults. The experiment results show that the proposed rack force estimation method can not only effectively improve the estimation accuracy even in aggressive steering condition and uneven road profiles, but also achieve accurate estimation of the rack force in the case of resolver faults. The proposed method obtains high-precision and high-reliability rack force estimation results while being easily embedded applied in engineering practice.

Microwave pretreatment effects on the aroma precursors, sensory characteristics and flavor profiles of fragrant rapeseed oil

Microwave technology offers a rapid and uniform heating method. This study investigated how microwave pretreatment affects the aroma precursors and flavor of fragrant rapeseed oils (FROs). Microwave pretreatment led to decreased levels of polyunsaturated fatty acids, sugars, protein-bound amino acids, and glucosinolates. Using gas chromatography–mass spectrometry, we identified 66 volatile compounds in the oil samples. Among these, based on odor activity values (OAV ≥ 1), we found 9 aldehydes, 1 ketone, 6 pyrazines, 1 isothiocyanate, and 7 nitriles as the key aroma-active compounds, contributing fatty-like, nutty-like, and pungent-like odors, respectively. The electronic nose results highlighted W5S and W1W as primary sensors for determining the flavor profiles of FROs. Notably, aroma-active pyrazines exhibited strong negative correlations with sucrose, cysteine, lysine, and isoleucine. This research provides essential insights for enhancing the aroma of FROs.

Making the unmodulated Pyramid wavefront sensor smart

Almost all current and future high-contrast imaging instruments will use a Pyramid wavefront sensor (PWFS) as a primary or secondary wavefront sensor. The main issue with the PWFS is its nonlinear response to large phase aberrations, especially under strong atmospheric turbulence. Most instruments try to increase its linearity range by using dynamic modulation, but this leads to decreased sensitivity, most prominently for low-order modes, and makes it blind to petal-piston modes. In the push toward high-contrast imaging of fainter stars and deeper contrasts, there is a strong interest in using the PWFS in its unmodulated form. Here, we present closed-loop lab results of a nonlinear reconstructor for the unmodulated PWFS of the Magellan Adaptive Optics extreme (MagAO-X) system based on convolutional neural networks (CNNs). We show that our nonlinear reconstructor has a dynamic range of >600 nm root-mean-square (RMS), significantly outperforming the linear reconstructor that only has a 50 nm RMS dynamic range. The reconstructor behaves well in closed loop and can obtain >80% Strehl at 875 nm under a large variety of conditions and reaches higher Strehl ratios than the linear reconstructor under all simulated conditions. The CNN reconstructor also achieves the theoretical sensitivity limit of a PWFS, showing that it does not lose its sensitivity in exchange for dynamic range. The current CNN’s computational time is 690 µs, which enables loop speeds of >1 kHz. On-sky tests are foreseen soon and will be important for pushing future high-contrast imaging instruments toward their limits.

Web-Based Integration of IoT and Artificial Intelligence for Monitoring Aquariums

Technology is now an indispensable part of work across all fields and locations. For instance, in fish cultivation and among ornamental fish enthusiasts, technology plays a pivotal role in regulating and notifying when ornamental fish's environmental conditions require attention. This includes monitoring and alerting for cleaning or feeding needs. Ornamental fish are particularly sensitive, often carrying a higher price tag. Due to their susceptibility to stress and their relatively costly nature, those involved in ornamental fish cultivation and collection frequently face significant losses. These losses can occur when fish are collected, raised, and fall ill or, in the worst-case scenario, die. In response to these challenges, technological innovations have emerged to simplify the monitoring of ornamental fish's environmental conditions. This is achieved through IoT-based solutions accessible anytime and anywhere via the web. These innovations incorporate three primary sensors: pH sensors, clarity sensors, and temperature sensors. Additionally, Thingsboard is used to facilitate real-time monitoring of sensor data. These aquarium monitoring systems for ornamental fish hold the potential to enhance fish welfare and health, mitigate risks, and provide hobbyists and professionals with a more effective and informed experience in maintaining ornamental fish. However, it's important to note that the success of this system relies heavily on the quality of hardware, meticulous software development, and a profound understanding of the specific needs of ornamental fish

Abstract 729: IDO1 inhibition enables IFNγ-elicited responsiveness of pulmonary breast cancer metastases to hypoxia-directed tumoricidal agents

