The relatively high rate of injuries in construction is not surprising, as site work by its very nature ranks highly on fundamental risk factors. Working at heights often magnifies these risk factors. The literature reveals that falls from heights accounts for a large percentage of injuries in construction worldwide. Thailand is no exception, where fall accidents constitute the majority of high-rise construction accidents despite preventive measures being implemented. This paper examines how the use of a simple Ambient Intelligence (AmI) system—a device comprising a microcontroller, microwave sensors, Light Emitting Diode (LED) and audio alarm—could help to affect safety behavioural change of on-site construction workers in order to decrease the potential for fall accidents. An experiment was conducted at a high-rise building construction site in Bangkok, Thailand to examine the effectiveness of the AmI in helping workers mitigate the risk of falling from heights. The analysis of the data collected over two work weeks from the pre- and post-AmI application using X-bar charts and one-way analysis of variance (ANOVA) revealed a significant reduction of about 78% in the number of workers passing through the fall hazard zones. The finding established the potential of a simple AmI for reducing the risk of fall accidents. Globally, the construction industry continues to strive to improve workers safety as they typically perform physically demanding tasks in poor work conditions  and are frequently exposed to work-related hazards  . TragicaFalls from a height (FFH) still consistently have the highest rates amongst construction accidents compared with others types of accidents , and when compared to other industries’ accidents . In 2016As stated previously, the most common factors associated with FFH are risky activities and individual characteristics. Therefore, it is not surprising that understanding human behavior has attracted several researchers’ attention. Researchers have argued that human behaviour accounts for most accidents and so understanding and being able to modify behaviour should be crucial to improving safety performance. Although behaviour, and by extension, safety behaviour, is influenced by activators/antecedents and consequences , unsafe In the context of FFH, how well are the risks associated with falling from heights perceived? Situational awareness and risk perception of workers are key factors that have a direct influence on falls . InadequSituational awareness is a three-step process which includes detection of hazardous signals, perception and comprehension of the risks associated with the hazard, and projection of the consequences associated with the decision options . ConsequAttempting to change behaviours can prove challenging especially when the safe behaviour to be engaged in is not known. Geller  places bThe term “ambient intelligence” (AmI) is about sensitive adaptive electronic environments that respond to actions of persons, objects and cater for their needs . Given tTo prevent construction-related accidents, safety supervisors need to monitor the construction site conditions thoroughly. Based on the site observation, actions can be made to protect workers under safety risks . HoweverThe presence of the above technologies makes possible the development of an AmI system that can be used to improve construction site safety. AmI, as proposed herein, refers to a digital environment that proactively supports workers working at height, where its functionality is based on the ability to use many inter-related technologies. Despite many existing AmI-enabling technologies however, there has been very limited empirical research attempting to examine how an AmI system can be developed and applied to affect positive change in workers’ safety behaviour at a construction site. Therefore, the research presented in this paper was aimed to examine the efficacy of a simple AmI system in reducing the risk of FFH. The purpose of the AmI was to improve the worker’s awareness of areas where fall hazards exist. This is achieved by using an alarm triggered by microwave sensors to warn workers when they are in the proximity of a fall hazard area, which may not be clearly visible or obvious in various site conditions. The outcome from this research would be of interest to developing countries seeking to implement a simple AmI system constructed from generally available technologies to improve construction workers’ safety behaviour.Due to the nature of this present study, an on-site experiment was designed and implemented to explore AmI’s potential in decreasing FFH. The experiment involved an installation of a simple AmI system at a high-rising building construction site in Bangkok, Thailand to collect and anlayse the data before and after the implementation of the AmI system. The following sections describe the AmI system used, the experiment site and the data collection process.A simple AmI system consisting of a set of microwave sensors  and a miThe developed AmI system can detect a movement within a specific area and sound the alarm whenever the movement is detected. It can also collect and store the frequency of movement detection in a memory card, which can then be transferred to a computer for further analysis. As the name implies, the sensors utilise microwaves as a medium for motion detection. Each sensor emits a microwave which on reflection off the contact surface, changes its wavelength according to the type of contact surface, and gets detected by the receiver. A microwave sensor has adjustable wave intensity for suitable sensitivity, with the scale ranging from 1–7. An intensity of 1 is capable of detecting a large solid object in motion with a range of approximately 3–4 m and an area of 0.5 × 0.4 m. The maximum intensity of 7 allows a detection range of 6–7 m with an area of 3 × 1.5 m. It is worth nothing that the sensor cannot distinguish between a single object and multiple objects. The microwave emitted by a sensor can penetrate several types of thin materials ; thus, the sensor will not detect such items. The microwave, however, does not penetrate such materials as steel, aluminium, stone and other rock-like materials.The components of the developed AmI system are presented in To examine the potential of AmI in improving construction site safety and particularly to help lower FFH risk, a 23-storey building construction site in central Bangkok, Thailand was chosen for this study to conduct the experiment. At the time of the experiment, the superstructure was already completed and the Mechanical, Electrical and Plumbing (MEP) work was underway. Upon consultation with the project manager and the site engineer, the landing platforms between the building and the passenger hoistway were identified as a hazardous location, suitable for the experiment where FFH risk could be identified from the gap and opening on the sides of the platforms  especialTo examine the efficacy of the AmI system in improving construction site safety by assisting workers to avoid FFH risk areas, the data collection process was broken down into the pre- and post-AmI deployment. In the pre-AmI deployment stage, only the warning alarm of the system was turned off, whereas the sensors were activated to count the number of workers passing through the designated hazardous areas. That is, the workers were not warned by the system when moving into the areas and the observation, in this stage, was effectively made under normal working conditions . In the post-AmI deployment stage, the warning alarm was turned on. When a worker moved into the designated hazardous areas detectable by the sensors, the audio and LED alarm would go off. On the first day of the AmI deployment stage, however, the research team explained clearly to all site workers the purpose of the alarm and highlighted the designated areas with a high risk of FFH.The duration of the experiment was determined in conjunction with the site engineer to ensure that it represented the normal working load for the MEP work on the observed floor. In other words, the duration was chosen such that it did not fall within a period of an unusually high workload  or a low workload . As a result, the experiment was conducted over a period of two working weeks with the pre-AmI stage taking place in the first week (Monday 22 April–Friday 26 April 2019) and the post-AmI stage in the second week (Monday 29 April–Friday 3 May 2019), with the data collected at one hour intervals from 8:00 a.m. to 7:00 p.m. daily. The daily count data were also normalised for subsequent analysis based on the total number of workers working at the observed location as provided by the project manager. This is to account for the possible effect of the varying total number of workers at the site on the daily count data recorded by the sensors.Data analysis was primarily conducted in order to determine whether there was any significant difference between the number of workers passing through the designated hazardous areas before and after the deployment of the AmI system. The Statistical Process Control (SPC) chart was firstly employed to examine the trends of the collected data. The X-bar chart was considered appropriate as it deals with variable  data that involve a data sample  with multiple observations in the sample . Typically, the X-bar chart (and other SPC charts) will include the upper and lower control limits that can be used to indicate whether the variation of the data is statistically in control. For this research, the X-bar chart was used to detect any significant upward/downward trends, which indicate a significant increase or decrease in the number of risks attempted by the workers . This significant change between the pre- and post-AmI deployment data was further ascertained using One-Way Analysis of Variance (ANOVA). The results of both analyses are presented in the following sections.Following the experiment setup and the one-week test-and-run period mentioned in Initial data screening indicated that logging errors occurred on Monday 22 April and Friday 3 May 2019. As a result, the data obtained for these two dates were removed from the analysis. To examine the efficacy of the implemented AmI device, the X-bar chart was used to detect any significant change in the trends of the number of FFH risk attempts by workers  pre- and post-AmI deployment. To construct an X-bar chart, the following parameters were determined as follows:X-bar  = 2Lower Control Limit (LCL) = Based on the above parameters, the X-bar chart was plotted as presented in The ANOVA compares means between groups of interest and determines whether any of those means are statistically significantly different from each other . In thisp value and the Tukey pairwise comparison. The p value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis  is true. A p value of two groups less than a pre-chosen probability or significance level (α = 0.05 in this research) is considered statistically significantly different, and this implies that there is a 0.05 (or 5%) probability that the null hypothesis is true. In this case, it is judged that the null hypothesis “all means are equal” is not acceptable and the two groups are statistically significantly different. The Tukey statistic