Bottom-up Flow Research Articles

Effects of redox on the phosphorus removal ability of iron-rich phosphorus sorption materials

Iron-rich phosphorus (P) sorption materials (PSMs) are often used in P removal structures, a best management practice able to sequester dissolved P from surface runoff, subsurface drainage, and wastewater. The use of bottom-upward flow in these structures is of great interest, but it creates an intrinsic complication: the presence of stagnant water between flow events may cause structures to develop anoxic conditions. It is unknown whether the redox sensitivity of iron (Fe), the predominant element in Fe-rich PSMs, will affect P binding under anoxic conditions. Understanding the potential impact of intermittent anoxic conditions on the solubility of previously adsorbed P is imperative for determining the feasibility of the bottom-up flow design. The objective of this research was to investigate the (1) development of anoxic conditions in the presence of Fe-rich PSM and tile drainage, (2) Fe-bound P mobilization and solubility, and (3) changes in P sorption capacity of Fe-rich PSMs after oxic conditions are restored. Three Fe-rich PSMs were tested in batch incubation studies: acid mine drainage residual, Fe-coated alumina, and steel metal shavings. Non-treated and P-treated PSM samples were incubated in biogeochemical reactors for as long as necessary to reach Eh = −200 mV. After incubation, dissolved P concentrations in P-treated samples and non-treated samples were similarly low, indicating stability of P retention of PSMs under anoxic conditions. The P removal ability of non-treated PSMs before and after undergoing incubation was not significantly altered, as determined in flow-through experiments. Potentially harmful trace metals were not detected in the incubated solutions. Our research shows that the development of anoxic conditions does not significantly impact PSMs Fe-bound P dissolution, and the P removal ability of PSMs persists after oxic conditions are reestablished.

Restoring bottom-up communication in brain-heart interplay after trauma-focused psychotherapy in breast cancer patients with post-traumatic stress disorder

BackgroundThe psychological impact of breast cancer (BC) is substantial, with a significant number of patients (up to 32 %) experiencing post-traumatic stress disorder (PTSD). Exploring the emotional aspects of PTSD through the functional brain-heart interplay (BHI) offers valuable insights into the condition. BHI examines the functional interactions between cortical and sympathovagal dynamics. This study aims to investigate changes in functional directional BHI after trauma-focused (TF) psychotherapy, specifically Eye Movement Desensitization and Reprocessing (EMDR), in comparison to treatment as usual (TAU) among BC patients with PTSD. To our knowledge, this study represents the first examination of such changes. MethodsWe enrolled thirty BC patients who met the criteria for a PTSD diagnosis, with fourteen receiving EMDR and fifteen receiving TAU over a two- to three-month period. We analyzed changes in the emotional response during a script-driven imagery setting. Quantification of the functional interplay between EEG and sympathovagal dynamics was achieved using the synthetic data generation model (SDG) on electroencephalographic (EEG) and heartbeat series. Our focus was on the difference in the BHI index extracted at baseline and post-treatment. ResultsWe found statistically significant higher coupling in the heart-to-brain direction in patients treated with EMDR compared to controls. This suggests that the flow of information from the autonomic nervous system to the central nervous system is restored following EMDR-induced recovery from PTSD. Furthermore, we observed a significant correlation between improvements in PTSD symptoms and an increase in functional BHI after EMDR treatment. ConclusionsTF psychotherapy, particularly EMDR, appears to facilitate the restoration of the bottom-up flow of interoceptive information, which is dysfunctional in patients with PTSD. The application of BHI analysis to the study of PTSD not only aids in identifying biomarkers of the disorder but also enhances our understanding of the changes brought about by TF treatments.

What do popular YouTube videos say about genetically modified foods? A content analysis.

