Supply chain traceability and counterfeit detection of COVID-19 vaccines using novel blockchain-based Vacledger system.

We propose a novel framework, Vacledger, for supply chain traceability and counterfeit detection of COVID-19 vaccines using a blockchain network. It includes four smart contracts on a private-permissioned blockchain network for supply chain traceability and counterfeit detection of COVID-19 vaccine, more specifically to (i) handle the rules and regulations of vaccine importing countries and provide authorization for cross the borders (regulatory compliance and border authorization smart contract), (ii) register new and imported vaccines in the Vacledger system (vaccine registration smart contract), (iii) find the number of stocks that have arrived in the Vacledger system (stock accumulation smart contract), and (iv) identify the exact location of the stock (location tracing update smart contract). Our results show that the proposed system keeps track of all activities, events, transactions, and all other past transactions, permanently stored in an immutable Vacledger connected to decentralized peer-to-peer file systems. We observe no algorithm complexity differences between the proposed Vacledger system and existing supply chain frameworks based on different blockchain types. In addition, based on four use cases, we estimate our model's overall gasoline cost (transaction or gas price). The Vacledger system empowers distribution companies to manage their supply chain operations effectively and securely using an in-network, permissioned distributed network. This study employs the COVID-19 vaccine supply chain (the healthcare industry) to demonstrate how the proposed Vacledger system operates. Despite this, our proposed approach might be implemented in other supply chain industries, such as the food industry, energy trading, and commodity transactions.

A meta-analysis of in vitro exposures to weak radiofrequency radiation exposure from mobile phones (1990-2015).

To function, mobile phone systems require transmitters that emit and receive radiofrequency signals over an extended geographical area exposing humans in all stages of development ranging from in-utero, early childhood, adolescents and adults. This study evaluates the question of the impact of radiofrequency radiation on living organisms in vitro studies. In this study, we abstract data from 300 peer-reviewed scientific publications (1990-2015) describing 1127 experimental observations in cell-based in vitro models. Our first analysis of these data found that out of 746 human cell experiments, 45.3% indicated cell changes, whereas 54.7% indicated no changes (p = 0.001). Realizing that there are profound distinctions between cell types in terms of age, rate of proliferation and apoptosis, and other characteristics and that RF signals can be characterized in terms of polarity, information content, frequency, Specific Absorption Rate (SAR) and power, we further refined our analysis to determine if there were some distinct properties of negative and positive findings associated with these specific characteristics. We further analyzed the data taking into account the cumulative effect (SAR × exposure time) to acquire the cumulative energy absorption of experiments due to radiofrequency exposure, which we believe, has not been fully considered previously. When the frequency of signals, length and type of exposure, and maturity, rate of growth (doubling time), apoptosis and other properties of individual cell types are considered, our results identify a number of potential non-thermal effects of radiofrequency fields that are restricted to a subset of specific faster-growing less differentiated cell types such as human spermatozoa (based on 19 reported experiments, p-value = 0.002) and human epithelial cells (based on 89 reported experiments, p-value < 0.0001). In contrast, for mature, differentiated adult cells of Glia (p = 0.001) and Glioblastoma (p < 0.0001) and adult human blood lymphocytes (p < 0.0001) there are no statistically significant differences for these more slowly reproducing cell lines. Thus, we show that RF induces significant changes in human cells (45.3%), and in faster-growing rat/mouse cell dataset (47.3%). In parallel with this finding, further analysis of faster-growing cells from other species (chicken, rabbit, pig, frog, snail) indicates that most undergo significant changes (74.4%) when exposed to RF. This study confirms observations from the REFLEX project, Belyaev and others that cellular response varies with signal properties. We concur that differentiation of cell type thus constitutes a critical piece of information and should be useful as a reference for many researchers planning additional studies. Sponsorship bias is also a factor that we did not take into account in this analysis.

Lessons learned from the application of machine learning to studies on plant response to radio-frequency.

