Thermal Imaging – Theory and Applications

What is Thermal Imagery

We know that our eyes see reflected light, so it is easy for us to understand the principle of forming the image from Visual (daylight and night vision cameras). But if there is not enough light it is impossible for us or the camera to see. This is not the case in the thermal imagery domain. Thermal cameras measure temperature and emissivity of objects in the scene. In the thermal infrared technologies, most of the captured radiation is emitted from the observed objects, in contrast to visual and near infrared, where most of the radiation is reflected. Thus, knowing or assuming material and environmental properties, temperatures can be measured using a thermal camera (i.e., the camera is said to be radiometric).  But, let’s not forget: “Thermal cameras detect more than just heat though; they detect tiny differences in heat – as small as 0.01°C – and display them as shades of grey or with different colors.” [1]

Thermal image is different from visual camera image and cannot be treated as a grayscale visual image. In thermal infrared there are no shadows, and noise characteristics are different then in visual tracking. There are also no color patterns like in visual domain, but patterns come out from variations in material or temperature of objects.

The infrared wavelength band is usually divided into different sub-bands, according to their different properties: near infrared (NIR, wavelengths 0.7–1 µm), shortwave infrared (SWIR, 1–3 µm), midwave infrared (MWIR, 3–5 µm), and longwave infrared (LWIR, 7.5–12 µm). These bands are separated by regions where the atmospheric transmission is very low (i.e., the air is opaque) or where sensor technologies have their limits. LWIR, and sometimes MWIR, is commonly referred to as thermal infrared (TIR). TIR cameras should not be confused with NIR cameras that are dependent on illumination and in general behave in a similar way as visual cameras. Thermal cameras are either cooled or uncooled. Images are typically stored as 16 bits per pixel to allow a large dynamic range. Uncooled cameras give noisier images at a lower frame rate, but are smaller, silent, and less expensive. [2,3] 

Image source: from LTIR dataset


1. What is the biggest difference between a high and low cost thermal camera?

   The biggest difference is typically resolution. The higher the resolution, the better the picture clarity. This translates to a better picture at a greater distance as well, similar to the megapixels of a regular digital camera.

2. Can thermal imaging cameras see through objects?

   No. Thermal imaging cameras only detect heat; they will not “see” through solid objects, clothing, brick walls, etc. They see the heat coming off the surface of the object.

3. Is there a difference between night vision and thermal imaging?

    Yes. Night vision relies on at least a very low level of light (less than the human eye can detect) in order to amplify it so that it can produce a picture. Night vision will not work in complete darkness whereas thermal imaging will  

    because it only “sees” heat.

4. Can rain and heavy fog limit the range of thermal imaging cameras?

    Yes. Rain and heavy fog can severely limit the range of thermal imaging cameras because light scatters off of droplets of water.


Image source:


Applications of  thermal vision are numerous, in civil as well as in military sector, but here we will focus on applications in civil sector that can be of help in every day life. So, this technology can be used to observe and analyze human activities from a distance in a noninvasive manner, for example. Traditional computer vision utilizes RGB cameras, but problems with this sensor include its light dependency. Thermal cameras operate independently of light and measure the radiated infrared waves representing the temperature of the scene. In order to showcase the possibilities, both indoor and outdoor scenarios applications which use thermal imaging only are presented.

Image source:

Surveillance: People counting in urban environments

Human movement can be automatically registered and analyzed. For both real-time and long-term perspectives, this knowledge can be beneficial in relation to urban planning and for shopkeepers in the city. Information in real-time can be used for analyzing the current flow and occupancy of the city, while long-term analysis can reveal trends and patterns related to specific days, time or events in the city.

Security: Analyzing the use of sports arenas

The interest in analyzing and optimizing the use of public facilities in cities has a large variety of applications in both indoor and outdoor spaces. Here, the focus is on sports arenas, but other possible applications could be libraries, museums, shopping malls, etc. The aim is to estimate the occupancy of sports arenas in terms of the number of people and their positions in real time. Potential use of this information is both online booking systems, and post-processing of data for analyzing the general use of the facilities. For the purpose of analyzing the use of the facilities, we also try to estimate the type of sport observed based on people’s positions.

In indoor spaces, the temperature is often kept constant and cooler than the human temperature. Foreground segmentation can therefore be accomplished by automatic thresholding the image. In some cases, unwanted hot objects, such as hot water pipes and heaters, can appear in the scene. In these situations, background subtraction can be utilized.

Health and safety: Gas leaking location and event alert

Some public buildings of interest can be monitored with thermal cameras, while gas or water leakage can be discovered before a hazardous situation happens.

Localizing a suspected leak in a building can turn to be delicate, sometimes requiring stopping the operations, if not probe walls or floors. Whatever the mix of construction materials, thermal imaging can be the right answer: in most cases, a leakage translates into an abnormal temperature pattern. Thermal imaging is de facto a non-contact operation, increasing inspector safety, capable of visualizing fluid leakage as well as electrical dysfunction. Thermal imaging can of course also detect thermal bridges and, as such, is a key tool to generate property investigation report.

Water leakage can be both hot and cold, and thermal imagers can catch them both. It can sometimes be close to impossible to spot a water leak on your own, especially when they are behind walls. That is why thermal cameras prevent dangerous situations.

Traffic control: Traffic monitoring and specific event alert

As for monitoring heterogenous traffic, thermal imaging can be a precious camera type reducing overall system costs and increasing reliability. On contrary to Visible and NIR-based detectors, LWIR cameras are not affected by the lighting conditions of the scene: e.g. night vs day, and sun orientation. This remains true over long distances, enabling the detection of a child, a biker, a car or a truck. Once coupled with relevant processing, LWIR cameras turn to be a key asset of ITS, reducing the number of cameras while increasing alarms reliability. This helps the manager on duty to take quickly the right decision in case of e.g. obstacle detection, reverse direction vehicle, abnormal traffic jam, etc. to ensure road-users security as well as optimal commuting time.

Energy saving: Building occupancy

Monitoring building occupancy turns to be highly relevant for management of commercial complex or public infrastructure: optimal adjustment of energy supply, scheduling of maintenance services, as well as comfort and health of occupants.
It is also useful for sizing security services, and of crucial importance in case of event requiring building evacuation. Advanced solutions, relying on thermal sensors, integrate thermal imaging: low resolution detectors (detecting presence / human activity) and/or a high-resolution thermal camera spotting relevant doorways (for people counting / human activity characterization).

This time, our goal was to explain more the science behind thermal cameras and its applications. If there are some additional questions or anything else you would like to know about this topic, feel free to ask via mail or comments.


How can Artificial Intelligence help solve environmental problems like Air Pollution

Air pollution

Air pollution is caused by solid and liquid particles and certain gases that are suspended in the air. These particles and gases can come from car and truck exhaust, factories, dust, pollen, mold spores, volcanoes and wildfires. The solid and liquid particles suspended in our air are called aerosols.