Abstract In mouse models of lung carcinoma and pulmonary breast cancer metastasis, genetic ablation of the tryptophan catabolizing enzyme IDO1 (indoleamine 2,3-dioxygenase 1) resulted in restricted tumor outgrowth associated with attenuated induction of the inflammatory cytokine IL6 (interleukin 6). Utilizing the 4T1 breast cancer metastasis model to further elucidate the mechanistic basis underlying IDO1’s pro-tumor activity in the lungs established that IDO1 acts as an integral determinant of a tumor-promoting inflammatory state that supports neovascular retention. Ido1−/− (nullizygous) mice exhibited impaired pulmonary metastases neovascularization and outgrowth that was driven by IFNγ (interferon gamma) in the absence of IDO1-dependent IL6 production. Hypoxia was likewise elevated, and these stress conditions increased the susceptibility of the metastatic tumor cells to cytotoxicity associated with administration of GSK2656157, an inhibitor of PERK (protein kinase RNA-like ER kinase), which acts as a primary sensor for activating the UPR (unfolded protein response) survival pathway. In WT (wild type) mice with established 4T1 lung metastases, administration of the IDO1 inhibitors epacadostat, navoximod or indoximod each similarly resulted in rapid collapse of the metastatic tumor neovasculature and concomitantly increased intratumoral hypoxia and sensitivity to PERK inhibition. Therefore, while IDO1 inhibition does not directly promote effective tumor cell killing in this context, the disruption of blood supply due to IFNγ-mediated neovascular regression subjects the tumor cells to hypoxic environmental stress, raising the possibility for therapeutic intervention with selectively targeted agents. Combining chemotherapy with immune checkpoint blockade (ICB) is recognized as an effective strategy for treating lung cancer, with first-line therapy for some patients including the alkylating agent carboplatin in conjunction with pembrolizumab. The challenge remains, however, that chemotherapy causes systemic side effects that can negatively impact quality of life and limit efficacy. Evofosfamide is an alkylating prodrug that was designed to be selectively activated in regions of hypoxia, thereby limiting systemic exposure. We report that enhanced metastatic tumor cell killing with evofosfamide can be achieved by co-administration of an IDO1 inhibitor. Elevated intratumoral hypoxia has also previously been linked to increased expression levels of PDL1, a favorable indicator of ICB responsiveness, and we have confirmed the upregulation of PDL1 in 4T1 lung metastases following IDO1 inhibitor administration. Our data in the lung metastasis model support future studies to evaluate whether IDO1 inhibition can be effectively combined with evofosfamide and anti-PD1 treatment to improve therapeutic outcomes. Citation Format: Shih-Chun Shen, Souvik Dey, James B. DuHadaway, Erika Sutanto-Ward, George C. Prendergast, Alexander J. Muller. IDO1 inhibition enables IFNγ-elicited responsiveness of pulmonary breast cancer metastases to hypoxia-directed tumoricidal agents [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 729.

Comparing Mfd- and UvrD-dependent models of transcription coupled DNA repair in live Escherichia coli using single-molecule tracking

During transcription-coupled DNA repair (TCR) the detection of DNA damage and initiation of nucleotide excision repair (NER) is performed by translocating RNA polymerases (RNAP), which are arrested upon encountering bulky DNA lesions. Two opposing models of the subsequent steps of TCR in bacteria exist. In the first model, stalled RNAPs are removed from the damage site by recruitment of Mfd which dislodges RNAP by pushing it forwards before recruitment of UvrA and UvrB. In the second model, UvrD helicase backtracks RNAP from the lesion site. Recent studies have proposed that both UvrD and UvrA continuously associate with RNAP before damage occurs, which forms the primary damage sensor for NER. To test these two models of TCR in living E. coli, we applied super-resolution microscopy (PALM) combined with single particle tracking to directly measure the mobility and recruitment of Mfd, UvrD, UvrA, and UvrB to DNA during ultraviolet-induced DNA damage. The intracellular mobilities of NER proteins in the absence of DNA damage showed that most UvrA molecules could in principle be complexed with RNAP, however, this was not the case for UvrD. Upon DNA damage, Mfd recruitment to DNA was independent of the presence of UvrA, in agreement with its role upstream of this protein in the TCR pathway. In contrast, UvrD recruitment to DNA was strongly dependent on the presence of UvrA. Inhibiting transcription with rifampicin abolished Mfd DNA-recruitment following DNA damage, whereas significant UvrD, UvrA, and UvrB recruitment remained, consistent with a UvrD and UvrA performing their NER functions independently of transcribing RNAP. Together, although we find that up to ∼8 UvrD-RNAP-UvrA complexes per cell could potentially form in the absence of DNA damage, our live-cell data is not consistent with this complex being the primary DNA damage sensor for NER.

Endothelium-specific SIRT7 targeting ameliorates pulmonary hypertension through Krüpple-like factor 4 deacetylation.

