Data security is a fundamental aspect to be considered in Internet of Things (IoT) information gathering systems, as IoT is a network of interconnected devices that collect and share real-time data, becoming increasingly prevalent in our lives. However, data security in IoT systems presents unique challenges due to the large number of devices and access points involved. This study aims to conduct a literature review on IoT security to analyze the performance of security mechanisms on current development platforms, specifically on a Raspberry Pi 3. Some functions from the OpenSSL library were used, including popular hash functions and cipher algorithms. Additionally, a bash code was developed to obtain the time spent in seconds and the memory consumption in kilobytes. In addition to time and memory calculations, statistical values such as variance and standard deviation were also obtained and compared with results obtained on a personal computer. The tests conducted in this study demonstrated that it is possible to implement these algorithms on platforms with more limited resources, with AES and RSA algorithms being the most suitable for IoT scenarios.

More than 700 thousand human deaths from mosquito bites are observed annually in the world. It is more than 2 times the number of annual murders in the world. In this regard, the invention of new more effective methods of protection against mosquitoes is necessary. In this article for the first time, comprehensive studies of mosquito neutralization using machine vision and a 1 W power laser are considered. Developed laser installation with Raspberry Pi that changing the direction of the laser with a galvanometer. We developed a program for mosquito tracking in real. The possibility of using deep neural networks, Haar cascades, machine learning for mosquito recognition was considered. We considered in detail the classification problems of mosquitoes in images. A recommendation is given for the implementation of this device based on a microcontroller for subsequent use as part of an unmanned aerial vehicle. Any harmful insects in the fields can be used as objects for control.

This manuscript presented an autonomous home farm for predicting metrological characteristics that can not only automate the process of growing crops but also, due to a neural network, significantly increase the productivity of the farm. The developed farm monitors and manages the following indicators: illumination, soil PH, air temperature, earth temperature, air humidity, CO2 concentration, and soil moisture. The presented farm can also be considered as a device for testing various weather conditions to determine the optimal temperature characteristics for different crops. This farm as a result is completely autonomous grows tomatoes at home.

Military medical service is an important component of the Armed Forces of any country [1]. In wartime, healthcare performs critical functions of medical support for military operations, providing effective and qualified care to the wounded and sick. Consequently, it is imperative to equip military hospitals with state-of-the-art materials and technical resources commensurate with the predefined requirements. In this study, we examine a series of enhancements applied to the TurtleBot 3 Waffle Pi-based medical assistant robot, tailored for deployment in military hospital settings.

Located between Asia and Europe, Türkiye is very rich in terms of wild edible fruit diversity. One of the most important wild edible fruits in Türkiye is red raspberries and they widely found in cold to warm temperate regions in the country. The wild raspberries in general have red fruit color but yellow fruited genotypes are evident. A total of 11 wild grown red raspberry genotypes sampled in Northern Anatolia region was used in this study and their sensory, biochemical and antioxidant characteristics were determined. For sensory analysis aroma, taste and juiciness were used as the main criteria to compare ecotypes each other. Biochemical and antioxidant characteristics of ecotypes included SSC (Soluble Solid Content), vitamin C, organic acids, total anthocyanins (TA), total phenolic content, total flavonoid content and total antioxidant capacity. FRAP (Ferric Reducing Antioxidant Power) assay was used to determine antioxidant capacity. There were significant differences among ecotypes for most of the searched parameters even they found similar growing condition. The ecotypes had fruit weight between 1.04 g and 1.33 g, and fruit Chroma value between 26.11 and 33.70. Vitamin C content of ecotypes were quite variable and ranged from 29.3 mg/100 g to 41.4 mg/100 g. The total phenolic content, total anthocyanin content, total flavonoid content was between 164-362 mg gallic acid equivalent/100 g; 17.3-33.3 mg cyanidin-3-glucoside equivalent/100 g and 10.3-17.2 mg quercetin equivalent/100 g, respectively. Citric acid was the dominant organic acid for all genotypes and ranged from 10.3-14.6 g/100 g fresh weight base. According to the results V-4, V-8, V-3 and V-10 had attractive bigger fruits indicate their suitability for fresh consumption. V-3 and V-5 had sweeter fruits indicated their suitability for processing and V-6, V-7 and V-11 had richer for human health promoting compounds (higher total phenolic content and antioxidant capacity), making them suitable for future use as functional foods and as promising sources of natural antioxidants.