Certain gases in the atmosphere can cause air pollution. For example, in cities, a gas called ozone is a major cause of air pollution. Ozone is also a greenhouse gas that can be both good and bad for our environment. It all depends where it is in Earth’s atmosphere.

Ozone high up in our atmosphere is a good thing. It helps block harmful energy from the Sun, called radiation. But, when ozone is closer to the ground, it can be really bad for our health. Ground level ozone is created when sunlight reacts with certain chemicals that come from sources of burning fossil fuels, such as factories or car exhaust.

When particles in the air combine with ozone, they create smog. Smog is a type of air pollution that looks like smoky fog and makes it difficult to see. (

Polluted city – image source:

The major outdoor pollution sources include vehicles, power generation, building heating systems, agriculture/waste incineration and industry. In addition, more than 3 billion people worldwide rely on polluting technologies and fuels (including biomass, coal and kerosene) for household cooking, heating and lighting, releasing smoke into the home and leaching pollutants outdoors.

Air quality is closely linked to earth’s climate and ecosystems globally. Many of the drivers of air pollution (i.e. combustion of fossil fuels) are also sources of high CO2 emissions. Some air pollutants such as ozone and black carbon are short-lived climate pollutants that greatly contribute to climate change and affect agricultural productivity. Policies to reduce air pollution, therefore, offer a “win-win” strategy for both climate and health, lowering the burden of disease attributable to air pollution, as well as contributing to the near- and long-term mitigation of climate change.

Air pollution can be significantly reduced by expanding access to clean household fuels and technologies, as well as prioritizing: rapid urban transit, walking and cycling networks; energy-efficient buildings and urban design; improved waste management; and electricity production from renewable power sources. (

How does air pollution affect our health?

Breathing in polluted air can be very bad for our health. Long-term exposure to air pollution has been associated with diseases of the heart and lungs, cancers and other health problems. That’s why it’s important for us to monitor air pollution.

Polluted city – image source: WHO

AI might be used to improve urban sustainability and quality of life. It is about time that Artificial Intelligence is used for something important for the whole planet. That is why we will talk about AI solutions that address the problem of air pollution.

Air pollution – AI solutions

Artificial Intelligence for cleaner air in Smart Cities

In Singapore, where air pollution and related health costs are particularly high, a team of researchers investigated the possibility to combine the power of sensor technologies, Internet of things (IoT) and AI to get reliable and valid environmental data and feed bettergreener policy-making. As reported by The Business Times, through the computation of real-time IoT sensor data measuring spatial and temporal pollutants, user-friendly air quality heat maps and executive dashboards can be created, and the most severe pollution hotspots can be determined with the help of machine learning algorithms for predictive modelling. This is the first step to take proactive actions towards further decarbonizing the economy, including incentives for virtuous businesses, the development of wiser land use plans, the revitalization of urban precincts, and more. (

Polluted city – image source:

An Artificial Intelligence-Based Environment Quality Analysis System

The paper describes an environment quality analysis system based on a combination of some artificial intelligence techniquesartificial neural networks and rule-based expert systems. Two case studies of the system use are discussed: air pollution analysis and flood forecasting with their impact on the environment and on the population health. The system can be used by an environmental decision support system in order to manage various environmental critical situations (such as floods and environmental pollution), and to inform the population about the state of the environment quality. (An Artificial Intelligence-Based Environment Quality Analysis System –

AI non-profit to track air pollution from every power plant in the world and make data public

A nonprofit artificial intelligence firm called WattTime is going to use satellite imagery to precisely track the air pollution (including carbon emissions) coming out of every single power plant in the world, in real time. And it’s going to make the data public. This system promises to effectively eliminate poor monitoring and gaming of emissions data.

The plan is to use data from satellites that make theirs publicly available, as well as data from a few private companies that charge for their data. The images will be processed by various algorithms to detect signs of emissions., Google’s philanthropic wing, is getting the project off the ground…with a $1.7 million grant. WattTime made a splash earlier this year with Automated Emissions Reduction. AER is a program that uses real-time grid data and machine learning to determine exactly when the grid is producing the cleanest electricity.

Author: David Roberts, Vox, Published on: 8 May 2019

“We’ll soon know the exact air pollution from every power plant in the world. That’s huge.”, 7 May 2019. (

A fresher breeze: How AI can help improve air quality

As part of our AI for Earth commitment, Microsoft supports five projects from Germany in the areas of environmental protection, biodiversity and sustainability. In the next few weeks, we will introduce the project teams and their innovative ideas that made the leap into our global programme and group of AI for Earth grantees.

AI for Earth

The AI​​for Earth program helps researchers and organizations to use artificial intelligence to develop new approaches to protect water, agriculture, biodiversity and the climate. Over the next five years, Microsoft will invest $ 50 million in “AI for Earth.” To become part of the “AI for Earth” program, developers, researchers and organizations can apply with their idea for a so-called “Grant”. If you manage to convince the jury of Microsoft representatives, you´ll receive financial and technological support and also benefit from knowledge transfer and contacts within the global AI for Earth network. As part of Microsoft Berlin´s EarthLab and beyond, five ideas have been convincing and will be part of our “AI for Earth” program in the future in order to further promote their environmental innovations. (

Environmental Protection – image source:

Artificial Intelligence For Air Quality Control Systems: A Holistic Approach


Recent environmental regulations introduced by the United States environmental protection agency such as the Mercury Air Toxics Standards and Hazardous Air Pollution Standards have challenged environmental particulate control equipment especially the electro-static precipitators to operate beyond their design specifications. The impact is exacerbated in power plants burning a wide range of low and high-ranking fossil fuels relying on co-benefits from upstream processes such as the selective catalytic reactor and boilers. To alleviate and mitigate the challenge, this manuscript presents the utilization of modern and novel algorithms in machine learning and artificial intelligence for improving the efficiency and performance of electrostatic precipitators reflecting a holistic approach by considering upstream processes as model parameters. In addition, the paper discusses input relevance algorithms for neural networks and random forests such as partial derivatives, input perturbation and GINI importance comparing their performance and applicability for our case study. Our approach comprises of applying random forests and neural network algorithms to an electrostatic precipitator extending the model to include upstream process parameters such as the selective catalytic reactor and the air heaters. To study variable importance differences and model generalization performance between our employed algorithms, we developed a statistical approach to compare features data distributions impact on input relevance.

Read more here:


Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of Delhi, India


•Neural network and fuzzy logic are combined for forecasting of PM2.5 during haze conditions.

•The haze occurs when the level of PM2.5 is more than 50 μg/m3 and relative humidity is less than 90%.

•Neuro-fuzzy model is capable for better forecasting of haze episodes over urbanized area than ANN and MLR models.


