SPIRO - the automated Petri plate imaging platform designed by biologists, for biologists.

Phenotyping of model organisms grown on Petri plates is often carried out manually, despite the procedures being time-consuming and laborious. The main reason for this is the limited availability of automated phenotyping facilities, whereas constructing a custom automated solution can be a daunting task for biologists. Here, we describe SPIRO, the Smart Plate Imaging Robot, an automated platform that acquires time-lapse photographs of up to four vertically oriented Petri plates in a single experiment, corresponding to 192 seedlings for a typical root growth assay and up to 2500 seeds for a germination assay. SPIRO is catered specifically to biologists' needs, requiring no engineering or programming expertise for assembly and operation. Its small footprint is optimized for standard incubators, the inbuilt green LED enables imaging under dark conditions, and remote control provides access to the data without interfering with sample growth. SPIRO's excellent image quality is suitable for automated image processing, which we demonstrate on the example of seed germination and root growth assays. Furthermore, the robot can be easily customized for specific uses, as all information about SPIRO is released under open-source licenses. Importantly, uninterrupted imaging allows considerably more precise assessment of seed germination parameters and root growth rates compared with manual assays. Moreover, SPIRO enables previously technically challenging assays such as phenotyping in the dark. We illustrate the benefits of SPIRO in proof-of-concept experiments which yielded a novel insight on the interplay between autophagy, nitrogen sensing, and photoblastic response.

Inhibition of Benzo[a]pyrene-induced DNA Adduct in Buccal Cells of Smokers by Black Raspberry Lozenges.

Using LC-MS/MS analysis we previously showed for the first time (Carcinogenesis 43:746-753, 2022) that levels of DNA damage-induced by benzo[a]pyrene (B[a]P), an oral carcinogen and tobacco smoke (TS) constituent, were significantly higher in buccal cells of smokers than those in non-smokers; these results suggest the potential contribution of B[a]P in the development of oral squamous cell carcinoma (OSCC) in humans. Treating cancers, including OSCC at late stages even with improved targeted therapies, continues to be a major challenge. Thus interception/prevention remains a preferable approach for OSCC management and control. In previous preclinical studies we and others demonstrated the protective effects of black raspberry (BRB) against carcinogen-induced DNA damage and OSCC. Thus, to translate preclinical findings we tested the hypothesis, in a Phase 0 clinical study, that BRB administration reduces DNA damage induced by B[a]P in buccal cells of smokers. After enrolling 27 smokers, baseline buccal cells were collected before the administration of BRB lozenges (5/day for 8 weeks, 1 gm BRB powder/lozenge) at baseline, at the middle and the end of BRB administration. The last samples were collected at four weeks after BRB cessation (washout period). B[a]P-induced DNA damage (BPDE-N2-dG) was evaluated by LC-MS/MS. BRB administration resulted in a significant reduction in DNA damage: 26.3% at the midpoint (p = 0.01506) compared to baseline, 36.1% at the end of BRB administration (p = 0.00355), and 16.6% after BRB cessation (p = 0.007586). Our results suggest the potential benefits of BRB as a chemopreventive agent against the development of TS-initiated OSCC.

Bacteria Associated with Spores of Arbuscular Mycorrhizal Fungi Improve the Effectiveness of Fungal Inocula for Red Raspberry Biotization.