This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discern whether there are consistent biological impacts of exposure to non-thermal low power radio-frequency electromagnetic fields (RF-EMF). Expanding on previous analysis that identified sensitive plant species, we extracted data from 45 articles published between 1996 and 2016 that included 169 experimental case studies of plant response to RF-EMF. Raw-data from these case studies included six different attributes: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). This dataset has been tested with two different classification algorithms: k-Nearest Neighbor (kNN) and Random Forest (RF). The outputs are estimated using k-fold cross-validation method to identify and compare classifier mean accuracy and computation time. We also developed an optimization technique to distinguish the trade-off between prediction accuracy and computation time based on the classification algorithm. Our analysis illustrates kNN (91.17%) and RF (89.41%) perform similarly in terms of mean accuracy, nonetheless, kNN takes less computation time (3.38 s) to train a model compared to RF (248.12 s). Very strong correlations were observed between SAR and frequency, and SAR with power flux density and electric field strength. Despite the low sample size (169 reported experimental case studies), that limits statistical power, nevertheless, this analysis indicates that ML algorithms applied to bioelectromagnetics literature predict impacts of key plant health parameters from specific RF-EMF exposures. This paper addresses both questions of the methodological importance and relative value of different methods of ML and the specific finding of impacts of RF-EMF on specific measures of plant growth and health. Recognizing the importance of standardizing nomenclature for EMF-RF, we conclude that Machine Learning provides innovative and efficient RF-EMF exposure prediction tools, and we propose future applications in occupational and environmental epidemiology and public health.

Review: Weak radiofrequency radiation exposure from mobile phone radiation on plants.

AIM: The aim of this article was to explore the hypothesis that non-thermal, weak, radiofrequency electromagnetic fields (RF-EMF) have an effect on living plants. SUBJECT AND METHODS: In this study, we performed an analysis of the data extracted from the 45 peer-reviewed scientific publications (1996-2016) describing 169 experimental observations to detect the physiological and morphological changes in plants due to the non-thermal RF-EMF effects from mobile phone radiation. Twenty-nine different species of plants were considered in this work. RESULTS: Our analysis demonstrates that the data from a substantial amount of the studies on RF-EMFs from mobile phones show physiological and/or morphological effects (89.9%, p < 0.001). Additionally, our analysis of the results from these reported studies demonstrates that the maize, roselle, pea, fenugreek, duckweeds, tomato, onions and mungbean plants seem to be very sensitive to RF-EMFs. Our findings also suggest that plants seem to be more responsive to certain frequencies, especially the frequencies between (i) 800 and 1500 MHz (p < 0.0001), (ii) 1500 and 2400 MHz (p < 0.0001) and (iii) 3500 and 8000 MHz (p = 0.0161). CONCLUSION: The available literature on the effect of RF-EMFs on plants to date observed the significant trend of radiofrequency radiation influence on plants. Hence, this study provides new evidence supporting our hypothesis. Nonetheless, this endorses the need for more experiments to observe the effects of RF-EMFs, especially for the longer exposure durations, using the whole organisms. The above observation agrees with our earlier study, in that it supported that it is not a well-grounded method to characterize biological effects without considering the exposure duration. Nevertheless, none of these findings can be directly associated with human; however, on the other hand, this cannot be excluded, as it can impact the human welfare and health, either directly or indirectly, due to their complexity and varied effects (calcium metabolism, stress proteins, etc.). This study should be useful as a reference for researchers conducting epidemiological studies and the long-term experiments, using whole organisms, to observe the effects of RF-EMFs.

Reduced growth of soybean seedlings after exposure to weak microwave radiation from GSM 900 mobile phone and base station.

The aim of this work was to study possible effects of environmental radiation pollution on plants. The association between cellular telephone (short duration, higher amplitude) and base station (long duration, very low amplitude) radiation exposure and the growth rate of soybean (Glycine max) seedlings was investigated. Soybean seedlings, pre-grown for 4 days, were exposed in a gigahertz transverse electromagnetic cell for 2 h to global system for mobile communication (GSM) mobile phone pulsed radiation or continuous wave (CW) radiation at 900 MHz with amplitudes of 5.7 and 41 V m(-1) , and outgrowth was studied one week after exposure. The exposure to higher amplitude (41 V m(-1)) GSM radiation resulted in diminished outgrowth of the epicotyl. The exposure to lower amplitude (5.7 V m(-1)) GSM radiation did not influence outgrowth of epicotyl, hypocotyls, or roots. The exposure to higher amplitude CW radiation resulted in reduced outgrowth of the roots whereas lower CW exposure resulted in a reduced outgrowth of the hypocotyl. Soybean seedlings were also exposed for 5 days to an extremely low level of radiation (GSM 900 MHz, 0.56 V m(-1)) and outgrowth was studied 2 days later. Growth of epicotyl and hypocotyl was found to be reduced, whereas the outgrowth of roots was stimulated. Our findings indicate that the observed effects were significantly dependent on field strength as well as amplitude modulation of the applied field.

