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Meltable metal–organic frameworks (MOFs) offer significant accessibility to chemistry and moldability for developing carbon-based materials. However, the scarcity of low melting point MOFs poses challenges for related design. Here, we propose a MOFs melt-foaming strategy toward Ni single atoms/quantum dots-functionalized carbon foams (NiSA/QD@CFs). Melt-foaming highly depends on two factors: flexible metal–phosphorus bonds with cage-like ligands bridged in a zipper configuration via hydrogen bonds, facilitating MOFs conformational melting below 200 °C, and high annealing rates that lead to MOFs foaming by reducing the energy barrier and enhancing the pyrolysis enthalpy. When used as hosts for sodium metal anodes, foam structure regulates metallic Na to preferentially deposit inside the pores, while Ni SA and QD synergistically enhance Na absorption. Consequently, NiSA/QD@CF electrodes exhibit stable cyclic performance for 1000 h in symmetrical cells, with a low hysteresis voltage of 98 mV at 100 mA/cm2, 100 mAh/cm2, and 100% depth of discharge. Moreover, both full cells and anode-free ones exhibit excellent rate and cyclic performances. This strategy enriches the liquid MOFs family and their applications in mild-processing CFs for electrochemical energy storage.

Deep learning (DL) systems suffer from low resource utilization due to 1) monolithic server model that tightly couples compute and memory; and 2) limited sharing between different inference applications, and across inference and training, because of strict service level objectives (SLOs). To address this problem, we present DistMind, a disaggregated DL system that enables efficient multiplexing of DL applications with near-optimal resource utilization. DistMind decouples compute from host memory, and exposes the abstractions of a GPU pool and a memory pool, each of which can be independently provisioned. The key challenge is to dynamically allocate GPU resources to different applications based on their real-time demands while meeting strict SLOs. We tackle this challenge by exploiting the power of high-speed 100 Gbps networks, and design three-stage pipelining, cache-aware load balancing, and DNN-aware sharding mechanisms based on the characteristics of DL workloads, to achieve millisecond-scale application loading overhead and improve system efficiency. We have implemented a prototype of DistMind and integrated it with PyTorch. Experimental results on AWS EC2 show that DistMind achieves near 100% resource utilization, and compared with NVIDIA MPS and Ray, DistMind improves the throughput by up to 279% and reduces the inference latency by up to 94%.

Tetracycline repressor (TetR) family regulators (TFRs) are the largest group of DNA-binding transcription factors and are widely distributed in bacteria and archaea. TFRs play vital roles in controlling the expression of various genes and regulating diverse physiological processes. Recently, a TFR protein Pseudomonas virulence regulator A (PvrA), was identified from Pseudomonas aeruginosaas the transcriptional activator of genes involved in fatty acid utilization and bacterial virulence. Here, we show that PvrA can simultaneously bind to multiple pseudo-palindromic sites and upregulate the expression levels of target genes. Cryo-electron microscopy (cryo-EM) analysis indicates the simultaneous DNA recognition mechanism of PvrA and suggests that the bound DNA fragments consist of a distorted B-DNA double helix. The crystal structure and functional analysis of PvrA reveal a hinge region that secures the correct domain motion for recognition of the promiscuous promoter. Additionally, our results showed that mutations disrupting the regulatory hinge region have differential effects on biofilm formation and pyocyanin biosynthesis, resulting in attenuated bacterial virulence. Collectively, these findings will improve the understanding of the relationship between the structure and function of the TetR family and provide new insights into the mechanism of regulation of P. aeruginosavirulence.

Large-scale GPU clusters are widely used to speed up both latency-critical (online) and best-effort (offline) deep learning (DL) workloads. However, similar to the common practice, the DL clusters at ByteDance dedicate each GPU to one workload or share workloads in time dimension, leading to very low GPU resource utilization. Existing techniques like NVIDIA MPS provide an opportunity to share multiple workloads in space on widely-deployed NVIDIA GPUs, but it cannot guarantee the performance of online workloads. We present MuxFlow, the first production system that can scale over massive GPUs to support highly efficient space-sharing for DL workloads. MuxFlow introduces a two-level protection mechanism for both memory and computation to guarantee the performance of online workloads. MuxFlow leverages dynamic streaming multiprocessor (SM) allocation to improve the efficiency of offline workloads. Based on our practical error analysis, we design a mixed error-handling mechanism to improve system reliability. MuxFlow has been deployed at ByteDance on more than 18000 GPUs. The deployment results indicate that MuxFlow substantially improves the GPU utilization from 26% to 76%, SM activity from 16% to 33%, and GPU memory usage from 42% to 48%.

