Smoke Signals Research Articles

Design of an Intelligent Alarm System Based on Multi-sensor Data Fusion

The fire alarm system plays a very important role in life, but the system has problems such as false alarms and false alarms. Therefore, this paper proposes the application of fire detection based on GA-BP neural network. Firstly, the algorithm takes temperature, smoke concentration and CO concentration as the input of BP neural network, and the output is whether there is fire or not. Secondly, it combines the characteristics of genetic algorithm with strong global search ability and strong robustness. The algorithm has achieved 100% correct classification on the test set through simulation experiments. At the same time, the absolute error of the sample prediction is only 0.006, which proves that it has strong robustness, reliability and generalization ability. Finally, the model was transplanted to STM32 to prove its feasibility. This method provides a new method for intelligent identification of fire signals for early warning of fires and accurate identification of non-fire signals.

A Study on the Real-time Fire Classification Algorithm Based on Stacked LSTM

Several researchers have been drawn to the development of fire detector in recent years, to protect people and property from the catastrophic disaster of fire. However, studies related to fire monitoring are affected by some unique characteristics of fire sensor signals, such as time dependence and the complexity of the signal pattern based on the variety of fire types,. In this study, a new deep learning-based approach that accurately classifies various types of fire situations in real-time using data obtained from multidimensional channel fire sensor signals was proposed. The contribution of this study is to develop a stacked-LSTM model that considers the time-series characteristics of sensor data and the complexity of multidimensional channel sensing data to develop a new fire monitoring framework for fire identification based on improving existing fire detectors.

Smoke signals: A study of the neurophysiological reaction of smokers and non-smokers to smoking cues inserted into antismoking public service announcements

Tobacco addiction is one of the biggest health emergencies in the world, Antismoking Public Service Announcements (PSAs) represent the main public tool against smoking; however, smoking-related cues (SCs) often included in PSAs can trigger ambiguous cerebral reactions that could impact the persuasiveness and efficacy of the antismoking message.This study aimed to investigate the electroencephalographic (EEG) response in adult smokers and non-smokers during the exposure to SCs presented in antismoking PSAs video, in order to identify eventual neurophysiological features of SCs' ‘boomerang effect’ elicited in smokers.EEG frontal Alpha asymmetry and frontal Theta were analyzed in 92 adults (30 no smokers, 31 low smokers, 31 high smokers) from EEG recorded during the vision of 3 antismoking PSAs, statistical analysis was conducted using ANOVA.Main results showed a significant interaction between smoking cue condition (Pre and Post) and smoking habit (in particular for female heavy smokers) for the frontal Alpha asymmetry. Since the relative higher right frontal Alpha activity is associated with approach towards a stimulus, it is suggested that the relative left frontal Alpha increase in response to SCs might reflect an appetitive approach in response to it. In the light of the Incentive Sensitization Theory, this pattern can be interpreted as a neurophysiological signal in response to SCs that could undermine the message's effectiveness contributing to the maintenance of the addiction.

Estimation of fire-induced carbon emissions from Equatorial Asia in 2015 using in situ aircraft and ship observations

Abstract. Inverse analysis was used to estimate fire carbon emissions in Equatorial Asia induced by the big El Niño event in 2015. This inverse analysis is unique because it extensively used high-precision atmospheric mole fraction data of carbon dioxide (CO2) from the commercial aircraft observation project CONTRAIL. Through comparisons with independent shipboard observations, especially carbon monoxide (CO) data, the validity of the estimated fire-induced carbon emissions was demonstrated. The best estimate, which used both aircraft and shipboard CO2 observations, indicated 273 Tg C for fire emissions from September–October 2015. This 2-month period accounts for 75 % of the annual total fire emissions and 45 % of the annual total net carbon flux within the region, indicating that fire emissions are a dominant driving force of interannual variations of carbon fluxes in Equatorial Asia. Several sensitivity experiments demonstrated that aircraft observations could measure fire signals, though they showed a certain degree of sensitivity to prior fire-emission data. The inversions coherently estimated smaller fire emissions than the prior data, partially because of the small contribution of peatland fires indicated by enhancement ratios of CO and CO2 observed by the ship. In future warmer climate conditions, Equatorial Asia may experience more severe droughts, which risks releasing a large amount of carbon into the atmosphere. Therefore, the continuation of aircraft and shipboard observations is fruitful for reliable monitoring of carbon fluxes in Equatorial Asia.