YouTube is one of the most popular media sharing platforms that facilitates both professionals and lay people to participate in dissemination of knowledge and opinions. Its wide-reaching impact allows both top-down and bottom-up flow of information between experts and lay audience. With a vast proportion of Americans obtaining health-related information digitally, the purpose of this study was to describe the content of 100 most viewed YouTube videos in the English language, specific to genetically modified foods (GMFs). Using the search terms "genetically modified foods" the URLs and metadata for 100 English YouTube videos with the highest viewership were curated. Each video was viewed, and dichotomously coded for the absence or presence of ten content categories. Descriptive statistics, percentages of categorical variables and independent one-tailed t-tests (α=.05) were conducted to assess the statistical effect of the absence or presence of these categories on the number of views and likes garnered by the videos. Cumulatively, the 100 videos observed received 65,536,885 views and 1,328,605 likes. Only 7% of the videos were created by professionally credentialed individuals or organizations. More than 90% of the sampled videos described GMFs with an example, 50% mentioned their role in alleviating hunger, and 65% mentioned ecological concerns attributed to GMFs. Our results underscore the need for health professionals to increase their digital presence on online media sharing platforms such as YouTube, and capitalize on its pervasiveness as potential conduits of accurate scientific information to equip consumers make evidence-based, informed decision regarding GMFs.

Frequency-specific directed interactions between whole-brain regions during sentence processing using multimodal stimulus

Neural oscillations subserve a broad range of speech processing and language comprehension functions. Using an electroencephalogram (EEG), we investigated the frequency-specific directed interactions between whole-brain regions while the participants processed Chinese sentences using different modality stimuli (i.e., auditory, visual, and audio-visual). The results indicate that low-frequency responses correspond to the process of information flow aggregation in primary sensory cortices in different modalities. Information flow dominated by high-frequency responses exhibited characteristics of bottom-up flow from left posterior temporal to left frontal regions. The network pattern of top-down information flowing out of the left frontal lobe was presented by the joint dominance of low- and high-frequency rhythms. Overall, our results suggest that the brain may be modality-independent when processing higher-order language information.

Nonlinear Activation-Free Contextual Attention Network for Polyp Segmentation

The accurate segmentation of colorectal polyps is of great significance for the diagnosis and treatment of colorectal cancer. However, the segmentation of colorectal polyps faces complex problems such as low contrast in the peripheral region of salient images, blurred borders, and diverse shapes. In addition, the number of traditional UNet network parameters is large and the segmentation effect is average. To overcome these problems, an innovative nonlinear activation-free uncertainty contextual attention network is proposed in this paper. Based on the UNet network, an encoder and a decoder are added to predict the saliency map of each module in the bottom-up flow and pass it to the next module. We use Res2Net as the backbone network to extract image features, enhance image features through simple parallel axial channel attention, and obtain high-level features with global semantics and low-level features with edge details. At the same time, a nonlinear n on-activation network is introduced, which can reduce the complexity between blocks, thereby further enhancing image feature extraction. This work conducted experiments on five commonly used polyp segmentation datasets, and the experimental evaluation metrics from the mean intersection over union, mean Dice coefficient, and mean absolute error were all improved, which can show that our method has certain advantages over existing methods in terms of segmentation performance and generalization performance.

A neural mechanism underlying predictive visual motion processing in patients with schizophrenia

Psychotic symptoms may be traced back to sensory sensitivity. Thereby, visual motion (VM) processing particularly has been suggested to be impaired in schizophrenia (SCZ). In healthy brains, VM underlies predictive processing within hierarchically structured systems. However, less is known about predictive VM processing in SCZ. Therefore, we performed fMRI during a VM paradigm with three conditions of varying predictability, i.e., Predictable-, Random-, and Arbitrary motion. The study sample comprised 17 SCZ patients and 23 healthy controls. We calculated general linear model (GLM) analysis to assess group differences in VM processing across motion conditions. Here, we identified significantly lower activity in right temporoparietal junction (TPJ) for SCZ patients. Therefore, right TPJ was set as seed for connectivity analyses. For patients, across conditions we identified increased connections to higher regions, namely medial prefrontal cortex, or paracingulate gyrus. Healthy subjects activated sensory regions as area V5, or superior parietal lobule. Reduced TPJ activity may reflect both a failure in the bottom-up flow of visual information and a decrease of signal processing as consequence of increased top-down input from frontal areas. In sum, these altered neural patterns provide a framework for future studies focusing on predictive VM processing to identify potential biomarkers of psychosis.