As the significance of rare earth elements (REEs) as a strategic resource in the industrial economy and the scarcity of the resource itself gain prominence worldwide, technologies for capturing and recovering REEs from wastewater are crucial for a sustainable future. To gain a comprehensive insight into research focuses and emerging trends related to the recovery of REEs from wastewater, the Web of Science Core Collection (WOSCC) database was used for the literature search. Bibliometrix, VOSviewer, and CiteSpace software were employed for bibliometric analysis of research literature published between 2000 and 2023, investigating aspects such as the prominent contributing countries/regions, prolific authors, source journals, and major research institutions. The results revealed a steady increase in research pertaining to the recovery of REEs from wastewater during the studied period. China, India, and the USA were identified as leading contributors, with the Chinese Acad Sci being recognized as the most productive institution. Based on the co-occurrence and burst analysis of keywords, it was found that the adsorption and solvent extraction of REEs from wastewater currently dominate current research, with the development of emerging technologies such as the electrochemical method and biological extraction method representing potential future hotspots. Additionally, emerging types of highly selective and high-capacity adsorbents for REEs are anticipated to become a prominent area of interest. This paper provided a comprehensive overview of the current state of the recovery of REEs from wastewater and forecasts future trends, offering a valuable reference for understanding the evolving dynamics in this field.

The orchid mantis Hymenopus coronatus(Insecta: Mantodea) is famous for its unique appearance, which resembles a flower blossom with petal-like legs and a whitish pink colour. Based on this appearance, H. coronatushas been hypothesized to be an aggressive mimic that attracts pollinators. Research on the orchid mantis has been sparse due to the difficulty of collection and maintenance. The first record of orchid mantis from Xishuangbanna, China, was originally named H. coronatoidesbut later considered synonymous with H. coronatusbased on a morphological study. In this study, the complete mitochondrial genome of the orchid mantis from Xishuangbanna, China, was determined to be a circular molecule of 15,589 bp containing 37 typical mitochondrial genes in the same gene arrangement as those of other praying mantises. The relatively low degree of genetic difference between orchid mantises from China and Malaysia suggests that they belong to the same species. The phylogenetic relationships inferred from 13 protein-coding genes (PCGs) show that H. coronatusis a sister to Parymenopus davisoni, another rare species from Malaysia, and supports monophyletic Hymenopodinae, whereas Acromantinae is polyphyletic. This study provides useful data for studies on the population genetics, phylogeny and mimicry mechanism of the orchid mantis.

High-performance integrated electro-optic modulators operating at low temperature are critical for optical interconnects in cryogenic applications. Existing integrated modulators, however, suffer from reduced modulation efficiency or bandwidth at low temperatures because they rely on tuning mechanisms that degrade with decreasing temperature. Graphene modulators are a promising alternative because graphene’s intrinsic carrier mobility increases at low temperature. Here, we demonstrate an integrated graphene-based electro-optic modulator whose 14.7 GHz bandwidth at 4.9 K exceeds the room temperature bandwidth of 12.6 GHz. The bandwidth of the modulator is limited only by high contact resistance, and its intrinsic RC-limited bandwidth is 200 GHz at 4.9 K.

To break through a critical barrier in the practical application of graphene biosensors, namely, device-to-device performance inhomogeneity, this work presents a novel scenario employing a fully solid-state (FSS) transistor configuration. Herein, the graphene sensing unit is completely encapsulated by a high-κ solid dielectric material, which isolates the sensing unit from solution contaminants and thus homogeneously maintains the extraordinary carrier mobility of pristine graphene in batch-made devices. To create an interface sensitive to biomolecular interactions based on the FSS configuration, a metallic floating gate functionalized by conductive mercapto-phenyl molecular linkers is defined on the top-layer solid dielectric. As the solid dielectric layer beneath the metal floating gate enables a higher capacitive gating efficiency than the regular graphene-solution electrical double layer (EDL) interface, the overall transistor amplification gain is further enhanced. As a proof of principle, a label-free DNAzymatic bioassay of Pb2+is conducted. Without the traditional one-by-one device normalization, an excellent concentration detection limit of 929.8 fM is achieved, which is almost 2 orders of magnitude lower than that in existing works. The FSS configuration allows enhanced sensitivity and homogeneity, thereby providing new developmental guidelines for graphene biosensors beyond the laboratory investigation stage. Additionally, it has the potential to be universally applicable for cost-efficient single-device bioassays.

