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Abstract Background The research gap addressed in this study is the applicability of deep neural network (NN) models on wearable sensor data to recognize different activities performed by patients with Parkinson’s Disease (PwPD) and the generalizability of these models to PwPD using labeled healthy data. Methods The experiments were carried out utilizing three datasets containing wearable motion sensor readings on common activities of daily living. The collected readings were from two accelerometer sensors. PAMAP2 and MHEALTH are publicly available datasets collected from 10 and 9 healthy, young subjects, respectively. A private dataset of a similar nature collected from 14 PwPD patients was utilized as well. Deep NN models were implemented with varying levels of complexity to investigate the impact of data augmentation, manual axis reorientation, model complexity, and domain adaptation on activity recognition performance. Results A moderately complex model trained on the augmented PAMAP2 dataset and adapted to the Parkinson domain using domain adaptation achieved the best activity recognition performance with an accuracy of 73.02%, which was significantly higher than the accuracy of 63% reported in previous studies. The model’s F1 score of 49.79% significantly improved compared to the best cross-testing of 33.66% F1 score with only data augmentation and 2.88% F1 score without data augmentation or domain adaptation. Conclusion These findings suggest that deep NN models originating on healthy data have the potential to recognize activities performed by PwPD accurately and that data augmentation and domain adaptation can improve the generalizability of models in the healthy-to-PwPD transfer scenario. The simple/moderately complex architectures tested in this study could generalize better to the PwPD domain when trained on a healthy dataset compared to the most complex architectures used. The findings of this study could contribute to the development of accurate wearable-based activity monitoring solutions for PwPD, improving clinical decision-making and patient outcomes based on patient activity levels.

This article presents complementary results on experimental data on the process of reducing the size of plant biomass by mechanical cutting. According to the source of the experimental data, the problem of statistical modelling of the main parameters describing the process of biomass reduction was approached. A more comprehensive formulation of the list of main system parameters was also attempted, which should be considered in a minimal mathematical model. The results of this article are starting points for a systemic approach to this biomass processing process. A first systematization is fixing 13 parameters that are included in the model of the biomass size reduction phenomenon. The 13 model parameters are divided into three categories: input parameters (5), adjustment parameters (4) and output parameters (4). The performances of the interpolation formulas are evaluated using the global error and the maximum error, varying between 1% and 0.1% for the prior and 3.8% and 0.34% for the latter. Some mathematical models suggest the existence of optimal operating points. Their exploitation can only come as a result of new high-resolution experimental research, at least in terms of rotation speed.

Motivated by the increased use of UAS in commercial applications, in this paper we tackle the problem of path planning when requests are submitted by UAS managed by different operators. We propose the new problem of generalized path planning for UAS Traffic Management, where the UAS path is described by operators with a sequence of waypoint groups and a solution trajectory must pass through a waypoint in each group. This problem is typical for applications where multiple charging stations and pickup/drop-off locations are distributed in a flight area. Our solution builds upon prior work on discretized space-time graph path planning and proposes a novel multi-source/multi-destination graph search algorithm that generates collision-free trajectories for pre-flight CDR. Our efficient algorithm has runtime proportional to the number of groups and avoids combinatorial explosion. We apply our mechanism to the energy-constrained UAS package delivery problem with multiple warehouses and battery charging stations. Simulation results show that our algorithm is efficient and scalable with the number of requests and graph size. The addition of charging stations and the option for multiple warehouses increases the request admission ratio and reduces the overall trajectory duration, effectively improving both the planner’s quality of service and the efficiency of airspace usage.

The article continues the exposition of the results obtained in researching an agricultural machine for processing soil, designed for research with applications including exploitation. The MCLS (complex machine for soil tillage) was designed to research the working processes of the instruments intended for soil processing. The MCLS cultivator is a modulated machine (it can work for three working widths: 1, 2, and 4 m, with tractors of different powers) that is designed to use a wide range of working bodies. The experimental data obtained with the structure with a working width of 1 m and the results of their processing within the framework of the theory of random vibrations are presented in this article. The experimental results are analysed as random vibrations of the supports of the active working bodies. As a result, the main characteristics of random vibrations are exposed: the distribution function, the average value, the autocorrelation, and the frequency spectrum. These general results regarding random vibrations are used for several critical applications in the design, execution, and exploitation of some subassemblies and assemblies of agricultural machines of this type. The main applications include estimating the probability of the occurrence of dangerous load peaks, counting and selecting the load peaks that produce fatigue accumulation in the material of the supports of the working bodies, identifying some design deficiencies or defects in the work regime, and estimating the effects of vibrations on the quality of soil processing. All of the outcomes are composed of applications in MCLS research and exploitation. The applications pursue well-known objectives of modelling the working processes of agricultural machines: safety at work, increasing the quality of work, optimising energy consumption, and increasing productivity, all in a broad context to obtain a compromise situation. The material and the method are based on experimental data acquisition, processing, and interpretation.