with a 95% confidence interval compares means between pre- and post-AmI deployment. If the interval of the means does not contain 0, then they are judged to be significantly different [According to ifferent . MoreoveSafety in construction sites remains a high priority for every company, yet the number of accidents and casualties persist. Statistically, falling from heights is the most common cause of death in construction sites, but can be eliminated or reduced by an implementation of preventative and protective site safety measures. Protective site safety measures are commonly seen, for example, through the installations of fall protection and the use of personal protective equipment (PPE), while prevention could focus on instilling a good safety culture through, for example, the use of sign boards and routine safety meetings. This research focuses on reducing the risk of fall accidents at construction sites using a preventative warning system aimed at assisting workers to keep away from hazardous areas.p value was less than the pre-chosen probability or significance level (0.05), implying the rejection of the null hypothesis that “all means are equal” and accept the alternative hypothesis . In addition to the p value, the Tukey difference of means analysis indicated that the difference between means was not 0 and the data groups were different; thus, the results were statistically significantly different. The implementation of AmI was proven to provide a real impact on construction site safety. In addition to physical preventative safety measures, AmI offers an augmenting method to improve construction site safety for companies to consider. Although it may not have the ability to completely replace the traditional site safety protections, it could further improve construction site safety by altering human behaviour and awareness of risk. Since the attitude of workers towards safety behaviours contributes to hazardous actions [Subject to some limitations, the results and analysis of the pre- and post-AmI deployment stages are promising as the number of daily counts was notably reduced. However, visual inspection of the data alone cannot prove whether AmI affects workers’ behaviour, or whether this was a coincidence. Hence, an X-bar chart and one-way ANOVA were used to verify the statistical significance of the improvement as a result of the AmI implementation. As shown in the results, the data exceeded the upper and lower control limits, reflecting the fact that the data exceeded the expected variation range. In this instance, post-AmI data moving below the lower control limit is a very positive result. The drop in the number of risk attempts per worker was also reinforced statistically via one-way ANOVA. The estimated  actions , every p actions . The AmITo enhance the results further, the adage of “old habits die hard” must be observed. Since the number of observations (hourly observations) and the sample (observed dates) were limited, there is a risk that workers ignore the alarm and over time resume their risky behaviour. Research conducted by Stewart et al.  indicateIn this paper, a simple but effective AmI device has been designed and developed as a tool to decrease the potential for fall accidents at a construction site. The device was applied to a real high-rise building construction site in Bangkok, Thailand, and was tested to examine its effectiveness in helping workers mitigate the risk of falling from heights. The results indicate that the device has the potential to improve workers’ behaviours when working in areas with a high risk of FFH. The X-bar chart shows a clear reduction in the average normalised daily counts of the number of workers passing through the fall hazard areas . The results from the ANOVA also confirmed the statistical significance of this reduction.Understanding the degree of severity when working in high-elevation zones will greatly reduce the potential for falls; hence, developing an awareness around FFH risk-prone areas should be institutionalised. Providing pre-education and training only mitigates a moderate amount of perceived risk, since such knowledge varies by location and environmental conditions, among others. The application of AmI could therefore provide an effective augmented safety prevention mechanism by reinforcing and sustaining such risk awareness required throughout the entire construction project.It is important to note that the findings from this study be interpreted with some reservation due to several limitations. Experimentally, the period of the observation was relatively short as there was an administrative restriction making the longitudinal study impossible. As pointed out in the previous section, there is a possibility that in the longer term, the workers may gradually ignore the alarm and revert to their previous behaviour. Therefore, an extended period of observation is required in future studies to fully assess the longer-term effects of AmI. Technically, due to the study being conducted in the context of a developing country, the aspect of “intelligence” of the AmI system employed in this study is limited to only an audio alarm, which could be considered too simplistic with respect to existing technologies. Future studies could benefit from integrating different types of technologies that enable the AmI the collection of various types of data, such as images, location and temperature, which could allow for a more comprehensive data analysis, on-site monitoring and the provision of a better feedback mechanism to the workers and managers.


Gesture recognition has been widely used for human-robot interaction. At present, a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains. For each new domain, it is required to collect and annotate a large amount of data, and the training of the algorithm does not benefit from prior knowledge, leading to redundant calculation workload and excessive time investment. To address this problem, the paper proposes a method that could transfer gesture data in different domains. We use a red-green-blue (RGB) Camera to collect images of the gestures, and use Leap Motion to collect the coordinates of 21 joint points of the human hand. Then, we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning. This paper compares the effects of three classification algorithms, i.e., support vector machine (SVM), broad learning system (BLS) and deep learning (DL). We also compare learning performances with and without using the joint distribution adaptation (JDA) algorithm. The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion. In addition, we found that when using DL to classify the data, excessive training on the source domain may reduce the accuracy of recognition in the target domain.


Among a wide range of enhanced oil-recovery techniques, polymer flooding has been selected by petroleum industries due to the simplicity and lower cost of operational performances. The reason for this selection is due to the mobility-reduction of the water phase, facilitating the forward-movement of oil. The objective of this comprehensive study is to develop a mathematical model for simultaneous injection of polymer-assisted nanoparticles migration to calculate an oil-recovery factor. Then, a sensitivity analysis is provided to consider the significant influence of formation rheological characteristics as type curves. To achieve this, we concentrated on the driving mathematical equations for the recovery factor and compare each parameter significantly to nurture the differences explicitly. Consequently, due to the results of this extensive study, it is evident that a higher value of mobility ratio, higher polymer concentration and higher formation-damage coefficient leads to a higher recovery factor. The reason for this is that the external filter cake is being made in this period and the subsequent injection of polymer solution administered a higher sweep efficiency and higher recovery factor. Enhanced oil-recovery  techniques are known as efficient methods in oil production form hydrocarbon reservoirs when natural drive mechanisms could not provide sustainable and economic production ,2,3. EORThe common types of polymers that are used in the polymer flooding procedures divided into biopolymers and synthetic polymers. Synthetic polymers such as polyacrylamide (PAM), hydrolyzed polyacrylamide (HPAM), xanthan, biologic polysaccharide and some of the natural polymers like hydroxyl ethyl cellulose (HEC), sodium carboxyl-methyl cellulose and guar gum that are modified in specific processes are the most ubiquitous polymers in petroleum industries. The advantages of these polymers are as follows:PAM (polyacrylamide): Due to its high value of molecular weight (approximately more than 106 gr./mole) that usually performed as a thickening agent for the aqueous solutions. Moreover, PAM has its stable properties at the temperature of 90 °C at the normal salinity and at the salinity of seawater; the temperature is set up at 62 °C Hence, it is worthwhile that this type of polymer would be restricted on the on-shore performances [ormances .HPAM (hydrolyzed polyacrylamide): It is considered as one of the popular polymer types in the operational circumstances. It is constructed by the hydrolysis procedure of PAM or by the copolymerization of acrylamide and sodium acrylate. HPAM has some advantages rather other polymer types that would be considered as the preferable type for its administration in the polymer flooding performances. These advantages are entailed low costs, the ability of this polymer to tolerate in the presence of high value of mechanical forces and its high potential to resist against bacterial attack. In addition, this type of polymer could be utilized up to the temperature of 99 °C that is utterly depended to the hardness of reservoir brines. On the contrary, the disadvantage of HPAM is the high sensitivity to the salinity of brines, surfactant present and hardness of brine [of brine .Xanthan gum: Xanthan has very high amount of molecular weight of 2−50 × 103 Kg/mole with the sever chains of polymer which made them approximately insensitive to the hardness and high value of salinity. One of the advantages of this polymer is to be compatible with different types of fluid additives and most of the surfactants. On the other hand it is very sensitive to degradation by bacterial activity when it was injected to the reservoir at low temperature regions [ regions .Thereby, to select the best type of polymers that has the most appropriate compatibility with the properties of reservoir rock and fluid, numerous parameters should be taken into consideration. First, it is required to consider the oil viscosity and the permeability of the reservoir to obtain the best molecular weight for polymer solution. Second, it is essential to determine the cloud point of polymer that is clearly related to the thermal stability of polymer in the high temperatures and high salinity of brines. Another essential parameter is the polymer retention, which encompasses possible mechanisms responsible for the reduction of mean velocity of polymer molecules during their flow through porous media. A schematic of polymer flooding performances is schematically depicted in Nanoparticles  are an