This paper presents an approach to the real-time, label-free, specific, and sensitive monitoring of insulin using a graphene aptameric nanosensor. The nanosensor is configured as a field-effect transistor, whose graphene-based conducting channel is functionalized with a guanine-rich IGA3 aptamer. The negatively charged aptamer folds into a compact and stable antiparallel or parallel G-quadruplex conformation upon binding with insulin, resulting in a change in the carrier density, and hence the electrical conductance, of the graphene. The change in the electrical conductance is then measured to enable the real-time monitoring of insulin levels. Testing has shown that the nanosensor offers an estimated limit of detection down to 35 pM and is functional in Krebs–Ringer bicarbonate buffer, a standard pancreatic islet perfusion medium. These results demonstrate the potential utility of this approach in label-free monitoring of insulin and in timely prediction of accurate insulin dosage in clinical diagnostics.

Point-of-care monitoring of small molecules in biofluids is crucial for clinical diagnosis and treatment. However, the inherent low degree of recognition of small molecules and the complex composition of biofluids present significant obstacles for current detection technologies. Although nanopore sensing excels in the analysis of small molecules, the direct detection of small molecules in complex biofluids remains a challenge. In this study, we present a method for sensing the small molecule drug gentamicin in whole blood based on the mechanosensitive channel of small conductance in Pseudomonas aeruginosa(PaMscS) nanopore. PaMscS can directly detect gentamicin and distinguish its main components with only a monomethyl difference. The ’molecular sieve’ structure of PaMscS enables the direct measurement of gentamicin in human whole blood within 10 min. Furthermore, a continuous monitoring device constructed based on PaMscS achieved continuous monitoring of gentamicin in live rats for approximately 2.5 h without blood consumption, while the drug components can be analyzed in situ. This approach enables rapid and convenient drug monitoring with single-molecule level resolution, which can significantly lower the threshold for drug concentration monitoring and promote more efficient drug use. Moreover, this work also lays the foundation for the future development of continuous monitoring technology with single-molecule level resolution in the living body.

OCCUPATIONAL APPLICATIONSA single wearable sensor (accelerometer) on the chest was employed to classify static and dynamic activities commonly observed in manual handling jobs. Utilizing only two features obtained from this sensor, 15 different simulated activities were classified with 93%–98% accuracy. The classification models developed here could be used to objectively quantify workers’ tasks through the course of their work shifts, thereby enabling more accurate and efficient ergonomic assessments while requiring using only a simple wearable device.TECHNICAL ABSTRACTBackground: The use of accelerometers for recognizing physical activity has increased substantially; however, there is a need to develop activity classification models that are effective for a large range of static and dynamic work activities commonly seen in manual handling. Purpose: This study aimed to develop an efficient classification model to recognize static and dynamic work activities from a single accelerometer attached to the chest. Methods: Accelerometer data were obtained during 15 simulated activities from 27 (13 males, 14 females) healthy adults. First-order differencing was employed to account for between-subject variability. Eighty-one datasets were created using nine different time windows and nine different data samples with randomly-selected start times. Medians of postural angles and the area-under-the-curve of transformed triaxial data were used as predictors in classification models (random forest and support vector machines). Results: In the random-forest models, the highest (98.2%) and lowest (93%) overall accuracies across the 15 activities were associated with time windows of 6 and 2 s, and data samples of 5000 and 1000, respectively. The findings also indicated that accuracy of the random-forest models improved with increasing time window durations and data samples. Finally, activity classification with the support vector machine model, corresponding to the random-forest model with the highest performance, achieved an accuracy of 95.5%. Conclusions: These results suggest that the use of only two features from a single accelerometer can help classify work activities, which in turn can improve real-time or remote ergonomic risk assessments of manual handling tasks. Future work is needed, though, to examine the generalizability of the current models to efficiently classify complex manual handling tasks in diverse worker groups.

