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Road accident data includes detailed information about incidents that occurred, such as where they happened, the severity of the accident, and the number of people on the road at the time. Such information is useful in determining the causes of accidents and developing potential countermeasures. This research aims to determine the factors that contribute to pedestrian fatalities and injuries in traffic accidents. This study examined 150 pedestrian-vehicle accidents that took place between 1990 and 2021 in forty countries. Eleven factors have been identified as the major causes of accidents. The categorical principal component analysis (CATPCA) technique is used to reduce the number of dimensions and identify the elements that contribute to accidents. The eleven variables are classified into three groups: human factors, roadway environment, and vehicle attributes. The study found that car speed, weather, lighting, traffic conditions, area types, accident locations, and road conditions all had a significant impact on pedestrian accidents and fatalities. The findings show that a pedestrian's state (walking, running) and intention significantly increase the risk of serious injuries and death. The analysis of the driver's status suggests that the driver's intentions may also play a role in car accidents.

Background: Anxiety may be defined as “apprehension, uneasiness or tension that stems from an anticipation of danger, which may be external or internal”. Amputation is “A removal of the limb or its part owing to medical causes/reasons”. Low-level of social support, religiosity & self-efficacy, may have a role in commencement and continuation of mental health condition. Empathy & support are imperative for helping amputees to cope-up with an uncertainty, emotions, mobility issues, and to en-able them to adjust to new normality. Objective: The aim of study was to find an impact of duration and social support on anxiety amongst amputees. Study Design: Cross-sectional study. Place and duration of Study: Physical Rehabilitation Centers of Quetta. Duration of this study was 3 months. Methodology: Primary data was taken from all amputees visiting rehabilitation centers, through structured adopted questionnaire “Hospital Anxiety and Depression Scale” (HADS), with the help of trained data collectors along with principal investigator. Results: 54 subjects were included in this research, of whom. Majority 49 were earning/receiving up to 30000 Pk Rs monthly. Regarding no of dependents, majority (57.5%) had 9 or above no of dependents.

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

We address the application of using short erasure correcting codes for cognitive radio applications. In order to optimize the efficiency of secondary user in cognitive radio we need to find the optimum functioning point on receiver operating characteristic (ROC) curve for sensing impairments. This optimum functioning point depends on the parameters of sensing impairments, the activity of PU, and code parameters including minimum distance, length and rate. We have given preference to short codes over long codes in order to minimize the latency of whole system. We have compared the efficiency achieved using short codes with short and long LDPC codes. We have also discussed the scenario of multiple secondary users for cognitive radio application (for example using WiFi in TV white spaces), erasure correcting codes can be used for the recovery of erased data of all secondary users. We show that there is a trade-off among PU's activity, the code properties (length, rate, minimum distance, etc.) and the maximum achievable efficiency.

Steroidal spiroazetidinone derivatives (3, 10-12) were obtained by the multi-step reactions of ketosteroids. It involved Staudinger ketene-imine [2+2] cycloaddition reaction of steroidal iminophenylcholest-5-enes (2, 7-9). The structural assignment of the products was confirmed on the basis of IR, 1H NMR, 13C NMR, MS and analytical data. The synthesized compounds were screened for in vitro antimicrobial activity against different bacterial and fungal strains by agar diffusion method and in vitro antioxidant activity by using DPPH method. Docking studies were performed to investigate the hypothetical binding mode of the steroidal spiroazetidinones. The results suggest that steroidal bearing a core spiroazetidinone scaffold would be potent phramacological agents., European Chemical Bulletin, Vol 4, No 1-3 (2015): European Chemical Bulletin