Effects of lithium on locomotor activity and circadian rhythm of honey bees.

Lithium has been considered a potential acaricidal agent against the honey bee (Apis mellifera) parasite Varroa. It is known that lithium suppresses elevated activity and regulates circadian rhythms and light response when administered to humans as a primary therapeutic chemical for bipolar disorder and to other bipolar syndrome model organisms, given the crucial role of timing in the bee's foraging activity and the alternating sunlight vs dark colony environment bees are exposed, we explored the influence of lithium on locomotor activity (LMA) and circadian rhythm of honey bees. We conducted acute and chronic lithium administration experiments, altering light conditions and lithium doses to assess LMA and circadian rhythm changes. We fed bees one time 10 µl sucrose solution with 0, 50, 150, and 450 mM LiCl in the acute application experiment and 0, 1, 5, and 10 mmol/kg LiCl ad libitum in bee candy in the chronic application experiment. Both acute and chronic lithium treatments significantly decreased the induced LMA under constant light. Chronic lithium treatment disrupted circadian rhythmicity in constant darkness. The circadian period was lengthened by lithium treatment under constant light. We discuss the results in the context of Varroa control and lithium's effect on bipolar disorder.

COVID-19 forecasting using shifted Gaussian Mixture Model with similarity-based estimation.

The COVID-19 pandemic has caused a pronounced disturbance in the social environments and economies of many countries worldwide. Credible forecasting methods to predict the pandemic's progress can allow countries to control the disease's spread and decrease the number of severe cases. This study presents a novel approach, called the Shifted Gaussian Mixture Model with Similarity-based Estimation (SGSE), that forecasts the future of a specific country's daily new case values by examining similar behavior in other countries. The model uses daily new case values collected since the pandemic began and finds countries with similar trends using a specific time offset. The daily new case values data between the first day and ( t o d a y - N ) th day are transformed by employing the Gaussian Mixture Model (GMM) and, subsequently, a new vector of features is obtained for each country. Using these feature vectors, countries that show similar statistics in the past are found for any forecasted country. The future of the corresponding country is forecasted by taking the mean of the time-series plots after the offset points of similar countries are calculated. A brand new metric called a trend similarity score, which calculates the similarity between forecasted and actual values is also presented in this study. While the SGSE trend similarity score median varies between 0.903-0.947, based on the selection of the distance metric, the ARIMA model yields only 0.642. The performance of the SGSE was compared in seven European countries using four different public projects submitted to The European COVID-19 Forecast Hub. The SGSE gives the most accurate forecasts compared to all other models. The test sets' results show that trends and plateaus are predicted accurately for many countries.

A Minimally Invasive Approach Towards "Ecosystem Hacking" With Honeybees.

Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen bee, a central colony element, has the potential to change the collective behavior of the entire colony and thus also improve its interaction with the surrounding ecosystem. Such a system can be used to study and understand many elements of bee behavior within hives that have not been adequately researched. We discuss here the applicability of this technology for ecosystem protection: A novel paradigm of a minimally invasive form of conservation through "Ecosystem Hacking". We discuss the necessary requirements for such technology and show experimental data on the dynamics of the natural queen's court, initial designs of biomimetic robotic surrogates of court bees, and a multi-agent model of the queen bee court system. Our model is intended to serve as an AI-enhanceable coordination software for future robotic court bee surrogates and as a hardware controller for generating nature-like behavior patterns for such a robotic ensemble. It is the first step towards a team of robots working in a bio-compatible way to study honey bees and to increase their pollination performance, thus achieving a stabilizing effect at the ecosystem level.

Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy.

Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m.

Multimodal wireless sensor network-based ambient assisted living in real homes with multiple residents.

Human activity recognition and behavior monitoring in a home setting using wireless sensor networks (WSNs) provide a great potential for ambient assisted living (AAL) applications, ranging from health and wellbeing monitoring to resource consumption monitoring. However, due to the limitations of the sensor devices, challenges in wireless communication and the challenges in processing large amounts of sensor data in order to recognize complex human activities, WSN-based AAL systems are not effectively integrated in the home environment. Additionally, given the variety of sensor types and activities, selecting the most suitable set of sensors in the deployment is an important task. In order to investigate and propose solutions to such challenges, we introduce a WSN-based multimodal AAL system compatible for homes with multiple residents. Particularly, we focus on the details of the system architecture, including the challenges of sensor selection, deployment, networking and data collection and provide guidelines for the design and deployment of an effective AAL system. We also present the details of the field study we conducted, using the systems deployed in two different real home environments with multiple residents. With these systems, we are able to collect ambient sensor data from multiple homes. This data can be used to assess the wellbeing of the residents and identify deviations from everyday routines, which may be indicators of health problems. Finally, in order to elaborate on the possible applications of the proposed AAL system and to exemplify directions for processing the collected data, we provide the results of several human activity inference experiments, along with examples on how such results could be interpreted. We believe that the experiences shared in this work will contribute towards accelerating the acceptance of WSN-based AAL systems in the home setting.