Comparative study on heat transfer characteristics of molten PCM in PV/PCM air-based system based on grey relation analyses

The operating temperature of photovoltaic (PV) panel have significant influence on its generating efficiency and life span. To improve the performance of PV panel, we performed comparative analyses on a novel PV panel cooling strategy by integrating PV panel with phase change material (PCM) and ventilation system (PV/PCM air-based system). The heat transfer characteristics of molten PCM in an inclined cavity with constant heat flux on one side and air flow boundary on another side are numerically investigated. The thermal performance including dimensionless temperature and temperature uniformity, the heat transfer capability including local Nusselt number and average Nusselt number, are compared and analyzed. The results indicate that flow boundary has a significant effect on the PV cooling. Cavity at top-down flow direction has better temperature uniformity and at bottom-up flow direction has greater heat transfer capability. Increasing Reynolds number can enhance the heat transfer efficiency and reduce the temperature at the same time. Further, the impact of each factor on heat transfer capability are quantified based on the grey relation analyses. Grey relation grade for average Nusselt number with Ra number, Re number, AR, ϕ, and flow direction is 0.51, 0.66, 0.69, 0.62, and 0.53, respectively. This indicates that heat transfer capacity is the most sensitive to AR, followed by Re number, inclination angle, flow direction, and Ra number.

Using Ecopath Models to Explore Differences in Ecosystem Characteristics Between an Artificial Reef and a Nearby Natural Reef on the Coast of the North Yellow Sea, China

The comparison of trophic structure and energy flow between natural and artificial reefs is imperative to evaluate whether these man-made structures work similarly to comparable natural reefs. Here, to characterize the potential difference in functioning between two types of reef ecosystems, two trophic models (Ecopath) at an artificial reef and an adjacent natural reef on the coast of the north Yellow Sea, China, were established. Both Ecopath models were divided into 18 functional groups from primary producers (algae and phytoplankton) and detritus to predatory species (e.g., Sebastes schlegelii). Model outputs showed that the ecosystem scale was smaller in the artificial reef (total system throughput (TPP) = 6,455.47 t·km−2·year−1) relative to its natural counterpart (TPP = 9,490.48 t·km−2·year−1). At both reef types, a large proportion of energy occurred at trophic levels I and II, and most of the primary production was utilized through a detritus pathway. This result implies a bottom-up energy flow control for both cases. However, two types of reef systems were behaving in a reasonable manner, as mean transfer efficiencies were similar to the Lindeman efficiency (10%). The ecosystem maturity of the artificial reef is not comparable to that of the natural reef for its inferior value of total primary production/total respiration (TPP/TR). Moreover, both the connectance index (CI) and system omnivory index (SOI) were slightly higher at the artificial reef relative to the natural reef as well as other coastal systems with parallel latitudes, suggesting that the current artificial system has formed complicated interspecies relations and high-level stability. This work updates our knowledge about the functioning evolvement of established artificial reefs and provides a baseline for the efficient management of coastal zones and further investigations.

Few-Shot Object Detection of Remote Sensing Images via Two-Stage Fine-Tuning

In recent years, there have been many developments in object detection on remote sensing images. However, those deep convolutional neural network (CNN) models always demand a great number of labeled samples, which leads to a significant decrease in performance on rare categories. Recently, the fine-tuning-based method of few-shot learning has drawn attention in the field of computer vision. In this letter, we proposed a multiscale few-shot object detection approach for remote sensing images, which is built upon faster region-CNN (R-CNN) architecture. First, we build the whole backbone of the detector with the involution operator, which enhances the classification ability of the features extractor. Then, our proposed detector learns multiscale features with the assistance of a path-aggregation module, which shortens the information transmission path by a bottom-up flow and uses semantic information of low-level features for localization. Eventually, we increase the shape bias in the detector in the training phase, which further improves the robustness and performance of the model. Experiments on two optical remote sensing datasets demonstrate that the proposed method outperforms the current few-shot detection models in the remote sensing field.

ARUS TOP-DOWN DAN BOTTOM-UP PADA GERAKAN DIALOG ANTAR AGAMA DI INDONESIA

The interfaith dialogue movement in the top-down current as described above, namely the movement originating from the state, was welcomed by various communities in Indonesia as a bottom-up current, namely the interfaith dialogue movement originating from the people. At least in this bottom-up flow, there are two communities, namely dialogue developed in academic institutions, and dialogue conducted or facilitated by civil society institutions, such as NGOs (Non-Governmental Organizations), both focusing on dialogue and raising issues. -Other issues related to dialogue. In this research, the researcher focuses only on two groups, namely the state (top-down current) and academic institutions (bottom-up current) trying to examine religious movements in the realm of inter-religious dialogue using social movement theory. There are three key concepts in social movement theory which usually play a very important role in determining the success of collective action. The three concepts include (1) political opportunity structure, (2) mobilizing structures, and (3) framing of action.