Online courses in horticulture increase the breadth of students who may be able to enroll. However, it is challenging to create hands-on learning experiences in online classes that are valuable for student learning. In an online introduction to horticulture class at the University of Maine, we created a hands-on project that is appropriate for students to work on independently at home. Students built an environmental monitoring system using a relatively inexpensive Raspberry Pi microcomputer and sensors for monitoring environmental factors that impact plant growth with a particular focus on monitoring temperature and humidity. They monitored the growing environment in their homes while growing house plants and used the information from their environmental monitoring system to determine whether their home environment was suitable for growing plants. Students were asked to use a pre-existing computer program in the Python language to monitor the environment. They also learned about how components of the code function and changed some simple parts of the code. A majority of students working on this project felt moderately confident, somewhat more confident, or very confident about their ability to use a Raspberry Pi microcomputer in the future. This project provides students with valuable hands-on experience in building environmental monitoring systems and provides them with a deeper understanding of the impact of the environment on plant growth.

Leaves of Rubus idaeus are a raw material, ingredients of herbal blend and a source of antioxidants. There are no data concerning histochemistry of trichomes and little is known about the leaves structure of this species. The aim of this study was to determine the histochemistry of active compounds and the structure of glandular trichomes, micromorphology, anatomy and ultrastructure of leaves as well as content of elements. To determine the histochemistry of glandular trichomes different chemical compounds were used. The leaves structure was analysed using light, scanning, and transmission electron microscopes. The content of elements was determined with atomic absorption spectrometry and the microanalysis of the epidermis ultrastructure was carried out with transmission electron microscope equipped with a digital X-ray analyser. In glandular trichomes: polyphenols, terpenes, lipids, proteins, and carbohydrates were identified. The main elements in the ultrastructure of the epidermis were: Na, S, Ca, Mg, B, Mo, and Se. In dry matter of leaves K, Mg, Ca, P, and Fe were dominant. Infusions from leaves are safe for health in terms of the Cd and Pb concentrations. Leaves can be a valuable raw material. Non-glandular trichomes prevent clumping of mixed raw materials in herbal mixtures.

Commonly, professors, students and researchers from universities around the world use software distributed under a license agreement for computer simulation purposes, which requires a computer with considerable hardware capabilities. Consequently, this implies a high cost to conduct simulations that require implementing numerical methods in all areas of engineering, particularly in the field of robotics and nonlinear control. This paper presents the design and results analysis of a low-cost and open-source frugal computer simulation tool with applications to robotic nonlinear control, for instance, for numerical simulations of manipulator robot control based on dynamic models. Using a single board minicomputer with with reduced computing power, Raspberry Pi, together with free software, GNU-Octave, trajectory tracking control simulations in the joint space of a Selective Conformal Assembly Robot Arm (SCARA) are achieved by solving a system of nonlinear differential equations, represented in matrix form, which includes the control law and the system model. The results of the proposed alternative are compared by running the same simulation code on a laptop computer using MATLAB and GNU Octave, which show minimal deviations and reasonable time complexity. Moreover, considering the frugality curve calculated for this approach, in addition to the low acquisition cost of the simulation software tool, it would allow the creation of a simulation laboratory in some universities with budgetary constraints for educational and research purposes.

The aim of this study was to determine the changes in chemical composition of the fresh red raspberry ‘Willamette’ fruits, caused by the presence of raspberry leaf blotch emaravirus (RLBV). In three experimental orchards of ‘Willamette’ raspberry, fruits were harvested from RLBV‒free and RLBV-infected plants in 2019 and 2020. Fruits were collected at appropriate maturity stages and further analyzed on total phenolics, total anthocyanins, and selected individual phenolics. In all three experimental orchards, the phenolic profiles of infected and uninfected fruit samples were considerably different during both studied years. Nonetheless, the intensity of the modifications varied greatly depending on location and harvest year. Statistical analysis revealed that the influence of RLBV infection on the studied features is undeniable, although the influences of weather conditions and soil composition outweigh the influence of RLBV. Taking into consideration all experimental and statistical data, it can be concluded that raspberry 'Willamette' fruits may be RLBV-tolerant, while sensitivity to environmental conditions and soil composition is emphasized.