The Smoke Signals Radio Show Archive Project

In this multimedia report we introduce our ongoing collaborative archival project to organize, describe, and provide access to digitized audio files from the long-running Indigenous radio broadcast called Smoke Signals, produced and hosted by Indigenous activists, community leaders, educators, and Elders Dan Smoke and Mary Lou Smoke.

Too many or not enough? A methodology for evaluating fire and gas detector layouts at LNG facilities

AbstractNFPA 59A Standard for the Production, Storage, and Handling of Liquefied Natural Gas (LNG) requires facilities to have the equipment necessary for the detection and control of fires, leaks and spills of hazardous materials, yet provides no requirements or guidance on the location of the detectors. Literature available in the public domain outlines key factors to consider in developing layouts for flame and gas detectors, but a methodology to use this information in evaluating a hazard detector layout is not clearly defined; instead, generic and non‐quantifiable terminology such as “quick and reliable” is often used. As a result, there is no consistent approach to developing flame and gas detector layouts for land‐based LNG facilities and there is no systematic method for regulators to evaluate these designs. The ability to quantify hazard detector performance and define performance targets are of particular interest to the Department of Transportation (DOT) Pipeline and Hazardous Materials Safety Administration (PHMSA), which regulates numerous LNG facilities in the United States. Blue Engineering and Consulting is working on a DOT‐PHMSA sponsored project to develop a risk‐based approach and criteria for hazard detector layouts at LNG facilities. This project builds upon performance‐based design principles outlined in NFPA 72 National Fire Alarm and Signaling Code and the International Society of Automation (ISA) technical report 84.00.07 Guidance on the Evaluation of Fire, Combustible Gas, and Toxic Gas System Effectiveness. The methodology divides the LNG facility into Detection Areas based on the hazards present and the plant layout, identifies the appropriate hazard scenarios to evaluate the detector layout, establishes performance targets for each Detection Area, and quantifies hazard detector coverage. This paper will describe and demonstrate the proposed methodology. It should be noted that, while performance targets must be chosen for the purposes of the demonstrative included in this paper, these targets should not be interpreted as requirements of DOT‐PHMSA nor as acceptable to them.