Intensive crop production leads to the disruption of the symbiosis between plants and their associated microorganisms, resulting in suboptimal plant productivity and lower yield quality. Therefore, it is necessary to improve existing methods and explore modern, environmentally friendly approaches to crop production. One of these methods is biotization, which involves the inoculation of plants with appropriately selected symbiotic microorganisms which play a beneficial role in plant adaptation to the environment. In this study, we tested the possibility of using a multi-microorganismal inoculum composed of arbuscular mycorrhizal fungi (AMF) and AMF spore-associated bacteria for biotization of the red raspberry. Bacteria were isolated from the spores of AMF, and their plant growth-promoting properties were tested. AMF inocula were supplemented with selected bacterial strains to investigate their effect on the growth and vitality of the raspberry. The investigations were carried out in the laboratory and on a semi-industrial scale in a polytunnel where commercial production of seedlings is carried out. In the semi-industrial experiment, we tested the growth parameters of plants and physiological response of the plant to temporary water shortage. We isolated over fifty strains of bacteria associated with spores of AMF. Only part of them showed plant growth-promoting properties, and six of these (belonging to the Paenibacillus genus) were used for the inoculum. AMF inoculation and co-inoculation of AMF and bacteria isolated from AMF spores improved plant growth and vitality in both experimental setups. Plant dry weight was improved by 70%, and selected chlorophyll fluorescence parameters (the contribution of light to primary photochemistry and fraction of reaction centre chlorophyll per chlorophyll of the antennae) were increased. The inoculum improved carbon assimilation, photosynthetic rate, stomatal conductance and transpiration after temporary water shortage. Raspberry biotization with AMF and bacteria associated with spores has potential applications in horticulture where ecological methods based on plant microorganism interaction are in demand.

Comparative Responses of Two Major Cucurbit Pests, Zeugodacus cucurbitae and Zeugodacus tau to Phenylbutanoid Male Lures.

Zeugodacus cucurbitae and Z. tau are two major fruit fly pests of cucurbitaceous plants in the tropical and subtropical regions. The former species has a broader host range and wider world distribution than the latter. With global climate change, Z. tau shows great potential for geographical expansion with several invasion records in recent years. Males of both species are attracted to cue lure (CL) (and raspberry ketone (RK), a deacetyl derivative of CL), a common male lure used in fruit fly population detection, monitoring and control programs. Males of both species are also known to respond to zingerone (ZN), which are produced by some rainforest orchids. Previous studies have shown that fruit fly-male lure interactions are unique to species and lure types, and significantly impact the success of a lure-based fruit fly control program. We seek to compare the attraction of Z. cucurbitae and Z. tau males to CL, RK and ZN via Probit behavioral assays. Our results showed that CL is more attractive to Z. cucurbitae and Z. tau males than RK, while ZN is a poor lure for both species. Attraction Z. tau to CL is slightly lower than Z. cucurbitae, but the former is at least 1.71 times less attractive to RK than the latter. Together with published information on species' sexual development, our current study indicates a lure-based control program via male annihilation technique for Z. tau will be more challenging than Z. cucurbitae and should incorporate other integrated pest management strategies for a desirable outcome.

A review on natural phenylbutanoid attractants: Occurrence, distribution, and role in nature, especially in relation to Dacini fruit fly behavior and pollination.

The natural occurrence, distribution (within a plant) and roles of four phenylbutanoid compounds (anisyl acetone, cue-lure, raspberry ketone and zingerone) are elucidated for the Asia-Pacific and Oceania regions. These phenylbutanoids may act individually or in combination to attract true fruit fly males belonging to a tribe Dacini of subfamily Dacinae (Diptera: Tepritidae). Of special interest are the mutualistic interactions between the Dacini fruit fly males and the tropical daciniphilous (attracting exclusively Dacini fruit flies) orchids - leading to cross pollination for the orchids and enchanced mating success for the flies. When offered to male flies, anisyl acetone and cue-lure are generally converted to raspberry ketone. Upon consumption, raspberry ketone and zingerone are individually sequestered in the male rectal (pheromonal) gland unchanged. Attracted male flies readily imbibe the phenylbutanoid(s) in the floral synomone to compliment the endogenously synthesized male sex pheromonal components - to enhance attraction of conspecific females during courtship as well as attract conspecific males to form 'leks'. The phenylbutanoid(s) may also act as an allomone to deter vertebrate predators, especially geckos, besides possessing antimicrobial and antioxidant activities. Cue-lure, raspberry ketone and zingerone are important attractants/lures used in pest surveillance and mass trapping under the integrated pest management (IPM) program against quarantine Dacini fruit fly pest species, particularly Bactrocera tryoni and Zeugodacus cucurbitae.

Research on a Method for Identification of Peanut Pests and Diseases Based on a Lightweight LSCDNet Model.

