Search Results (73)

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age. Full article

(1) Background: For the residents of long-term care (LTC) units, a humanoid social robot (HSR) may be not only a caregiver but also a companion. The aim of our study was to analyse changes in its perception following a real-world interaction; (2) Methods: One hundred LTC residents were assessed twice with the Godspeed Questionnaire Series (GQS): after viewing a photograph of HSR TIAGo only and after interacting with it in a practical manner. The perception parameters were evaluated on a scale of 1–5 in five series: I-Anthropomorphism, II-Animation, III-Likeability, IV-Perceived intelligence, and V-Perceived safety. (3) Results: In the post-interaction assessment of the TIAGo robot, no lower scores were observed relative to the first (photo-based) scoring. Positive changes were observed in III (p < 0.001), I (p < 0.01), II (p < 0.05), and IV (p < 0.05). In multivariable analysis, high levels of loneliness constituted a correlate for improvement after interaction in I (p < 0.05); computer skills—in III (p < 0.01), and GDS score corresponding to depression—in IV (p < 0.01). (4) Conclusions: Our study reveals a positive change in older people’s perception of an HSR after interacting with it. Interaction is thus an indispensable element in the development process. Developers and implementers should pay particular attention to the robot’s smart functions, movements, and responsiveness. Full article

To solve the missed and wrong detection problems of the object detection model in identifying soybean companion weeds, this paper proposes an enhanced multi-scale channel feature model based on RT-DETR (EMCF-RTDETR). First, we designed a lightweight hybrid-channel feature extraction backbone network, which consists of a CGF-Block module and a FasterNet-Block module working together, aiming to reduce the amount of computation and the number of parameters while improving the efficiency of feature extraction. Second, we constructed the EA-AIFI module. This module enhances the extraction of detailed features by combining the in-scale feature interaction module with the Efficient Additive attention mechanism. In addition, we designed an Enhanced Multiscale Feature Fusion (EMFF) network structure, which first differentiates the inputs of the three feature layers and then ensures the effective flow between the original and enhanced features of each feature layer by two multiscale feature fusions as well as one diffusion. The experimental results demonstrate that the EMCF-RTDETR model improves the average precision mAP50 and mAP50:95 by 3.3% and 2.2%, respectively, compared to the RT-DETR model, and the FPS is improved by 10%. Moreover, our model outperforms other mainstream detection models in terms of accuracy and speed, revealing its significant potential for soybean weed detection. Full article

The increasing use of Unmanned Aerial Vehicles (UAVs) demands enhanced flight safety systems. This study presents the development of an affordable and efficient Sense and Avoid (S&A) system for small fixed-wing UAVs, typically under 25 kg and fly at speeds of up to 15 m/s. The system integrates multiple non-cooperative sensors, two ultrasonic sensors, two laser rangefinders, and one LiDAR, along with a Pixhawk 6X flight controller and a Raspberry Pi CM4 companion computer. A collision avoidance algorithm utilizing the Vector Field Histogram method was implemented to process sensor data and generate real-time trajectory corrections. The system was validated through experiments using a ground rover, demonstrating successful obstacle detection and avoidance with real-time trajectory updates at 10 Hz. Full article

As healthcare costs rise due to aging populations and chronic illnesses, optimized care solutions are urgently needed. Gesture recognition and fall detection are critical for intelligent companion robots in healthcare. However, current deep learning models struggle with accuracy and real-time performance in complex backgrounds due to high computational demands. To address this, we propose an improved RT-DETR R18 model tailored for companion robots. This lightweight, efficient design integrates YOLOv9’s ADown module, the RepNCSPELAN4 module, and custom attention-based AdaptiveGateUpsample and AdaptiveGateDownsample modules for enhanced multi-scale feature fusion, reducing weight and complexity while optimizing real-time detection. Experiments show our model achieves a 51.7% reduction in parameters, a 46.7% decrease in GFLOPS, and higher FPS compared to RT-DETR R18, with mAP@0.5, mAP@0.5-0.95, precision, and recall improving to 99.4%, 86.4%, 99.6%, and 99.4%, respectively. Testing in complex indoor environments confirms its high accuracy for gesture recognition and fall detection, reducing manual workload and offering a novel solution for human behavior recognition in intelligent companionship. Full article

Future smart cities will consist of a heterogeneous environment, including UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles), used for different applications such as last mile delivery. Considering the vulnerabilities of GNSS (Global Navigation System Satellite) in urban environments, a resilient PNT (Position, Navigation, Timing) solution is needed. A key research question within the PNT community is the capability to deliver a robust and resilient time solution to multiple devices simultaneously. The paper is proposing an innovative time dissemination framework, based on IQuila’s SDN (Software Defined Network) and quantum random key encryption from Quantum Dice to multiple users. The time signal is disseminated using a wireless IEEE 802.11ax, through a wireless AP (Access point) which is received by each user, where a KF (Kalman Filter) is used to enhance the timing resilience of each client into the framework. Each user is equipped with a Jetson Nano board as CC (Companion Computer), a GNSS receiver, an IEEE 802.11ax wireless card, an embedded RTC (Real Time clock) system, and a Pixhawk 2.1 as FCU (Flight Control Unit). The paper is presenting the performance of the fusion framework using the MUEAVI (Multi-user Environment for Autonomous Vehicle Innovation) Cranfield’s University facility. Results showed that an alternative timing source can securely be delivered fulfilling last mile delivery requirements for aerial platforms achieving sub millisecond offset. Full article