Pulmonary hypertension (PH) is a pulmonary vascular disease characterized by a high mortality rate. Pulmonary arterial endothelium cells (PAECs) serve as a primary sensor of various environmental cues, such as shear stress and hypoxia, but PAEC dysfunction may trigger vascular remodelling during the onset of PH. This study aimed to illustrate the role of Sirtuin 7 (SIRT7) in endothelial dysfunction during PH and explore the potential therapeutic strategy for PH. SIRT7 levels were measured in human and murine experimental PH samples. Bioinformatic analysis, immunoprecipitation, and deacetylation assay were used to identify the association between SIRT7 and Krüpple-like factor 4 (KLF4), a key transcription factor essential for endothelial cell (EC) homeostasis. Sugen5416 + hypoxia (SuHx)-induced PH mouse models and cell cultures were used for the study of the therapeutic effect of SIRT7 for PH. SIRT7 level was significantly reduced in lung tissues and PAECs from PH patients and the SuHx-induced PH mouse model as compared with healthy controls. Pulmonary endothelium-specific depletion of Sirt7 increased right ventricular systolic pressure and exacerbated right ventricular hypertrophy in the SuHx-induced PH model. At the molecular level, we identified KLF4 as a downstream target of SIRT7, which deacetylated KLF4 at K228 and inhibited the ubiquitination-proteasome degradation. Thus, the SIRT7/KLF4 axis maintained PAEC homeostasis by regulating proliferation, migration, and tube formation. PAEC dysfunction was reversed by adeno-associated virus type 1 vector-mediated endothelial overexpression of Sirt7 or supplementation with nicotinamide adenine dinucleotide (NAD)+ intermediate nicotinamide riboside which activated Sirt7; both approaches successfully reversed PH phenotypes. The SIRT7/KLF4 axis ensures PAEC homeostasis, and pulmonary endothelium-specific SIRT7 targeting might constitute a PH therapeutic strategy.

TurtleBot Operation with Barrier Bypass and Point Navigation Functionality

In the quest for a simple, efficient, and cost-effective method for autonomous robot navigation, it is crucial to develop a reliable obstacle avoidance system. This project explores how obstacles affect navigation and the time required for the robot to manoeuvre around them. An obstacle avoidance method that relies solely on the LIDAR as the primary sensor is proposed, which is both cheap to manufacture and easy to install. TurtleBot3 is used for experimentation, as it is easy to work with and allows for quick adjustments to the algorithm. The approach involves an optimized algorithm designed to navigate various obstacle scenarios effectively as well as multiple points considered to be target positions. By leveraging LIDAR data for obstacle detection, the algorithm addresses diverse obstacle situations. Experimental results demonstrate the efficiency of the method in navigating through different barrier scenarios and different coordinates.

Analysis of three-phase asymmetrical currents in the secondary voltage of signal change sensors in the power supply system using graph models

This article presents the principles of modeling three-phase asymmetrical currents in the form of secondary voltages of signal change sensors in the power supply system. The classical methods of signal transformation modeling do not provide the necessary accuracy due to the complexity in representing the interdependence between input and output quantities and parameters, as well as the difficulty in capturing the interchain dependencies and associated physical-technical effects. The analysis in this work focuses on the principles of signal transformation based on graph models, including the physical and technical effects of the three-phase primary current electromagnetic sensor that creates the interacting magnetic fields. The modifications to elements with nonlinear parameters and the values of the electric network currents in the power supply system during estimation have been considered as an object with concentrated parameters. In contrast to traditional single-phase current transformers, the complex transducing components, weight, and volume of the items are laborintensive when used in combined power measurement and control systems. They also do not provide the universality of the output value when interfacing with microprocessors and electronic technologies in intelligent systems.

Shaking Up Activity Counts: Assessing the Comparability of Accelerometers and Activity Count Computation

Accelerometers have been at the forefront of free-living activity capture for decades, and accordingly ActiGraph the largest distributor. Historically, limitations in data storage and battery power led to the use of summary metrics, which have been termed activity counts. Recently, ActiGraph publicly released their count-based algorithm, marking a notable development in the field. This study aimed to assess and compare activity counts generated through different processing techniques (ActiLife and open-source), filters that are available through ActiGraph count generation (normal- and low-frequency extension), and data from various ActiGraph models and GENEActiv devices. We evaluated ActiGraph GT3X+ (n = 8), ActiGraph wGT3X-BT (n = 10), ActiGraph GT9X (n = 8; primary and secondary sensors), OPAL (n = 6), and GENEActiv (n = 5), subjected to oscillations across their full dynamic range (0.005–8 G) using a multiaxis shaker table. Results indicated that the low-frequency extension produced significantly higher counts compared to the normal frequency across the devices and processing techniques. Notably, open-source counts (R and Python) were statistically equivalent to ActiLife-generated counts (p < .05) for the GT9X, wGT3X-BT, and the GT3X+. Overall, many of the counts generated by different ActiGraph models were statistically equivalent or had mean differences <5.03 counts. Conversely, the GENEActiv, OPAL, and GT9X secondary monitor exhibited significantly higher responses than the other ActiGraph models at higher frequencies with mean differences ranging from 55.50 to 104.91 counts. This study provides insights into accelerometer data processing methods and highlights the comparability of counts across different devices and techniques.