Delhi has been listed as the worst performer across the world with respect to the presence of alarmingly high level of haze episodes, exposing the residents here to a host of diseases including respiratory disease, chronic obstructive pulmonary disorder and lung cancer. This study aimed to analyze the haze episodes in a year and to develop the forecasting methodologies for it. The air pollutants, e.g., CO, O3, NO2, SO2, PM2.5 as well as meteorological parameters (pressure, temperature, wind speed, wind direction index, relative humidity, visibility, dew point temperature, etc.) have been used in the present study to analyze the haze episodes in Delhi urban area. The nature of these episodes, their possible causes, and their major features are discussed in terms of fine particulate matter (PM2.5) and relative humidity. The correlation matrix shows that temperature, pressure, wind speed, O3, and dew point temperature are the dominating variables for PM2.5 concentrations in Delhi. The hour-by-hour analysis of past data pattern at different monitoring stations suggest that the haze hours were occurred approximately 48% of the total observed hours in the year, 2012 over Delhi urban area. The haze hour forecasting models in terms of PM2.5 concentrations (more than 50 μg/m3) and relative humidity (less than 90%) have been developed through artificial intelligence based Neuro-Fuzzy (NF) techniques and compared with the other modeling techniques e.g., multiple linear regression (MLR), and artificial neural network (ANN). The haze hour’s data for nine months, i.e. from January to September have been chosen for training and remaining three months, i.e., October to December in the year 2012 are chosen for validation of the developed models. The forecasted results are compared with the observed values with different statistical measures, e.g., correlation coefficients (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA). The performed analysis has indicated that R has values 0.25 for MLR, 0.53 for ANN, and NF: 0.72, between the observed and predicted PM2.5 concentrations during haze hours invalidation period. The results show that the artificial intelligence implementations have a more reasonable agreement with the observed values. Finally, it can be concluded that the most convincing advantage of artificial intelligence based NF model is capable for better forecasting of haze episodes in Delhi urban area than ANN and MLR models.

Read more here:


AI – image source:

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification


In recent years, the application of titanium dioxide (TiO2) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO2 on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO2 solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

Read more here:


Neuro Fuzzy Modeling Scheme for the Prediction of Air Pollution


The techniques of artificial intelligence based in fuzzy logic and neural networks are frequently applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Hybrid of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The structure of the model is based on three-layered neural fuzzy architecture with back propagation learning algorithm. The main objective of this paper is two folds. The first objective is to develop Fuzzy controller, scheme for the prediction of the changing for the NO2 or SO2, over urban zones based on the measurement of NO2 or SO2 over defined industrial sourcesThe second objective is to develop a neural net, NN; scheme for the prediction of O3 based on NO2 and SO2 measurements.

Read more here:


Sensing the Air We Breathe — The OpenSense Zurich Dataset


Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem, introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.

Read more here:


This article is good for getting started and gives a dataset to work with!

Clean air – image source:

Development of artificial intelligence based NO2 forecasting models at Taj Mahal, Agra


The statistical regression and specific computational intelligence based models are presented in this paper for the forecasting of hourly NO2 concentrations at a historical monument Taj Mahal, Agra. The model was developed for the purpose of public health oriented air quality forecasting. Last ten–year air pollution data analysis reveals that the concentration of air pollutants increased significantly. It is also observed that the pollution levels are always higher during the months of November at around Taj Mahal, Agra. Therefore, the hourly observed data during November were used in the development of air quality forecasting models for Agra, India. Firstly, multiple linear regression (MLR) was used for building an air quality–forecasting model to forecast the NO2 concentrations at Agra. Further, a novel approach, based on regression models, principal component analysis (PCA) was analyzed to find the correlations of different predictor variables between meteorology and air pollutants. Then, the significant variables were taken as the input parameters to propose the reliable physical artificial neural network (ANN)-multi layer perceptron model for forecasting of air pollution in Agra. MLR and PCA–ANN models were evaluated through statistical analysis. The correlation coefficients (R) were 0.89 and 0.91 respectively, for PCA–ANN and were 0.69 and 0.89 respectively for MLR in the training and validation periods. Similarly, the values of normalized mean square error (NMSE), index of agreement (IOA) and fractional bias (FB) were in good agreement with the observed values. It was concluded that PCA–ANN model performs better and can be used for forecasting air pollution at Taj Mahal, Agra.

Read more here:


A Novel Air Quality Early-Warning System Based on Artificial Intelligence


The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

Read more here:


Octopus – image source:

How AI and IoT could help people combat air pollution issues

It is with little surprise that the UN’s 2019 World Environment Day Is a call to action to #beatairpollutionIT, as a sector, influences air quality in terms of the energy used to drive our electronics, data centers and, indeed, through business travel. With a large-scale industry presence in Asia, home to some of the most polluted cities in the world, we need to do what we can to minimize these impacts.

But technology can also be part of the solution. Last year, Capgemini announced a new global ambition to leverage technology to help organizations with their sustainability challenges, recognizing that this is the biggest impact we can make. Technology can be an enabler to help address prevention at source, helping organizations optimize their operations and reduce their impact. But with 4.2 million deaths every year as a result of exposure to ambient outdoor air pollution, how can we also leverage technology to monitor, inform, and ultimately change the behaviors of those most affected as they head into our many cities?

The advances in technology give us the opportunity to reach people directly and build a more sophisticated monitoring and communication network. We could leverage both artificial intelligence (AI) and the internet of things (IOT) with the capabilities from an increasing range of personal devices whether it be the 2.5 billion smart phones or the estimated 278 million smart watches in the world.[3] Indeed, the wearable health and fitness technology sector is set to grow 10–20% in the next five years, with an expanding set of capabilities. These devices measure elements such as heart rate, blood pressure, and breathing rate, which are indicators of overall health and are also measurables that change with exposure to air pollutants such as PM, nitrogen oxide and sulfur oxides. Yet they also monitor spatial and GPS data, which if combined could demonstrate the impact of the external environment on health factors, and better inform people of the issues. Data from different sources and AI technology could allow us to drill down on very local issues.

If we overlay current air quality monitoring data sources onto an individual, it would allow us to give a very precise prediction of local air quality issues. We could then integrate AI, to both refine and include a wider range of factors such as weather conditions and traffic levelsAdded to this, if automatic number plate recognition (ANPR) is integrated, we could discern the proportion of vehicle fuel types being used in specific locations. This is important because diesel vehicles emit 90% of particulate matter.

Data analytics over time would allow people to understand impacts on their health – and change behavior.

Over time, as an individual’s health and diagnostics data are inputted into a data analytics model alongside their own spatial data and air pollution exposure data, they could receive an analysis of how air pollution is impacting their physiology. Based on this, they could receive tailored suggested actions to take as wellThe ability to overlay a Google Map of your walk to school or work to the air quality data around you could, instead of highlighting traffic congestion, show air quality issues and provide the options to re-route to avoid, or offer alternative options for time to start a journey.