indispensable part of reservoir materials, are considered as in situ particles in porous media and are released with flowing flow. The primary function of these nanoparticles is to be easily transferred in the fractures. Thereby, they can fill tight, small cracks and pores and induce fundamental changes in permeability reduction due to their strong impact on pore plugs. Furthermore, there are a wide variety of factors that exert a considerable influence on controlling nanoparticles migration, such as increasing flow velocity and rise of pressure drop during the low-salinity water-flooding procedures . In thisp is the pressure and q is the well flow rate. J is the dimensionless pressure drop when the rate of the well is constant [To define well injectivity, we use as the following equation for the injectivity index (II). This index is the well rate per pressure drop unit between the reservoir and the well, in which the impedance is defined as the normalized reciprocal well index:constant .Although many studies and laboratory investigations in the literature espouse the importance of polymer flooding techniques on oil-recovery enhancement, we have tried to derive a mathematical equation for simultaneously calculating the recovery factor through the axisymmetric two-phase polymer-nanoparticle-assisted extraction. Inasmuch, polymer impedance is modeled for heavy oil reservoirs. Moreover, the rheological properties of the formation and the polymer concentration on the recovery factor were parametrically analyzed to compare the considerable influence of each reservoir characteristics. Therefore, the structure of this paper is divided in three sections: (1) governing equations for the displacement of oil by the polymer solution, (2) calculating the pressure drop value by considering and comparing rheological properties and last, (3) investigating the profound impact of each parameter on the recovery factor.The injectivity of a polymer solution greatly affects sweep efficiency and subsequent oil recovery. Thus, it can greatly affect project costs and has a significant effect on decision-making. In this section, governing equations for flow of polymer solution containing nanoparticles  in porous media are reviewed. The effect of damage-free solutions, solutions without nanoparticles and solutions with polymer and slug-particle damage in heavy oil reservoir are discussed.BLJ is the damage-free impedance, Dx) and time , for displacement of oil by water is used for describing well-injectivity behavior during water injection into oil reservoirs with different viscosity. They assumed incompressible and immiscible flow of oil and water and used mass conservation of oil and water, generalized Darcy’s law for water and oil. Then, equations for one dimensional axisymmetric flow were solved. Finally, impedance due to damage free water injection were derived as follows :(2)JBL(tHigh-viscosity oil causes rapid water injection and increases in well injectivity (decrease of well impedance or pressure due to displacement of heavy oil by low viscous water). Injectivity depends on the water permeability end-point relative to oil at initial water saturation, well and reservoir dimensions and saturation-dependent mobility of oil and water.Normally, injected water contains dispersed nanoparticles  that may be retained in the porous media with flowing water and decrease permeability. Once massive pore blockage occurs and permeability declines, solid particles can no longer penetrate into the formation due to narrowed pores and a buildup of external porous media on the injection wall .Kalantariasl et al. (2014) derived equations for the simultaneous effect of damage-free impedance changes due to variation in mobility (Equation2) and nanoparticle retention near the wellbore, i.e., deep filtration of particles and external cake formation on the injection face as follows:Addition of Equation (2) to Equation (3) gives the final expression for the impedance as Equation 4:Oil displacement by polymer solutions containing dispersed particles from polymers added into the system must be considered, since polymers highly affect injectivity. Injectivity behavior due to displacement of oil by polymer solutions containing dispersed nanoparticles has three separate parts: changes in injectivity due to damage-free oil–water mobility variation, changes in injectivity due to damage resulting from retention of dispersed particles and changes in injectivity due to addition of polymer into the system.q is assumed in the flooding scenario, Therefore, the explicit formula is derived for the simultaneous injection of polymer and nanoparticles as the following equation:max—obtained from the Langmuir adsorption isotherm—and nonlinearity of the isotherm (b). These principal factors and how such parameters would emphasize the polymer injectivity procedures are detailed below.The following assumptions are taken into consideration to espouse the importance of driving mathematical equations for calculating pressure drop: there is incompressibility and flow direction in one dimension for the aqueous polymer solution in the porous media by the consideration of mechanical entrapment and polymer adsorption; the concentration of the polymer is not large enough to severely emphasize the volumetric balance of solution in the aqueous phase, i.e., the profound impact of polymer on the solvent density is being ignored and the proportionality coefficient is considered as the filtration coefficient, which is in good agreement with the experimental data. In addition, it has been elaborated that polymer solution viscosity has a rising pattern regarding the polymer concentration increase, i.e., (see Farajzadeh 2016 and Bedrikovetski 2008 for further detailed discussion about these assumptions). The rheological properties of formation play a significant role in the pressure drop of the reservoir, and subsequently affect the recovery factor. These rheological specifications are the mobility ratio (M), formation-damage coefficient (βf), resistance—or permeability-reduction factor (R), porosity (Φ), filtration coefficient (٨), concentration of the polymer solution, maximum dimensionless adsorbed-polymer concentration . As shown in Polymer flooding is considered as one of the most technical and economic improved oil-recovery procedures to develop sweep efficiency by reducing water viscosity, which, in turn, leads to producing more oil volume in the wellbore. In this way, an appropriate estimation of the properties of injected polymer solutions with the reservoir properties would be the essential first step before flooding processes and considered as the major challenges of polymer application in hydrocarbon reservoirs. A proposed analytical model (Equation 3) was developed for injectivity scenarios in the homogeneous reservoir. To investigate the profound impact of each phenomenon on the pressure drop and subsequently on the impedance, we concentrated on each parameter in detail to compare them properly.To find the considerable influence of mobility ratio, polymer concentration and formation-damage coefficient variations on the pressure drop, it is evident that mobility enhancement has provided higher pressure drop, due to the more possible spaces for oil movement, which directly leads to a higher pressure drop value. In addition, the propagation of polymers into the porous media is strongly controlled by the physical adsorption of polymer molecules and the polymer entrapment in the provided solvent. The polymer retention rate utterly depends on the flow rate, size of the pore-throats, concentration of injected polymers and the molecular form of the polymers. Thereby, a higher polymer concentration would lead to a greater pressure drop and increasing sweep efficiency. However, as shown, according to the analytical solutions for the dominant influence of formation-damage coefficient on the pressure drop, it has a similar increase pattern-like mobility ratio and polymer concentration alteration. An increasing trend for this parameter leads to a gradual increase which would then reach a plateau after the stabilization of particle distribution near the wellbore. Otherwise, this extensive model could be utilized in the viscous dominated waterflooding operations which has occurred in reservoirs with layer-cake.Neglecting the combination of the effects of three-phase displacement while deriving the mathematical model may lead to an inappropriate interpretation of well-injectivity history. To ensure the significant impact of the rheological properties of the reservoir and polymer, the characteristics of the total impedance, coupled with simultaneous effects of formation damage are taken into consideration. By the considering the constant assumptions for the comparison of each parameter, it is demonstrated that higher mobility ratio, polymer concentration and formation-damage coefficient values would be directly related to higher values of total impedance for polymer injectivity with the coupling of formation-damage effects. The permeability-reduction factor has not altered the recovery factor, thus it would be negligible in the calculations.Polymer flooding is an EOR technique that is particularly relevant to heavy oil reservoirs. In the coming decades, petroleum industries will concentrate more on the improvement of this technique, how to virtually eliminate its inefficiencies to obtain the best results and subsequently, how to obtain the most recoverable oil from reservoirs. The injection of polymer solutions could be strictly emphasized by the rheology of polymers, pore-throat plugs caused by polymer junk, polymer adsorption and the high-viscosity of the polymer in the solution used. According to the conceptual nurture of formation rheological parameters and polymer concentration, the main results of this study are as follows:The principal parameters that play a substantial role in the pressure drop and recovery of the polymer flooding technique are the mobility ratio (M), formation-damage coefficient (βf) and polymer concentration. In other words, the other parameters such as the resistance, or permeability-reduction factor (R), porosity (Φ), filtration coefficient (٨), concentration of the polymer solution, maximum dimensionless adsorbed-polymer concentration (Cmax) and nonlinearity of the isotherm (b) would not be an effective on the calculation of pressure drop and recovery factor. Thereby, the effect of these parameters can be considered negligible.Analytical-type curves show that higher values of mobility ratio, formation-damage coefficient and polymer concentration lead to a steep rise in both pressure drop and recovery curves versus the time of pore volume injection. In this way, after the external cake was performed with the maximum thickness, the pressure drop reached a constant value at the equilibrium time and hence, the recovery factor has stabilized in this period.In this extensive study, we coupled filtration theory with the simultaneous injection of a polymer solution to compare the results of each step and distinguish the differences of each method. In addition, the dominant influence of formation rheological parameters and polymer concentration were added to the mathematical model to investigate the importance of them.