Timely and accurate identification of peanut pests and diseases, coupled with effective countermeasures, is pivotal for ensuring high-quality and efficient peanut production. Despite the prevalence of pests and diseases in peanut cultivation, challenges such as minute disease spots, the elusive nature of pests, and intricate environmental conditions often lead to diminished identification accuracy and efficiency. Moreover, continuous monitoring of peanut health in real-world agricultural settings demands solutions that are computationally efficient. Traditional deep learning models often require substantial computational resources, limiting their practical applicability. In response to these challenges, we introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet. LSCDNet preserves only the transition layers to reduce feature map dimensionality, simplifying the model's complexity. The inclusion of a sandglass block bolsters features extraction capabilities, mitigating potential information loss due to dimensionality reduction. Additionally, the incorporation of coordinate attention addresses issues related to positional information loss during feature extraction. Experimental results showcase that LSCDNet achieved impressive metrics with accuracy, precision, recall, and Fl score of 96.67, 98.05, 95.56, and 96.79%, respectively, while maintaining a compact parameter count of merely 0.59 million. When compared with established models such as MobileNetV1, MobileNetV2, NASNetMobile, DenseNet-121, InceptionV3, and X-ception, LSCDNet outperformed with accuracy gains of 2.65, 4.87, 8.71, 5.04, 6.32, and 8.2%, respectively, accompanied by substantially fewer parameters. Lastly, we deployed the LSCDNet model on Raspberry Pi for practical testing and application and achieved an average recognition accuracy of 85.36%, thereby meeting real-world operational requirements.

First Report of Fusarium avenaceum Causing Root Rot of Raspberry (Rubus corchorifolius) in China.

Raspberry (Rubus corchorifolius) plants hold historical, economic, and medicinal importance in China (Yang et al. 2022). Raspberries are cultivated to generate income for local farmers in Lintao County, Dingxi City, Gansu Province. However, farmers encountered challenges due to raspberry plants exhibiting root rot disease, resulting in plant death. During a thorough field survey conducted in June 2022, symptoms ranging from leaf yellowing and wilting to necrotic lesions and root rots were observed, where approximately 30% of raspberry plants were affected. Five diseased and healthy plants were collected from the farmers' fields in Lintao (35.53oN, 103.84oE) for pathogen identification. Symptomatic and asymptomatic root tissues were surface sterilized with 75% ethanol for 30 s and 3% NaOCl for 5 min, followed by three rinses in sterile water. Small pieces (0.5 × 0.5 cm) were cut and incubated on potato dextrose agar (PDA) plates at 25°C for 7-10 days. Twenty-two pure Fusarium isolates, which displayed four distinct colony groups morphologically, were obtained. Pathogenicity tests on isolates RB10, RB1, RB30, and RB23, representing each colony group, revealed that RB10 exhibited symptoms similar to those observed in the field. The RB10 strain produced yellowish-white to greyish-white colonies on PDA and was then cultured in a carboxymethylcellulose (CMC) broth for enhanced conidia production (Zhang et al. 2020). Macroconidia were sickle-shaped or slightly curved, with three to five septa (19.2 to 38.5 x 3.1 to 5.8 µm, n =40). Microconidia were oval to ellipsoidal, non-septate or featuring 1 to 2 septa (4.8 to 10.5 x 2.1 to 5.2 µm, n=20). These morphological features indicated the isolate was similar to Fusarium avenaceum (Leslie and Summerell, 2006). For further identification of the strains, genomic regions (ITS-rDNA, TEF-1α, and RPB2) were amplified and sequenced using specific primers ITS1/ITS4, EF-1/EF-2, and 5f2/7cr, respectively (O'Donnell et al. 2010; Uwaremwe et al. 2021; Zarrin et al. 2016). PCR BLASTn queries of NCBI GenBank revealed a 99.8% (522 bp), 99.4% (355 bp) and 99.6% (985 bp) homology with F. avenaceum (MZ724839.1, MN271631.1, and MK185026.1), respectively. Sequences were deposited in GenBank (ITS, OR735571; TEF-1α, PP216660; RPB2, PP857820). One-year-old raspberry seedlings were planted in pots with a sterile soil mix (2:2:1 v/v ratio of soil, peat, and vermiculite) under controlled greenhouse conditions (23-26°C, 16h light/8h dark). A month post-planting, taproots were wounded in six pots and inoculated with 20 ml of conidia suspension (106 conidia/ml), while the other six pots were maintained as controls. After 14 days, RB10-infected plants showed symptoms similar to field observations, while controls remained healthy. The experiment was conducted twice, and re-isolation confirmed both the pathogenicity and identity of the pathogen. In the concatenated phylogenetic tree of ITS, TEF-1α and RPB2, strain RB10 was clustered with the F. avenaceum representative strains KG502, KG431 and F094. Studies revealed F. avenaceum varied pathogenicity across plants (Bugingo, 2022; Moparthi et al. 2020& 2024; Yli-Mattila et al. 2018), and it has been reported to induce raspberry fruit rot (Wang et al. 2017). However, no previous reports linked this fungus to raspberry root rot. This report is crucial for understanding the impact of root rot disease on raspberry cultivation and developing effective management strategies.

