M5-Tracker (Dementia, Elderly, Visually Impaired and Intellectual Disabilities Tracking Device)

Symbol labelling improves advantageous decision-making on the Iowa Gambling Task in people with intellectual disabilities

Antipsychotic drugs are often used outside their licensed indication to manage aggression in adults with intellectual disabilities in the absence of a diagnosed psychiatric disorder. The current case series shows that antipsychotics could be effective, but they are often used with non‐drug measures and sometimes cause side‐effects. Clinicians did not always carry out a thorough assessment followed by a formulation before prescribing medications, as recommended in the national and international guides (Deb et al, 2009; Unwin & Deb, 2010). It is also apparent that clinicians did not carry out tests to detect side‐effects of risperidone such as metabolic syndrome. Clinicians should use psychotropic medication for a minimum period of time and at the lowest possible dose, and should consider non‐drug management of aggression at the outset and at a regular interval.

Trends in Models and Algorithms for Fleet Management

Relevance. Electricity supply to remote settlements in the North is carried out using autonomous power systems, which are a combination of diesel generator units. These units consume significant volumes of expensive diesel fuel. Reducing the volume of fuel consumption by using renewable energy facilities has practical relevance and demand. Aim. Evaluation of the possibility of using manual tracker units in micro-power PV-power plants operating as part of autonomous power systems in the North by performing in-kind studies. Methods. Methods of determining the efficiency of using manual and automatic tracker units for PV-power plants, and processessing experimental data. Results and conclusions. The authors have identified the indicators of increase in electricity generation before and after the introduction of tracker units. The volumes of reduction in diesel fuel consumption in an autonomous power system are calculated depending on the type of tracker unit. Recommendations for the sustainable operation of manual tracker units for micro-power PV-power plants are formed. The authors substantiated the impossibility of using manual tracker units in small and large-capacity solar energy facilities due to high labor costs. The obtained results of applied research can be used to update the existing list of technical measures to improve PV-power plants located in the North. The capital cost of manual tracker units is 70–80% less than their automatic counterparts. This type of units increases the average daily electricity generation from a PV-power plant by 17–18% provided that the orientation of the PV-panels relative to the Sun changes hourly. Based on the results of the technical and economic assessment, it was determined that in the case of introducing manual tracker units into a PV-power plant with an installed capacity of 20 kW in the village of Toyon-Aryy, an average annual saving of diesel fuel of up to 8.8 tons is achieved with a payback period of up to 11.7 years. The carbon footprint in the energy system under study is reduced in the form of a decrease in CO2 emissions to 27.7 tons per year.

A high percentage of people with intellectual disability experience communication impairments that limit their use of spoken language and make the use of alternative and augmentative communication (AAC) devices potentially beneficial. However, AAC devices often present barriers for people with intellectual disability due to the complexity of switching between levels and overlays and by hardware configurations that limit the number of words or phrases that can be programmed into a device. This study reports on an evaluation of GeoTalk, a handheld device application that incorporates universal design in the user interface and integrates global positioning system (GPS), electronic schedule timers, and other sensor technology to automatically switch between vocabulary sets when the user enters a designated geographic zone, such as a school, bank, and grocery store. The system also triggers automated switches in vocabulary sets based upon time of day or remote sensors in situations that require a unique vocabulary set. Findings from the pilot test showed that people with intellectual disability were able to operate the GPS-enabled GeoTalk device with fewer errors, requiring fewer prompts, and with greater speed than two commercially available comparison AAC devices.

STCP is the main bus operator in the Porto Metropolitan Area. The experimental phase of using fleet tracking and management systems fitted on public transport vehicles began in the late eighties, but only in 2002 the current system (SAEI) covered all the buses of the company. Nowadays, these systems are integral company management systems. In this paper, the experience of the past six years or so is presented. The main reasons for the use of such systems are detailed, the architecture and functionalities are described and the importance of accessing real-time information together with subsequent analysis of the data obtained is underlined.