A food-web comparative modeling approach highlights ecosystem singularities of the Gulf of Alicante (Western Mediterranean Sea)

A food-web comparative modeling approach using Ecopath with Ecosim (EwE) was built to characterize the ecosystem of the Gulf of Alicante (GoA) and investigate its singularities. The GoA differs from neighboring ecosystems of the Western Mediterranean Sea because of its different oceanographic characteristics. We developed a model of the study area representing early 2010s, covering a total area of 7085 km2, and including the continental shelf and upper slope with depths from 50 to 800 m. In total, 45 functional groups considering all components of the food web (fish, marine mammals, seabirds, invertebrates, primary producers, and detritus) were selected to build the ecological model. The fishery was represented by the four main fleets operating in the area (i.e., bottom trawl, purse seine, longlines, and small-scale fisheries) and we included official landing data and estimated percentage of discarded species. Results were then compared with available outputs of available models representing adjacent ecosystems such as the Gulf of Cadiz (in the Atlantic side) and the North Western Mediterranean Sea (north of the study area). The study revealed that the major differences found between models were due to the lower primary production in the GoA. This led to lower catches and higher importance of the demersal compartment, where the most biomass was associated to the detritus. The main trophic flows in the GoA were originated at the basis of the food web, likely related to bottom-up flow control. Interactions between pelagic and demersal groups were weaker than in neighboring areas. Despite the high values of ecotrophic efficiencies and mortality rates, results showed a lower fishing pressure than in neighboring areas, suggesting a moderate exploitation level of marine resources in the GoA. This study fills a knowledge gap in the area and sets the baseline to develop future studies to test scenarios of change and management options.

Performance of Field-Scale Phosphorus Removal Structures Utilizing Steel Slag for Treatment of Subsurface Drainage

Reducing dissolved phosphorus (P) losses from legacy P soils to surface waters is necessary for preventing algal blooms. Phosphorus removal structures containing steel slag have shown success in treating surface runoff for dissolved P, but little is known about treating subsurface (tile) drainage. A ditch-style and subsurface P removal structure were constructed using steel slag in a bottom-up flow design for treating tile drainage. Nearly 97% of P was delivered during precipitation-induced flow events (as opposed to baseflow) with inflow P concentrations increasing with flow rate. Structures handled flow rates approximately 12 L s−1, and the subsurface and ditch structures removed 19.2 (55%) and 0.9 kg (37%) of the cumulative dissolved P load, respectively. Both structures underperformed relative to laboratory flow-through experiments and exhibited signs of flow inhibition with time. Dissolved P removal decreased dramatically when treated water pH decreased <8.5. Although slag has proven successful for treating surface runoff, we hypothesize that underperformance in this case was due to tile drainage bicarbonate consumption of slag calcium through the precipitation of calcium carbonate, thereby filling pore space, decreasing flow and pH, and preventing calcium phosphate precipitation. We do not recommend non-treated steel slag for removing dissolved P from tile drainage unless slag is replaced every 4–6 months.

Graph Theory Teaches Us Something About Grammaticality

Abstract Graph theory, which quantitatively measures the precise structure and complexity of any network, uncovers an optimal force balance in sentential graphs generated by the computational procedures of human natural language (CHL). It provides an alternative way to evaluate grammaticality by calculating ‘feature potential’ of nodes and ‘feature current’ along edges. An optimal force balance becomes visible by expressing ‘feature current’ through different point sizes of lines. Graph theory provides insights into syntax and contradicts Chomsky’s current proposal to discard tree notations. We propose an error minimization hypothesis for CHL: a good sentential network possesses an error-free self-organized force balance. CHL minimizes errors by (a) converting bottom-up flow (structure building) to top-down flow (parsing), (b) removing head projection edges, (c) preserving edges related to feature checking, (d) deleting DPmovement trajectories headed by an intermediate copy, (e) ensuring that covert wh-movement trajectories have infinitesimally small currents and conserving flow directions, and (f) robustly remedying a gap in wh-loop by using infinitesimally inexpensive wh-internally-merged (wh- IM) edge with the original flow direction. The CHL compels the sensorimotor (SM) interface to ground nodes so that Kirchhoff’s current law (a fundamental balance law) is satisfied. Internal merges are built-in grounding operations at the CHL–SM interface that generate loops and optimal force balance in sentential networks.

