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The importance evaluation of power grid transmission lines is crucial for preventing catastrophic grid failures, enhancing grid resilience, and ensuring the safe and stable operation of the power system. To address the limitations in existing transmission line evaluation methods, such as reliance on single evaluation metrics, insufficient consideration of information transmission between adjacent lines, and the impact of line failures on system load loss, this paper proposes a comprehensive evaluation method based on multidimensional information feature mapping. First, evaluation metrics are established from three dimensions—power transmission characteristics, grid network structure, and resilience improvement—each reflecting the importance of transmission lines from different perspectives. Then, a hybrid weighting method is introduced, combining subjective and objective weights through a game theory approach to calculate the comprehensive weights, enabling the multidimensional information-based importance evaluation of transmission lines. Finally, an improved network efficiency index, representing the current operational performance of the grid, is developed to validate the proposed evaluation method. A simulation analysis using the IEEE-39 bus test system verifies the feasibility and accuracy of the proposed approach.

The hybrid yellow catfish exhibits advantages over pure yellow catfish in terms of fast growth, fast development, a high feeding rate, and strong immunity; additionally, it is almost sterile, thus ensuring the conservation of the genetic stock of fish populations. To investigate the sterility mechanism in hybrid yellow catfish (P. fulvidraco (♀) × P. vachelli (♂)), the mRNA and miRNA of the gonads of P. fulvidraco, P. vachelli, and a hybrid yellow catfish were analyzed to characterize the differentially expressed genes; this was carried out to help establish gene expression datasets to assist in the further determination of the mechanisms of genetic sterility in hybrid yellow catfish. In total, 1709 DEGs were identified between the hybrid and two pure yellow catfishes. A KEGG pathway analysis indicated that several genes related to reproductive functions were upregulated, including those involved in the cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis, and genes associated with ECM–receptor interaction were downregulated. The spermatogenesis-related GO genes CFAP70, RSPH6A, and TSGA10 were identified as being downregulated DEGs in the hybrid yellow catfish. Sixty-three DEmiRNAs were identified between the hybrid and the two pure yellow catfish species. The upregulated DEmiRNAs ipu-miR-194a and ipu-miR-499 were found to target the spermatogenesis-related genes CFAP70 and RSPH6A, respectively, playing a negative regulatory role, which may underscore the miRNA–mRNA regulatory mechanism of sterility in hybrid yellow catfish. The differential expression of ipu-miR-196d, ipu-miR-125b, and ipu-miR-150 and their target genes spidr, cep85, and kcnn4, implicated in reproductive processes, was verified via qRT-PCR, consistent with the transcriptome sequencing expression trends. This study provides deep insights into the mechanism of hybrid sterility in vertebrate groups, thereby contributing to achieving a better understanding and management of fish conservation related to hybrid sterility.

A new type of multiphase nanoparticle-reinforced TiAl matrix composites ((Ti2AlC + Al2O3)p/TiAl composites) was successfully prepared by vacuum hot-pressing sintering using Ti powder and Al powder, which were ball-milled with different contents of stearic acid (CH3(CH2)16COOH). The component, microstructure, reaction mechanism, and mechanical properties were studied. The results indicated that the composites prepared by adding stearic acid as a process control agent during the ball-milling process not only contained γ-TiAl and α2-Ti3Al phases but also Ti2AlC and Al2O3 phases. The results of SEM and TEM showed that the composites were composed of equiaxed TiAl and Ti3Al grains, and the Ti2AlC and Al2O3 particles were mainly distributed along the TiAl grain boundary in chain form, which can effectively reduce the TiAl grain size. Through the room-temperature compression test, the maximum compression stress was significantly improved in those composites that added the stearic acid, due to the reinforcement particles. The maximum compression stress was 1590 MPa with a 24.3% fracture strain. In addition, the generated crack deflection and Ti2AlC and Al2O3 particles could also enhance the toughness of the TiAl alloy. (Ti2AlC + Al2O3)p/TiAl composites generated by adding stearic acid played a key role in improving the mechanical properties of the TiAl matrix.

Cancer incidence and mortality are rapidly increasing worldwide; therefore, effective therapies are required in the current scenario of increasing cancer cases. Polysaccharides are a family of natural polymers that hold unique physicochemical and biological properties, and they have become the focus of current antitumour drug research owing to their significant antitumour effects. In addition to the direct antitumour activity of some natural polysaccharides, their structures offer versatility in synthesizing multifunctional nanocomposites, which could be chemically modified to achieve high stability and bioavailability for delivering therapeutics into tumor tissues. This review aims to highlight recent advances in natural polysaccharides and polysaccharide-based nanomedicines for cancer therapy.