Ultralow Power Sensor Package for Early Warning of Wildland Fires

Introduction: The wildfires in California and throughout the western USA have been devastating, especially over the past 3 years, causing loss of lives, incredible property losses, and the bankruptcy of a giant utility [Pacific Gas and Electric] with over $50B of debt. And it is not just the USA that suffers from such fiery disasters but there are devastating fires throughout Australia, Siberia, and around the world. The premise is that early detection could provide avenues for emergency response and the limitation of lost lives and property damage. Current methods to detect these fires ranges from deployment of local forest rangers, satellite optical and thermal IR imaging, some local monitoring, and public education. But none of these are adequate as satellites may not have resolution required for small startup fires and can be obscured by weather. Since fires produce emissions that can provide an atmospheric signature including gases and particles as well as thermal plumes, the hypothesis is that detection of these “fire” signatures with sensitivity could provide early warning if a low-cost distributed network of sensors is deployed.The case can be made for sensing atmospheric emissions in that Carbon Monoxide is produced in high concentrations especially in smoldering fires and has been used for early warning of fires in coal mines for half a century or more. Figure 1 illustrates the use of CO to map the impact of 2018 fires in California through IR imaging through 18000 feet of the atmosphere1 at 200 ppb and lower concentrations. Clearly CO is related to the fire activity but how early can a fire be sensed? What confounding variables will be present such as T, P, RH, and wind velocity or local activities and the natural variability of the chemical composition of the air at low levels? Are CO or particles the best emission parameters to monitor or are better variables available? The answers to these questions are key to the design and deployment of an early warning system based on measurement of atmospheric conditions and atmospheric fire signature emissions! Methods The approach has two objectives in this early research: 1] to understand the emission signatures from fires as recorded by low cost environmental sensors, and 2] to evaluate the feasibility of a practical early warning fire detector based on emission signatures in addition to changes in T, RH, and wind direction. Clearly, we know CO2 (~1500g/kg forest fuel) and CO (~100g/kg) are the largest gaseous emissions by >2 orders of magnitude, but other variables include particles (~20-50g/kg), as well as gases hazardous at very low concentrations like O3, NO2, SO2 and other gases such as methane, CH2O and other NMVOCs2 that could come from different sources.A monitoring system was designed made from low cost COTS [commercially off the shelf] sensors and electronics that could be used to gather field data. The system is complete with sensor hardware, communication alternatives, and computation capability. SPEC sensors3 and KWJ4 electronic hardware and algorithms were selected to partner with commercial CO2, particulate and environmental [T, P, RH] sensors5 in order to build a system that could address Objective 1. Evaluation of actual field data will then be used to achieve Objective 2 as it is clear that a deployed sensor system must have low cost, near zero power, and long-term stability as well as be free from interfering signals. Results The construction of the prototype integrated monitoring and analysis system node [at left] was completed and included SPEC Sensors for CO, NO2, O3; Alphasense SO2 sensor, Sensirion SPD30 (NDIR) CO2 and SPS30 particulate sensor as well as Wind Speed and Direction, a 4.4 A∙h LiPo Battery, two 3.4 W adjustable solar panels, and USB Connection for power/communications.Data from a deployed SPEC printed CO sensor - taken by a third party (SCAQMD) - is illustrated at right. The SPEC CO sensor was only calibrated once at the start of the deployment and the data from it are plotted vs a co-located EPA reference instrument. The agreement over months supports the hypothesis that an ultra-low-cost screen-printed electrochemical sensor can possess the stability that will be needed in a practical application scenario. There is significant agreement over 0-2000ppb between a frequently calibrated instrument with 100 ppb resolution reading 1/hr and the hourly average of a tiny low-cost real-time sensor! Concluding Remarks: The systems and sensor and the application data will be discussed further in the presentation. The data supports the possibilities for complete systems and remote nodes that can both detect wildfires and then better understand wildfire emissions. Early fire warnings though emission monitoring will become one of the methods contributing to the protection of lives, property and health. Study of the application and improving sensor systems is ongoing.

Comparison of Cortical Autonomic Network-Linked Sympathetic Excitation by Mueller Maneuvers and Breath-Holds in Subjects With and Without Obstructive Sleep Apnea.

In healthy young volunteers, acquisition of blood oxygen level-dependent (BOLD) magnetic resonance (MR) and muscle sympathetic nerve (MSNA) signals during simulation of obstructive or central sleep apnea identified cortical cardiovascular autonomic regions in which the BOLD signal changed synchronously with acute noradrenergic excitation. In the present work, we tested the hypothesis that such Mueller maneuvers (MM) and breath-holds (BH) would elicit greater concomitant changes in mean efferent nerve firing and BOLD signal intensity in patients with moderate to severe obstructive sleep apnea (OSA) relative to age- and sex-matched individuals with no or only mild OSA (Apnea Hypopnea Index, AHI, <15 events/h). Forty-six participants, 24 with OSA [59 ± 8 years; AHI 31 ± 18 events/h (mean ± SD); seven women] and 22 without (58 ± 11 years; AHI 7 ± 4; nine women), performed a series of three MM and three BH, in randomly assigned order, twice: during continuous recording of MSNA from the right fibular nerve and, on a separate day, during T2∗-weighted echo planar functional MR imaging. MSNA at rest was greater in those with OSA (65 ± 19 vs. 48 ± 17 bursts per 100 heart beats; p < 0.01). MM and BH elicited similar heart rate, blood pressure, and MSNA responses in the two cohorts; group mean BOLD data were concordant, detecting no between-group differences in cortical autonomic region signal activities. The present findings do not support the concept that recurring episodes of cyclical apnea during sleep alter cortical or peripheral neural responsiveness to their simulation during wakefulness by volitional Mueller maneuvers or breath-holds.