Interpretable machine learning models for predicting and explaining vehicle fuel consumption anomalies

Steady requirements of consumers to the quality of to the development of automated systems, make it necessary to the information provided, on the basis of which the control actions constantly process or create higher quality products. The require-are carried out, as well as the acceleration of technical means related ment to improve the basic information solutions using modern technical resources offered by industry was the reason for the rework of the existing business process for managing the fleet of passenger cars implemented in the automated system for managing operation and repair of the passenger car fleet (ASUPV). The article gives a brief analysis of the 20-year work of the linear enterprises (LP) with the ASUPV - (ASUPV LP) subsystem, with the help of which the source documents are entered into the car park, trains are formed and the passenger car fleet maintenance is monitored. This subsystem is the main supplier of information for obtaining analytical data at all levels of passenger car fleet management. The article also outlines the main goal of the new information technology - obtaining, through single processing of primary data, new quality information, on the basis of which optimal management solutions should be developed. The purpose of the newly developed ASUPV LP-2 system is to automate the technological processes of centralized management of the passenger fleet of linear enterprises to improve performance and efficiency of the industry, which will allow optimizing the work of passenger line enterprises, monitoring compliance with technological processes and ensuring the availability of reliable information to interested users. The article describes the main tasks of the developed system: ensuring the need for the necessary and sufficient number of passenger cars for the fulfillment of the transportation plan at the linear enterprise; creating of guaranteed traffic safety of the formed passenger trains; improving the quality of repairs and maintenance. It is noted that the sources of increased complexity of managing the passenger car fleet are the constantly increasing complexity of business processes automated at the linear enterprise, which conflicts with the existing regulatory and technological base, as well as increasing requirements for the speed and accuracy of execution of all operations with cars. It is also indicated that automatic monitoring of the input sequence of source documents and its information interconnection should be created in the developed system and a system of automated control and prevention of conscious violations performed by users should be developed in order to improve the performance of their own work. The system should control the user's actions as much as possible and check the information entered by the user for compliance with regulatory requirements.

Services and warranties of large fleets of engineering assets is a very profitable business where original equipment manufacturers and independent service providers offer contracts designed to cover events in day-to-day service as well as major maintenance and repairs over the life of the asset. Accurate reliability modeling, as a way to understand how the complex stochastic interactions between operating conditions and component capability define useful life, is key for services profitability. The modeling task is daunting as factors such as aggressive mission mixes introduced by operators, exposure to harsh environment, inadequate maintenance, and problems with mass production (bad batch of materials) can lead to large discrepancies between designed and observed useful lives. This paper is focused on how to quantify the impact of infant mortality in fleets of industrial assets. A simple numerical experiment is used to address the fundamental question: how does number of observations and fleet size interact with each other in fleet management? The results demonstrate that material capability, penetration of bad batch of material in the fleet, and commissioning time can drastically influence fleet unreliability. Moreover, infant mortality due to manufacturing problems/material capability is a manifestation of an outlier problem. As a consequence, the propensity to observe first failures depend on the actual fleet size. Since failure observations are used to build/update the reliability models, small fleet operators have to deal with large uncertainties when quantifying infant mortality. This impacts their ability to make provisions for service and maintenance (inventory, labor, loss of productivity, etc.). Although the large number of failure observations causes a financial burden in large fleet operators, it also allows for reduced uncertainty in building/updating the reliability models. In turn, this improves their ability to forecast future failures and make provisions for service and maintenance.

Federated Learning (FL) has emerged as a suitable option for collaborative, user-centric machine learning while protecting sensitive user data. FL differs significantly from traditional centralized frameworks that collect raw data on a central server. FL focuses on collecting model updates generated locally on edge devices. With traditional FL approaches, data must be downloaded to a central server for model training. In contrast, training occurs on the edge and only model updates are sent. This decentralized framework, unlike traditional FL approaches, is ideal for ubiquitous applications—systems woven seamlessly into everyday surroundings like smart homes, advanced mobile devices, and IoT systems—where sensitive data is constantly and automatically generated. In these systems, user privacy is paramount, and advanced methods must be implemented to ensure privacy and system efficacy. This paper discusses the integration of FL into ubiquitous environments, addressing the relevance, advantages, and disadvantages of this approach. I’d like to focus on privacy-preserving methods, but their preservation against data leakage and adversary attacks adds significant value. There are case studies to show the challenges encountered, lessons learned, and the use of FL within privacy-centric ubiquitous systems. In closing this paper, I summarize the key points and underscore the transformational potential of FL for privacy-sensitive, innovative systems. I also promote further research in this area to support its practical relevance. This paper demonstrates that among the many approaches to privacy within pervasive computing, FL is the only one that is practical and scalable.