Read more here:


So, this time we listed some novel AI solutions for solving the environmental air pollution problem. Next time we talk about this topic, expect the idea how we are going to include Smart Imaging and AI in Smart city solution for cleaner air. Do you have any suggestions?


Do you like these innovative ideas and you want to boost your creativity? Check out my story here:

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Doba lažnih stručnjaka

Ovaj blog post bih želela da posvetim jednoj temi za koju mislim da je veoma aktuelna danas, a to je sve veći broj “stručnjaka” iz raznih oblasti poput fitnesa, ishrane, psihologije uspeha, životnih trenera, itd. Svedoci smo, ovih dana, šta nam je ovo doba donelo, sa sve većim brojem lažnih lekara, odnosno nadrilekara koji su unakazili svoje mušterije. U celoj priči se postavlja pitanje, kako je do toga došlo? Kako je moguće da posluju priučeni ljudi koji nemaju dovoljno znanja o oblastima kojima se bave?

Na ovo razmišljanje me je navela i pojava sve većeg broja teoretičara zavera, kao i ljudi koji se predstavljaju kao stručnjaci, a baziraju svoje znanje na zastarelim, opovrgnutnim ili odbačenim naučnim tezama, a nekad i još gore, samo na svom ličnom ili tuđem mišljenju. Kao što sam u jednom članku pročitala da je jako lepo napisano: “to su stručnjaci čije se znanje bazira na tome što su pročitali uputstvo za lek i sada znaju da ga koriste”.

Još jednu rečenicu često čujem, a to je da je ovo doba kada nam ne treba znanje u glavi, nego je sve dostupno na internetu. U tom smislu, razumem, dosta toga i jeste dostupno na internetu, ali je džaba što postoji ako niste svesni da postoji. I da li biste dete odveli kod lekara koji će da počne na internetu da traži moguće bolesti, ili ga ipak vodite kod lekara koji jednim pregledom odmah vidi šta je? Znanje, pa na znanje ide iskustvo i još mnogo znanja i veština koje se u hodu usvajaju.

U tom smislu bih volela sa Vama da podelim neke od načina da proveravate činjenice koje Vam drugi serviraju, da Vam ukažem na to da je opasno verovati onome što se, iz neproverenih izvora, servira na mreži. I to ne samo opasno fizički, kao što je nadrilekarstvo u plastičnoj hirurgiji ili anti age medicini, već i psihički u vidu raznih stručnjaka za život i misli. Opasno je da Vam bilo ko govori o tome na koji način da se hranite, vežbate ili razmišljate, a da iza tog nekog ne stoji ozbiljna škola i sa njom i provera znanja. Ovo je nešto o čemu ću više reći u blog postu: Formalno i neformalno obrazovanje.

Evo su neke od smernica koje ja sledim kada razmatram čije savete da slušam, kod kojeg lekara ću se lečiti, kako da odaberem fitnes trenera ili fizioterapeuta, ili šta god Vam padne na pamet:

  1. PROVERENO OBRAZOVANJE – Osim toga što akreditovane institucije i programi nude znanje, oni ne daju diplome bez prethodne provere usvojenog znanja. Sertifikat bez provere znanja je samo to, potvrda da ste nešto slušali, a koliko ste čuli, to ne možemo znati.
  2. NAUČNE REFERENCE koje se mogu proveriti – Svaki put kada neko tvrdi neku naučnu činjenicu, ili citira nekog naučnika, mora dati referencu. Reference služe tome da se radovi mogu proveriti, pročitati i videti da li su još uvek validne izložene teorije. Naučna zajednica, kakve god mane imala, je i dalje skup ljudi koji se trude da održe kvalitet te zajednice.
  3. OPOVRGNUTE TEORIJE ili stare teorije koje se propagiraju nisu samo zastarele, već dokazano netačne. Dakle, da se nadovežem na prethodnu stavku, neka naučna činjenica može biti izložena u radu iz 1950. godine. Ali, to ne znači da je i danas tačna. Dakle, validne su savremene teorije koje su dokazane. Ako je neka teorija iz 1950. opovrgnuta, ona više ne važi. Ali ako nije, i dalje se mogu koristiti njene pretpostavke i zaključci. Zato i postoje stručnjaci u svojim oblastima koji to prate.
  4. KREDIBILITET se gradi. Nije dovoljno samo čuti iskustva ljudi sa odredjenim programom. Svi mi predstavimo uvek naš najbolji rad. Vidim dosta online programa koji se baziraju na tome da su iskustva ljudi jako dobra, i to je super praksa. Međutim, nije uvek dovoljno. Šta time želim da kažem? Da bismo postali stručnjaci, i bili prepoznati kao takvi, potrebno je da naše znanje i veštine proveravaju oni koji su od nas u tom trenutku bolji i stručniji, a ne samo oni od kojih smo mi sami stručniji. Dakle, logično je da elektro inženjera juniora proverava senior, jer je on to prošao već i sada više zna. Isto tako fitnes trenera početnika proverava fitnes trener sa više znanja i iskustva, a ne samo ljudi koji treniraju tu.

Za sada bih zaključila ovaj tekst sa nadom da ćemo se svi malo više kritički odnositi prema informacijama koje su nam dostupne sa raznih društvenih mreža. Ako ne zbog nas samih, onda zbog naše dece, da ne bi odrastala u doba manipulatora, lažnih stručnjaka i obilja netačnih informacija.

Nije svaka informacija moć.

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Serijal: Prezentacija

Želeli biste nešto da promenite u svom životu, ali ne znate ni šta, ni kako?

Samo znaš da je vreme za promenu. Imaš znanje u svojoj oblasti, ali ti se čini da ne doprinosiš dovoljno tvojoj firmi u kojoj radiš. Imaš neprestani osećaj da su svi drugi kreativniji, da svi drugi idu u željenom pravcu, dok kod tebe to nije slučaj. Možda misliš da ti fali kreativnosti, dobrih ideja. Misliš da postoje ljudi koji su se prosto tako rodili, da stvaraju i uvek imaju neku inicijativu za promenu, ali ti prosto nisi taj tip. Ili sa druge strane misliš da će se okolnosti same namestiti.

Okolnosti se same nameštaju kada ih mi poželimo i kada na njima krenemo da radimo i aktivno da razmišljamo. Kreativno razmišljanje, istraživanje i predstavljanje ideja je nešto što može da se nauči. Isticanje tvog doprinosa iz mora stvari koje ti se čine nebitnim, a uložila si trud je takođe veština koja se uči. Pronalaženje sfere gde možeš da daš svoj doprinos je još jedna veština.

Serijal pod nazivom PREZENTACIJA je zamišljen tako da ti pomogne da naučiš na koji način se ideje oblikuju tako da se na najbolji način predstave onima kojima želiš. Ovaj serijal obuhvata celokupan proces od toga kako da znaš gde da tražiš, kako da istražuješ, kako da istakneš jedinstveni doprinos, i kako da uobličiš i predstaviš ideju.