Mixed plantation of Chinese fir (Cunninghamia lanceolata) is an effective artificial forest management for tree productivity. However, the mixing strategies, site conditions, and subsurface properties that affect tree productivity are not yet fully understood. In this study, we conducted a meta-analysis of 96 publications to consolidate insights on the effects of mixing strategies (e.g., planting density, mixing proportion, mixed species, and tree age), site conditions (e.g., mean annual precipitation (MAP), mean annual temperature (MAT), elevation, and total nitrogen (TN) or total phosphorus (TP) of sample sites), and subsurface properties (e.g., soil characteristics, microbial communities, and extracellular enzyme activity) on tree height, diameter at breast height, and individual volume of Chinese fir. We used the Web of Science and China National Knowledge Infrastructure for searching peer-reviewed papers, and the searching words were: (“Cunninghamia lanceolata” OR “Chinese fir”) AND “mix*”. Following the data screening process, the natural logarithm of the response ratio (lnRR) was computed for subsequent analysis. The results showed that introduced companion species generally increased the individual volume of Chinese fir by an average of 20%. Densities ranging from 1200 to 2000 trees per hectare and moderate mixing proportions (1:1 to 3:1) optimized individual tree growth and thereby boosted productivity. Broadleaf species may be beneficial companions, and trees aged 10 to 20 years grew fastest. At sites with low MAT and high MAP, mixed plantations enhanced the tree productivity of Chinese fir. The optimal elevation range for mixed plantations may be 200 to 600 m. Further, mixed plantations significantly changed soil properties by improving soil structure, increasing soil pH and soil water content, and soil total and available N and P, which were crucial for boosting the productivity of Chinese fir. Soil microbial biomass and enzyme activities were also significantly increased by mixed plantations. Overall, these findings highlight the importance of mixing strategies and site conditions in increasing tree productivity of Chinese fir by improving soil physicochemical characteristics, increasing resource availability, and reducing interspecific and intraspecific competition through niche separation. Full article

Pansharpening is a traditional image fusion problem where the reference image (or ground truth) is not accessible. Machine-learning-based algorithms designed for this task require an extensive optimization phase of network parameters, which must be performed using unsupervised learning techniques. The learning phase can either rely on a companion problem where ground truth is available, such as by reproducing the task at a lower scale or using a pretext task, or it can use a reference-free cost function. This study focuses on the latter approach, where performance depends not only on the accuracy of the quality measure but also on the mathematical properties of these measures, which may introduce challenges related to computational complexity and optimization. The evaluation of the most recognized no-reference image quality measures led to the proposal of a novel criterion, the Regression-based QNR (RQNR), which has not been previously used. To mitigate computational challenges, an approximate version of the relevant indices was employed, simplifying the optimization of the cost functions. The effectiveness of the proposed cost functions was validated through the reduced-resolution assessment protocol applied to a public dataset (PairMax) containing images of diverse regions of the Earth’s surface. Full article

This survey provides a comprehensive comparison of prominent open-source unmanned aerial vehicle (UAV) autopilots, focusing on their hardware compatibility, software features, and communication protocols. Additionally, it assesses the impact of these autopilots on research and education by examining their potential for integration with companion computers, compatibility with robot operating system (ROS) middleware and the MATLAB/Simulink environment, and the availability of simulation-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulation tools. The paper concludes with a discussion of the advantages and disadvantages of these leading open-source autopilots. Full article

Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave communications, and, more recently, communications assisted using intelligent reflecting surfaces (IRSs), and unmanned aerial vehicles (UAVs). In a companion paper, we provided an overview of cooperative communication systems from a tensor modeling perspective. The objective of the present paper is to provide a comprehensive tutorial on semi-blind receivers for MIMO one-way two-hop relay systems, allowing the joint estimation of transmitted symbols and individual communication channels with only a few pilot symbols. After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). Some new variants of nested models are introduced. Uniqueness and identifiability conditions, depending on the algorithm used to estimate the parameters of these models, are established. Two families of algorithms are presented: iterative algorithms based on alternating least squares (ALS) and closed-form solutions using Khatri–Rao and Kronecker factorization methods, which consist of SVD-based rank-one matrix or tensor approximations. In a second part of the paper, the overview of cooperative communication systems is completed before presenting several two-hop relay systems using different codings and configurations in terms of relaying protocol (AF/DF) and channel modeling. The aim of this presentation is firstly to show how these choices lead to different nested tensor models for the signals received at destination. Then, by capitalizing on these models and their correspondence with the generic models studied in the first part, we derive semi-blind receivers to jointly estimate the transmitted symbols and the individual communication channels for each relay system considered. In a third part, extensive Monte Carlo simulation results are presented to compare the performance of relay systems and associated semi-blind receivers in terms of the symbol error rate (SER) and channel estimate normalized mean-square error (NMSE). Their computation time is also compared. Finally, some perspectives are drawn for future research work. Full article

