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How To Benefit From Emerging Technologies From the daggers and axes of the cavemen societies to today's spacecraft, self-driving cars, metaverses, and AI-filled societies, technology has significantly emerged and brought about a massive transformation to our lives. The pace of this innovation has been particularly colossal in this industrial era, continuously disrupting our lives. Where will this imminent tech take us in the future? This book will dissect how various aspects of our lives will be transformed in the years to come, with a particular focus on how to benefit from these emerging technologies. You will gain a 360 degree view by getting a historical perspective of technology because discussions about the future are seldom complete without history. The ongoing debate on whether technology will replace our jobs is causing great panic. However, failure to catch up to technology is guaranteed to be catastrophic. This book will provide a freight of the latest tech-driven trends to equip everyone to face the future, like a one-time software upgrade. Whether you are a student, a fresh graduate, a bewildered parent, or a tech enthusiast, this book offers everything you need to be ahead of the game. It will also help budding entrepreneurs, business owners, and corporate professionals identify opportunities to incorporate the right tech into their businesses and be at the forefront of innovation.

The Internet of Battlefield Things (IoBT) refers to interconnected battlefield equipment/sources for synchronized automated decision making. Due to difficulties unique to the battlefield, such as a lack of infrastructure, the heterogeneity of equipment, and attacks, IoBT networks differ significantly from regular IoT networks. In war scenarios, real-time location information gathering is critical for combat effectiveness and is dependent on network connectivity and information sharing in the presence of an enemy. To maintain connectivity and guarantee the safety of soldiers/equipment, location information must be exchanged. The location, identification, and trajectory of soldiers/devices are all contained in these messages. A malicious attacker may utilize this information to build a complete trajectory of a target node and track it. This paper proposes a location privacy-preserving scheme in IoBT networks using deception-based techniques. Dummy identifier (DID), sensitive areas location privacy enhancement, and silence period concepts are used to minimize the attacker’s ability to track a target node. In addition, to consider the security of the location information, another security layer is proposed, which generates a pseudonym location for the source node to use instead of its real location when sending messages in the network. We develop a Matlab simulation to evaluate our scheme in terms of average anonymity and probability of linkability of the source node. The results show that the proposed method improves the anonymity of the source node. It reduces the attacker’s ability to link the old DID of the source node with its new DID. Finally, the results show further privacy enhancement by applying the sensitive area concept, which is important for IoBT networks.

Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis. A common problem are high cardinality features, i.e. unordered categorical predictor variables with a high number of levels. We study techniques that yield numeric representations of categorical variables which can then be used in subsequent ML applications. We focus on the impact of these techniques on a subsequent algorithm’s predictive performance, and—if possible—derive best practices on when to use which technique. We conducted a large-scale benchmark experiment, where we compared different encoding strategies together with five ML algorithms (lasso, random forest, gradient boosting, k-nearest neighbors, support vector machine) using datasets from regression, binary- and multiclass–classification settings. In our study, regularized versions of target encoding (i.e. using target predictions based on the feature levels in the training set as a new numerical feature) consistently provided the best results. Traditionally widely used encodings that make unreasonable assumptions to map levels to integers (e.g. integer encoding) or to reduce the number of levels (possibly based on target information, e.g. leaf encoding) before creating binary indicator variables (one-hot or dummy encoding) were not as effective in comparison.

Bu çalışmada, regresyon analizindeki kalitatif değişkenlerin rolünün açıklanmasına çalışılmaktadır. Kalitatif değişkenler gölge değişken (dummy variables) olarak adlandırılır. Gözlemsel çalışmalardaki birçok ilginç problemin çözümü için doğrusal regresyonda oldukça esnek bir araç olarak gölge değişkenleri kullanırız.

The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the ‘effective number’ of clusters and simulate placebo laws with dummy variable regressors.

Using a nationwide survey of provincial institutional quality and a sample of private manufacturing small and medium scale enterprises (the SMEs), this paper contributes to the literature by considering for the first time the effects of corruption on the financial performance of Vietnamese private SMEs. Interestingly, contrary to previous findings, we find that corruption when measured by a dummy variable, does not affect firms' financial performance after controlling for heterogeneity, simultaneity and dynamic endogeneity. However, the intensity of bribery and the majority of the forms of corruption were found to have negative impacts on firms' financial performance. Hence, a typical approach using only a dummy variable for bribery might not adequately evaluate the impact of bribe intensity or even ignores the negative impacts of some types of bribes on firms' financial performance. The findings suggest that anti-corruption measures are vital for the development of the Vietnamese private SMEs.