The purpose of this review is to evaluate the use and effectiveness of the local administration of tramadol in reducing post-operative pain during surgical interventions.The PubMed, EMBASE and Cochrane Central Register of Controlled Trials (CENTRAL) databases will be searched for this review. This systematic review will include studies evaluating the clinical efficacy of the local infiltration of tramadol, with no study design restrictions. Only studies that present clear descriptions of local tramadol administration are published in peer-reviewed journals in the English, Italian, Spanish, French, Portuguese or German language and are published in full will be taken into consideration. A meta-analysis will be performed when there is sufficient clinical homogeneity among the retrieved studies, and only randomized controlled studies and quasi-randomized controlled studies will be included. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach will be used to assess the certainty in the evidence. If a quantitative analysis cannot be conducted, a qualitative description of the results of the retrieved studies will be provided.A high-quality synthesis of the current evidence on the local administration of tramadol for managing post-surgical pain will be illustrated using subjective reports and objective measures of performance. The primary outcomes will include the magnitude of post-operative pain intensity improvement, with improvement being as defined by a reduction by at least 2 points in the visual analogue scale (VAS) score or numerical rating scale (NRS) score. The secondary outcomes will be the magnitude of reduction in tramadol rescue doses and in other analgesic drug doses.This protocol will present evidence on the efficacy of tramadol in relieving post-surgical pain.CRD42018087381PROSPERO  The use of multimodal perioperative and intraoperative techniques for pain control may decrease the incidence of acute pain and related complications such as chronic pain [Several surgical procedures are performed every day worldwide, and most of them are painful or uncomfortable for patients. Currently, due to advances in knowledge and anaesthetic techniques, many diagnostic procedures and an increasing number of surgical procedures can be performed under local anaesthesia. A ‘short’ procedure may be described as an intervention that is anticipated to take fewer than 60 min and can be performed safely in day surgery . The prenic pain . Conventnic pain . In mostnic pain . The toxCompared with local anaesthetics, tramadol seems to have fewer side effects and good efficacy and safety. In 1998, Pang and colleagues described for the first time the anaesthetic property of commercially available tramadol when it is injected intradermally [To date, according to studies on rats, we suppose that tramadol has an early local analgesic effect, decreasing mechanical hyperalgesia induced by plantar incisions. This analgesic effect is not mediated by peripheral opioid receptors.Tramadol modifies both motor and sensory neural conduction with a mechanism similar to that of lidocaine and acts on the voltage-dependent sodium channel (NaV 1.2).A high concentration of tramadol can indirectly and reversibly suppress sodium inward currents, blocking voltage-gated sodium channels (NaV 1.2) .Some authors have reported that tramadol can interfere with nerve conduction, increasing the concentration of extracellular calcium (Ca+2) and raising the activation threshold of voltage-dependent channels .Furthermore, it has been reported that the presence of a high Ca+2 concentration in the external medium increases tramadol activity.Actually, the local anaesthetic effect of tramadol is weaker than that of lidocaine, but tramadol has fewer side effects and a lower risk of adverse events if it is accidentally injected inside a peripheral nerve .There is evidence in the literature on the use of tramadol as a local anaesthetic, but to date, only the systemic administration of tramadol has been approved. In addition, limitations regarding paediatric use were recently announced: On September 21, 2015, the US Food and Drug Administration (FDA) issued a drug safety communication on the use of tramadol in children aged 17 years and younger because of the rare but serious risk of slowed or difficult breathing. This risk may be increased in children treated with tramadol for post-surgical pain due to tonsillectomy and/or adenoidectomy . SubsequTramadol is an atypical synthetic opioid analgesic available on the market in a multitude of formulations. The analgesic potency of tramadol is approximately 10% of that of morphine and is considered a weak opioid because of its relatively low affinity for the μ-opioid receptor . CompareThe review protocol was registered on PROSPERO , and the registration number is CRD42018087381 . The lasThis review will consider studies evaluating the clinical efficacy of the local infiltration of tramadol, with no study design restrictions. Only studies that clearly describe local tramadol administration, are published in peer-reviewed journals in the English, Italian, Spanish, French, Portuguese or German language and are published in full will be taken into consideration. Studies describing tramadol administration for non-surgical procedures will be excluded; reviews, abstracts, letters and comments will be excluded. A meta-analysis will be performed when there is sufficient clinical homogeneity among the retrieved studies, and only randomized controlled studies and quasi-randomized controlled studies will be included.This review will consider all relevant studies in humans undergoing surgical interventions in which the use of tramadol as a local anaesthetic is clearly described. In vitro and animal studies will be excluded.This review will consider studies in which there is a clear description of tramadol use as local or locoregional anaesthetic. We will describe the efficacy of the local administration of this drug through subcutaneous, submucosal or intra-articular administration routes in controlling post-surgical pain. Using these administration routes, we will analyse the local action of tramadol in a circumscribed region, that is, the surgical incision site. Furthermore, studies concerning the use of tramadol as a locoregional anaesthetic  alone or as an adjuvant to other anaesthetics will be considered because when the drug is administered by a locoregional technique, it may act directly on the central nervous system. We limited the types of interventions to increase the level of between-study homogeneity; therefore, studies regarding the oral administration, intravenous administration or intramuscular administration of tramadol will be excluded because the drug has systemic effects with these types of administration. Studies concerning the use of tramadol for non-surgical procedures will be excluded.The comparator will be the administration of local anaesthetics or a placebo with the same mode of administration as used for tramadol for post-surgical pain control. Studies in which different administration routes were used for tramadol and the comparator(s) will be excluded.The primary outcome will be the magnitude of post-operative pain control, with successful pain control being defined as a reduction by at least 2 points or by achieving a score of < 3 on the VAS, NRS or if necessary, another age-appropriate pain assessment tool.The secondary outcomes will be the effects on drug consumption and vital signs.The magnitude of reduction in tramadol rescue dosesThe magnitude of reduction in other analgesic drug dosesRegarding drug consumption, the following factors will be considered:3.Tachycardia 4.Hypertension 5.Desaturation  with/without the occurrence of respiratory depression  and/or ICU admission.Regarding vital signs, the following factors will be considered:The PubMed, EMBASE and Cochrane Central Register of Controlled Trials (CENTRAL) databases will be used for this review. The reference lists of all the identified reports and articles will be searched as well with backward and forward citation tracking. No restrictions on the dates of publication of the studies will be applied. The reference lists of all the included studies will be screened for additional eligible studies that were not identified by previous searches.The search strategy and the search string will be formulated following the PICO method.In this review, a research strategy based on three steps has been designed to find and analyse only studies published in peer-reviewed journals.The first search will be conducted in PubMed, followed by a detailed examination of the titles, abstracts and the index terms used to describe the articles. The information on the complete search strategy will be derived from previous research. The proposed search strategy is detailed in Table A second search will be performed using the keywords and index terms identified through the following databases: PubMed, EMBASE and CENTRAL.A third search will be performed using the bibliographies of the identified reports. The search will not include any language restrictions. Nevertheless, if the title/abstract of a study is in English and the full text is in another language, the study will be considered if the text is in the English, Italian, Spanish, French, Portuguese or German language.Studies will be selected by a two-step process. Initially, the studies will be screened according to the titles and abstracts. Subsequently, the full text will be read for the final inclusion process. Duplicates will be excluded. Microsoft Excel software will be used to manage the study selection process and to remove all duplicates. Two authors will independently evaluate the eligibility of the studies. When there is missing information, the authors of the primary studies will be contacted. Explanations for the exclusion of full-text articles that do not meet the inclusion criteria will be recorded and reported in the review. Any disagreements that arise between the two authors during the study selection process will be resolved by discussion or with a third reviewer. Every result from each step of the search will be reported in full in the final report and presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram .All identified citations will be imported into Endnote X8.0 , and duplicates will be removed. Data will be extracted from the included studies using a standardized data extraction tool by two independent reviewers; these data will include specific details about the populations, study methods, interventions and outcomes of significance to the review objective. Furthermore, patient characteristics, such as age, sex, ASA class and intervention characteristics, such as the dose, timing of administration, route of administration and pharmacological combination, will be extracted..Two authors will independently evaluate the methodological quality of the retrieved studies before inclusion in the review. Study quality will be independently assessed by two authors. The Newcastle-Ottawa quality assessment form will be used to assess the quality of the observational studies, while the Cochrane tool (Rob2) will be used to assess the quality of the randomized controlled trials (RCTs) . Any disIf required, the authors of the studies will be contacted to request missing or additional data for explanation. Any disagreements between the authors will be resolved by discussion or with a third author. The results will be reported in narrative form and summarized in tables.If there are missing or incomplete data for the primary results, we will contact the corresponding authors for the missing data. If the missing data cannot be obtained, it will be included in a narrative analysis.We may