Task-Oriented Over-the-Air Computation for Multi-Device Edge AI

Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmission), and computation (data processing). Aligned with the paradigm shift, a task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system. In the considered system, local feature vectors, which are extracted from the real-time noisy sensory data on devices, are aggregated over-the-air by exploiting the waveform superposition in a multiuser channel. Then the aggregated features as received at a server are fed into an inference model with the result used for decision making or control of actuators. To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy. The problem is made tractable by measuring the inference accuracy using a surrogate metric called discriminant gain, which measures the discernibility of two object classes in the application of object/event classification. It is discovered that the conventional AirComp beamforming design for minimizing the mean square error in generic AirComp with respect to the noiseless case may not lead to the optimal classification accuracy. The reason is due to the overlooking of the fact that feature dimensions have different sensitivity towards aggregation errors and are thus of different importance levels for classification. This issue is addressed in this work via a new task-oriented AirComp scheme designed by directly maximizing the derived discriminant gain

Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead

A Two-Step Strategy for Fabrication of Biocompatible 3D Magnetically Responsive Photonic Crystals

Extremely stable and biocompatible 3D magnetically responsive photonic crystals (MRPCs) are successfully prepared in aqueous solution. Classic hydrothermal synthesis was applied for preparation of the Fe3O4@C core. Modified Stöber method was then employed for synthesis of the different size of Fe3O4@C@SiO2. Unlike the traditional magnetic nanoparticles, the highly negative charged superparamagnetic nanospheres (SMNs), i.e., the double-shell structure Fe3O4@C@SiO2 are capable of rapidly self-assembling into 3D MRPCs with full visible and various colors that can be periodically and reversibly tuned under different kinds of external magnetic fields (EMFs) within 1 s. The assembling behavior and mechanism of the 3D MRPCs under EMF were monitored and analyzed. The preparation is simple and the size of the SMN is easily controllable by adjusting the amount of catalyst. Compared with the previous works, the synthesized 3D MRPCs are hydrophilic, and exhibit extremely high stability after 6-month storage. To conclude, our study provides an effective two-step strategy for fabrication of biocompatible 3D MRPCs and it reveals great potentials in biological fields

Highly efficient Cu(II) coordination polymer catalyst for the conversion of hazardous volatile organic compounds

Three novel coordination polymers (CPs), namely [Cu(μ-1κO,2κN-L)2]n (1), [Zn (μ-1κO,2κN-L)2(H2O)2]n (2) and [Cd (μ-1κOO’,2κN-L)2]n (3) [where HL = 4-(pyrimidin-5-ylcarbamoyl)benzoic acid], were synthesized and characterized by elemental analysis, ATR-IR, TGA, XPS and single-crystal X-ray diffraction. Despite having the same organic ligand, the various metal cations had an impact in the subsequent frameworks. Hirshfeld surface analysis was performed to investigate the intermolecular interactions and to examine the stability of the crystal structures of the three polymers. Their catalytic performances were screened for the peroxidative oxidation of Volatile Organic Compounds (VOCs), with toluene and p-xylene selected as model substrates. Tert-butyl hydroperoxide (t-BuOOH or TBHP) (aq. 70 %) was employed as the oxidant. The catalytic oxidation of toluene yielded benzyl alcohol, benzaldehyde and benzoic acid. The copper CP 1 exhibited the highest total yield for toluene oxidation, reaching approximately 36% in an aqueous medium. For p-xylene oxidation, tolualdehyde, methylbenzyl alcohol, and toluic acid were produced as the primary products, accompanied by minor ones. The experiments were conducted under diverse conditions, manipulating key parameters such as the choice of solvent (water or acetonitrile), type of oxidant (t-BuOOH or H2O2), the concentration of the oxidant and reaction temperature. In the presence of catalyst 1, a maximum total yield of ca. 80% was achieved for p-xylene oxidation

Simultaneous and Ultrasensitive Detection of Foodborne Bacteria by Gold Nanoparticles-Amplified Microcantilever Array Biosensor