Prvi video u serijalu je na temu: PREZENTACIJA U TRI KORAKA, gde pričam o tome koji je ključ za dobru prezentaciju, za sve one koji u manje od 10 minuta žele da saznaju kako da naprave dobru prezentaciju. To nije ni broj slika, niti količina teksta. U mom kratkom videu možeš saznati ŠTA jeste KLJUČ DOBRE PREZENTACIJE i na koji ga način možeš primeniti na svaku ideju. Kada prođeš ova tri koraka, garantujem da će ti tačan sadržaj prezentacije biti pred očima i nećeš imati problem da ga pretočiš u pisanu formu.

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Šta ako ostavim kafu?

Evo jednog kratkog teksta o mom ličnom eksperimentu, a to je prestanak konzumiranja kafe. Dosta je kontradiktornih razmišljanja na temu kafe. Neki je preporučuju, a i neka istraživanja govore da je dobra za mozak, za prevenciju Alchajmerove bolesti, Parkinsonove bolesti, itd. Sa druge strane, ima ljudi koji misle da je jako loša, da je otrov i da utiče na naše zdravlje na način da pospešuje osteoporozu, kardiovaskularne bolesti, itd. Čak je u nekim državama kafa počela da ima oznake da je kancerogena. Kako nisam lekar, ali me ne košta da probam kakav efekat bi na mene bio da ostavim kafu, prenosim svoja iskustva.

Ujedno bih i naglasila, da je najbolji način da vidimo kako nešto što radimo utiče na nas, da to prestanemo (ako smo radili), ili počnemo (ako nismo radili) da radimo. Tako sam i ja otpočela eksperiment: da ne pijem više kafu. Da budem iskrena, na mene je dosta uticalo ono što sam čula da priča primarijus dr Petar Borović o kafi. Sa druge strane, i Nikola Tesla je, nakon saveta lekara, prestao da pije kafu.

Polazne tačke:

  • pila sam jednu kafu dnevno
  • pila sam kafu pre podne, oko 9h
  • nisam pila kafu na prazan stomak, uvek sam pila nakon čaja sa kurkumom i cimetom , zelenog soka od povrća i doručka
  • kafu sam pila tek 3-4 h nakon ustajanja
  • popodne mi se često dešavalo da mi se prispava oko 14h
  • volim ukus kafe
  • imam nizak pritisak

Efekti prestajanja, u prvih nedelju dana:

  • glavobolje koje su trajale dva dana
  • nemogućnost koncentrisanja u vreme kada sam inače pila kafu
  • snažan osećaj da mi kafa nedostaje i da volim da je pijem
  • iritabilnost
  • stalna pospanost

Jako je bilo teško izdržati tih prvih par dana, a pri tome ići na posao i raditi normalno. Međutim, istrajala sam u nameri i mogu da kažem kakva su mi zapažanja nakon mesec i više dana.

Efekti nakon mesec dana:

  • nema pospanosti popodne koja je ranije bila nezaobilazna
  • čajevi su mi u potpunosti zamenili kafu
  • nemam nagle uspone i padove koncentracije
  • ceo dan imam postojani fokus
  • nisam zavisna od toga da moram da popijem kafu svaki dan, inače će me boleti glava, pa se i osećam slobodnije

Na kraju, sledi zaključak. Iako ne mislim da nikada neću popiti kafu, desiće se da prolazim pored omiljenog kafića i da uzmem za poneti ili popijem kapućino u Italiji, ipak mislim da je za mene bolje što sam prestala da je pijem. Kao da sam preuzela kontrolu nad svojom koncentracijom, a ne zavisi mi od toga da li ću nešto popiti ili ne. Meni je svakako ovaj eksperiment uspešan, jer sam uspela da vidim šta za mene radi dobro, a šta još bolje. Ne treba da prihvatamo zdravo za gotovo to što drugi kažu ili rade. Svako od nas je jedinstven, tako da ovim tekstom želim sve da ohrabrim da eksperimentišu i na taj način dođu to toga šta najviše odgovara Vašoj ličnosti.

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Jutarnji podsetnik

Po jutru se dan poznaje, zar ne? Naše misli i akcije koje se pojave ujutru uglavnom oboje čitav dan. Zato je važno da se svako jutro podsetimo kako želimo da nam dan protiče i kako želimo da reagujemo na događaje u našoj okolini. Kada smo nervozni, kako da u par sekundi promenimo tok misli, ili kada nemamo samopouzdanja i loše smo raspoloženi, kako da što pre to promenimo koliko možemo. Mi smo ti koji govore našem telu šta da radi, ali na osnovu prethodno usvojenih obrazaca. Kako bismo zamenili neke od obrazaca koji nam ne pomažu, moramo naučiti nove. A da bismo naučili, ponavljamo što češće. Zato sam napravila ovaj jutarnji podsetnik, sa nekim rečima i pitanjima koja nam pomažu da se na pravi način promenimo na bolje. Mogu reći da je to skup raznih korisnih saveta iz knjiga, podcast-a, blogova, za koje sam videla da pomažu. Neću ulaziti u detalje, ali ću podeliti sa vama moju listu, uz objašnjenje.