Unmanned Aircraft Systems (UAS) provide the platform of many applications such as efficient package delivery, monitoring, surveillance, search and rescue, and more. As the density of UAS deployed in urban areas for delivery and monitoring operations is expected to increase in the near future, it becomes critical to design an efficient collision-free UAS path planning system to be used by a centralized traffic manager. In this article we propose an on-demand, scalable, collision-free trajectory planning mechanism that processes routing requests in batches, with the objective of improving the ratio of successfully admitted requests. The planner transforms the input map to a unit graph discretized on the safety distance and computes trajectories using search in the derived space-time graph. The planner generates trajectories that are by design conflict-free and deadlock-free. The performance of our algorithm is analyzed with simulations. We measure admission ratio, the delay overhead vs. optimal path delay, the algorithm running time, and we compare results with prior work. The proposed batch planner yields better admission ratio and delay overhead in low and medium density scenarios. In all scenarios it has a running time that is 4-7 times shorter, showing its scalability in practical situations.

Sensors and actuators are fundamental units in Cyber–Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), which are patterns that describe abstract conceptual entities. From AEPs, we can derive concrete patterns; a structure combining related AEPs is an Entity Solution Frame (ESF). This paper concentrates on the functional aspects of these devices and defines conceptual units that can be used to design any system that requires sensors and actuators; that is, almost any Cyber–Physical system. For concreteness, we explore them in this study in the context of autonomous cars. An autonomous car is a complex system because, in addition to its own complex design, it interacts with other vehicles and with the surrounding infrastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber–Physical System, where some of its functions are performed via Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appropriate physical actions. Both sensors and actuators are susceptible to malicious attacks due to the large attack surface of the system in which they are used. Our work is intended to make autonomous cars more secure, which also increases their safety. Our final objective is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups together and organizes related security patterns.

Diabetes mellitus is often associated with a risk of developing some types of cancer. The association between head and neck cancers and diabetes as well as prognosis and treatment tolerance remains a controversy. Acute toxicities associated with treatment may be amplifi ed by the presence of comorbidities, including hypertension, diabetes and collagen diseases. Another factor implicated in the treatment tolerance is also the limitation by the presence of hyperglycemia of the corticosteroids dose used for the control of pain and edema associated with chemo-irradiation and for the treatment of thrombocytopenia. The purpose of the study was to evaluate the involvement of diabetes mellitus in the toxicities associated with chemo-radiotherapy treatment in multimodal treated patients for advanced local head and neck cancers. For patients with locally advanced non-metastatic head and neck treated with multimodal (chemo-radiotherapy) acute toxicities (radio-dermitis, radio-mucositis, dysphagia) was analyzed comparatively in patients who associate or not cancer with diabetes. It was also compared if the diagnostic of diabetes influenced the intensity of chemotherapy. Identifying the predictive value of diabetes mellitus for the severity of toxicities in multimodal curative treatment for head and neck cancers can lead to limitation of radiation dose to some radiosensitive anatomical structures in the context of the modern IMRT and VMAT irradiation techniques implementation in clinical practice.

Abstract Wireless sensor networks (WSNs) have many applications in climate monitoring, fire detection, smart homes, and smart cities, as well as detecting and monitoring dynamic, spatio-temporal events such as storms, traffic, and animal movement. WSNs are an important component of the Internet of Things (IoT), and they play an important role in monitoring and collecting surrounding context and reporting the sensed data. In this paper, we consider a WSN deployed to monitor spatio-temporal events and a mobile sink which acts as the gateway between the IoT and the WSN. Data gathering is a challenging problem in WSNs and IoT since the algorithms have to be energy-efficient and the information has to be available to the end user in a timely manner and without redundancies. In this paper, we propose an improvement of the reactive, anchor-based routing protocol with constrained flooding and dynamic clustering from Aranzazu-Suescun and Cardei (Spatio-temporal event detection and reporting in mobile-sink wireless sensors networks, 2017). We propose a new event-based clustering mechanism and a new dynamic clustering technique. This improvement results in reduced energy consumption and a higher number of packets processed successfully by the sink compared to Aranzazu-Suescun and Cardei (Spatio-temporal event detection and reporting in mobile-sink wireless sensors networks, 2017). Data collected by the mobile sink are shared to the end users through the IoT infrastructure. We conducted simulations using WSNet, an event-driven simulator for WSNs. We measured various performance metrics, such as the average residual energy, the number of active clusters, and the percentage of events processed successfully by the sink.