Foodborne pathogens, especially bacteria, are explicitly threatening public health worldwide. Biosensors represent advances in rapid diagnosis with high sensitivity and selectivity. However, multiplexed analysis and minimal pretreatment are still challenging. We fabricate a gold nanoparticle (Au NP)-amplified microcantilever array biosensor that is capable of determining ultralow concentrations of foodborne bacteria including Escherichia coli O157:H7, Vibrio parahaemolyticus, Salmonella, Staphylococcus aureus, Listeria monocytogenes, Shigella, etc. The method is much faster than using conventional tools without germiculturing and PCR amplification. The six pairs of ssDNA probes (ssDNA1 + ssDNA2 partially complementary to the target gene) that originated from the sequence analysis of the specific gene of the bacteria were developed and validated. The ssDNA1 probes were modified with -S-(CH2)6 at the 5′-end and ready to immobilize on the self-assembled monolayers (SAMs) of the sensing cantilevers in the array and couple with Au NPs, while 6-mercapto-1-hexanol SAM modification was carried out on the reference cantilevers to eliminate the interferences by detecting the deflection from the environment induced by non-specific interactions. For multianalyte sensing, the target gene sequence was captured by the ssDNA2-Au NPs in the solution, and then the Au NPs-ssDNA2-target complex was hybridized with ssNDA1 fixed on the beam of the cantilever sensor, which results in a secondary cascade amplification effect. Integrated with the enrichment of the Au NP platform and the microcantilever array sensor detection, multiple bacteria could be rapidly and accurately determined as low as 1–9 cells/mL, and the working ranges were three to four orders of magnitude. There was virtually no cross-reaction among the various probes with different species. As described herein, it holds great potential for rapid, multiplexed, and ultrasensitive detection in food, environment, clinical, and communal samples

The Effect of Myosin Light Chain Kinase on the Occurrence and Development of Intracranial Aneurysm

Myosin light chain kinase is a key enzyme in smooth muscle cell contraction. However, whether myosin light chain kinase plays a role in the occurrence or development of intracranial aneurysms is not clear. The present study explored the function of myosin light chain kinase in human intracranial aneurysm tissues. Five aneurysm samples and five control samples were collected, and smooth muscle cells (SMCs) were dissociated and cultured. A label-free proteomic analysis was performed to screen the differentially expressed proteins between aneurysm and control samples. The expression and function of myosin light chain kinase in aneurysms were examined. We found that 180 proteins were differentially expressed between the aneurysm and control samples, among which 88 were increased and 92 (including myosin light chain kinase) were decreased in aneurysms compared to control tissues. In a model of the inflammatory environment, contractility was weakened and apoptosis was increased in aneurysm SMCs compared to human brain SMCs (p < 0.05). The knock down of myosin light chain kinase in human brain SMCs caused effects similar to those observed in aneurysm SMCs. These results indicated that myosin light chain kinase plays an important role in maintaining smooth muscle contractility, cell survival and inflammation tolerance

Evaluation of Different Anthropometric Indicators for Screening for Nonalcoholic Fatty Liver Disease in Elderly Individuals

Objective. To explore the anthropometric indicators suitable for screening for nonalcoholic fatty liver disease (NAFLD) in the elderly population. Methods. This cross-sectional study screened subjects over 65 years, who had undergone a physical examination in 2019. Their height, weight, waist circumference, and fasting blood glucose and triglyceride levels were measured. Body mass index (BMI), waist circumstance (WC), waist-to-height ratio (WHtR), relative fat mass (RFM), ponderal index (PI), conicity index (CI), lipid accumulation product (LAP), and body shape index (ABSI) were calculated. Statistical analyses were performed using the Chi-square test, logistic regression, and receiver operating characteristic (ROC) curve. Subjects. Of a total of 4985 subjects, 1173 diagnosed with NAFLD and 3812 without NAFLD were included. Results. The NAFLD group had increased BMI, WC, WHtR, RFM, PI, CI, and LAP. ABSI was only significantly different in males between the groups. Logistic regression analysis showed that RFM was an effective prognostic factor for males with NAFLD, and LAP, BMI, and WC were effective prognostic factors for females. ROC curve analysis showed that LAP played a significant role in the prediction of NAFLD. Conclusion. LAP is closely related to the occurrence of NAFLD and could be an efficient screening and treatment tool for NAFLD in the elderly people. Lay Summary. We conducted a screening and study of nonalcoholic fatty liver disease in the elderly population by determining the association between obesity indexes and nonalcoholic fatty liver disease. We found that LAP is practical, easy-to-measure tool for screening and studying NAFLD in the high-risk community elderly population, making it a valuable indicator in research