  1. Postavi se u “heroj” pozu
    Čim ustanete iz kreveta, ispravite se, udahnite duboko i ostanite par sekundi u ovoj pozi. Možda deluje glupo, ali bar probajte, pa recite da ne deluje. Jer, hteli to mi ili ne, nije moguće da budemo ucveljeni, a da smo ispravljeni i dignute glave, zar ne?
  2. Zahvalnost umesto nervoze
    Hajde umesto toga što smo nervozni zbog obaveza koje imamo da budemo srećni što je to tako. Moram da spremim doručak i meni i mužu i deci, pretvorite u imam divnu porodicu kojoj volim da spremam zdrav doručak.
  3. Osećaj života na visokoj nozi
    A zašto ne bismo pisali u prelep notes naše misli ili pili čaj iz najlepše šolje? Formirajte svoj osećaj života na visokoj nozi sa onim što već imate. Ne čuvajte za specijalne prilike.
  4. Kreiraj svoj bol – zadovoljstvo
    Šta je ono što nas zaboli ako ne uradimo, a šta je ono što radimo zbog toga što nam pričinjava zadovoljstvo? Svaka situacija, sama po sebi, nije ni ružna ni lepa. Ako mi neko kaže da sam debela krava, a ja sam žgolja, mene za to baš briga. Ali ako jesam debela, onda me pogađa. I to je, u oba slučaja ista rečenica. E pa zato kreirajte sami svoje mišljenje o situacijama. Nešto je loše, svi kažu, a vi vidite šta je u tome ipak dobro.
  5. Kako mogu da iskoristim ovu situaciju?
    Pretvorićemo naše: “Zašto se stalno to meni dešava” u “Kako mogu da iskoristim ovu situaciju?”. Jer naš mozak traži rešenja za ono što ga pitamo, uvek se setite toga. I odgovoriće baš na to što se pitamo.
  6. Prekini negativan šablon
    Setite se da tokom dana prilikom neke rasprave sa ljudima koji su Vam bitni prekinete negativan naboj nekim smešnim pitanjem. To automatski smanjuje negativnost i pretvara raspravu u konstruktivan razgovor.
  7. Koji je i zašto IZLAZ/ koju akciju preduzimam / da li pomaže ostvarenju cilja / ako ne, menjaj akciju
    Mislim da je jutro pravo vreme da se preispitamo koji su to naši ciljevi i šta želimo da dobijemo. Onda ako na tome radimo već neko vreme i ne dobijamo šta želimo, da promenimo akcije, jer ne ide.
  8. Ako sebi postaviš pitanje na koje želiš odgovor, gledaj kako ćeš se osećati kada ga postaviš
    Nema ništa bolje što možete da uradite za slebe nego da slušate svoju intuiciju. Ako Vas neko pitanje, odgovor ili misao navodi da se osećate lepo, onda znaš da je to to. Ako to želite da radite ili ako želite tu da idete, to je to. Samo vodite uvek računa o tome šta je ono što ne želimo, a šta je ono što nas plaši, ali ćemo biti bolji ako uradimo. Na primer, želim da sam profesor, ali imam strah od javnog nastupa. Nemojte odmah zaključiti kako ipak nije to to.
  9. Šta bi buduća ti uradila?
    Kako zamišljaš idealnu sebe? Šta oblači savršena ti? Kako miriše? Da li pije kafu ili čaj? Da li se raspravlja ili shvata sve opuštenije i kao igru? E zato se, u svakoj škakljivoj situaciji zapitaj… Šta bi buduća ti uradila?
  10. Promeni reakciju na „okidač“ situacije
    Znate tačno koje reči Vas najviše iznerviraju kada Vam muž kaže. I tad budete van sebe od besa. Sad probajte da to promenite. Probajte da kažete sebi da ćete od sad na to gledati kao na igru uloga. Probajte da Vam to bude smešno, umesto da iznervira. I sledeći put kada Vas na poslu iznerviraju tako što vam stalno daju pogrešne podatke, a vi onda lovite greške, nasmejte se i recite da je to samo test strpljenja.

Dakle, ovo je samo podsetnik. Za one koji nisu probali ni jednu tehniku sa ove liste, krenite polako. Jedno po jedno. I probajte da urežete sebi u pamćenje kad god se nađete u nekoj situaciji koja Vam i nije po volji.

Jutarnja rutina je nešto drugo potpuno. U nekom od sledećih postova ću pričati o tome kako da kreirate idealnu jutarnju rutinu koju ćete voleti i koja će Vam promeniti dan. Za sad, jutarnji podsetnik možete dobiti na mejl u pdf-u ukoliko se priključite mojoj mejling listi:

Da li želite jutarnji podsetnik u pdf-u? Priključite se mejling listi!

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7 načina da povećate produktivnost

napravljeni da rade baš po vašoj meri

Pitate se verovatno uvek kako da imate bolju koncentraciju, kako da poboljšate produktivnost na poslu tako da uspete, uz manje ometanja sa strane, da završite više posla. Iako dosta postižem kada pogledam šta sam sve uspela da uradim u toku prethodnog perioda, ipak, mislim da dani mogu da mi budu produktivniji. I to na način da više radim fokusirano, i da imam više pauza kada se stvarno odmaram od rada. U prevodu, htela bih manje da radim gluposti u kojima brzo prodje vreme, a više da se posvetim onome što je za mene bitno. Medjutim, nije to baš tako lako. I naravno svi saveti koje možete naći nisu uvek prilagođeni Vama i Vašim potrebama.

Zato sam spremila ovih 7 načina koji su meni pomogli i pitanja koja je potrebno da postavite sebi da uredite sve kako Vama odgovara.


  1. Koje je vreme kada sam najefikasnija i imam najbolju koncentraciju? Da li je ujutru ili popodne?
    U zavisnosti od odgovora na ovo pitanje možemo odrediti vreme za fokusirani rad u periodu kada smo najproduktivniji.
    Primer: Radimo od 9-17h, a najproduktivniji smo ujutru, čim stignemo na posao, pa do nekih 12-13h. Problem je što su se tada već nakupili važni mejlovi, ili ostale kolege prave kafu, pa je to lepa prilika za socijalizaciju. Naravno, teško možete pobeći od okoline. Ono što možete je da napravite kompromis. Mejlove ostavite za posle ručka i pre nego što krećete kući, kako bi ostalo malo toga za rano jutro. Naravno, bacite pogled čim stignete na mail i sve što nije hitno u smislu da odmah morate odgovoriti,ostavite za period manje produktivnosti. Kafu i druženje ostavite za posle ručka, kada ste manje produktivni.
  2. Ko su ljudi kojima se uvek moram javiti na telefon ili odgovoriti na poruku?
    Te ljude (poput dadilje koja čuva dete, muža, šefa itd.) stavite na listu ljudi kojima se javljate u svako doba dok radite. Za sve ostale uključite do not disturb mode na telefonu.
  3. Koliko vam traje koncentracija? Da li bolje radite kada imate više kraćih pauza češće ili duže pauze ređe?
    Na osnovu ovog pitanja možete podesiti vreme kada radite, a kada pravite pauzu. I same pauze koriste da odmorimo oči od računara ako radimo dosta za računarom na primer, ili da uopšteno odmorimo posle fokusiranog rada. Ali neko radi bolje po principu da radi npr. 90 minuta, pa da pravi pauze od 10 minuta, a neko radi bolje kada na pola sata napravi 3 minuta pauze. Na Vama je da vidite kako Vama odgovara najviše.
  4. Šta su mi prioriteti za narednu godinu, mesec, dan?
    Nikako ne možete produktivno raditi ukoliko ne znate Vaše ciljeve i prioritete. Recimo, ja sam kao prioritet (u okviru poslovnih aktivnosti) postavila završetak doktorskih studija, kao i rad na projektu na kojem sam angažovana. Sve ostalo mi nije prioritet. Naravno, desi se da se pojavi hitan zadatak koji znači celokupnoj firmi i tada sve drugo odlažem. Ali, za uobičajene dane znam koji su mi ciljevi i koji posao radim u moje produktivno vreme.
  5. Koje je najbolje vreme da napišem svoj plan, ciljeve i ostvareni napredak?
    Obavezno moramo voditi evidenciju o tome koji su nam planovi za sledeći dan, mesec ili godinu. Na dnevnom nivou je, na primer, dobro razmatrati koji su zadaci za tekuči dan i nedelju važni. Tu evidenciju je, meni lično, najlakše da vodim na kraju dana, pred kraj radnog vremena. Poslednjih 5 minuta pre nego što krenem zabeležim gde sam stala, šta sam do sad uspela, šta je plan za sutra. To uvek radim na kraju radnog dana jer se mnogih sitnica neću setiti sutradan ujutru. Ali, nekom dugom možda više odgovara da ovakvu evidenciju uradi ujutru. Na Vama je da podesite kako Vam odgovara.
  6. Koja hrana me uspava?
    Ovo je veoma važno pitanje, jer u zavisnosti od toga šta jedemo pre nego što sednemo da radimo fokusirano zavisi i naša mogučnost da se skoncentrišemo. I sami znate da se dešava da Vam se prispava nakon neke jake hrane. Koja je to hrana? Eksperimentišite… Svakome prija različito. Meni recimo prija laganiji doručak sa žitaricama, voćkicama, kakaom, orašastim puterima. Nekome prija više avokado tost. Pronadjite šta Vam daje energiju, a ne pada teško. Ista stvar za ručak. To je perid dana kada se mnogima prispava, posle ručka. Lagan obrok bi mogao da reši taj problem. Manje slatkiša svakako, jer oni izazivaju nagli skok, pa nagli pad energije. Ovo je široka tema, kojoj ću posvetiti više prostora u nekom sledećem tekstu.
  7. Koliko sati sna mi je potrebno noću za optimalno funkcionisanje danju?
    Od ove teme ne može da se pobegne. Ni kafa, ni lagano jelo, ni planovi Vam neće pomoći ako niste naspavani i odmorni. Koliko sati sna Vam je potrebno da biste funkcionisali kako treba? Kada je potrebno da legnete, a kada da ustanete? Ovoga se pridržavajte uvek!