Research on the Design of Fire Alarm and Pre-treatment Robot System

With the development of society and economy, the application of fire warning robots based on sensors and artificial intelligence technology is a new research field. This paper proposes a design scheme for fire warning and pre-processing robots. The robot is based on visual tracking instead of human Uninterrupted patrol of space sites; comprehensive analysis of environmental data collected by sensors based on fuzzy theory algorithms, and issuing fire warning and alarm signals; analysis of infrared images of electrical equipment based on machine vision to monitor operating conditions; construction of fire site maps based on slam algorithm through laser radar. The fire early warning and pre-processing robot designed in this paper realizes the functions of autonomous inspection, fire early warning, fire detection, fire extinguishing, and obstacle removal. It makes full use of the golden extinguishing time to kill the fire and protect people’s lives and property.

The role of climate and disturbance regimes upon temperate rainforests during the Holocene: A stratigraphic perspective from Lago Fonk (∼40°S), northwestern Patagonia

Climate and disturbance regimes play key roles in shaping the structure, composition and functioning of terrestrial ecosystems. Despite this importance, very few stratigraphic studies in the temperate rainforests from northwestern Patagonia have explored this relationship in detail along a time continuum through the entire Holocene. Here we present a high-resolution fossil pollen and charcoal record from Lago Fonk (median resolution: 20 years), a small closed-basin lake in the lowlands of the Chilean Lake District (41°S), where wildfires and explosive volcanism have intermittently taken place during the Holocene, along with pronounced human-induced disturbance in post-colonial time. Our results show persistence of temperate rainforest throughout the Holocene, with changes in the composition and structure of Valdivian rainforests (VRF) at millennial timescales. We detect centennial-scale alternations in dominance between the VRF tree Eucryphia/Caldcluvia and generalist trees found in VRF and North Patagonian rainforests after ∼6.5 cal ka BP. Intervals dominated by VRF coincide with enhanced fire occurrence signaling negative hydroclimate anomalies with a mean duration of ∼150 years, which alternate with positive hydroclimate anomalies lasting ∼312 years on average.Our results suggest that the magnitude and rapidity of vegetation changes detected at 10.2–9.9, 4.0–3.0, ∼1.0, and ∼0.7 cal ka BP were amplified by disturbance regimes, and led to the establishment and maintenance of Eucryphia/Caldcluvia-dominated forests in the Longitudinal Valley of the Chilean Lake District. On several occasions the higher incidence of fire disturbance during warm/dry climate intervals coincided with episodes of heightened explosive volcanic activity from multiple eruptive centers within the Southern Andean Volcanic Zone.

Involvement of NMDA and GABA(A) receptors in modulation of spontaneous activity in hippocampal culture: Interrelations between burst firing and intracellular calcium signal