Lavender Harbors More Viruses than Previously Thought: First Report of Raspberry Ringspot Virus and Phlox Virus M in Lavandula × intermedia.

Plants of the genus Lavandula are thought to be rarely infected by viruses. To date, only alfalfa mosaic virus, cucumber mosaic virus, tobacco mosaic virus, and tomato spotted wilt virus have been reported in this host. In this study, we identified for the first time raspberry ringspot virus (RpRSV) and phlox virus M (PhlVM) in lavender using herbaceous indexing, enzyme-linked immunosorbent assay, and high-throughput sequencing. Nearly complete genome sequences for both viruses were determined. Phylogenetic and serological characterizations suggest that the obtained RpRSV isolate is a raspberry strain. A preliminary survey of 166 samples indicated RpRSV was spread only in the lavender cultivar 'Grosso', while PhlVM was detected in multiple lavender cultivars. Although RpRSV raspberry strain may have spread throughout Auckland and nearby areas in New Zealand, it is very likely restricted to the genus Lavandula or even to the cultivar 'Grosso' due to the absence or limited occurrence of the nematode vector. Interestingly, all infected lavender plants, regardless of their infection status (by RpRSV, PhlVM, or both) were asymptomatic. RpRSV is an important virus that infects horticultural crops including grapevine, cherry, berry fruits, and rose. It remains on the list of regulated pests in New Zealand. RpRSV testing is mandatory for imported Fragaria, Prunus, Ribes, Rosa, Rubus, and Vitis nursery stock and seeds for sowing, while this is not required for Lavandula importation. Our study revealed that lavender could play a role not only as a reservoir but also as an uncontrolled import pathway of viruses that pose a threat to New Zealand's primary industries.

Host Range of Phytophthora spp. from Berry Crops in Huelva, Spain.

Crop declines have been observed in raspberry and blueberry farms in the southwest region of Spain, which is the most important berry-producing area in the country. This study aimed to identify and characterize the pathogens associated with these diseases using molecular and morphological methods. Additionally, pathogenicity tests were performed on different raspberry, blueberry, and strawberry cultivars to determine possible susceptible hosts in the area. An isolate of Phytophthora cactorum was obtained from a symptomatic strawberry plant, an isolate of P. cinnamomi was obtained from a symptomatic blueberry plant, and isolates identified as P. rosacearum, P. rubi, and a previously unknown species named P. balkanensis were recovered from symptomatic raspberry plants. Results from the pathogenicity tests reported, for the first time, P. rubi causing root rot and wilting complex in Spanish raspberry crops. Additionally, P. cinnamomi was found to affect highbush blueberry production in Spain. Thus, this study provides valuable insights into the identification and characterization of Phytophthora spp. associated with the decline of blueberry and raspberry crops in Huelva. It also provides essential recommendations regarding the potential risks associated with the use of other types of berries as rotational crops and emphasizes the necessity for effective management strategies to mitigate crop losses. This is particularly critical given the limited soil disinfection alternatives available in Spain.