About 425 million adults around the world were living with diabetes in 2017. A relevant condition called Hypoglycemia is characterized by a dangerous low level of blood sugar that could be fatal to diabetic patients. Continuous glucose monitoring systems (CGMS) are the most popular commercially available technology for detecting diabetic hypoglycemia. However, CGMSs are invasive, costly, and not user-centric thereby not sustainable for diabetes management. This paper documents our initial efforts in designing an inexpensive, non-invasive, wearable physiological tremor sensory system to detect the onset of hypoglycemic events of diabetic patients. The design cycle briefly presented here includes: 1) determination of system (technology and user) requirements, 2) development of the tremor detection prototype, and 3) testing and validation of the system in non-clinical and clinical settings using human factors, data analytics, and biomedical sciences techniques and approaches.

Extensive research has been conducted to study the effects of physical and sleep related fatigue on occupational health and safety. However, fatigue is a complex multidimensional construct, that is task- and occupation-dependent, and our knowledge on how to measure this complex construct is limited. A scoping review was conducted to: 1) review sensors and their metrics currently employed in occupational fatigue studies, 2) identify overlap between sensors and associated metrics that can be leveraged to assess comprehensive fatigue, 3) investigating the effectiveness of the sensors/metrics, and 4) recommended potential sensor/metric combinations to evaluate comprehensive fatigue. 512 unique abstracts were identified through Ovid-MEDLINE, MEDLINE, Embase and Cinal databases and application of the inclusion/exclusion criteria resulted in 27 articles that were included for the review. Heart rate sensors and actigraphs were identified to be the most suitable devices to study comprehensive fatigue. Heart rate trend within the heart rate sensor, and sleep length and sleep efficiency within actigraphs were found to be the most popular and reliable metrics for measuring occupational fatigue.

Here, we report a facile approach for highly sensitive and selective detection of aqueous lead ions that uses a real-time quantitative polymerase chain reaction technology and a lead-dependent DNAzyme, termed GR-5. In this method, the substrate DNA is cleaved at the site of the adenosine ribonucleotide by GR-5 DNAzyme in the presence of lead ions, resulting in a decrease in template DNA available for PCR and a consequent change in signal detection (cycle threshold (Ct) value). This novel approach takes advantage of the exponential amplification of PCR and the specific recognition of the GR-5 lead-dependent DNAzyme to provide Pb2+-specific detection with an excellent linear relationship between Ct value and Pb2+concentration within a range of 1–500 nM. The correlation coefficient of the standard curve was 0.9898, and the limit of detection was 0.7 nM. Moreover, this sensor showed good selectivity for Pb2+ions over other metal ions.

Geo-routing protocols can provide the scalability, robustness, lower overhead and energy efficiency requirements for underwater sensor networks (UWSNs). However, these protocols require localisation service, which is another challenging problem. In this paper, we introduce a novel energy efficient schema that does not need location information. In this schema, nodes assign themselves to concentric layers around sink nodes and routing paths are determined on the fly based on nodes' layers and residual energy information. Nodes can adaptively adjust their transmission power level to cater for network mobility and residual energy constraints. Extensive simulation results show that our routing protocol achieves high delivery ratio and low energy consumption while reducing end to end delay when compared with other routing strategies. In particular, our schema can guarantee a relatively higher delivery ratio in presence of mobility.

The influences of fractal pore structure in coal reservoir on coalbed methane (CBM) migration were analyzed in detail by coupling theoretical models and numerical methods. Different types of fractals were generated based on the construction thought of the standard Menger Sponge to model the 3D nonlinear coal pore structures. Then a correlation model between the permeability of fractal porous medium and its pore-size-distribution characteristics was derived using the parallel and serial modes and verified by Lattice Boltzmann Method (LBM). Based on the coupled method, porosity (φ), fractal dimension of pore structure (Db), pore size range (rmin, rmax) and other parameters were systematically analyzed for their influences on the permeability (κ) of fractal porous medium. The results indicate that: ① the channels connected by pores with the maximum size (rmax) dominate the permeability κ, approximating in the quadratic law; ② the greater the ratio of rmaxand rminis, the higher κis; ③ the relationship between Dband κfollows a negative power law model, and breaks into two segments at the position where Db≌2.5. Based on the results above, a predicting model of fractal porous medium permeability was proposed, formulated as κ=Cfrmaxn, where Cand n(approximately equal to 2) are constants and fis an expression only containing parameters of fractal pore structure. In addition, the equivalence of the new proposed model for porous medium and the Kozeny-Carman model κ=Crnwas verified at Db=2.0.

