Loneliness and social isolation are prevalent concerns among older adults and can lead to negative health consequences and a reduced lifespan. New technologies are increasingly being developed to help address loneliness and social isolation in older adults, including monitoring systems, social networks, robots, companions, smart televisions, augmented reality (AR) and virtual reality (VR) applications. This systematic review maps human-centered design (HCD) and user-centered design (UCD) approaches, human needs, and contextual factors considered in current technological interventions designed to address the problems of loneliness and social isolation in older adults. We conducted a scoping review and in-depth examination of 98 papers through a qualitative content analysis. We found 12 studies applying either an HCD or UCD approach and observed strengths in continuous user involvement and implementation in field studies but limitations in participant inclusion criteria and methodological reporting. We also observed the consideration of important human needs and contextual factors. However, more research is needed on stakeholder perspectives, the functioning of applications in different housing environments, as well as studies that include diverse socio-economic groups.

Compacted bentonite, used as an engineering barrier for permanent containment of high-level radioactive waste, is susceptible to mineral evolution resulting in compromise of the expected barrier performance due to alkaline–thermal chemical interaction in the near-field. To elucidate the mineral-evolution mechanisms within bentonite and the transformation of the nuclide adsorption properties during that period, experimental evolution of bentonite was conducted in a NaOH solution with a pH of 14 at temperatures ranging from 60 to 120°C. The results showed that temperature significantly affects the stability of minerals in bentonite under alkali conditions. The dissolution rate of fine-grained cristobalite in bentonite exceeds that of smectite, with the phase-transition products of smectite being temperature-dependent. As the temperature rises, smectite experiences a three-stage transformation: initially, at 60°C, the lattice structure thins due to the collapse of the octahedral sheets; at 80°C, the lattice disintegrates and reorganizes into a loose framework akin to albite; and by 100°C, it further reorganizes into a denser framework resembling analcime. The adsorption properties of bentonite exhibit a peak inflection point at 80°C, where the dissolution of the smectite lattice eliminates interlayer pores and exposes numerous polar or negatively charged sites which results in a decrease in specific surface area and an increase in cation exchange capacity and adsorption capacity of Eu3+. This research provides insights into the intricate evolution of bentonite minerals and the associated changes in radionuclide adsorption capacity, contributing to a better understanding of the stability of bentonite barriers and the effective long-term containment of nuclear waste.

The inverse dynamics model of an industrial robot can predict and control the robot’s motion and torque output, improving its motion accuracy, efficiency, and adaptability. However, the existing inverse rigid body dynamics models still have some unmodelled residuals, and their calculation results differ significantly from the actual industrial robot conditions. The bootstrap aggregating (bagging) algorithm is combined with a long short-term memory network, the linear layer is introduced as the network optimization layer, and a compensation method of hybrid inverse dynamics model for robots based on the BLL residual prediction algorithm is proposed to meet the above needs. The BLL residual prediction algorithm framework is presented. Based on the rigid body inverse dynamics of the Newton–Euler method, the BLL residual prediction network is used to perform error compensation on the inverse dynamics model of the Franka robot. The experimental results show that the hybrid inverse dynamics model based on the BLL residual prediction algorithm can reduce the average residuals of the robot joint torque from 0.5651 N·m to 0.1096 N·m, which improves the accuracy of the inverse dynamics model compared with those of the rigid body inverse dynamics model. This study lays the foundation for performing more accurate operation tasks using industrial robots.

The high-power narrow-linewidth fiber laser has become the most widely used high-power laser source nowadays. Further breakthroughs of the output power depend on comprehensive optimization of stimulated Brillouin scattering (SBS), stimulated Raman scattering (SRS) and transverse mode instability (TMI). In this work, we aim to further surpass the power record of all-fiberized and narrow-linewidth fiber amplifiers with near-diffraction-limited (NDL) beam quality. SBS is suppressed by white-noise-signal modulation of a single-frequency seed. In particular, the refractive index of the large-mode-area active fiber in the main amplifier is controlled and fabricated, which could simultaneously increase the effective mode field area of the fundamental mode and the loss coefficient of higher-order modes for balancing SRS and TMI. Subsequent experimental measurements demonstrate a 7.03 kW narrow-linewidth fiber laser with a signal-to-noise ratio of 31.4 dB and beam quality factors of Mx2 = 1.26, My2 = 1.25. To the best of our knowledge, this is the highest reported power with NDL beam quality based on a directly laser-diode-pumped and all-fiberized format, especially with narrow-linewidth spectral emission.