Drug innovation traditionally follows a de novo approach with new molecules through a complex preclinical and clinical pathway. In addition to this strategy, drug repositioning has also become an important complementary approach, which can be shorter, cheaper, and less risky. This review provides an overview of drug innovation in both human and veterinary medicine, with a focus on drug repositioning. The evolution of drug repositioning and the effectiveness of this approach are presented, including the growing role of data science and computational modeling methods in identifying drugs with potential for repositioning. Certain business aspects of drug innovation, especially the relevant factors of market exclusivity, are also discussed. Despite the promising potential of drug repositioning for innovation, it remains underutilized, especially in veterinary applications. To change this landscape for mutual benefits of human and veterinary drug innovation, further exploitation of the potency of drug repositioning is necessary through closer cooperation between all stakeholders, academia, industry, pharmaceutical authorities, and innovation policy makers, and the integration of human and veterinary repositioning into a unified innovation space. For this purpose, the establishment of the conceptually new “One Health Drug Repositioning Platform” is proposed. Oncology is one of the disease areas where this platform can significantly support the development of new drugs for human and dog (or other companion animals) anticancer therapies. As an example of the utilization of human and veterinary drugs for veterinary repositioning, the use of COX inhibitors to treat dog cancers is reviewed. Full article

Background/Objectives: Feelings of loneliness are common in people living with dementia (PLWD) in long-term care (LTC). The goals of this study were to describe the development of a novel virtual companion for PLWD living in LTC and assess its feasibility and acceptability. Methods: The computer-generated virtual companion, presented using a head-mounted virtual reality display, was developed in two stages. In Stage 1, the virtual companion asked questions designed to encourage conversation and reminiscence. In Stage 2, more powerful artificial intelligence tools allowed the virtual companion to engage users in nuanced discussions on any topic. PLWD in LTC tested the application at each stage to assess feasibility and acceptability. Results: Ten PLWD living in LTC participated in Stage 1 (4 men and 6 women; average 82 years old) and Stage 2 (2 men and 8 women; average 87 years old). Session lengths ranged from 0:00 to 5:30 min in Stage 1 and 0:00 to 53:50 min in Stage 2. Speech recognition issues and a limited repertoire of questions limited acceptance in Stage 1. Enhanced conversational ability in Stage 2 led to intimate and meaningful conversations with many participants. Many users found the head-mounted display heavy. There were no complaints of simulator sickness. The virtual companion was best suited to PLWD who could engage in reciprocal conversation. After Stage 2, response latency was identified as an opportunity for improvement in future versions. Conclusions: Virtual reality and artificial intelligence can be used to create a virtual companion that is acceptable and enjoyable to some PLWD living in LTC. Ongoing innovations in hardware and software will allow future iterations to provide more natural conversational interaction and an enhanced social experience. Full article

The use of companion diagnostics has become a standard in precision oncology in the context of ongoing therapeutic innovation. However, certain limitations make their application imperfect in current practice. This position paper underscores the need to broaden the notion of companion testing, considering the potential of emerging technologies, including computational biology, to overcome these limitations. This wave of progress should impact not only our representation of the analytical tool itself but also the nature of the tumoral sample under analysis (liquid biopsies). The complex inter-relationship between companion test guided-personalized therapy, and health agency policies for new drug agreements will also be discussed. Full article

Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning capability, including the hardware pipeline for the hardware–software integration to realize the proposed real-time re-planning algorithm. The framework is based on a modified version of the PX4 Autopilot and a Raspberry Pi 5 companion computer. The planning algorithm utilizes minimum-snap trajectory generation, taking advantage of the differential flatness property of quadrotors, to realize computationally light, real-time re-planning using an onboard computer. We first verify the control system and the planning algorithm through simulation experiments, followed by implementing and demonstrating the system on hardware using a quadcopter. Full article

Nutritional secondary hyperparathyroidism (NSH) in dogs is a condition that develops in response to a vitamin D deficiency or an imbalanced calcium-to-phosphorus ratio in dog food. Puppies of large-breed dogs exclusively fed a non-supplemented, boneless raw meat diet are especially susceptible to developing NSH due to their elevated calcium requirement. Reports on NSH in companion animals have been sparse in the last decades due to dog owners having easy access to commercially balanced dog foods. However, with the rising popularity of meat-based raw feeding, this condition has re-emerged. In this case series, four large-breed puppies fed exclusively non-supplemented, boneless raw meat diets presented with complaints of acute onset of pain and paresis. Radiographs and/or computed tomography (CT) scans showed reduced radio density of the skeleton in all four puppies. Two of the dogs had pathological fractures, and these two puppies were euthanized. One was subjected to a post mortem examination, which revealed cortical bone resorption and hypertrophy of the parathyroid glands. The remaining two puppies rapidly improved after receiving pain medication and a commercial, balanced diet. This case series demonstrates a risk of young dogs developing severe neurological deficits when fed a non-supplemented, boneless raw meat diet. Full article