Modern data centers are massive, and support a range of distributed applications across potentially hundreds of server racks. As their utilization and bandwidth needs continue to grow, traditional methods of augmenting bandwidth have proven complex and costly in time and resources. Recent measurements show that data center traffic is often limited by congestion loss caused by short traffic bursts. Thus an attractive alternative to adding physical bandwidth is to augment wired links with wireless links in the 60 GHz band.We address two limitations with current 60 GHz wireless proposals. First, 60 GHz wireless links are limited by line-of-sight, and can be blocked by even small obstacles. Second, even beamforming links leak power, and potential interference will severely limit concurrent transmissions in dense data centers. We propose and evaluate a new wireless primitive for data centers, 3D beamforming, where 60 GHz signals bounce off data center ceilings, thus establishing indirect line-of-sight between any two racks in a data center. We build a small 3D beamforming testbed to demonstrate its ability to address both link blockage and link interference, thus improving link range and number of concurrent transmissions in the data center. In addition, we propose a simple link scheduler and use traffic simulations to show that these 3D links significantly expand wireless capacity compared to their 2D counterparts.

HtrA-family proteases are commonly employed to sense the protein folding stress and activate the regulated intramembrane proteolysis (RIP) cascade in Gram-negative bacteria. Here, we reveal the unique dual-signal activation and dynamic regulation properties of AlgW, an HtrA-type protease triggering the AlgU stress-response pathway, which controls alginate production and mucoid conversion in Pseudomonas aeruginosa.

Office workers are at high-risk for work-related musculoskeletal disorders (WMSDs), which can inhibit productivity and perceived comfort through associated disorders (Piligian et al, 2000). An increase in WMSDs has created a demand for ergonomic office interventions. Recent ergonomic advancements have focused on dynamic office furniture that aim to promote chairs featuring structural designs (Ellegast et al, 2012). Dynamic office furniture lacking a range of specific and modifiable adjustments can lead to adverse outcomes, such as nerve compression, resulting in perceived discomfort and increased risk of musculoskeletal disorders (Groenesteijn et al, 2009; Legg et al, 2002). However, there is limited research regarding the relationship of posture changes and the effects on perceived comfort and task performance. The objective of the study was to explore the relationship between perceived comfort, productivity, postural changes, and adjustability in chairs with varying degrees of adjustability. The study consisted of 44 participants (54.5% males, 22.8 (2.89) years) attending three different days, working together in teams of 2-3, to complete a 3-hour simulated office task related to construction science. Tasks required team collaboration to evaluate construction bids, develop plan of actions, and present conclusions to each task. To determine the ergonomic impact of office furniture on team creativity and productivity on the design project, two different types of chairs were employed that varied in the level of user involvement for chair adjustments – no involvement without adjustment and high involvement with adjustment, each presented on separate days. Subjective surveys were used at the end of every session to evaluate the chair used in that session that determined individual satisfaction, usability, and perceived discomfort levels. Continuous video obtained during the sessions were analyzed to obtain the number of postural changes (e.g., bending from side to side, shoulders or back ‘drooping’), chair adjustments made, unintended jerks, and chair switches throughout each time period (for a total of 3 time periods) on each day. Performance on the simulated construction science tasks were assessed using a predetermined rubric that included components such as document completion, completed summaries, and presentations to obtain a total performance score. Reported here are preliminary findings from the video analyses and discomfort surveys. A 2-chair x 3-time repeated measures analysis of variance (ANOVA) on postural change over time revealed that postural changes on the chair associated with no involvement without adjustment was higher than that of high involvement with adjustment, however this was only seen in times 1 and 2. This implies that the benefit of adjustments are likely lost over time. Separate one-way ANOVAs were performed on discomfort scores to test the effects of the two chairs. Overall, perceived discomfort across different body regions did not differ between chairs. It is likely that more postural changes were needed to maintain initial comfort levels in the no involvement no adjustment chair studied and were necessary to minimize discomfort. Preliminary findings obtained here will help identify the extent to which postural deviations affect team productivity and responses on discomfort and strain.