The Internet of Things has completely changed the way we interact with our surroundings. The upsurge in the use of IoT devices has helped us to do and monitor our day-to-day tasks with ease. With the increasing use of IoTs, there also comes their own set of security issues that need to be considered. IoT devices generate vast amounts of data, most of which can be shared or connected to social networking sites or online platforms. Due to malicious activity or attacks in the network, the data can be manipulated. Digital deception poses a significant threat in the era of AI-driven social networks, enabling the spread of misinformation and cyber intrusions at an unprecedented scale. Here comes the role of the intrusion detection system, which helps us to detect and prevent security breaches in IoT. In recent years, it has become a critical issue to secure IoT systems as they are more prone to be hacked. So, it has become very important to develop and optimize our traditional intrusion detection systems. Previously used intrusion detection systems suffered from several limitations, such as centralized data storage, privacy, and security concerns. They require a significant amount of processed data to work effectively according to our criteria. This can be challenging to us in case of very few historical attacks that are not enough to train our systems. To overcome these issues, federated learning-based IDS for IoT systems has been proposed. Federated Learning is a machine learning technique that provides a system that can collaboratively learn a model without sharing its data. In federated learning, the machine learning model is trained on the device itself, and only the updates are pushed to the central server for improvement of the central model. It helps us preserve the privacy of the device and, at the same time, enables us to build and train the central model server that can detect any intrusion attacks. This can also reduce the computational cost of machine training by distributing the work across several devices. Federated learning models can adapt to the changes in the environment as they are designed to continuously learn from the recently generated data from servers and devices in the network. This way, the model can progress over time to better detect the changes in the network traffic.

The ability to track vehicles is useful in many applications including security of personal vehicles, public transportation systems, fleet management and others. Furthermore, the number of vehicles on the road globally is also expected to increase rapidly. Therefore, the development of vehicle tracking system using the Global Positioning System (GPS), Global System for Mobile Communications (GSM) modem, General Packet Radio Service (GPRS) and Google or Open street map respectively is undertaken with the aim of enabling users to locate their vehicles in real time with ease and in a convenient manner. The system will provide users with the capability to track vehicle remotely through the mobile network. This project presents the development of the vehicle tracking system's server and client software. Specifically, the system will utilize GPS to obtain a vehicle's coordinate and transmit it using GSM modem to the user's phone through the mobile network. The system is divided into two parts which are the tracking and monitoring part. The tracking part consist of GPS and an android/IOS mobile phone for navigation purpose. The GPS will provide information about the location of the vehicle. After receiving the location data from the web server, the data is monitored by a personal computer. After processing the data, the location of the vehicle can be viewed on the map.

This investigation introduces a new charge controller for GPS trackers, with the objective of offering long-lasting and efficient tracking solutions. The article emphasizes the widespread use of GPS trackers in various sectors, as well as the critical need for sustainable power sources to maintain their effectiveness. The charge controller ensures optimal performance and durability by meticulously choosing components. Experimental investigations confirm the efficacy of the charge controller to deliver a steady power supply to the GPS tracker. The integration of GPS technology with solar PV charging allows for continuous monitoring. The results show that the solar photovoltaic (PV) system reached a peak output voltage of 10 V, ensuring that the battery remained at a stable level of 12.4 V and continually providing the GPS tracker with a steady 5 V. These findings validate the system's capacity to deliver dependable and enduring electricity, signifying notable progress in remote monitoring, asset tracking, and fleet management.

A low-cost Arduino-based datalogger with cellular modem and FTP communication for irrigation water use monitoring to enable access to CropManage

Loss-of-function mutations in SCN1A cause Dravet syndrome (DS), a catastrophic childhood epilepsy in which patients experience comorbid behavioral conditions, including movement disorders, sleep abnormalities, anxiety, and intellectual disability. To study the functional consequences of voltage-gated sodium channel mutations, we use zebrafish with a loss-of-function mutation in scn1lab, a zebrafish homolog of human SCN1A. Homozygous scn1labs552/s552 mutants exhibit early-life seizures, metabolic deficits, and early death. Here, we developed in vivo assays using scn1labs552 mutants between 3 and 6 d postfertilization (dpf). To evaluate sleep disturbances, we monitored larvae for 24 h with locomotion tracking software. Locomotor activity during dark (night phase) was significantly higher in mutants than in controls. Among anticonvulsant drugs, clemizole and diazepam, but not trazodone or valproic acid, decreased distance moved at night for scn1labs552 mutant larvae. To monitor exploratory behavior in an open field, we tracked larvae in a novel arena. Mutant larvae exhibited impaired exploratory behavior, with increased time spent near the edge of the arena and decreased mobility, suggesting greater anxiety. Both clemizole and diazepam, but not trazodone or valproic acid, decreased distance moved and increased time spent in the center of the arena. Counting inhibitory neurons in vivo revealed no differences between scn1labs552 mutants and siblings. Taken together, our results demonstrate conserved features of sleep, anxiety, and movement disorders in scn1lab mutant zebrafish, and provide evidence that a zebrafish model allows effective tests of treatments for behavioral comorbidities associated with DS.