Development of a digital star-shot analysis system for comparing radiation and imaging isocenters of proton treatment machine.

PURPOSE: To directly compare the radiation and imaging isocenters of a proton treatment machine, we developed and evaluated a real-time radiation isocenter verification system. METHODS: The system consists of a plastic scintillator (PI-200, Mitsubishi Chemical Corporation, Tokyo, Japan), an acrylic phantom, a steel ball on the detachable plate, Raspberry Pi 4 (Raspberry Pi Foundation, London, UK) with camera module, and analysis software implemented through a Python-based graphical user interface (GUI). After kV imaging alignment of the steel ball, the imaging isocenter defined as the position of the steel ball was extracted from the optical image. The proton star-shot was obtained by optical camera because the scintillator converted proton beam into visible light. Then the software computed both the minimum circle radius and the radiation isocenter position from the star-shot. And the deviation between the imaging isocenter and radiation isocenter was calculated. We compared our results with measurements obtained by Gafchromic EBT3 film (Ashland, NJ, USA). RESULTS: The minimum circle radii were averaged 0.29 and 0.41 mm while the position deviations from the radiation isocenter to the laser marker were averaged 0.99 and 1.07 mm, for our system and EBT3 film, respectively. Furthermore, the average position difference between the radiation isocenter and imaging isocenter was 0.27 mm for our system. Our system reduced analysis time by 10 min. CONCLUSIONS: Our system provided automated star-shot analysis with sufficient accuracy, and it is cost-effective alternative to conventional film-based method for radiation isocenter verification.

Development and Validation of Low-Cost Indoor Air Quality Monitoring System for Swine Buildings.

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

Portable Head-Mounted System for Mobile Forearm Tracking.

Computer vision (CV)-based systems using cameras and recognition algorithms offer touchless, cost-effective, precise, and versatile hand tracking. These systems allow unrestricted, fluid, and natural movements without the constraints of wearable devices, gaining popularity in human-system interaction, virtual reality, and medical procedures. However, traditional CV-based systems, relying on stationary cameras, are not compatible with mobile applications and demand substantial computing power. To address these limitations, we propose a portable hand-tracking system utilizing the Leap Motion Controller 2 (LMC) mounted on the head and controlled by a single-board computer (SBC) powered by a compact power bank. The proposed system enhances portability, enabling users to interact freely with their surroundings. We present the system's design and conduct experimental tests to evaluate its robustness under variable lighting conditions, power consumption, CPU usage, temperature, and frame rate. This portable hand-tracking solution, which has minimal weight and runs independently of external power, proves suitable for mobile applications in daily life.

Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning-A Comparison between High-End and Low-Cost Multispectral Sensors.

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated.

ML-Based Edge Node for Monitoring Peoples' Frailty Status.

The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelligent system that takes the RGB frames of a video as input to classify the occurrence of brushing teeth, washing hands, and fixing hair. No action activity is considered. The RGB frames are first processed by two Mediapipe algorithms to extract body keypoints related to the pose and hands, which represent the features to be classified. The optimal feature extractor results from the most complex Mediapipe pose estimator combined with the most complex hand keypoint regressor, which achieves the best performance even when operating at one frame per second. The final classifier is a Light Gradient Boosting Machine classifier that achieves more than 94% weighted F1-score under conditions of one frame per second and observation times of seven seconds or more. When the observation window is enlarged to ten seconds, the F1-scores for each class oscillate between 94.66% and 96.35%.

Looping Flexible Fluoropolymer Microcapillary Film Extends Analysis Times for Vertical Microfluidic Blood Testing.