From “Propaganda” to “Guided Communication”. Animating Political Communication in Digital China

This essay investigates the recent boom in the use of animated cartoons for political communication in China which began in late 2013. A series of political cartoons are examined against the background of a comprehensive media revolution designed by top-following the Chinese Communist Party’s (hereafter CCP) new understanding of the role of media and public opinion. I argue, by looking closely at the creative use of political cartoons, that the CCP has adjusted its views on the role of media in the digital age – from propaganda mouthpiece, to guiding opinion unifier for popularizing the Party’s rule. Their efforts and success in stimulating a significant number of responses through the use of animated cartoons has given rise to a new communication model of mixing top-down and bottom-up flow of message. Behind the new model was the CCP’s changing understanding of the public: from “target audience of propaganda” to guided audience, and then to central players in popularizing the Party. The major media reform since Xi took office in early 2013 has laid institutional, managerial and editorial foundations to sustain this conceptual change in practice. The boom in political cartoons is the most conspicuous result of that.

Multi-scale object detection by top-down and bottom-up feature pyramid network

While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network (TDBU-FPN), which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement sub-original information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes (PASCAL VOC) dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.

Revisiting the Dissemination of Marxism in May Fourth China: A Perspective of Cultural Field

The dissemination of Marxism in May Fourth China has always been a heated topic among Chinese historians, and the perspective of social history has influenced this issue over the last 20 years. Current opinion tends to regard the spread of Marxism as a rebellion of marginalized intellectuals against the whole establishment while the cultural elite tried to return to the center of society. Nevertheless, I argue that the dissemination of Marxism in the May Fourth period relied heavily on the existing cultural hierarchy and that a three-tiered configuration began to form among intellectuals that was dominated by top-down rather than bottom-up flow of knowledge of Marxism. The farewell to existing authority had to wait until these intellectuals gradually transformed themselves into revolutionaries after 1922.

Causal cortical dynamics of a predictive enhancement of speech intelligibility

Speech perception may be underpinned by a hierarchical cortical system, which attempts to match “external” incoming sensory inputs with “internal” top-down predictions. Prior knowledge modulates internal predictions of an upcoming stimulus and exerts its effects in temporal and inferior frontal cortex. Here, we used source-space magnetoencephalography (MEG) to study the spatiotemporal dynamics underpinning the integration of prior knowledge in the speech processing network. Prior knowledge was manipulated to i) increase the perceived intelligibility of speech sentences, and ii) dissociate the perceptual effects of changes in speech intelligibility from acoustical differences in speech stimuli. Cortical entrainment to the speech temporal envelope, which accounts for neural activity specifically related to sensory information, was affected by prior knowledge: This effect emerged early (∼50 ms) in left inferior frontal gyrus (IFG) and then (∼100 ms) in Heschl's gyrus (HG), and was sustained until latencies of ∼250 ms. Directed transfer function (DTF) measures were used for estimating direct Granger causal relations between locations of interest. In line with the cortical entrainment result, this analysis indicated that prior knowledge enhanced top-down connections from left IFG to all the left temporal areas of interest – namely HG, superior temporal sulcus (STS), and middle temporal gyrus (MTG). In addition, intelligible speech increased top-down information flow between left STS and left HG, and increased bottom-up flow in higher-order temporal cortex, specifically between STS and MTG. These results are compatible with theories that explain this mechanism as a result of both ascending and descending cortical interactions, such as predictive coding. Altogether, this study provides a detailed view of how, where and when prior knowledge influences continuous speech perception.