Dielectric properties of liquid phase molecular clusters using the external field method: molecular dynamics study.

We analyzed the dielectric properties of molecular liquids using the external field method with reaction field approximations. The applicability of this method to determine the dielectric properties of molecules with zero (1,4-dioxane) and non-zero (water and bio-molecular aqueous solutions) permanent dipole moment was studied. The relative static dielectric constant obtained using the external field method for polar and non-polar molecular liquids, including molecules with zero permanent dipole moment, agreed well with the experimental values presented in the literature. Our results indicate that the Debye relaxation time constants estimated from the non-equilibrium simulations using the external field method were accurate for molecules whose permanent dipole moments were less than 12 D.

Critical time delay of the pineal melatonin rhythm in humans due to weak electromagnetic exposure.

Electromagnetic fields (EMFs) can increase free radicals, activate the stress response and alter enzyme reactions. Intracellular signalling is mediated by free radicals and enzyme kinetics is affected by radical pair recombination rates. The magnetic field component of an external EMF can delay the "recombination rate" of free radical pairs. Magnetic fields thus increase radical life-times in biological systems. Although measured in nanoseconds, this extra time increases the potential to do more damage. Melatonin regulates the body's sleep-wake cycle or circadian rhythm. The World Health Organization (WHO) has confirmed that prolonged alterations in sleep patterns suppress the body's ability to make melatonin. Considerable cancer rates have been attributed to the reduction of melatonin production as a result of jet lag and night shift work. In this study, changes in circadian rhythm and melatonin concentration are observed due to the external perturbation of chemical reaction rates. We further analyze the pineal melatonin rhythm and investigate the critical time delay or maturation time of radical pair recombination rates, exploring the impact of the mRNA degradation rate on the critical time delay. The results show that significant melatonin interruption and changes to the circadian rhythm occur due to the perturbation of chemical reaction rates, as also reported in previous studies. The results also show the influence of the mRNA degradation rate on the circadian rhythm's critical time delay or maturation time. The results support the hypothesis that exposure to weak EMFs via melatonin disruption can adversely affect human health.

Smart Magnetotactic Bacteria Enable the Inhibition of Neuroblastoma under an Alternating Magnetic Field.

Magnetotactic bacteria are ubiquitous microorganisms in nature that synthesize intracellular magnetic nanoparticles called magnetosomes in a gene-controlled way and arrange them in chains. From in vitro to in vivo, we demonstrate that the intact body of Magnetospirillum magneticum AMB-1 has potential as a natural magnetic hyperthermia material for cancer therapy. Compared to chains of magnetosomes and individual magnetosomes, the entire AMB-1 cell exhibits superior heating capability under an alternating magnetic field. When incubating with tumor cells, the intact AMB-1 cells disperse better than the other two types of magnetosomes, decreasing cellular viability under the control of an alternating magnetic field. Furthermore, in vivo experiments in nude mice with neuroblastoma found that intact AMB-1 cells had the best antitumor activity with magnetic hyperthermia therapy compared to other treatment groups. These findings suggest that the intact body of magnetotactic bacteria has enormous promise as a natural material for tumor magnetic hyperthermia. In biomedical applications, intact and living magnetotactic bacteria play an increasingly essential function as a targeting robot due to their magnetotaxis.

Intelligent sensing in dynamic environments using markov decision process.

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor's sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning.

Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells.