Inherent and induced defects in mixed-phase CuO nanoparticles

Copper (Cupric) oxide is a readily synthesized, non-toxic metal oxide with a wide range of uses, including the treatment of water, the manufacture of electronic devices, solar cells, cathodes for lithium-ion primary batteries, gas sensors, electrochromic devices, supercapacitors, and field effect transistors. The effects of Zn doping in CuO in three different concentrations (weights %) were investigated. The band gap and carrier concentration were altered due to the formation of defect energy states, lattice stresses, and the formation of ZnO, and Cu2O in addition to CuO. Temperature increases from room temperature resulted in the production of metal oxides with preferred crystal growth in specific orientations. An increase in temperature, from 300 °C to 500 °C, generated residual strain release and atomic diffusions, resulting in grain growth and a reduction in the band gap. The changes in UV and PL spectra predicted the growth kinetics involved

STING agonists in cancer immunotherapy: a brief review

The “STimulator of INterferon Genes” (STING) represents the primary sensors of cytosolic double-stranded DNA (dsDNA). The STING cellular signaling pathway is considered an attractive pharmacological target for cancer immunotherapy due to its immunostimulatory potential. In fact, activation of the intracellular STING protein triggers the secretion of type I IFNs, which results in immune-mediated tumor elimination and generation of antitumor immune memory. Two types of STING agonists were developed: cyclic dinucleotides (CDNs) and non-nucleotide small molecule agonists. Preclinical studies of STING agonists have demonstrated remarkable results in many tumor models, resulting in complete and durable therapeutic responses in a majority of treated mice. This review provides a brief summary of the latest research findings on STING agonists, their delivery to the tumor and the strategies being employed to enhance their efficacy in cancer immunotherapy.

Fiber-Optic Hydraulic Sensor Based on an End-Face Fabry–Perot Interferometer with an Open Cavity

The paper describes the design and manufacturing process of a fiber optic microphone based on a macro cavity at the end face of an optical fiber. The study explores the step-by-step fabrication of a droplet-shaped macro cavity on the optical fiber’s end surface, derived from the formation of a quasi-periodic array of micro-cavities due to the fuse effect. Immersing the end face of an optical fiber with a macro cavity in liquid leads to the formation of a closed area of gas where interfacial surfaces act as Fabry–Perot mirrors. The study demonstrates that the macro cavity can act as a standard foundational element for diverse fiber optic sensors, using the droplet-shaped end-face cavity as a primary sensor element. An evaluation of the macro cavity interferometer’s sensitivity to length alterations is presented, highlighting its substantial promise for use in precise fiber optic measurements. However, potential limitations and further research directions include investigating the influence of external factors on microphone sensitivity and long-term stability. This approach not only significantly contributes to optical measurement techniques but also underscores the necessity for the continued exploration of the parameters influencing device performance.

AN EVALUATION OF SOLID-STATE LIDAR FOR LOCALIZATION AND HD POINT CLOUD MAPPING

Abstract. Cost-effective navigation and positioning systems for autonomous vehicles has become a key focus of research in recent years. Having an accurate position within a lane is vital to enabling high levels of automation and improving safety. Traditionally, vehicle navigation and positioning systems have relied heavily on the Global Navigation Satellite System (GNSS), particularly in open-sky scenarios. However, GNSS signals can be easily disrupted by environmental interferences. These include phenomena such as urban canyons, which result from multi-path interferences, as well as challenges posed by Non-Line-of-Sight (NLOS) situations. In the pursuit of developing robust systems resilient to such issues, the concept of sensor fusion has been widely employed. Among all sensors used in commercial self-driving vehicles, mechanical LiDAR is the primary sensor. Utilizing point cloud data from LiDAR and registering it with a prior point cloud map can result in highly accurate position results. However, the high cost of mechanical LiDAR has limited the mass production of point cloud map and autonomous vehicle. In this paper, we evaluate several successful Simultaneous Localization and Mapping (SLAM) architectures from LiDAR-based to LiDAR-Inertial-based using single Solid-State LiDAR (SSL). Last, we proposed a single SSL mapping and localization framework that can achieve 36 centimeters 3D RMSE and 0.5 degree accuracy in heading estimation.