conduct a narrative synthesis if a meta-analysis is not appropriate.Risk differences (RDs) will be used as the meta-analytic measure of the association between the administration of tramadol and post-surgical pain relief. We will calculate risk differences and corresponding 95% confidence intervals (CIs) using a 2 × 2 table.The route of administration used  subcutaneous, (b) submucosal, (c) intra-articular; locoregional: (d) intrathecal, (e) nerve block)The age of the patients treated If possible, a subgroup analysis will be conducted for the following factors:Q and I2 statistics, and the I2 statistic is defined as the proportion of total variance observed among the studies that is attributed to differences among the studies rather than to sampling error. I2 values of 25%, 50% and 75% correspond to cut-off points for low, moderate and high degrees of heterogeneity, and a P value of less than 0.10 for the Q statistic will be considered significant. If the overall heterogeneity is significant, a random-effects model will be used; otherwise, a fixed-effects model will be used.Heterogeneity between studies will be assessed by using the The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach for grading the certainty of evidence will be used to assess the certainty of the findings, and a summary of findings (SoF) will be created using GRADE-Pro GDT software. The SoF will present the following information where appropriate: absolute risks for the treatment and control, estimate of the relative risk and ranking of the quality of the evidence based on the risk of bias, directness, heterogeneity, precision and risk of publication bias of the review results. All primary outcomes will be included in the summary of findings.The purpose of this protocol is to investigate the potential off-label use of a drug commonly used in clinical practice for systemic administration. Its use as an adjuvant in local anaesthesia is described in the literature –32 and m


Imbalanced class distribution in the medical dataset is a challenging task that hinders classifying disease correctly. It emerges when the number of healthy class instances being much larger than the disease class instances. To solve this problem, we proposed undersampling the healthy class instances to improve disease class classification. This model is named Hellinger Distance Undersampling (HDUS). It employs the Hellinger Distance to measure the resemblance between majority class instance and its neighbouring minority class instances to separate classes effectively and boost the discrimination power for each class. An extensive experiment has been conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three state-of-the-art undersampling models. The outcomes display that HDUS can perform better than other models in terms of sensitivity, F1 measure, and balanced accuracy. Classification is a standard data mining process. It consists of two steps, building a model and testing a model. A classification model is built to learn from training data which is then tested to predict the category of unknown samples. Most classification algorithms were mainly built to classify the balanced dataset, whereas a problem occurs when a dataset is imbalanced, which degrades the recognition power of the classifier . The imbIn general, the problem of classifying imbalanced data is due to the lack of training with a few minority class samples which are inadequate to predict accurately . PreviouIn this work, we propose a novel undersampling technique, named the Hellinger Distance Undersampling (HDUS) model, aimed at solving the imbalanced classification problem in medical datasets. The proposed model reduces healthy class samples to improve the classifying performance of the rare disease class. It adopts the Hellinger distance to measure the similarity between majority class instance and its neighbouring minority class instances, then chooses a number of the highest Hellinger distance values, and sums them up to be a similarity value for each majority instance. Finally, the model selects a subset from the majority instances, having top similarity values, and combined with the original minority class instances. This model could effectively separate major class instances and minor class instances and boost the discrimination power for each class, thereby improving the classification accuracy for rare class. We compared the HDUS with four models, including a baseline model without using any sampling technique and three state-of-the-art undersampling models. The experiment was conducted on four imbalanced medical datasets using three classifiers.The remainder of this paper is organized as follows. Recently, the problem of imbalanced classification has drawn much attention in the literature because the traditional classification algorithms were not initially built to train the imbalanced dataset . This prTo overcome the imbalanced data problem, current approaches may be categorized to the algorithm level and data level. The first group works to change the classification algorithm, to support the minority class cases, by specifying weights to cases from various classes or by ensemble methods , 12. ThePrevious studies indicated that solving the imbalanced problem at the data level is simple and efficient for unbalanced classification . TherefoK neighbour that belongs to the minor samples. When the number of neighbours in each major class is higher in the minor class, the major class sample shall be omitted as noise or borderline [Undersampling is another reasonable data sampling technique which attempts to reduce the number of samples in the majority class. The undersampling concept is how to eliminate majority class instances in a manner that retains the practical distinction among classes . Numerourderline .Previous research studies revealed that there is no optimal rule to attain the best fit with over- or undersampling. They have shown that usually undersampling process of the major class is used to outperform the results obtained through the oversampling of the minor class . More thRecently, a lot of undersampling methods have been reported in the literature to improve the imbalanced data classification. Tsai et al. in  introducThe work is aimed at providing a method that handles the problem of imbalanced data distribution which affects classification performance of minority class samples. In the imbalanced dataset, the class with a larger number of instances takes up most of the space. Unequal class distribution makes the classifier to be inadequately qualified to classify the smaller class instances, and the class with a larger number of instances overlaps the identification ability of the class with a smaller number of instances. In this case, the classifier would favour the majority class instances and scoring false high accuracy.In this work, we proposed an undersampling model by following the principle of one-sided selection to extract instances from the major class, while the data in the minor class will remain without change. This is based on the premise that it is better to keep the instances of a minor class as real as they are, in such a manner that no greater or no less quantity is exercised on them. So, the classifier will be provided by an accurate recognition power for the original minor class samples.a(x) and b(x) be two probability functions; then the HD between a(x) and b(x) can be expressed as follows:Instance selection in the undersampling technique depends on how to select majority class instances in a manner that retains the compatible distinction among classes. In our proposed model, we used Hellinger distance (HD) , 28 to cConsidering the problem of classification of the imbalanced class dataset and being motivated by the properties of the HD, we proposed an undersampling model using Hellinger similarity measure. The proposed model works to reduce the number of major class instances, aimed at upgrading the prediction performance of minority class which is the class of teh highest interest in medical datasets. In this section, we display the details of the experiment to test the proposed HDUS model. We present the nature of the datasets, the used classification algorithms, the evaluation metrics, and the undersampling methods used for comparison.The code for the whole experiment was conducted in Python Programming language and spyder tools using the available utilities to provide all the necessary preprocessing and classification techniques besides the evaluation functions.In this work, we have exercised four imbalanced medical datasets to evaluate the performance of the suggested (HDUS) model. For each dataset, the number of features (attributes), the number of instances, the number of majority cases, and the number of minority cases are presented. These datasets are described in the following.This dataset is from the Southampton University Hospital and has been used with approval from the responsible surgeon (co-author), and the data are all anonymous. The data are for patients having primary cancer at 12 colorectal sites, who then have cancer resection surgery. There are 1005 instances (patients), each of which acts as a record of a single patient with 14 features (attributes), including the target label. Out of 1005 instances, 760 are for patients having primary CRC who do not have metastasis, representing the majority samples, and another 245 cases are for patients having primary CRC growing to metastasis in other organs of the body, representing the minority samples. The data type is categorical (groups into multiple categories) and mapped to numeric values. The dataset of PIMA Indians was taken from the UCI machine learning repository . It has The thoracic data was taken from the UCI machine learning repository . This daThe BC dataset was taken from the UCI machine learning repository  which isK-Nearest Neighbour (KNN). The primary purpose of using these classifiers was to evaluate the performance of the proposed model on four imbalanced medical datasets. The experiment was initially done on a CRC dataset and then tested on three datasets selected from the UCI repository.In this study, three classification algorithms with different characteristics were explored: decision tree (DT), Support Vector Machine (SVM), and X) with the particular label denoting its class. KNN counts the spaces between X and all training tuples, then specifies to X the maximum repeat class in the nearest k tuple [KNN is a classification technique that relies on feature similarity measures to find the closest neighbours. For the classification of a new point, the KNN reviews each training sample as a tuple  and the class boundaries trying to find the optimal hyperplane that removes some insignificant data from the training data set. However, when the data is intrinsically nonlinear, SVM will use kernel function to construct a separating hyperplane that transforms the data from the original dimension into a high-dimensional space. Popularly used kernel functions are the linear, polynomial, sigmoid, and Gaussian kernel , 34.A decision tree classifier involves several simpler decisions to build a tree model. The tree model builds three types of nodes: root, internal, and leaf. The root represents the starting point which has no incoming edges but outgoing edges. The internal nodes are represented by the data attribute, which has only one incoming branch and at least two leaving branches for each possible attribute. The leaf nodes are represented by the classes. These patterns of the decision tree express sets of if-then rules that can be employed to classify novel samples .A classifier is, typically, evaluated by a confusion matrix which contains four values from classification