Organic ultraviolet filters (OUVFs) are extensively incorporated into both cosmetic items and industrial products and have been commonly found in water ecosystems. This study aims to examine the environmental levels, sources, ecological and human health risks of 14 commonly used OUVFs both in coastal water and beach deposit samples collected from the nearshore regions of Hainan Island and the South China Sea. This is first study highlighting the contamination of OUVFs in Hainan Island and utilizing economic and tourism data to confirm the potential source of OUVF pollution in costal aquatic and coastal ecosystem. Along the coastal tourist regions of Hainan Island, the median concentrations in coastal waters and beach deposits of these OUVFs fall within the range from 1.2 to 53.2 ng/L and 0.2-17.0 ng/g dw, respectively. In coastal water and beach deposit, the concentration of BP-3 was the highest, with median concentrations of 53.2 ng/L and 17.0 ng/g dw, respectively. Regarding human health risks, the daily intake of all 14 OUVFs through swimming was found to be 40-48 ng/kg/day. Ecological risk assessment indicates that BP-3 presents a medium risk for marine microalgae with a concurrent low risk for corals. The correlation analysis underscores a substantial interrelation of OUVFs in both coastal waters and beach deposits with various economic indicators, including annual rainfall, overnight tourists, total hotel rooms (unit), room occupancy rate, and sewage treatment capacity.

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance.

Objective. We intend to chronically restore somatosensation and provide high-fidelity myoelectric control for those with limb loss via a novel, distributed, high-channel-count, implanted system.Approach.We have developed the implanted Somatosensory Electrical Neurostimulation and Sensing (iSens®) system to support peripheral nerve stimulation through up to 64, 96, or 128 electrode contacts with myoelectric recording from 16, 8, or 0 bipolar sites, respectively. The rechargeable central device has Bluetooth® wireless telemetry to communicate to external devices and wired connections for up to four implanted satellite stimulation or recording devices. We characterized the stimulation, recording, battery runtime, and wireless performance and completed safety testing to support its use in human trials.Results.The stimulator operates as expected across a range of parameters and can schedule multiple asynchronous, interleaved pulse trains subject to total charge delivery limits. Recorded signals in saline show negligible stimulus artifact when 10 cm from a 1 mA stimulating source. The wireless telemetry range exceeds 1 m (direction and orientation dependent) in a saline torso phantom. The bandwidth supports 100 Hz bidirectional update rates of stimulation commands and data features or streaming select full bandwidth myoelectric signals. Preliminary first-in-human data validates the bench testing result.Significance.We developed, tested, and clinically implemented an advanced, modular, fully implanted peripheral stimulation and sensing system for somatosensory restoration and myoelectric control. The modularity in electrode type and number, including distributed sensing and stimulation, supports a wide variety of applications; iSens® is a flexible platform to bring peripheral neuromodulation applications to clinical reality. ClinicalTrials.gov ID NCT04430218.

This paper introduces a novel, Simple-based Dynamic Decentralized Community Detection Algorithm (S-DCDA) for Socially Aware Networks. This algorithm aims to address the resource-intensive nature, instabilities and inaccuracies of traditional distributed community detection algorithms. The dynamics of decentralization is evident in the threefold nature of the algorithm: (i) each node of the community is the core of the entire network or community for a certain period of time dependent on their need, (ii) nodes are not centralized around themselves, requiring the consent of the other node to join a community, and (iii) Communities start from a single node to form an initial scale community, the number of nodes and the relationship among them are constantly changing. The algorithm requires low processor performance and memory capacity size of each node, to a certain extent, effectively improve the accuracy and stability of community detection and maintenance. Experimental results demonstrate that in comparison to classical and classical-based improved community detection algorithms, S-DCDA yields superior detection results.

The utilization of the human immune system as a therapeutic modality has materialized in the form of novel biologics known as immune effector cells (IECs). However, currently approved IECs rely on autologous cells for manufacturing that are funneled through costly centralized supply chains leading to long wait times and potentially increased mortality. Alternative models for manufacturing at or near the point-of-care in a distributed and local approach are being proposed to overcome such a bottleneck. Cell processing facilities for minimally manipulated products, as well as academic good manufacturing practice facilities, are being considered for such manufacturing tasks. However, the infrastructure and the practices of these facilities remains unstudied. Here, we surveyed the cell processing facilities accredited by the Foundation for Accreditation of Cellular Therapy (FACT) in the United States to better understand their preparedness for local manufacturing of IECs. A structured survey consisting of 40 items was distributed to the directors of 157 facilities. The survey evaluated 6 domains, including facility characteristics, quality practices, personnel, use of automation, experience with IECs, and the perception of the point-of-care model. Thirty-eight facilities completed the survey (24.2%). Most facilities were involved in handling IEC products (35/38, 92.1%), and the majority had infrastructure to support basic operations and quality control such as viability (36/36, 100%), identity (33/36, 91.7%), and sterility (33/36, 91.7%). The quality practices varied among the facilities depending on the types of products processed. A slight majority implemented automation in their workflows (22/38, 57.9%). Facilities expressed a general interest in adopting point-of-care models (23/38, 61%), with financial and human resources identified as the most significant constraints. In conclusion, FACT-accredited cell processing facilities may provide the infrastructure required for local manufacturing. However, there is a need for standardization and minimum quality requirements to effectively implement such models.

Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor-object interface. In this paper, we present an optical-based six-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate the local XYZ force and displacement at ten distinct locations and provide the global XYZ force and torque measurements. Its compact size, comparable to that of a human thumb, and minimal thickness allow seamless integration onto existing robotic fingers, eliminating the need for complex modifications to the gripper. The proposed sensor design uses a simple, low-cost fabrication method. Moreover, the optical transduction approach uses light angle and intensity sensing to infer force and displacement from deformations of the individual sensing units that form the overall sensor, providing distributed six-axis sensing. The local force precision at each sensing unit in the X, Y, and Z axes is 20.89 mN, 19.19 mN, and 43.22 mN, respectively, over a local force range of approximately ±1.5 N in X and Y and 0 to -2 N in Z. The local displacement precision in the X, Y, and Z axes is 56.70 μm, 50.18 μm, and 13.83 μm, respectively, over a local displacement range of ±2 mm in the XY directions and 0 to -1.5 mm in Z (i.e., compression). Additionally, the sensor can measure global torques, Tx, Ty, and Tz, with a precision of of 1.90 N-mm, 1.54 N-mm, and 1.26 N-mm, respectively. The fabricated design is showcased by integrating it with an OnRobot RG2 gripper and illustrating real-time measurements during in simple demonstration task, which generated changing global forces and torques.

Simulation-based training is increasingly used to acquire basic laparoscopic skills. Multiple factors can influence training, e.g., distributed practice is superior to massed practice in terms of efficiency. However, the optimal interval between training sessions is unclear. The objective of this trial was to investigate if shorter intervals between sessions are more efficient than longer intervals during proficiency-based laparoscopy simulator training.
A randomized simulation-based trial where medical students (n = 39) were randomized to proficiency-based training with either 1-2 days (intervention group) or 6-8 days (control group) between training sessions. Both groups practiced a series of basic tasks and a procedural module until proficiency level on the LapSim® simulator. Both groups were given instructor feedback upon request. After reaching proficiency, participants were invited back for a retention test 3-5 weeks later and practiced the same tasks to proficiency again.
The mean time to reach proficiency during training was 291 (SD 89) and 299 (SD 89) min in the intervention and control group, respectively (p = 0.81). During the retention test, the mean time to reach proficiency was 94 (SD 53) and 96 (SD 39) minutes in the intervention and control groups, respectively (p = 0.91).
We found no difference whether practicing with shorter intervals or longer intervals between training sessions when examining time to proficiency or retention.

Multi-agent systems are promising for applications in various fields. However, they require optimization algorithms that can handle large number of agents and heterogeneously connected networks in clustered environments. Planning algorithms performed in the decentralized communication model and clustered environment require precise knowledge about cluster information by compensating noise from other clusters. This article proposes a decentralized data aggregation algorithm using consensus method to perform COUNT and SUM aggregation in a clustered environment. The proposed algorithm introduces a trust value to perform accurate aggregation on cluster level. The correction parameter is used to adjust the accuracy of the solution and the computation time. The proposed algorithm is evaluated in simulations with large and sparse networks and small bandwidth. The results show that the proposed algorithm can achieve convergence on the aggregated data with reasonable accuracy and convergence time. In the future, the proposed tools will be useful for developing a robust decentralized task assignment algorithm in a heterogeneous multi-agent multi-task environment.

Speech comprehension is severely compromised when several people talk at once, due to limited perceptual and cognitive resources. In such circumstances, top-down attention mechanisms can actively prioritize processing of task-relevant speech. However, behavioral and neural evidence suggest that this selection is not exclusive, and the system may have sufficient capacity to process additional speech input as well. Here we used a data-driven approach to contrast two opposing hypotheses regarding the system\'s capacity to co-represent competing speech: Can the brain represent two speakers equally or is the system fundamentally limited, resulting in tradeoffs between them? Neural activity was measured using magnetoencephalography (MEG) as human participants heard concurrent speech narratives and engaged in two tasks: Selective Attention, where only one speaker was task-relevant and Distributed Attention, where both speakers were equally relevant. Analysis of neural speech-tracking revealed that both tasks engaged a similar network of brain regions involved in auditory processing, attentional control and speech processing. Interestingly, during both Selective and Distributed Attention the neural representation of competing speech showed a bias towards one speaker. This is in line with proposed \'bottlenecks\' for co-representation of concurrent speech and suggests that good performance on distributed attention tasks may be achieved by toggling attention between speakers over time.

Over the past three decades, we have seen significant advances in the field of wireless implantable medical devices (IMDs) that can interact with the nervous system. To further improve the stability, safety, and distribution of these interfaces, a new class of implantable devices is being developed: single-channel, sub-mm scale, and wireless microelectronic devices. In this research, we describe a new and simple technique for fabricating and assembling a sub-mm, wirelessly powered stimulating implant. The implant consists of an ASIC measuring 900 × 450 × 80 µm3, two PEDOT-coated microelectrodes, an SMD inductor, and a SU-8 coating. The microelectrodes and SMD are directly mounted onto the ASIC. The ultra-small device is powered using electromagnetic (EM) waves in the near-field using a two-coil inductive link and demonstrates a maximum achievable power transfer efficiency (PTE) of 0.17% in the air with a coil separation of 0.5 cm. In vivo experiments conducted on an anesthetized rat verified the efficiency of stimulation.