The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data better. We develop a prediction strategy, for the first time, using supervised ML to observe the possible impact of weak radiofrequency electromagnetic field (RF-EMF) on human and animal cells without performing in-vitro laboratory experiments. We extracted laboratory experimental data from 300 peer-reviewed scientific publications (1990-2015) describing 1127 experimental case studies of human and animal cells response to RF-EMF. We used domain knowledge, Principal Component Analysis (PCA), and the Chi-squared feature selection techniques to select six optimal features for computation and cost-efficiency. We then develop grouping or clustering strategies to allocate these selected features into five different laboratory experiment scenarios. The dataset has been tested with ten different classifiers, and the outputs are estimated using the k-fold cross-validation method. The assessment of a classifier's prediction performance is critical for assessing its suitability. Hence, a detailed comparison of the percentage of the model accuracy (PCC), Root Mean Squared Error (RMSE), precision, sensitivity (recall), 1 - specificity, Area under the ROC Curve (AUC), and precision-recall (PRC Area) for each classification method were observed. Our findings suggest that the Random Forest algorithm exceeds in all groups in terms of all performance measures and shows AUC = 0.903 where k-fold = 60. A robust correlation was observed in the specific absorption rate (SAR) with frequency and cumulative effect or exposure time with SAR×time (impact of accumulated SAR within the exposure time) of RF-EMF. In contrast, the relationship between frequency and exposure time was not significant. In future, with more experimental data, the sample size can be increased, leading to more accurate work.

Predicting the mean first passage time (MFPT) to reach any state for a passive dynamic walker with steady state variability.

Idealized passive dynamic walkers (PDW) exhibit limit cycle stability at steady state. Yet in reality, uncertainty in ground interaction forces result in variability in limit cycles even for a simple walker known as the Rimless Wheel (RW) on seemingly even slopes. This class of walkers is called metastable walkers in that they usually walk in a stable limit cycle, though guaranteed to eventually fail. Thus, control action is only needed if a failure state (i.e. RW stopping down the ramp) is imminent. Therefore, efficiency of estimating the time to reach a failure state is key to develop a minimal intervention controller to inject just enough energy to overcome a failure state when required. Current methods use what is known as a Mean First Passage Time (MFPT) from current state (rotary speed of RW at the most recent leg collision) to an arbitrary state deemed to be a failure in the future. The frequently used Markov chain based MFPT prediction requires an absorbing state, which in this case is a collision where the RW comes to a stop without an escape. Here, we propose a novel method to estimate an MFPT from current state to an arbitrary state which is not necessarily an absorbing state. This provides freedom to a controller to adaptively take action when deemed necessary. We demonstrate the proposed MFPT predictions in a minimal intervention controller for a RW. Our results show that the proposed method is useful in controllers for walkers showing up to 44.1% increase of time-to-fail compared to a PID based closed-loop controller.

Radio Hazard Safety Assessment for Marine Ship Transmitters: Measurements Using a New Data Collection Method and Comparison with ICNIRP and ARPANSA Limits.

We investigated the levels of radio frequency electromagnetic fields (RF EMFs) emitted from marine ship transmitters. In this study, we recorded the radio frequency (RF) electric field (EF) levels emitted from transmitters from a marine vessel focusing on the areas normally occupied by crew members and passengers. Previous studies considered radiation hazard safety assessment for marine vessels with a limited number of transmitters, such as very high-frequency (VHF) transceivers, radar and communication transmitters. In our investigation, EF levels from seven radio transmitters were measured, including: VHF, medium frequency/high frequency (MF/HF), satellite communication (Sat-Com C), AISnavigation, radar X-band and radar S-band. Measurements were carried out in a 40 m-long, three-level ship (upper deck, bridge deck and bridge roof) at 12 different locations. We developed a new data-collection protocol and performed it under 11 different scenarios to observe and measure the radiation emissions from all of the transmitters. In total, 528 EF field measurements were collected and averaged over all three levels of the marine ship with RF transmitters: the measured electric fields were the lowest on the upper deck (0.82-0.86 V/m), the highest on the bridge roof (2.15-3.70 V/m) and in between on the bridge deck (0.47-1.15 V/m). The measured EF levels were then assessed for compliance with the occupational and general public reference levels of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines and the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) standards. The ICNIRP and the ARPANSA limits for the general public were exceeded on the bridge roof; nevertheless, the occupational limits were respected everywhere. The measured EF levels, hence, complied with the ICNIRP guidelines and the ARPANSA standards. In this paper, we provide a new data collection model for future surveys, which could be conducted with larger samples to verify our observations. Furthermore, this new method could be useful as a reference for researchers and industry professionals without direct access to the necessary equipment.

Characterization of extremely low frequency magnetic fields from diesel, gasoline and hybrid cars under controlled conditions.

