A new algorithm for the identification of dives reveals the foraging ecology of a shallow-diving seabird using accelerometer data

The identification of feeding events is crucial to our understanding of the foraging ecology of seabirds. Technology has made small devices, such as time-depth recorders (TDRs) and accelerometers available. However, TDRs might not be sensitive enough to identify shallow dives, whereas accelerometers might reveal more subtle behaviours at a smaller temporal scale. Due to the limitations of TDRs, the foraging ecology of many shallow-diving seabirds has been poorly investigated to date. We thus developed an algorithm to identify dive events in a shallowdiving seabird species, the Scopoli’s shearwater, using only accelerometer data. The accuracy in the identification of dives using either accelerometers or TDRs was compared. Furthermore, we tested if the foraging behaviour of shearwaters changed during different phases of reproduction and with foraging trip type. Data were collected in Linosa Island (35°51′33″N; 12°51′34″E) from 12 June to 8 September 2015 by deploying accelerometer data loggers on 60 Scopoli’s shearwaters. Four birds were also equipped with TDRs. TDRs recorded only 17.7% of the dives detected by the accelerometers using the algorithm. A total of 82.3% of dives identified by algorithm were too short or shallow to be detected by TDRs. Therefore, TDRs were not accurate enough to detect most of the dives in Scopoli’s shearwaters, which foraged mostly close to the sea surface. Our data showed that birds performed shorter foraging trips and dived more frequently in the early chick-rearing period compared with the late chick-rearing and incubation phases. Furthermore, parents dived more frequently during short foraging trips. Our results suggest that Scopoli’s shearwaters maximised their foraging effort (e.g. number of dives, short trips) during shorter foraging trips and during early chick-rearing

Identification of subgroups of early breast cancer patients at high risk of nonadherence to adjuvant hormone therapy: results of an italian survey.

The aim of this study was the identification of subgroups of patients at higher risk of nonadherence to adjuvant hormone therapy for breast cancer. Using recursive partitioning and amalgamation (RECPAM) analysis, the highest risk was observed in the group of unmarried, employed women, or housewives. This result might be functional in designing tailored intervention studies aimed at improvement of adherence. Background: Adherence to adjuvant endocrine therapy (HT) is suboptimal among breast cancer patients. A high rate of nonadherence might explain differences in survival between clinical trial and clinical practice. Tailored interventions aimed at improving adherence can only be implemented if subgroups of patients at higher risk of poor adherence are identified. Because no data are available for Italy, we undertook a large survey on adherence among women taking adjuvant HT for breast cancer. Patients and Methods: Patients were recruited from 10 cancer clinics in central Italy. All patients taking HT for at least 1 year were invited, during one of their follow-up visit, to fill a confidential questionnaire. The association of sociodemographic and clinical characteristics of participants with adherence was assessed using logistic regression. The RECPAM method was used to evaluate interactions among variables and to identify subgroups of patients at different risk of nonadherence. Results: A total of 939 patients joined the study and 18.6% of them were classified as nonadherers. Among possible predictors, only age, working status, and switching from tamoxifen to an aromatase inhibitor were predictive of nonadherence in multivariate analysis. RECPAM analysis led to the identification of 4 classes of patients with a different likelihood of nonadherence to therapy, the lowest being observed in retired women with a low level of education, the highest in the group of unmarried, employed women, or housewives. Conclusion: The identification of these subgroups of “real life” patients with a high prevalence of nonadherers might be functional in designing intervention studies aimed at improving adherenc

Inhalation therapy in the next decade : Determinants of adherence to treatment in asthma and COPD

Peer reviewedPublisher PD

Design methodology for the development of variable stiffness devices based on layer jamming transition

Variable stiffness mechanisms as Jamming Transition draw huge attention recently in Soft Robotics. This paper proposes a comprehensive design methodology for developing variable stiffness devices based on layer jamming. Starting from pre-existing modelling, we highlight the design parameters that should be considered, extracting them from literature and our direct experience with the phenomenon. Then we validated the methodology applying the design process to previous layer jamming cases presented in literature. The comparison between the results obtained from our methodology and those presented in the analyzed previous works highlights a good predictive capability, demonstrating that this methodology can be used as a valid tool to design variable stiffness devices based on layer jamming transition. Finally, in order to provide the scientific community with an easily usable tool to design variable stiffness structures based on layer jamming transition, we have elaborated a Matlab script that guides the user through the main design parameters implementing the proposed methodology in an interactive process

Design, fabrication and control of soft robots

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.National Science Foundation (U.S.) (Grant IIS-1226883

Soft Robots Proprioception Through Stretchable Laser-Induced Graphene Strain Sensors

Soft robotic grippers enable the safe manipulation of delicate objects, guaranteeing their integrity when handled and collected. Integrating sensors into these grippers can enable their proprioception but must avoid compromising flexibility or functionality. This study presents a pneumatic finger-based soft gripper with a novel piezoresistive sensor made of laser-induced graphene (LIG) embedded in dragon skin (DS), an elastomer matrix, offering continuous bending angle measurement. The LIG/DS composite is studied to confirm minimal impact on the gripper's stiffness. Mechanical and electromechanical characterizations are performed for two sensor designs, n1 and n2. Design n1 exhibits superior performance, with a gauge factor (Formula presented.), a linear response of up to 30% strain, and durability exceeding 10 000 cycles. A finite-element method analysis identifies the fingers’ neutral bending plane, guiding optimal sensor placement. Experimental validation confirms theoretical predictions and finds the ideal sensor location, achieving a linear response up to 110° with low hysteresis (8%). The sensor enables real-time monitoring of finger bending during grasping tasks, with a calibration curve linking resistance changes to bending angles. This cost-effective, stretchable, and durable sensor demonstrates high potential for soft robotic applications, offering precise and reliable proprioception without compromising the gripper's soft properties

One-shot additive manufacturing of robotic finger with embedded sensing and actuation

A main challenge in the additive manufacturing (AM) field is the possibility to create structures with embedded actuators and sensors: addressing this requirement would lead to a reduction of manual assembly tasks and product cost, pushing AM technologies into a new dimension for the fabrication of assembly-free smart objects. The main novelty of the present paper is the one shot fabrication of a 3D printed soft finger with an embedded shape memory alloy (SMA) actuator and two different 3D printed sensors (strain gauge and capacitive force sensor). 3D printed structures, fabricated with the proposed approach, can be immediately activated after their removal from the build plate, providing real-time feedback because of the embedded sensing units. Three different materials from two nozzles were extruded to fabricate the passive elements and sensing units of the proposed bioinspired robotic finger and a custom-made Cartesian pick and place robot (CPPR) was employed to integrate the SMA spring actuator into the 3D printed robotic finger during the fabrication processes. Another novelty of the present paper is the direct integration of SMA actuators during the 3D printing process. The low melting thermoplastic polycaprolactone (PCL) was extruded: its printing temperature of 70 °C is lower than the SMA austenitic start temperature, preventing the SMA activation during the manufacturing process. Two different sensors based on the piezoresistive principle and capacitive principle were studied, 3D printed and characterized, showing respectively a sensitivity ratio of change in resistance to finger bending angle to be 674.8 Ω∘Angle and a capacitance to force ratio of 0.53pFN . The proposed manufacturing approach paves the way for significant advancement of AM technologies in the field of smart structures with embedded actuators to provide real-time feedback, offering several advantages, especially in the soft robotics domain