Achieving cutting-edge mechanical properties of metal parts realized by additive manufacturing (AM) demands articulated process control strategies, due to the multitude of physical phenomena involved in this kind of manufacturing processes. Complexity is even higher for what concerns the direct energy deposition (DED) technique, which offers much more potential flexibility and efficiency with respect to other metal AM technologies, at the cost of more difficult process control. The present work presents a multisensor approach able to combine online signals, collected while monitoring the deposition process, and data coming from offline inspection devices, during the built part quality check phase. This data fusion approach constitutes the foundation for the process modeling phase and, consequently, for the implementation of an intelligent control strategy that would act online by adjusting the machine process parameters chasing part dimensional, mechanical, and quality targets. The benefits of the proposed solution are assessed through a dedicated experimental campaign on a DED machine.

Background: Studies focusing on the effects of telemanagement programs for chronic heart failure (CHF) on functional status are lacking, and the prognostic value of the clinical response to the programs is unknown. In the Lombardy region of Italy, a home-based telesurveillance program (HTP) including multidisciplinary management and remote telemonitoring for patients with CHF was introduced in 2000 and was formally adopted, as part of the services delivered by the regional healthcare system, in 2006. This article reports the effect of the HTP on the functional status and quality of life and describes the main outcomes observed within 1 year from the end of the program. Materials and Methods: Six-month variations of New York Heart Association (NYHA) class, left ventricular ejection fraction (LVEF), 6-min walking distance (6MWD), and Minnesota Living with Heart Failure Questionnaire (MLHFQ) score were evaluated in 602 CHF patients. Patients showing at least two of the following conditions—NYHA class reduction, increase in LVEF ≥5%, 6MWD >30 m, and a reduction of >24 points of MLHFQ—were defined as “responders.” One-year events included unplanned cardiovascular readmissions and mortality. Results: A significant improvement in NYHA class, LVEF, 6MWD, and MLHFQ was observed. Clinical events occurred in 24.1% of non-responders and in 15.9% of responders (p=0.03). An unfavorable response to the program, the presence of an implantable cardioverter defibrillator, and multiple comorbidities were predictors of poor outcome. Conclusions: The HTP was effective in improving CHF patient functional status, and an unsuccessful response to the intervention seems to be an independent marker of poor prognosis.

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.