This study characterizes extremely low frequency (ELF) magnetic field (MF) levels in 10 car models. Extensive measurements were conducted in three diesel, four gasoline, and three hybrid cars, under similar controlled conditions and negligible background fields. Averaged over all four seats under various driving scenarios the fields were lowest in diesel cars (0.02 µT), higher for gasoline (0.04-0.05 µT) and highest in hybrids (0.06-0.09 µT), but all were in-line with daily exposures from other sources. Hybrid cars had the highest mean and 95th percentile MF levels, and an especially large percentage of measurements above 0.2 µT. These parameters were also higher for moving conditions compared to standing while idling or revving at 2500 RPM and higher still at 80 km/h compared to 40 km/h. Fields in non-hybrid cars were higher at the front seats, while in hybrid cars they were higher at the back seats, particularly the back right seat where 16%-69% of measurements were greater than 0.2 µT. As our results do not include low frequency fields (below 30 Hz) that might be generated by tire rotation, we suggest that net currents flowing through the cars' metallic chassis may be a possible source of MF. Larger surveys in standardized and well-described settings should be conducted with different types of vehicles and with spectral analysis of fields including lower frequencies due to magnetization of tires.

Pineal melatonin level disruption in humans due to electromagnetic fields and ICNIRP limits.

The International Agency for Research on Cancer (IARC) classifies electromagnetic fields (EMFs) as 'possibly carcinogenic' to humans that might transform normal cells into cancer cells. Owing to high utilisation of electricity in day-to-day life, exposure to power-frequency (50 or 60 Hz) EMFs is unavoidable. Melatonin is a natural hormone produced by pineal gland activity in the brain that regulates the body's sleep-wake cycle. How man-made EMFs may influence the pineal gland is still unsolved. The pineal gland is likely to sense EMFs as light but, as a consequence, may decrease the melatonin production. In this study, more than one hundred experimental data of human and animal studies of changes in melatonin levels due to power-frequency electric and magnetic fields exposure were analysed. Then, the results of this study were compared with the International Committee of Non-Ionizing Radiation Protection (ICNIRP) limit and also with the existing experimental results in the literature for the biological effect of magnetic fields, in order to quantify the effects. The results show that this comparison does not seem to be consistent despite the fact that it offers an advantage of drawing attention to the importance of the exposure limits to weak EMFs. In addition to those inconsistent results, the following were also observedfrom this work: (i) the ICNIRP recommendations are meant for the well-known acute effects, because effects of the exposure duration cannot be considered and (ii) the significance of not replicating the existing experimental studies is another limitation in the power-frequency EMFs. Regardless of these issues, the above observation agrees with our earlier study in which it was confirmed that it is not a reliable method to characterise biological effects by observing only the ratio of AC magnetic field strength to frequency. This is because exposure duration does not include the ICNIRP limit. Furthermore, the results show the significance of disruption of melatonin due to exposure to weak EMFs, which may possibly lead to long-term health effects in humans.

An ab-initio computational method to determine dielectric properties of biological materials.

Frequency dependent dielectric properties are important for understanding the structure and dynamics of biological materials. These properties can be used to study underlying biological processes such as changes in the concentration of biological materials, and the formation of chemical species. Computer simulations can be used to determine dielectric properties and atomic details inaccessible via experimental methods. In this paper, a unified theory utilizing molecular dynamics and density functional theory is presented that is able to determine the frequency dependent dielectric properties of biological materials in an aqueous solution from their molecular structure alone. The proposed method, which uses reaction field approximations, does not require a prior knowledge of the static dielectric constant of the material. The dielectric properties obtained from our method agree well with experimental values presented in the literature.

Measurement and analysis of electromagnetic fields from trams, trains and hybrid cars.

Electricity is used substantially and sources of electric and magnetic fields are, unavoidably, everywhere. The transportation system is a source of these fields, to which a large proportion of the population is exposed. Hence, investigation of the effects of long-term exposure of the general public to low-frequency electromagnetic fields caused by the transportation system is critically important. In this study, measurements of electric and magnetic fields emitted from Australian trams, trains and hybrid cars were investigated. These measurements were carried out under different conditions, locations, and are summarised in this article. A few of the measured electric and magnetic field strengths were significantly lower than those found in prior studies. These results seem to be compatible with the evidence of the laboratory studies on the biological effects that are found in the literature, although they are far lower than international levels, such as those set up in the International Commission on Non-Ionising Radiation Protection guidelines.