outputs that report the number of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). The TP refers to the rate of correctly recognizing the rare positive. The TN refers to the rate of correctly recognized negative. The FP refers to the rate of frequent negative incorrectly recognized as rare positive, and the FN refers to the rate of rare positive incorrectly recognized as frequent negative. In the experiment, the minority class refers to positive and the majority class refers to negative. The most used performance measure of classification tasks is accuracy. However, it is not an appropriate metric when evaluating the imbalanced class distributions because the classifier has a strong bias towards the majority class and fails to classify the few samples of minority class .More proper metrics could be used to assess the performance measurement of classifying imbalanced class distribution, such as sensitivity or recall (True positive rate (TPR)), specificity (True negative rate (TNR)) , precisiThese metrics are given by equations in  as follon-fold cross-validation is used as an evaluation criterion. In n-fold cross-validation, the data were divided into n equal folds, then the model was trained on all folds except one fold as a validation set on which the prepared model was tested. The process repeats so that each fold gets an opportunity to act as the test set. Then, the n-test outcome was averaged [n value is set to 5.To ensure an unbiased evaluation of the models, the averaged . In our Tomek link (Tml): it is aimed at removing the noise and border points from majority class instances by examining pairs of samples belonging to different classes but are each other's nearest neighbour and eliminates the majority sample of the pair .Random undersampling (RUS): it eliminates instances from major class randomly until the desired balance of class distribution is achieved .K-Nearest Neighbour that belong to the minor samples. If the number of neighbours is predominant in each majority instance from minority instances, certain instances of the majority class are eliminated as overlap instances [Edited nearest neighbour (ENN): the basic idea of ENN is to eliminate samples of the major class based on the nstances .To allow a fear valuation of the validity of our proposed method, HDUS is compared against three other undersampling methods:To investigate the performance measures of the proposed HDUS method, we used four imbalanced medical datasets using three classification algorithms including DT, SVM, and KNN and they were compared with the baseline model (without any resampling method) and with three state-of-the-art undersampling methods . The results of the four datasets  are shown in Tables 5As shown in Tables 5nd, 3rd, and 4th columns of the tables represent the result of used undersampling methods: Tomek link, RUS and ENN, respectively. We can observe the improvement achieved by using these methods, as expressed by the values of sensitivity that reflects the ability of the models to detect the class of interest, i.e., the minor class. Although the Tomek link obtained worse performance in all datasets, it is better than the baseline model except in the THS dataset, which scored lower than the baseline model.The 2th column of all tables by the proposed HDUS method in terms of sensitivity, F1_m, and Bacc. It can be observed that the HDUS performance shows significant improvement over the baseline and the three undersampling methods. The HDUS results in the top rate of sensitivity overall datasets . It scores over 80% in both CRC and PIMA, near 70% in BC and near 60% in THS which is the lowest sensitivity. It also results in the highest rate for both F1_m and balanced accuracy.More improvement is achieved in the 5This study discussed about a preprocessing undersampling method named HDUS. The model handles the class inequality problem in medical datasets to improve the prediction performance of the minority class samples by using the instance selection based Hellinger distance similarity measure.It is crucial to refer to the need for handling the issue of class inequality by choosing appropriate approaches that address the skewed distributions of data. As noted in the previous section, the baseline classification of original datasets shows a very high value of specificity to predict the majority class samples but a very poor sensitivity to predict the minority class samples, which is the class of interest in the imbalanced medical datasets. The use of traditional undersampling techniques shows good progress, mainly by RUS. However, using RUS seems to be not convenient since it eliminates meaningful samples randomly and can also cause overfitting due to the expanding of scar samples without limitations . The perTo simplify the comparison among the different undersampling methods used in the experiment and to evaluate their efficiency, Figures 1Regarding the classification methods, it is worth to remark that the benefit of carrying out classification increases when the class imbalance issue is appropriately addressed. In our experiment, different classification algorithms may benefit from the adoption of the HDUS model. In particular, DT achieves the best performance with HDUS. It also obtained the best results with Tml and ENN, whereas SVM is more appropriate for the RUS method. As shown in Finally, the results of the proposed HDUS model should be considered as a preliminary experiment, but a promising method in the application of undersampling the imbalanced medical dataset to improve the classification performance of minor class samples.This paper proposed a novel model, HDUS, that handles the imbalanced classification problem in the medical datasets to improve the classification of the minority disease class. HDUS works to reduce the majority class instances by using the Hellinger distance to calculate the similarity between majority class instance and minority class instances. Then, HDUS selects a subset from the majority class instances having the highest similarity values that are shown to perform well in combination with the original minority class instances. The experiment was conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three selective undersampling models. The performance results show that HDUS could achieve significant improvement over the selective models in terms of sensitivity, which is highly desirable in the medical domain, F1_measure, and balanced accuracy. HDUS has proved to be a promising model for rebalancing the imbalanced medical datasets which contain a few but important cases of disease class.In a future work, we encourage comparing HDUS with other sampling techniques for the same classifiers or using other classifiers or even utilizing a larger number of medical datasets with different characteristics. We also suggest integrating the proposed model with other sampling techniques to handle the imbalanced classification problem in medical datasets.


The digital revolution has changed the way we implement and use connected devices and systems by offering Internet communication capabilities to simple objects around us. The growth of information technologies, together with the concept of the Internet of Things (IoT), exponentially amplified the connectivity capabilities of devices. Up to this moment, the Long Range (LoRa) communication technology has been regarded as the perfect candidate, created to solve the issues of the IoT concept, such as scalability and the possibility of integrating a large number of sensors. The goal of this paper is to present an analysis of the communication collisions that occur through the evaluation of performance level in various scenarios for the LoRa technology. The first part addresses an empirical evaluation and the second part presents the development and validation of a LoRa traffic generator. The findings suggest that even if the packet payload increases, the communication resistance to interferences is not drastically affected, as one may expect. These results are analyzed by using a novel Software Defined Radio (SDR) technology LoRa traffic generator, that ensures a high-performance level in terms of generating a large LoRa traffic volume. Despite the use of orthogonal variable spreading factor technique, within the same communication channel, the collisions between LoRa packets may dramatically decrease the communication performance level. Over the last years, the number of IoT (Internet of Things) technologies has continually increased in our daily lives. The main goal of these technologies is to increase the quality of life. These IoT architectures have generated new challenges and issues, many of them being impossible to be anticipated at the moment of design. These challenges are mostly often related to: (1) Providinggreater device autonomy (mainly by obtaining the lowest possible energy consumption); (2) The possibility of integrating a high number of sensors; (3) Network scalability; (4) Providing a larger coverage area; and (5) Ensuring the costs are accessible to consumers targeted by this technology.The main communication protocols that can be integrated into the IoT concept are presented in Thus, in order to cope with the new challenges and IoT applications, LPWAN (Low-Power Wide-Area Network) networks have been created. A new communication paradigm for applications featuring low data rates with low power use has been achieved through the integration of technologies, such as SigFox or LoRa , with thThe main protocols for solving the IoT concept challenge are briefly compared in In this paper, we focus on the most prominent of the LPWAN (Low-Power Wide-Area Network) technologies, pioneered by Semtech, using the modulation called LoRa, the acronym for Long Range. The LoRa modulation technique scheme is an integrated part of the LoRaWAN  communicThe main contribution of the paper is the evaluation of the orthogonality for the modulation of LoRa packets modulated with a different spreading factor within the same communication channel. Also, to further open new research directions regarding the scalability of LoRa networks a novel LoRa traffic generator able to emulate and simulate thousands of LoRa sensors without the need of further investments in a manner that is both low cost and effortless is designed and validated. From the authors’ best knowledge, an empirical approach backed by experimental results has not been covered yet in scientific literature, making for another novelty element of this paper. The ability to further test and develop new communication protocols, makes from the LoRa traffic generator an asset that can significantly contributeto large-scale LoRa networks research.This paper has the following structure: First, the presentation of a short introduction explaining the goal of this work, it mentions the communication protocols that can be integrated into the IoT concept. The second section presents the LoRaWAN communication architecture. The third section presents the evaluation of LoRa modulation. This section provides a detailed assessment of performance in terms of Bit Error Rate (BER) when different spreading factors and communication channels bandwidths are used. The fourth section presents a novel LoRa traffic generator based on SDR (Software Defined Radio) technology. The LoRa traffic generator contribute to experimentally measuring the performance provided by LoRa under capture effect and the quasi-orthogonality condition of LoRa spreading factors. The fifth section makes for the conclusion of this work and the discussion of the obtained results. The LoRa technology is part of the LPWAN family of technologies, especially employed when developing IoT networks, when the volume of data