Spontaneous burst firing is a hallmark attributed to the neuronal network activity. It is known to be accompanied by intracellular calcium [Са2+]i oscillations within the bursting neurons. Studying mechanisms underlying regulation of burst firing is highly relevant, since impairment in neuronal bursting accompanies different neurological disorders. In the present study, the contribution of NMDA and GABA(A) receptors to the shape formation of spontaneous burst -was studied in cultured hippocampal neurons. A combination of inhibitory analysis with simultaneous registration of neuronal bursting by whole-cell patch clamp and calcium imaging was used to assess spontaneous burst firing and [Са2+]i level.Using bicuculline and D-AP5 we showed that GABA(A) and NMDA receptors effectively modulate burst plateau phase and [Са2+]i transient spike which can further affect action potential (AP) amplitudes and firing frequency within a burst. Bicuculline significantly elevated the amplitude and reduced the duration of both burst plateau phase and [Са2+]i spike resulting in an increase of AP firing frequency and shortening of AP amplitudes within a burst. D-AP5 significantly decreases the amplitude of both plateau phase and [Са2+]i spike along with a burst duration that correlated with an increase in AP amplitudes and reduced firing frequency within a burst. The effect of bicuculline was occluded by co-addition of D-AP5 revealing modulatory role of GABA(A) receptors to the NMDA receptor-mediated formation of the burst.Our results provide new evidence on importance of NMDA and GABA(A) receptors in shaping burst firing and Ca2+transient spikes in cultured hippocampal neurons.

Delivery of Metals and Dissolved Black Carbon to the Southern California Coastal Ocean via Aerosols and Floodwaters Following the 2017 Thomas Fire

AbstractThe Thomas Fire began on December 4, 2017 and burned 281,893 acres over a 40‐day period in Ventura and Santa Barbara Counties, making it one of California's most destructive wildfires to date. A major rainstorm then caused a flash flood event, which led to the containment of the fire. Both airborne ash from the fire and the runoff from the flash flood entered into the Santa Barbara Basin (SBB). Here, we present the results from aerosol, river, and seawater studies of black carbon and metal delivery to the SBB associated with the fire and subsequent flash flood. On day 11 of the Thomas Fire, aerosols sampled under the smoke plume were associated with high levels of PM2.5, levoglucosan, and black carbon (average: 49 μg/m3, 1.05 μg/m3, and 14.93 μg/m3, respectively) and aerosol metal concentrations were consistent with a forest fire signature. Metal concentrations in SBB surface seawater were higher closer to the coastal perimeter of the fire (including 2.22 nM Fe) than further off the coast, suggesting a dependence on continental proximity rather than fire inputs. On days 37–40 of the fire, before, during, and after the flash flood in the Ventura River, dissolved organic carbon, dissolved black carbon, and dissolved metal concentrations were positively correlated with discharge allowing us to estimate the input of fire products into the coastal ocean. We estimated rapid aerosol delivery during the fire event to be the larger share of fire‐derived metal transport compared to runoff from the Ventura River during the flood event.

Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects

BackgroundSmoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers.MethodA smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989–1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA).ResultsAltogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results.ConclusionThis study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.

Professor Dr. Thomas M. Klapötke

Professor Dr. Thomas M. Klapötke

Real-Time Fire Detection Algorithm Based on Support Vector Machine with Dynamic Time Warping Kernel Function

In recent years, the development of fire detectors has attracted the attention of researchers for the purpose of protecting human lives and properties from catastrophic fire disasters. However, monitoring fires is challenging due to several unique characteristics of fire sensor signals, such as the existence of temporal dependency and diverse signal patterns for different fire types, including flaming, heating, and smoldering fires. In this study, we propose a new approach for fire detection based on machine learning and optimization techniques, to monitor various types of fire by utilizing information obtained via multichannel fire sensor signals. The contribution of this study is to improve an existing fire detector by developing a new fire monitoring framework to identify fire based on support vector machine with dynamic time warping kernel function (SVM-DTWK), which considers the temporal dynamics existing in the sensor signals of different fire types. In addition, multichannel sensor signals are further considered by the SVM-DTWK with a multi-modeling framework that constructs multiple classifiers for each sensor type and effectively utilizes sensor information that is critical for the detection of fires without prior knowledge of the fire type. Using real-life fire data, the proposed approach is compared with existing fire monitoring methods and achieves superior performance in terms of both fire detection time and false alarm rate.

Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

Farmer's behaviors toward pesticides use: insight from a field study in Oriental Morocco.