Eto, to bi bilo 7 načina da povećate produktivnost. Svaka nova dobra navika vuče mnogo drugih dobrih navika, kao što se i može naslutiti. Fokus tokom rada je više neki balans različitih faktora, nego što je sam napor da se ostane fokusiran. Ako sve ovo izgleda previše, nikako se nemojte uplašiti. Jedno po jedno i polako će se kockice složiti. Naravno, dopustite sebi period prilagodjenja.
Nekad ide lako, a nekim danima je teže.
Za pomoć i pitanja me možete naći na, a možete se i prijaviti na mejling listu, da uvek budete u toku 🙂

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Mit o motivaciji

Danas se mnogo priča o motivaciji. Imamo i motivacione govornike, motivacionu muziku za rad, za vežbanje, motivacione izreke… I onda, bombardovani sadržajem o motivaciji, mislimo da će tako doći jedan dan kada ćemo ustati i baš biti specijalno motivisani da počnemo napokon sa zdravom ishranom, ili treninzima, ili čime god o čemu razmišljamo.

Ili, još gore, dešava se da smo pročitali neki članak, knjigu o zdravoj ishrani i sve pripremili da krenemo. U ponedeljak smo bili uspešni, i to nam je bilo posebno zadovoljstvo, da jedemo zdravo. Mislimo motivacija je odradila svoje, to je to, falio je samo neko da nas inspiriše kako treba. I utorak, sve super, uživamo u , npr. novoj ishrani, ili treningu. već vidimo kako nam je bolje. Pa i cela nedelja prodje, sve kako treba. A onda, nakon nekog vremena, možda 10ak dana, popustimo. Ne uradimo trening, taj dan nismo motivisani. Pa ni sutra, ipak pada kiša. Ma ništa, ako nema motivacije, sačekaćemo da se vrati, ipak znamo kakav je to osećaj, kada jedva čekamo trening. Ako nije tako, znači da nam organizam poručuje da mu trenutno ne prija da se forsiramo.

Ali, nema motivacije. I dobar osećaj što treniramo se gubi, opet smo tromi. Pa nismo mi za to. Jer ipak, vrhunski sportisti svaki dan jedva čekaju da rade ono što vole. A ovo ipak nije za nas.

Zvuči li sve ovo poznato?

Da, to je zato što je MOTIVACIJA u stvari MIT koji se već duže vreme provlači svuda oko nas. To je kao poistovećivanje zaljubljenosti i ljubavi. Motivacija je kao zaljubljenost. A disciplina svaki dan je ono što u stvari dovodi do rezultata koje želimo.

Meni često kažu da sam kao robot. Ustajem ujutru, odradim trening ili trčim napolju, bez obzira na vremenske uslove i to da li mi se to radi ili ne. I muž me ponekad pita, pa da li ti se to stvarno radi? Ako je odgovor ne, on pita zašto to radim ako mi se ne radi. Odgovor je vrlo jednostavan. Ja se bolje osećam zato što vežbam i hranim se zdravo. Zato to radim. Znam kako ću se osećati ako to ne radim, i zato ustajem rano i poštujem svoju osmišljenu rutinu. Bez razmatranja šta mi se radi. Da to radim samo kad mi se radi, vežbala bih bar 50% manje, jela bih gluposti većinski i ne bih pola obaveza završila.

Ali, nisam ja jedina. Muhamed ALi je rekao da je mrzeo svaki minut svog treninga. Ali nije odustajao od svojih ciljeva. I da, jeste, možemo da kažemo lako je njemu, on je talenat. ALI, ŠTA AKO i mi probamo tu filozofiju?

ŠTA AKO donesemo odluku i pridržavamo se toga bez obzira na spoljašnje faktore? I to taman toliko vremena da zaboravimo i da razmišljamo o tome. Taman toliko da trening postane rutina, uslovna reakcija ujutru čim ustanemo. Hajde da probamo.

Ovo ne važi samo za trening. Važi i za posao. Ja jesam jedna od onih koji vole svoj posao. Studirala sam ono što volim, a sad i radim to što volim. Često mi ljudi kažu, da sam jedna od retkih koja voli to što radi i ima sreće da može. Znate li šta je iza svega toga?

RAD. Pa onda i disciplina. Pa onda i gomila trenutaka kada se pitam da li ima smisla to što radim. Da li ja to želim, da li bi bilo bolje da radim nešto drugo? Pa onda dobijem motivaciju, pa se opet setim zašto volim to što radim.

Ali sam se dosta pitala da li je to za mene, sve dok nisam shvatila da je sve to samo do odluke. Donela sam odluku da se bavim zanimanjem koje mi se svidelo, radim u struci i želim da se posvetim tome. Sama sebe svaki dan podsećam zašto radim to, a ne nešto drugo. I ne, ne radi mi se svaki dan. Ali imam rokove, imam i ideje koje želim da sprovedem. Imam i svest o tome da moram nekad da uradim i ono što me mrzi i što je dosadno, da bih radila ono u čemu najviše uživam. A nekada će i to u čemu uživam da mi bude mrsko.

Tako da, nadam se da nakon ovog teksta možete i sami da zaključite da je motivacija privid. Ono što je iza tog privida je disciplina da svaki dan uradite po jedan korak koji vas približava cilju koji ste zamislili.

