This paper presents the development of a novel contactless omnidirectional capacitive proximity sensor. The presented device has been designed to be energy-efficient (≈5 mW power consumption) by means of duty-cycling the power supply. A comprehensive methodological experiment has been carried out to extensively evaluate the performance within the sensing range (5–10 cm). A simple boot-up self-adjustment mechanism has been implemented using a digital potentiometer. This feature allows for an effortless utilization of the proposed device in a wide variety of potential applications, including mobile robotics and human–machine interaction.

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly read out by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.

As part of its HL-LHC upgrade program, the CMS collaboration is replacing its existing endcap calorimeters with a high-granularity calorimeter (CE). The new calorimeter is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic and hadronic compartments. Due to its compactness, intrinsic time resolution, and radiation hardness, silicon has been chosen as active material for the regions exposed to higher radiation levels. The silicon sensors are fabricated as 20 cm (8”) wide hexagonal wafers and are segmented into several hundred pads which are read out individually. As part of the sensor qualification strategy, 8” sensor irradiation with neutrons has been conducted at the Rhode Island Nuclear Science Center (RINSC) and followed by their electrical characterisation in 2020-21. The completion of this important milestone in the CE's R&D program is documented in this paper and it provides detailed account of the associated infrastructure and procedures. The results on the electrical properties of the irradiated CE silicon sensors are presented.

This study presents the design and evaluation of a plush smart toy prototype for teaching geometry shapes to young children. The hardware design involves the integration of sensors, microcontrollers, an LCD screen, and a machine learning algorithm to enable gesture recognition by the toy. The machine learning algorithm detects whether the child’s gesture outline matches the shape displayed on the LCD screen. A pilot study was conducted with 14 preschool children to assess the usability and performance of the smart toy. The results indicate that the smart toy is easy to use, engages children in learning, and has the potential to be an effective educational tool for preschool children. The findings suggest that smart toys with machine learning algorithms can be used to enhance young children’s learning experiences in a fun and engaging way. This study highlights the importance of designing user-friendly toys that support children’s learning and underscores the potential of machine learning algorithms in developing effective educational toys.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.