Pesticides are a vital component of modern agriculture. So, these chemical compounds could play a crucial role in maintaining high agricultural productivity. However, high-input intensive agricultural production systems in which more effective and widespread use of pesticides could harm human health and the environment. As we know, most of the farmers in developing countries used pesticides without taking the necessary precautions. The present study aims to assess farmers’ knowledge and understand the pattern of use of pesticides in Oriental Morocco. We based our approach on a standardized questionnaire with 140 farmers to evaluate their knowledge, attitude, and awareness about the pictograms used on pesticide labels. The results show that the farmer had a low level of education; only 14% were in university. More than 65% overuse pesticides and 60% did not respect the pre-harvest interval. Many farmers did not know the standard pictograms used on pesticide labels, 90% acknowledged only the crossbones and the fire sign, and 33% gave false interpretation or misunderstood these pictograms. These results show that the farmers in Eastern Morocco seem to be ignorant of pesticide danger and lack of safety education. They have no idea how to deal with the pictogram on the pesticide label. Pesticide safety education is necessary to induce protective behavior among farmers and prevent the residue of pesticides in foodstuff. The comprehension of pictograms on pesticide labels is crucial to reduce the risk of poisoning.

Full-Scale Video-Based Detection of Smoke from Forest Fires Combining ViBe and MSER Algorithms

Smoke, as a prominent character of combustion, is widely regarded as a signal of forest fire. Existing in a video-based smoke root detection methods on rely the distance between smoke and the lens, which is one of the most challenging parts. In relatively close distances, the dynamic region extraction method not only presents simplicity but also provides clear outlines and shapes, which is good for the smoke root extraction. However, when the distance increases, the advantage of this algorithm decreases and the rate of leak detection rises. To solve this challenge, this study developed a new algorithm which adopts Bayesian theory to combine the ViBe algorithm with the MSER algorithm. The likelihood functions are replaced by a small database, storing the descriptors of each frame and being updated in real time. The experiments demonstrate that the new method produces more complete shapes of candidate smoke root regions and lowers leak detection rate in full scale, compared with the ViBe results and MSER results, respectively. These improvements suggest that it can detect smoke in the forest more accurately.

Smoke signals - do they help to predict TAVI-associated stroke risk?

Smoke signals - do they help to predict TAVI-associated stroke risk?

Some Aspects of Modern Drones

Today drones have become so important that everyday life can no longer be conceived without them. The paper wishes to present some essential aspects regarding modern drones, their capabilities and their uses. Drones and minnies drones have penetrated deep into technology, industry, agriculture, the military and police area, security services, security and control, aerial surveillance, signaling, aerial filming, cartography, danger and fire signaling in a timely manner, surveillance missions, security, defense and attack. The major advantages of drones compared to conventional aircraft are the following: A much lower fuel consumption and a much lower polarization of the environment, sometimes the pole does not even exist the drones being electrically moved; much lower design, manufacturing, maintenance and operation prices. They do not require pilots, they are generally small and the manufacturing costs and materials are much reduced; in case of collisions they are very resistant, accidents with them being rare and without tragic consequences; they can operate various things without a pilot, without costs, without risks, with superior results to those of classic aviation. A simple way to reduce the consumption of raw materials needed by industries is the very economy of materials used in industries and the start must be made even with heavy industries and car manufacturers and primarily those in aviation and aerospace. With drones, the consumption of materials is extraordinary compared to the materials stored in old, classic, huge ships. Of course, large public aircraft are still needed for public passenger transport, which, no matter how redesigned, will still consume a lot of materials and energy, but for other daily air operations, drones will be able to successfully intervene in place of large and heavy ships. These areas will see huge savings in materials, not to mention the very low costs of people who will be replaced by robots, vending machines, cameras, low fuel consumption and the reduction and even elimination of massive pollution that was done by a massive use of large aircraft for any minor operations requiring flights. The drones come to usefully and efficiently complete large and important areas of flight, difficult and meticulous operations, the drones clearly having increased capabilities and much higher penetration.