Daću vam i par smernica:

  • Ciljevi i snovi nisu nešto veliko uvek, to su ponekad sitnice koje mi želimo, nekada da imamo koji kilogram manje, a nekada i velike stvari kao da započnemo naš biznis.
  • Samodisciplina i vežbanje samodiscipline su ključ
  • Sve što hoćete da Vam postane navika, od trčanja, pa do fokusiranog rada je VEŽBA
  • Ponoviću prethodno: Ponavljanje je majka znanja
  • Da je lako, svi bi to radili
  • Kada odlučiš da usvojiš novu naviku, napravi plan i više ne preispituj tu odluku dok ti navika ne postane rutina (primer: Ne preispituj se da li ćeš na trčanje svako jutro, ako je odluka već doneta)

Na kraju, daću Vam i par knjiga, podkasta, profila koji Vam mogu reći više o temi discipline i šta smo sve sposobni da uradimo:

  1. SpartanUp podcast ( )- kreiran da podstakne ljude da žive više kao spartanci, ali obradjuje i dosta zanimljivih tema, ima zanimljive stručnjake kao goste i svakako neguje disciplinu.
  2. Knjiga – Can’t hurt me ( od David-a Gogginsa, na čijem Instagram profilu takođe možete dosta korisnog čuti

Slike koje su korišćene su sa

Kako početi sa kašicama?

Sa vama bih htela da podelim svoje iskustvo sa otpočinjanjem nemlečne ishrane kod bebca. Za početak smo krenuli sa pirinčanom kašom. Prvu kasicu koju sam napravila sam bacila (a da ne pričam da mi je organski pirinač odstojao celu noć u vodi, kako bi se štetne materije izbacile). I tako skuvano sam bacila, jer mi nešto nije bio ok. Elem, sutradan sam opet pravila i uspelo je. Bebac je čak i htela da jede 💪
Evo ga postupak:
Uz @mojprvizalogaj i @avent blender uspešno je napravljena 1. kašica :
1. organski beli pirinač je odstojao u vodi celu noć
2. dobro sam oprala pirinač
3. kuvala u @rosa vodi 20min
4. kad se prohladilo izblendirala
To je to!

Kukuruzna kašica je bila sledeća po redu. Od uvođenja pirinčane kašice je počela i stolica bebe da se menja, odnosno nije tako česta. Zato sam nakon davanja malo pirinčane kaše napravila 2 dana pauze, pa nastavila sa kukuruzom. Po savetu @mojprvizalogaj knjižice. Odnosno, #mojaprvakasica da budem preciznija. I dalje se javljao zatvor, pa potop, da tako kažem. Pa sam napravila par dana pauze i stolica se vratila na normalu.

Kukuruznu kašicu sam pravila sa organskom palentom i malo vode. Brzo je gotova, a potrebno je samo slediti uputstva sa pakovanja.

Nadam se da sam ovim tekstom malo pomogla svima vama koji tek počinjete sa kašicama. Za dalje savete preporučujem tekst jelovnik za bebe od 6. meseca:

Napomena: slike u postu su autorske i podležu autorskim pravima.

Kako poboljšati kvalitet vazduha kod kuće

Još prošle godine sam počela svoja intezivna istraživanja na temu kako da poboljšam vazduh u svom domu, ako ne mogu da promenim ovaj napolju. Svi smo svedoci katastrofalnog kvaliteta vazduha u našoj zemlji, a i regionu, pa sam htela sa svima da podelim šta možemo uraditi da makar malo poboljšamo situaciju.

Najpre ću da kažem nešto više o uređajima koje možemo koristiti da poboljšamo kvalitet vazduha u kući. Sve što pišem, pišem iz ličnog iskustva.

  1. Prečišćavači vazduha sa filterima

    Na tržištu postoji dosta vrsta prečišćavača vazduha sa raznim vrstama filtera. Ono što biste, po mom mišljenju, trebali da gledate jeste da prečišćavač poseduje takozvani HEPA filter ( ). Zašto? Zato što Hepa filter može da pročisti čestice poput PM 2.5 i PM 10 koje su glavni zagađivači kod nas. Jonizator vazduha se ne preporučuje, jer je višak ozona u vazduhu štetan. Ja imam prečišćavač vazduha sa 5 različitih filtera već više od godinu dana i ono što mogu da kažem je:
    + oseti se razlika, lakše se diše kada je napolju veće zagađenje
    + pogodno je za sobe gde spavaju deca
    + nije mnogo bučno, ne smeta tokom spavanja

    – jedan filter se čisti svake nedelje OBAVEZNO, jer se dosta nakupi na njemu
    – obavezno je menjanje filtera po preporuci proizvođača inače ne radi kako treba
    – cena je visoka

    Ja vam neću preporučivati proizvođače, ali ono što mogu da kažem je da svakako ovako nešto PREPORUČUJEM za poboljšanje kvaliteta vazduha. Ja imam i jesam zadovoljna.

  2. Ovlaživači vazduha

    Znate onaj osećaj suvoće u grlu i nosu kada ustanete ujutru zimi? E to rešava ovlaživač vazduha. On dodaje vlažnost vazduhu u prostorijama, a u njega se mogu ubaciti i kapljice eteričnih ulja za dodatnu dezinfekciju prostorija. Nakon jednog dana korišćenja, ni deca, ni muž ni ja više nismo imali problema sa disanjem i suvoćom grla i nosa.
    + nema više ružnog osećaja u grlu i nosu
    + daje lepu svežinu u prostoriji
    + nezmenjiv zimi tokom grejne sezone

    – čisti se od kamenca na tri dana, obavezno
    – nikako ne sme duže stajati voda, pa se upotrebljavati, zbog bakterija koje se mogu stvoriti

    Opet, na tržišstu postoje razne vrste ovih aparata. Ja neću davati preporuke na tu temu, ali mogu da kažem da dosta koristi.

Ono na šta morate obratiti pažnju kada koristite i kupujete i prečišćavač i ovlaživač jeste:

  1. Kvadratura koju pokrivaju
  2. Mesto gde ih postavljate (smetaju jedan drugom, jer prečišćavač vidi vodenu paru kao da je vazduh zagađen, pa je alarm za zagađenje stalno uključen). Dakle, nikako jedan pored drugog.

Sledeće o čemu bih vam htela reći par reči jesu saveti za bolji vazduh bez tehnologija:

1. Sobne biljke – posebno Hlorofitum, Areka palma – prečišćavaju vazduh od toksina i zagađenja
2. Ne koristite sveće od parafina
3. Što više prirodnih sredstava za čišćenje (soda bikarbona, sirće)
4. Eterična ulja u sodi bikarboni u teglicama (menta, eukaliptus za antivirusna i antibakterijska dejstva)
5. Ne koristite veštačke mirise za prostorije, puni su štetnih hemikalija.

Eto, to je sve od mene za ovaj put. Ako imate pitanja, pišite u komentarima ili u DM na instagramu.

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