The chicken poultry business is becoming a crucial sector for our nation's sustainable food supply. To increase the health of chickens in coops and maybe increase the output of chicken meat and eggs, several innovations and inventions have been developed. Therefore, the creation of an automated chicken feeding device might be highly beneficial for the expansion of the poultry sector. This equipment may take the role of a worker in the chicken feeding process, solving the industry's labor shortage and introducing a semi-automatic procedure. The main processor of the automated chicken feeding system is a Raspberry Pi 3 Model B+. The machine consists of five main components: a Raspberry Pi with an amplifier that makes noise to draw the attention of free-range chickens, a Raspberry Pi with a pi camera that counts and detects the chickens, a Raspberry Pi with a servomotor that moves the food from storage to a container, a Raspberry Pi with a load cell that controls the number of feeds to avoid waste on foods, and a Raspberry Pi with a temperature sensor that monitors the food's freshness. This enhances the coop's atmosphere while lowering labor costs, conserving food, and ensuring that the chickens are fed on time. It also regulates the temperature sensor to ensure that the chicken food is fresh and prevents food from becoming polluted by insects and chicken excrement.

(Background:) Raspberry (Rubus idaeus L.) is very popular with consumers around the world for its intense flavor, attractive appearance and health benefits. In recent years, interest in healthy eating and natural products has increased, and raspberry fits perfectly into these trends, which translates into its greater importance on the consumer market. (Aim) The aim of the study was the commodity evaluation of raspberry fruits bearing fruit on 2-year-old shoots, cultivated under the conditions of varied nitrogen fertilization against the background of constant phosphorus-potassium fertilization. The first-order factor were cultivars ('Laszka' and ‘Glen Ample’), and the second-order factor was nitrogen fertilization (0, 50, 100 and 150 kg N ha-1), against the background of constant phosphorus-potassium fertilization (100 kg P2O5 and 120 kg K2O ha-1). The experiment was set up in a dependent split-plot design, in 3 repetitions. (Results) The importance of raspberry on the consumer market was shaped by: taste and quality of fruit, health benefits, naturalness and freshness, universality of use, availability and nutritional trends. (Conclusion) The tested cultivars were characterized by similar production and quality capabilities. Fertilization of the tested cultivars with a dose of 135 kg N·ha-1 turned out to be justified in terms of yield. Increasing nitrogen doses resulted in a significant increase in fresh fruit yield and 1000 fruit weight. Different doses of nitrogen increased fruit resistance to mechanical damage, firmness and improved quality indices.

Reductive amination is a powerful tool in sustainable organic synthesis that allows chemists to access a wide range of valuable amine products using renewable feedstocks, mild reaction con-ditions, and with minimal waste generation. Practical applications can be found in various fields, including pharmaceuticals, contributing to greener and more sustainable chemical processes. In this work we present a heterogeneous (Rh and Pt) catalysed protocol for the fast and efficient synthesis of ractopamine hydrochloride (-adrenergic drug) under MW-assisted reductive ami-nation protocol starting from raspberry ketone and octapamine. Microwave (MW) successfully accelerated the hydrogenation reaction and reduced the reaction time from 13 hours to only 3 hours under mild conditions (50°C at 10 bar). The best catalysts were Pt/C and Rh/C, which led to high conversion and selectivity towards ractopamine:HCl. The solvent has a significant effect on the reaction outcome. The replacement of methanol with other solvents had a negative impact on the reaction yield. We also replaced the raspberry ketone with other ketone substrates, especially cyclohexanone. These preliminary experiments may be useful for further process improvements in the synthesis of β-adrenergic agonists and related structures.

Phenotypic characterization of crop genotypes is an essential yet challenging aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agriculture research due to diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. Phenotypic traits of crop fresh biomass, dry biomass, and plant height estimated by CBM data had high correlation with ground truth manual measurements in wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

This paper presents the robotic platform, PiBot, that has been developed and that is aimed at improving the teaching of Robotics with vision to secondary students. Its computational core is the Raspberry Pi 3 controller board, and the greatest novelty of this prototype is the support developed for the powerful camera mounted on board, the PiCamera. An open software infrastructure written in Python language was implemented so that the student may use this camera, or even a WebCam, as the main sensor of this robotic platform. Also, higher level commands have been provided to enhance the learning outcome for beginners. In addition, a PiBot 3D printable model and the counterpart for the Gazebo simulator were also developed and fully supported. They are publicly available so that students and educational centers that do not have the physical robot or can not afford the costs of these, can nevertheless practice and learn or teach Robotics using these open platforms: DIY-PiBot and/or simulated-PiBot.