sent, oftentimes gathered from the sensors, is quite low . The operating range can reach up to tens of kilometers .A proprietary modulation scheme is used by the LoRa communication, alongside a CSS (Chirp Spread Spectrum) type mechanism. The spread spectrum technology of LoRa modulation, when merged with digital signal processing technology, enables a very good performance, despite interferences . Taking advantage of these mechanisms, LoRa communication can work over long distances in harsh environments. Different spreading factors are used, which provide different bit rates, providing an increase in the range of this communication model, leading to better spectral efficiency and an increase in network capacity. Physical bitrates are as it follows: They start from 11 kbits/s for SF7, and they go down to 250 bits/s for SF12 [The LoRaWAN communication system includes the following components: Sensors, also known as end-devices, a gateway to gather data and a network server, to allow user applications to access remote data with the help of an applications data server. The LoRaWAN communication architecture can be observed in In Europe, limitations of transmission time are being regulated by the ETSI (European Telecommunications Standards Institute). Thus, a duty cycle of less or equal to 1% is allowed, that needs to be distributed by three channels of the EU sub-bands . There are three functionality classes for the end-devices: Class A, B and C. Most LoRaWAN devices have a Class A rating, with transmission being initialized by the end-device node, in a completely asynchronous manner. After the end-device node sends data to the gateway (uplink), two well-defined time slots are allocated to it during in which a response from the gateway is expected (downlink). The first of these time slots is opened in the standard frequency band of 868 MHz, using the same channels as the uplink ones. The second time slot is opened in a reserved frequency band (869.525 MHz). This method increases data packets reception probability and the immunity to interferences. These interferences can originate from other signals transmitted in the same network, on the same frequency band when the network is clustered with too many sensors that send the information to the gateway. Class B devices must be compatible with Class A devices. Despite being very similar to Class A devices, Class B devices require an additional well-defined time slots, where a message from the gateway is expected. In this case, the sensor will receive a time-synchronized beacon-type signal from the gateway, to tell exactly when the node is in receiving mode. The largest energy consumers are Class C devices, since they shift to the reception mode after sending data to the gateway. Moreover, these Class C devices must be compatible with Class A devices only. Unlike other LPWAN wireless transmission technologies, LoRa uses the same phase between two chirp symbols for the preamble of the data packet at the physical layer of the communication protocol. This mechanism leads to improved time and frequency synchronizations, without the need of using expensive hardware elements as decoders in the configuration of the end-device node.In this section, we focus our attention onto the RF (Radio Frequency) and MAC (Medium Access Control) layer particular to the LoRa technology.Current studies are mostly based on the results obtained through simulations ,14, and In this work, the empirical results are justified by the obtained experimental results. The influence of collisions over performance is difficult to analyze in a classical setup, due to the fact that the integration of a large number of sensors demands a high cost. This is the reason we focus on the development, implementation and testing of a LoRa traffic generator that allows us to generate a large volume of data packets with the use of SDR technologies. This LoRa traffic generator must be easy to re-configure and flexible enough to allow for the customization of LoRa packets enabling testing in various configurations and structures. Because of these reasons, an SDR solution has been selected, meeting the requirements of flexibility, while being low cost at the same time. The traffic generator tests are performed within a semi-anechoic chamber in order to avoid any outside interference and obtain accurate results. The performance testing of the LoRa technology cannot be overlooked. The developers and those who implement networks which use the LoRa modulation need new solutions that can contribute to the direct increase in performance level meaning a detailed analysis of the influence the collision of packets, which can occur in LoRa communication architectures. These aspects can be considered the novelty of this work. In Reference , it is sc) Spreading Factor (SF), communication channel bandwidth (BW) and Coding Rate (CR). Notwithstanding all this, despite the ruggedness of the LoRa technology, its scalability is still undergoing research with a collective effort aimed at achieving this goal [The goal of introducing these separation systems  is within their possibility to integrate several nodes that are as high as possible capable of co-existing within the same cell provided by a gateway (GW) module. On these grounds, LoRa offers various possibilities for orthogonal transmissions, such as central channel frequency  triggers a decrease in performance level ,22.The LoRa specification, as defined by Semtech, has seven spreading factors (SF6–SF12) and four different channel bandwidths  following Semtech-defined LoRa specifications. In practice and current hardware implementations, SF can be varied from 7 to 12 with the employed BWs being 125 kHz, 250 kHz and 500 kHz. As per channel allocation possibilities are specified by one of the IoT platform named TTN (The Things Network) , there aBetween specific spreading factors and various bandwidths, theoretical orthogonality and more importantly, non-orthogonality can be easily determined. Not all combinations of SF and BW are orthogonal . TheoretThe LoRa communication model presented in Reference  has beenThus, the initial model , has beeThe modulated packet is then interfered in turn with a random LoRa packet modulated by a spreading factor, so that all possible variations are analyzed. The interfering packet is sent at random moments in time with an SIR  which increases by a 1 dB step starting from −30 dB. By implementing this mechanism, the BER (Bit Error Rate) characteristic can be determined, as a result of comparing later demodulated bits to initial modulated ones. It can be concluded that, the lower the spreading factor that interferes with the communication, the higher the SIR threshold necessary to obtain an admissible BER value. The model is implemented using MATLAB  environmWith the integration of the possibility of change for the bandwidth of the communication channel and applying the model on different LoRa packets featuring different payloads, the conclusion is that the applicability of the model is greatly extended. Thus, the new model offers the possibility of analysis of the influence of the communication channel bandwidth and of the payload dimension over the level of performance of the LoRa technology, by determining its scalability through the evaluation of the BER parameter. As expected, the worst-case scenario corresponds to when the packet interferes with a packet modulated with the same spreading factor. The limitations of this approach are related to the fact that an empirical method is used, and some limitations of the hardware design of the sensors and of the gateway modules are not taken into account. Moreover, it is important to mention that the assumption of perfect synchronization between transmitter and receiver is considered. Another major difference is in the fact that the LoRa traffic generator that has been developed and tested uses a method that allows the customization of generated packets with an innovative solution, unlike other approaches like the one presented in Reference . DiffereThus, two overlapped transmissions have been considered by a different use of spreading factors in order to evaluate the performance level. Thus, each packet modulated with an SF is overlapped  with a packet that has been progressively modulated with all other possible SF combinations. This is the emulated communication scenario. Following the implemented model, the correct synchronization between the sensor and GW is implied, the interference packet is randomly generated in time. All these elements make the degradation in performance of maximum importance.The SIR parameter has been chosen to evaluate performance as accurately as possible, considering the LoRa communication channel. One can observe that for an SIR value of approximative −1 dB, the BER value equals to about 70%. There is still the need to mention the fact that the most unfavorable case is analyzed when all sensors use the same SF12 spreading factor.Moreover, the spreading factor has been modified from SF12, representing the most unfavorable case (with the sensor located at a great distance from the GW) to SF6 corresponding the situation in which the sensor is located at a short distance from the GW. For each analyzed SF, every spreading factor is taken into consideration as possible interference. Obviously, the maximum value is reached when all the other sensors communicate on the same SF as the one undergoing analysis; this situation is also what we refer to as the worst-case scenario. This is a pessimistic approach and represents the evaluation of a boundary situation which is, however, often met with during the practical implementations of the LoRa technology. Due to the fact that one of the main advantages of the LoRa technology consists in large communication distances of the order of tens of kilometers; the operators of these networks will choose an SF with the maximum value available and implicitly ensuring communication at long distances, from an incentive to minimize the number of gateways  which serves a certain area. This feature determines a longer transmission time (due to the LoRa modulation mechanism) and implicitly the increase of probability of a collision occurring . The resThus, another varied parameter is the payload size associated with the LoRa packet ranging from the minimum value of 1 byte to a maximum LoRa specification defined value of 243 bytes. From the obtained results, it can be observed that with SIR  decrease the analyzed BER (Bit Error Rate) parameter increases, an expected aspect due to the increase of the transmission-related time. When a larger channel bandwidth is used, the performance level and the resistance to interferences is considerably enhanced.Furthermore, one can see that once the packet payload is increased, the resistance to interferences is not drastically affected, with results being similar. Since the studied scenario is of a worst-case type  and of best-case scenario , the payload influence is not a determining factor in the final overall result. This result is a surprising one, due to the fact that the answer to a wireless communication channel is oftentimes dependent on network packet dimension. A similar result has been reported in Reference . Thanks To investigate the influence of the payload on the resistance to interferences, another simulation scenario involving the BER parameter evaluation for a specific SF by varying the payload size while maintaining the same BW value was performed. The payload was varied from 60 bytes to 120 bytes and 180 bytes, respectively. The selected communication channel bandwidth in this setup is 125 kHz. In As stated before, one can observe the payload does not significantly influence the performance