Technique Blends Dimensionless Numbers and Data Mining To Predict Recovery Factors

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181024, “Recovery-Factor Prediction for Deepwater Gulf of Mexico Oil Fields by Integration of Dimensionless Numbers With Data-Mining Techniques,” by Priyank Srivastava and Xingru Wu, SPE, University of Oklahoma, and Amin Amirlatifi, SPE, Mississippi State University, prepared for the 2016 SPE Intelligent Energy International Conference and Exhibition, Aberdeen, 6–8 September. The paper has not been peer reviewed. Using attributes from a database of 395 deepwater Gulf of Mexico oil fields, a set of dimensionless numbers is calculated that helps in scaling attributes for all the oil fields. On the basis of these dimensionless numbers, various data-mining techniques are used to classify the oil fields. Subsequently, partial-least-square (PLS) regression is used to relate the dimensionless numbers to the recovery factor. This study shows that dimensionless numbers, together with data-mining techniques, can predict field behavior in terms of recovery factor for sparse data sets. Introduction The digitization of information and the rise of inexpensive sensor technologies have ushered in a new era of computing in which acquired data are used to show hidden patterns and trends. This method of computing is very efficient in solving inverse problems where parameters affecting system characteristics are not completely known. Hydrocarbon reservoirs provide a classic case of a natural system where engineers have limited control on the design of the system that they work with; thus, they have to rely on indirect measurements to determine properties of the reservoir and use these properties for prediction of future trends. Performance prediction is usually accomplished with either analytical material-balance equations or numerical reservoir simulation. However, both methods use a bottom-up work flow, which suffers from a drawback: the need for accurate representation of subsurface geology. Data mining, on the other hand, provides an alternative top-down intelligent-reservoir-modeling approach, which uses measured reservoir properties as the basis for modeling. Using publicly available information—including information on geology, geophysics, reserves, production, and infrastructure—the complete paper applies various data-mining and predictive-analytics algorithms to estimate recovery factor. Classical reservoir engineering assumes that recovery factor is dependent on rock properties, fluid properties, geological structures, and mode of production. Instead of using traditional deterministic methods, such as material balance or numerical simulation, this study uses data-driven analytics to estimate the recovery factor.

Conversation through journalism: Searching for organizing principles of public and citizen journalism

This article reviews the public and citizen journalism movements of recent years and offers some perspectives on how the current and future journalism can function to benefit democratic public life. A major argument of this article is that ‘conversation’ defines news as a process of negotiated social meaning and that it should be the organizing principle of today’s journalism. It is also argued that traditional journalistic principles such as objectivity and distance may no longer be useful to today’s citizen journalism and that we see a rise of new journalistic principles such as interactivity and transparency. Finally, borrowing from deliberative democracy literature, a bottom-up flow model to connect citizen and public journalism is discussed.

Outlook on moving of computing services towards the data sources

The internet of things (IoT) is potentially interconnecting unprecedented amounts of raw data, opening countless possibilities by two main logical layers: become data into information, then turn information into knowledge. The former is about filtering the significance in the appropriate format, while the latter provides emerging categories of the whole domain. This path of the data is a bottom-up flow. On the other hand, the path of the process is a top-down flow, starting at the strategic level of business and scientific institutions. Today, the path of the process treasures a sizeable amount of well-known methods, architectures and technologies: the so called Big Data. On the top, Big Data analytics aims variable association (e-commerce), data mining (predictive behaviour) or clustering (marketing segmentation). Digging the Big Data architecture there are a myriad of enabling technologies for data taking, storage and management. However the strategic aim is to enhance knowledge with the appropriate information, which does need of data, but not vice versa. In the way, the magnitude of upcoming data from the IoT will disrupt the data centres. To cope with the extreme scale is a matter of moving the computing services towards the data sources. This paper explores the possibilities of providing many of the IoT services which are currently hosted in monolithic cloud centres, moving these computing services into nano data centres (NaDa). Particularly, data-information processes, which usually are performing at sub-problem domains. NaDa distributes computing power over the already present machines of the IP provides, like gateways or wireless routers to overcome latency, storage cost and alleviate transmissions. Large scale questionnaires have been taken for 300 IT professionals to validate the points of view for IoT adoption. Considering IoT is by definition connected to the Internet, NaDa may be used to implement the logical low layer architecture of the services. Obviously, such distributed NaDa send results on a logical high layer in charge of the information-knowledge turn. This layer requires the whole picture of the domain to enable those processes of Big Data analytics on the top.