The microfluidic measurement of capillary flow can be used to evaluate the response of biological samples to stimulation, where distance and velocity are altered. Melt-extruded multi-bored microfluidic capillaries allow for high-throughput testing with low device cost, but simple devices may limit control over sample flow when compared to the more complex "lab-on-a-chip" devices produced using advanced microfluidic fabrication methods. Previously, we measured the dynamics of global haemostasis stimulated by thrombin by dipping straight vertical microcapillaries into blood, but only the most rapid response could be monitored, as flow slowed significantly within 30 s. Here, we show an innovative method to extend both the stimulation process and flow measurement time without increasing the cost of the device by adding simple loops to the flexible extruded device. The loops enable longer time-scale measurements by increasing resistance to flow, thereby reducing the dependence on high stimulus concentrations for rapid reactions. The instantaneous velocity and equilibrium heights of straight and looped vertical microcapillary films were assessed with water, plasma and whole blood, showing that the loops create additional frictional resistances, reduce flow velocity and prolong residence times for increased time scales of the stimulation process. A modified pressure balance model was used to capture flow dynamics with the added loop. Looped devices loaded with thrombin and collagen showed an improved detection of blood stimulation responses even with lower stimulus concentrations, compared to straight vertical capillaries. Thrombin-activated blood samples in straight capillaries provided a maximum measurement zone of only 4 mm, while the looped design significantly increased this to 11 mm for much longer time scale measurements. Our results suggest that extending stimulation times can be achieved without complex microfluidic fabrication methods, potentially improving concentration-response blood stimulation assays, and may enhance the accuracy and reliability. We conclude adding a loop to low-cost extruded microfluidic devices may bring microfluidic devices closer to delivering on their promise of widespread, decentralized low-cost evaluation of blood response to stimulation in both research and clinical settings.

An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System.

With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

A Performance Analysis of Security Protocols for Distributed Measurement Systems Based on Internet of Things with Constrained Hardware and Open Source Infrastructures.

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.

Cost-Effective Data Acquisition Systems for Advanced Structural Health Monitoring.

With the growing demand for infrastructure and transportation facilities, the need for advanced structural health monitoring (SHM) systems is critical. This study introduces two innovative, cost-effective, standalone, and open-source data acquisition devices designed to enhance SHM through the latest sensing technologies. The first device, termed CEDAS_acc, integrates the ADXL355 MEMS accelerometer with a RaspberryPi mini-computer, ideal for measuring strong ground motions and assessing structural modal properties during forced vibration tests and structural monitoring of mid-rise buildings. The second device, CEDAS_geo, incorporates the SM24 geophone sensor with a Raspberry Pi, designed for weak ground motion measurements, making it suitable for seismograph networks, seismological research, and early warning systems. Both devices function as acceleration/velocity Data Acquisition Systems (DAS) and standalone data loggers, featuring hardware components such as a single-board mini-computer, sensors, Analog-to-Digital Converters (ADCs), and micro-SD cards housed in protective casings. The CEDAS_acc includes a triaxial MEMS accelerometer with three ADCs, while the CEDAS_geo uses horizontal and vertical geophone elements with an ADC board. To validate these devices, rigorous tests were conducted. Offset Test, conducted by placing the sensor on a leveled flat surface in six orientations, demonstrating the accelerometer's ability to provide accurate measurements using gravity as a reference; Frequency Response Test, performed at the Gebze Technical University Earthquake and Structure Laboratory (GTU-ESL), comparing the devices' responses to the GURALP-5TDE reference sensor, with CEDAS_acc evaluated on a shaking table and CEDAS_geo's performance assessed using ambient vibration records; and Noise Test, executed in a low-noise rural area to determine the intrinsic noise of CEDAS_geo, showing its capability to capture vibrations lower than ambient noise levels. Further field tests were conducted on a 10-story reinforced concrete building in Gaziantep, Turkey, instrumented with 8 CEDAS_acc and 1 CEDAS_geo devices. The building's response to a magnitude 3.2 earthquake and ambient vibrations was analyzed, comparing results to the GURALP-5TDE reference sensors and demonstrating the devices' accuracy in capturing peak accelerations and modal frequencies with minimal deviations. The study also introduced the Record Analyzer (RECANA) web application for managing data analysis on CEDAS devices, supporting various data formats, and providing tools for filtering, calibrating, and exporting data. This comprehensive study presents valuable, practical solutions for SHM, enhancing accessibility, reliability, and efficiency in structural and seismic monitoring applications and offering robust alternatives to traditional, costlier systems.

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware.

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.