level of communication. We consider this payload analysis to be one of the main contributions of this work. This aspect is particularly important for LoRa network developers that can consider the use of large packets if the specifications of the application allows it.In order to evaluate the influence of the communication channel bandwidth on the level of performance, an additional test had been performed. We measured the BER parameter by varying the bandwidth of the communication channel, while maintaining the same packet payload size of 120 bytes. These results are presented in Our results show, as expected, that with the BW increase the performance level of the communication significantly increases. Thus, a larger communication channel bandwidth is preferred, if available, however, but it is dependent on the geographical area of the installation site.From the presented results, it can be seen the collision number contributes to the dramatically decrease in the performance level of the LoRa communication. Thus, this aspect, which cannot be overlooked, needs to be carefully addressed and further analyzed when a high level of performance for a high-density large-scale type network is mandatory .In this section, an experimental analysis has been made using SDR technologies , measurements being realized in a semi-anechoic chamber (in an interference-free environment). while considering the previously presented results. The first step has been the development, implementation and testing of a LoRa packet generator capable of generating packets that are compatible with the LoRaWAN specifications which have been defined by LoRa Alliance. For the implementation of the generator, the SDR technology is integrated by means of a LimeSDR  hardwareTo validate the developed LoRa traffic generator, some test scenarios created by means of using a TDK RF Solution  manufactThe used GW is implemented by using a Raspberry PI 3B+ and the iC880A concentrator board running a Linux-based operating system and integrating a forward-packet mechanism type towards the TTN data aggregation platform, connected to the Internet inside a semi-anechoic chamber. The designing and testing of the GW module are depicted in previous work published by the authors . The GW In the experiments carried out, the LoRa sensors that have been used feature an Atmega328 micro-controller and an RFM95  radio moThe main advantages are that the user has access in modifying parameters such as: Spreading factor, a packet payload, coding rate, the communication channel bandwidth and the communication channel central frequency. The second advantage of the traffic generator is to generate a very large number of LoRa packets so that it can allow for the emulation of LoRa networks of a high-density large-scale type and by doing so effectively opening new research directions within the field of wireless sensor networks.Thus, the architecture that has been initially presented  has been developed and perfected with the introduction of additional multiplexers with a large data volume of generated traffic as a result. Due to its low cost it can be easily implemented by researchers in a low effort manner. Another advantage is its scalability, by simply integrating other SDR devices the traffic generation capacity may be proportionally increased. The validation of the proposed traffic generator has been done in an evaluation scenario, where the spreading factor has been modified from SF7 to SF12, alongside the channel bandwidth, from 125 kHz to 250 kHz, only for SF7.It has to be mentioned, that all of the packets are correctly received by the GW when the LoRa traffic generator is not active. The LoRa traffic generator and the sensors use the same transmission power level. Thus, when the traffic generator is used, there is a collision that occurs between the packets sent by the LoRa generator and the packets sent on the same channel by the sensor. In the performed tests, the LoRa traffic generator acts as a source of interference. The monitored communication link from a performance standpoint belongs to the LoRa sensor node that uses an Atmega328 micro-controller integrating an RFM95 radio transceiver. The sensor communicates with the GW module by means of different SFs, on 868.3 MHz communication channel and generates one packet every two seconds. The received packets are sent by the GW module to the TTN platform. The LoRa sensor node can use one SF at a time, which can be modified by changing the software. The SDR LoRa traffic generator varies the SF parameter of the transmitted packet generating packets at random time moments using the same 868.3 MHz communication channel. These packets are received by the GW module and are reported to the TTN platform. The MIMO structure of the SDR platform allows the architecture to send different packets with different modulated SFs. Each transmitter can send packets modulated with a different SF. In the performed tests the payload was varied from 1 byte to 243 bytes. As the payload increases, the performance level is not affected, like the results shown in The LoRa traffic generator results are depicted in We need to mention that this evaluation setup corresponds to a pessimistic scenario that does not consider the geographical duty-cycle restriction that shows that a node must respect amongst many other node class restrictions. The emulated scenario corresponds to a high-density LoRa communication architecture that integrated thousands of nodes.The simulated results presented in Once an experimental setup is implemented, all these assumptions are no longer valid, since the hardware implementation of the GW module and of the LoRa sensor lead to results that differ from those determined on an empiric basis. The scenario that involves the generation of a high-volume data packets by the LoRa traffic generator is a pessimistic one, due to the fact that it takes into consideration a boundary condition by tackling the duty-cycle restrictions associated with the LoRa communication in some geographical areas. The synchronization between the LoRa sensor and the GW module is crucial for the correct demodulation of the received packets . The actHowever, in a high-density large-scale type of communication architectures , this pessimistic analyzed scenario corresponds to a scenario of mass-deployment. These analyses are necessary for the evaluation of scalability of the LoRa technology, so that whoever implements and develops such type of LoRa networks can take into account aspects that show the possibility of eventual collisions occurring with performance loss as a direct result. From a theoretical standpoint, the use of different spreading factors at different channel bandwidths onto the same communication channel can warrant orthogonality. However, the empirical and practical results show that collisions do occur, and that they drastically decrease the level of performance. The simulation results are validated with the implementation of a novel LoRa traffic generator by using SDR technology that ensures a high level of performance. The advantage of a traffic generator is represented by the possibility of emulating different LoRa sensor networks, without the need for additional costs in what can be described as an effortless and cheap manner. This aspect opens new research directions in the field of sensor networks by addressing the various issues which apply to the IoT concept. From the empirically obtained results, it can be seen that despite the existence of orthogonality between different spreading factors within the same communication channel, the collisions between certain LoRa packets may occur . This aspect cannot be overlooked and must be further analyzed with it being of utmost importance to developers and users who implement communication networks of a LoRa type.Another interesting result is that the payload influence is not a determining factor regarding the resistance to interferences. Thanks to the coding and spectrum spreading mechanism patented by Semtech, the influence of payload over the level of performance is minimal. If a larger channel bandwidth is used, the performance level and the resistance to interferences is considerably enhanced.These results have important implications for the LoRa operators and network planning professionals: Allotting larger SFs for users farther away would not necessarily improve the connectivity capability in high-density large scale sensor networks, since the transmissions are later received at low-power levels and are very prone to collisions due to longer transmission times.Moreover, if a CCA (Clear Channel Assessment) mechanism is not integrated, the ALOHA communication mechanism can lead to lowered performance. In scientific literature, there are some works that propose the integration of different control mechanisms for the communication channel assessment, such as the Listen Before Talk mechanism which significantly increases the performance of the LoRa communication system ,39. As the LoRa communication network increases , a real decrease in the number of collisions occurs, once a Listen Before Talk mechanism, based on a channel energy-detection procedure has been integrated at a physical layer of the communication protocol . The secFrom the author’s standpoint, at the moment, there is a requirement to find certain solutions which can contribute to the increase in the performance level of LoRa networks. From the obtained results at a simulation level, as well as the results obtained by experimental means within the LoRa communication collision, can occur if two or more LoRa packets are received on the same channel. Despite the advantages of the LoRa technology, namely, its ruggedness  and long communication range, its scalability is still undergoing research. This is a goal shared by the scientific community. At the moment, there are several adaptations of the LoRa protocol that allow its use in industrial applications with the help of some solutions that overcome the ALOHA communication scheme. Certain works promote the use of a time-slot schedule to reduce and eliminate the number of collisions and to allow the grouping of sensors present in certain areas based on the level of the received signal. In such conditions, one sensor can only communicate within the programmed time slot. The Listen Before Talk contention-based protocol verifies the degree of channel occupancy multiple times within the same scheduled time slot . By inteAnother goal is the improvement of the LoRa communication protocol, so that it becomes suitable for real-time environments and time-sensitive applications. One other method of nearing the LoRa communication protocol to the real-time concept involves the introduction of the ability of self-management and grouping of the devices, so that they themselves can determine their position on the communication time slots, as part of an architecture that is based on time-slotted communications. The use of a single slot dedicated to each frame is suggested in Reference  as a solThe novel LoRa traffic generator that has been developed is a useful and reliable instrument for the analysis of scalability and performance of LoRa communications. The traffic generator can be integrated into many applications, having immense potential. Thus, different manufacturers can use it in assessing the real throughput of their gateway architecture. Moreover, LoRa network operators can easily field-test their network distribution on a large-scale geographical area, reducing the initial deployment costs. The ability to further test and develop new communication protocols makes the LoRa traffic generator an asset that can provide a contribution to large-scale LoRa networks research.