At-home rapid antigen test (RAT) kits for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are valuable public health tools during the present coronavirus disease (COVID-19) pandemic. They provide fast identification of coronavirus infection, which can help to reduce the transmission rates and burden on the healthcare system. However, they have lower sensitivity when compared with the reverse transcription polymerase chain reaction (RT-PCR) tests. One of the reasons for the lower sensitivity is due to the RAT color indicators being indistinct or invisible to the naked eye after the measurements. For this reason, we propose a systematic approach, through which we investigated anonymously provided at-home RAT kit results by using our in-house open source image processing scripts developed for affordable Raspberry Pi computer and Raspberry Pi HQ camera systems (available at https://github.com/kmiikki/ratcv). Therefore, we aimed at minimizing the human-related analysis errors for such kits. We believe that our framework can contribute to reduced the delayed quarantines of infected individuals and spreading of the current infectious disease.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

As the embedded device and internet of things (IoTs) concept prevalent in today's world, there is an increasing demand for the security and performance requirements on deploying these devices to private and public sectors. The crucial part of it is to protect privacy, confidentiality and integrity, meanwhile, maintain an adequate level of performance during transmission, storage and access of critical information. While the conventional cryptography methods, such as the Advanced Encryption Standard (AES), SHA$-$2 hashing method and RSA and Diffie Hellman for message authentication and identification, work well on systems which have reasonable processing power and memory capabilities. These do not scale well into a world with embedded systems and sensor networks, in conjunction with their nature of smaller size and lower cost. Thus, in the context of resource constrained device, lightweight cryptography methods are proposed to overcome many of the problems of conventional cryptography possessed. This includes constraints related to physical size, processing requirement, memory limitation, energy drain and production cost. This paper provides a survey of the architectures that are defined as replacements for conventional ciphers within an IoTs space and discuss some trends in the design of future lightweight algorithms. The performance metrics are carefully chosen to reflect and assess the suitability for embedded devices. The aim of this research is to identify the various performance metrics applied on different types of symmetric block ciphers especially lightweight ciphers and compare the results on versatile platforms with stream ciphers and public key methods. The comparative analysis on efficient LW cipher will be tested against other similar block ciphers on both MacBook Pro with Intel core and resource constrained device Raspberry Pi with ARM processor.

Objectives: We aimed to clarify the endoscopic and clinicopathological features of raspberry-shaped gastric lesions (RSGLs) and to establish an endoscopic diagnostic algorithm for RSGLs. Methods: We collected RSGLs from an endoscopic database at our hospital between May 2009 and August 2021. All RSGLs were histopathologically classified and compared based on their endoscopic and clinicopathological characteristics. Results: Sixty-five RSGLs in 54 patients were classified into five histopathological types: gastric adenocarcinoma of foveolar type (GA-FV, n = 43), gastric adenocarcinoma of fundic-gland type (GA-FG, n = 2), gastric adenocarcinoma of fundic-gland mucosa type (GA-FGM, n = 4), hyperplastic polyp (HP, n = 12), and proton pump inhibitor-related lesion (PPI-L, n = 4). All RSGLs exhibited polygonal or curved marginal crypt epithelium (MCE). GA-FV lesions had homogenously reddish (95%) and irregular microvascular (MV) pattern (91%). GA-FG lesions were heterogeneously reddish with a submucosal tumor shape (100%) and had a regular MV pattern (50%). GA-FGM lesions were homogenously reddish (75%) and occasionally had a submucosal tumor shape (50%) with an irregular MV pattern (75%). HP and PPI-L were homogeneously reddish (93%), with linear or dotted MCE (81%) and a regular MV pattern (100%). Conclusion: Our diagnostic algorithm for RSGLs constructed using endoscopic features might be useful for the endoscopic differential diagnosis of RSGLs.