The article presents a model of an anthropometric dummy designed for low velocity crash tests, designed in ADAMS. The model consists of rigid bodies connected with special joints with appropriately selected stiffness and damping. The simulation dummy has the appropriate dimensions, shape, and mass of individual elements to suit a 50 percentile male. The purpose of this article is to draw attention to low speed crash tests. Current dummies such as THOR and Hybrid III are used for crash tests at speeds above 40 km/h. In contrast, the low-speed test dummy currently used is the BioRID-II dummy, which is mainly adapted to the whiplash test at speeds of up to 16km/h. Thus, it can be seen that there is a gap in the use of crash test dummies. There are no low-speed dummies for side and front crash tests, and there are no dummies for rear crash tests between 16 km/h and 25 km/h. Which corresponds to a collision of a passenger vehicle with a hard obstacle at a speed of 30 km/h. Therefore, in collisions with low speeds of 20 km/h, the splash airbag will probably not be activated. The article contains the results of a computer simulation at a speed of 20 km/h vehicle out in the ADAMS program. These results were compared with the experimental results of the laboratory crash test using volunteers and the Hybrid III dummy. The simulation results are the basis for building the physical model dummy. The simulation aims to reflect the greatest possible compliance of the movements of individual parts of the human body during a collision at low speed.

Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and by the effect of spectral reflectance from underlayer shrub, grass, and bare soil for young stands. This usually leads to overestimations and underestimations for smaller and larger values, respectively, and makes it very challenging to improve the estimation accuracy of forest AGB. In this study, a novel methodology was proposed by incorporating stand age as a dummy variable into four models to improve the estimation accuracy of the Pinus densata forest AGB in Yunnan of Southwestern China. A total of eight models, including two parametric models (LM: linear regression model and LMC: LM with combined variables), two nonparametric models (RF: random forest and ANN: artificial neural network) without the age dummy variable, and four corresponding models with the age dummy variable (DLM, DLMC, DRF, and DANN), were compared to estimate AGB. Landsat 8 Operational Land Imager (OLI) images and 147 sample plots were acquired and utilized. The results showed that (1) compared with the two parametric models, the two nonparametric algorithms resulted in significantly greater estimation accuracies of Pinus densata forest AGB, and the increases of accuracy varied from 8% to 32% for 100 modeling plots and from 12% to 35% for 47 test plots based on root mean square error (RMSE); (2) compared with the models without the age dummy variable, the models with the age dummy variable greatly reduced the overestimations for the plots with AGB values smaller than 70 Mg/ha and the underestimations for the plots with AGB values larger than 180 Mg/ha and, thus, significantly improved the overall estimation accuracy by 14% to 42% for the modeling plots and by 32% to 44% for the test plots based on RMSE; and (3) the texture measures derived from the Landsat 8 OLI images contributed more to improving the estimation accuracy than the original spectral bands and other transformations. This implied that two nonparametric models, coupled with the use of the age dummy variable and texture measures, offered a great potential for improving the estimation accuracy of Pinus densata forest AGB.

Because of the rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration, the peak angular velocity, brain injury criteria values (mean average errors < 13, 8, 13%, respectively), and the mouthguards with long enough sampling time windows are suitable for a convolutional neural network-based brain model to calculate the brain strain (mean average errors < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part.

Physical reconstructions are a valuable methodology for quantifying head kinematics in sports impacts. By recreating the motion of human heads observed in video using instrumented test dummies in a laboratory, physical reconstructions allow for in-depth study of real-world head impacts using well-established surrogates such as the Hybrid III crash test dummy. The purpose of this paper is to review all aspects of the physical reconstruction methodology and discuss the advantages and limitations associated with different choices in case selection, study design, test surrogate, test apparatus, text matrix, instrumentation, and data processing. Physical reconstructions require significant resources to perform and are therefore typically limited to small sample sizes and a case series or case–control study design. Their accuracy may also be limited by a lack of dummy biofidelity. The accuracy, repeatability, and sensitivity of the reconstruction process can be characterized and improved by good laboratory practices and iterative testing. Because wearable sensors have their own limitations and are not available or practical for many sports, physical reconstructions will continue to provide a useful and complementary approach to measuring head acceleration in sport for the foreseeable future.