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The world we live in today is becoming increasingly less tethered, with many applications depending on wireless signals to ensure safety and security. Proactive security measures can help prevent the loss of property due to actions such as larceny/theft and burglary. An IoT-based smart Surveillance System for High-Security Areas (SS-HSA) has been developed to address this issue effectively. This system utilizes a Gravity Microwave Sensor (GMS), which is highly effective due to its ability to penetrate nonmetallic obstructions. Combining GMS with Arduino UNO is a highly effective technique for detecting suspected objects behind walls. The GMS can also be integrated with the global system for mobile (GSM) communications, making it an IoT-based solution. The SS-HSA system utilizes machine learning AI algorithms operating at a GMS frequency to analyze and calculate accuracy, precision, F1-Scores, and Recall. After a thorough evaluation, it was determined that the Random Forest Classifier achieved an accuracy rate of 95%, while the Gradient Boost Classifier achieved an accuracy rate of 94%. The Naïve Bayes Classifier followed closely behind with a rate of 93%, while the K Nearest Neighbor and Support Vector Machine both achieved an accuracy rate of 96%. Finally, the Decision Tree algorithm outperformed the others in terms of accuracy, presenting a value of 97%. Furthermore, in the studied machine learning AI algorithms, it was observed that the Decision Tree was optimal for SS-HSA. Full article

(1) Background: Cardiocrinum cordatum (Thunb.) Makino (Liliaceae) is a forest perennial herb distributed in East Asia. Although flower visitors for this plant species have been well reported, their contribution to pollination remains unknown. (2) Methods: We evaluated pollination contribution for visitors of C. cordatum flowers in a natural cool temperate forest. We investigated visiting frequency, the number of pollen grains per body surface, fruit set, and the mean number of seeds per fruit produced after a single visit of each visiting species. Combining the results of these experiments, we determined the most important pollinators of this species. (3) Results: For the population investigated in the study, the three most essential pollinators were the bumblebee (Bombus diversus tersatus) (Apidae), sweat bee (Halictidae sp.), and marmalade hoverfly (Episyrphus balteatus) (Syrphidae). Additionally, we found that the contribution of a flower-visiting ant species (Myrmica ruginodis Nylander (s.l.)) (Formicidae) is small. (4) Conclusions: Pollinator contributions differed among flower visitors. Our results underscore the insufficiency of current information about flower-visiting species to evaluate pollination contribution. Full article

In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts. Full article