This study aims to evaluate the impact of low-carbohydrate diet, balanced dietary guidance and pharmacotherapy on weight loss among individuals with overweight or obesity over a period of 3 months. The study involves 339 individuals with overweight or obesity and received weight loss treatment at the Department of Clinical Nutrition at the Second Affiliated Hospital of Zhejiang University, School of Medicine, between 1 January 2020 and 31 December 2023. The primary outcome is the percentage weight loss. Among the studied patients, the majority chose low-carbohydrate diet as their primary treatment (168 (49·56 %)), followed by balanced dietary guidance (139 (41·00 %)) and pharmacotherapy (32 (9·44 %)). The total percentage weight loss for patients who were followed up for 1 month, 2 months and 3 months was 4·98 (3·04, 6·29) %, 7·93 (5·42, 7·93) % and 10·71 (7·74, 13·83) %, respectively. Multivariable logistic regression analysis identified low-carbohydrate diet as an independent factor associated with percentage weight loss of ≥ 3 % and ≥ 5 % at 1 month (OR = 0·461, P < 0·05; OR = 0·349, P < 0·001). The results showed that a low-carbohydrate diet was an effective weight loss strategy in the short term. However, its long-term effects were comparable to those observed with balanced dietary guidance and pharmacotherapy.

The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of PEML methods, expressed across the defining axes of physics and data, is discussed by engaging in a comprehensive exploration of its characteristics, usage, and motivations. In doing so, we present a survey of recent applications and developments of PEML techniques, revealing the potency of PEML in addressing complex challenges. We further demonstrate the application of select such schemes on the simple working example of a single degree-of-freedom Duffing oscillator, which allows to highlight the individual characteristics and motivations of different “genres” of PEML approaches. To promote collaboration and transparency, and to provide practical examples for the reader, the code generating these working examples is provided alongside this paper. As a foundational contribution, this paper underscores the significance of PEML in pushing the boundaries of scientific and engineering research, underpinned by the synergy of physical insights and machine learning capabilities.

In this paper, a capsule endoscopy system with a sensing function is proposed for medical devices. A single-arm spiral antenna is designed for data transmission and is combined with the voltage controlled oscillator to achieve sensing capabilities. The designed antenna operates at a 900 MHz industrial scientific medical band. By establishing a three-layer cylindrical model of the stomach, it was concluded that the antenna in the stomach has a high peak gain of −1.1 dBi. Additionally, the antenna achieved a −10 dB impedance bandwidth of 5%. The capsule endoscopy was experimentally measured in both actual stomach and simulated environments. The maximum working distance of the capsule endoscope was measured to be 6.8 m. Additionally, the proposed capsule endoscope was tested for its sensing function using solutions with different dielectric constants. Finally, it was confirmed through link analysis that it has good communication capabilities. The results and analysis confirm that the proposed capsule endoscope can be used for examining gastric diseases.

We introduce a novel human-centric deep reinforcement learning recommender system designed to co-optimize energy consumption, thermal comfort, and air quality in commercial buildings. Existing approaches typically optimize these objectives separately or focus solely on controlling energy-consuming building resources without directly engaging occupants. We develop a deep reinforcement learning architecture based on multitask learning with humans-in-the-loop and demonstrate how it can jointly learn energy savings, comfort, and air quality improvements for different building and occupant actions. In addition to controlling typical building resources (e.g., thermostat setpoint), our system provides real-time actionable recommendations that occupants can take (e.g., move to a new location) to co-optimize energy, comfort, and air quality. Through real deployments across multiple commercial buildings, we show that our multitask deep reinforcement learning recommender system has the potential to reduce energy consumption by up to 8% in energy-focused optimization, improve all objectives by 5–10% in joint optimization, and improve thermal comfort by up to 21% in comfort and air quality-focused optimization compared to existing solutions.

Purple nutsedge (Cyperus rotundus L.) is one of the world’s resilient upland weeds, primarily spreading through its tubers. Its emergence in rice fields has been increasing, likely due to changing paddy farming practices. This study aimed to investigate how C. rotundus, an upland weed, can withstand soil flooding and become a problematic weed in rice (Oryza sativa L.) fields. The first comparative analysis focused on the survival and recovery characteristics of growing and mature tubers of C. rotundus exposed to soil flooding conditions. Notably, mature tubers exhibited significant survival and recovery abilities in these environments. Based on this observation, further investigation was carried out to explore the morphological structure, non-structural carbohydrates, and respiratory mechanisms of mature tubers in response to prolonged soil flooding. Over time, the mature tubers did not form aerenchyma but instead gradually accumulated lignified sclerenchyma fibers, with lignin content also increasing. After 90 days, the lignified sclerenchyma fibers and lignin contents were 4.0 and 1.1 times higher than those in no soil flooding (CK). Concurrently, soluble sugar content decreased while starch content increased, providing energy storage, and alcohol dehydrogenase (ADH) activity rose to support anaerobic respiration via alcohol fermentation. These results indicated that mature tubers survived in soil flooding conditions by adopting a “low-oxygen quiescence strategy”, which involves morphological adaptations through the development of lignified sclerenchyma fibers, increased starch reserves for energy storage, and enhanced anaerobic respiration. This mechanism likely underpins the flooding tolerance of mature C. rotundus tubers, allowing them to endure unfavorable conditions and subsequently germinate and grow once flooding subsides. This study provides a preliminary explanation of the mechanism by which mature tubers of C. rotundus from the upland areas confer flooding tolerance, shedding light on the reasons behind this weed’s increasing presence in rice fields.