Impervious surfaces are key indicators for urbanization monitoring and watershed degradation assessment over space and time. However, most empirical studies only extracted impervious surface from spatial, temporal or spectral perspectives, paying less attention to integrating multiple dimensions in acquiring continuous changes in impervious surfaces. In this study, we proposed a neighborhood-based spatio-temporal filter (NSTF) to obtain the continuous change information of impervious surfaces from multi-temporal Landsat images in the Qinhuai River Basin (QRB), Jiangsu, China from 1988–2017, based on the results from semi-automatic decision tree classification. Moreover, we used the expansion intensity index (EII) and the landscape extension index (LEI) to further characterize the spatio-temporal characteristics of impervious surfaces on different spatial scales. The preliminary results showed that the overall accuracies of the final classification were about 95%, with the kappa coefficients ranging between 0.9 and 0.96. The QRB underwent rapid urbanization with the percentage of the impervious surfaces increasing from 2.72% in 1988 to 25.6% in 2017. Since 2006, the center of urbanization expansion was shaped from the urban built-up areas of Nanjing and Jiangning to non-urban built-up areas of the Jiangning, Lishui, and Jurong districts. The edge expansion occupied 73% on average among the different landscape expansion types, greatly beyond outlying (12%) and infilling (15%). The window size in the NSTF has a direct impact on the subsequent analysis. Our research could provide decision-making references for future urban planning and development in the similar basins.

The rapid development of chip technology has all put forward higher requirements for highly thermally conductive materials. In this work, a new type of material of Fishbone-like silicon carbide (SiC) material was used as the filler in a polyvinylidene fluoride (PVDF) matrix. The silicon carbide/polyvinylidene fluoride (SiC/PVDF) composites were successfully prepared with different loading by a simple mixing method. The thermal conductivity of SiC/PVDF composite reached 0.92 W m−1 K−1, which is 470% higher than that of pure polymer. The results show that using the filler with a new structure to construct thermal conductivity networks is an effective way to improve the thermal conductivity of PVDF. This work provides a new idea for the further application in the field of electronic packaging.

Over the past few decades, there has been a growing popularity in utilizing finite element analysis to study the spine. However, most current studies tend to use one specimen for their models. This research aimed to validate multiple finite element models by comparing them with data from in vivo experiments and other existing finite element studies. Additionally, this study sought to analyze the data based on the gender and age of the specimens. For this study, eight lumbar spine (L2–L5) finite element models were developed. These models were then subjected to finite element analysis to simulate the six fundamental motions. CT scans were obtained from a total of eight individuals, four males and four females, ranging in age from forty-four (44) to seventy-three (73) years old. The CT scans were preprocessed and used to construct finite element models that accurately emulated the motions of flexion, extension, lateral bending, and axial rotation. Preloads and moments were applied to the models to replicate physiological loading conditions. This study focused on analyzing various parameters such as vertebral rotation, facet forces, and intradiscal pressure in all loading directions. The obtained data were then compared with the results of other finite element analyses and in vivo experimental measurements found in the existing literature to ensure their validity. This study successfully validated the intervertebral rotation, intradiscal pressure, and facet force results by comparing them with previous research findings. Notably, this study concluded that gender did not have a significant impact on the results. However, the results did highlight the importance of age as a critical variable when modeling the lumbar spine.

Cervical degenerative disc diseases such as myelopathy and radiculopathy often require conventional treatments like artificial cervical disc replacement or anterior cervical discectomy and fusion (ACDF). When designing a medical device, like the stand-alone cage, there are many design inputs to consider. However, the precise biomechanics of the force between the vertebrae and implanted devices under certain conditions require further investigation. In this study, a new method was developed to evaluate the pressure between the vertebrae and implanted devices by embedding a sensor array into a 3D-printed C2-C3 cervical spine. The 3D-printed cervical spine model was subjected to a range of axial loads while under flexion, extension, bending and compression conditions. Cables were used for the application of a preload and a robotic arm was used to recreate the natural spine motions (flexion, extension, and bending). To verify and predict the total pressure between the vertebrae and the implanted devices, a 3D finite element (FE) numerical mathematical model was developed. A preload was represented by applying 22 N of force on each of the anterior tubercles for the C2 vertebra. The results of this study suggest that the sensor is useful in identifying static pressure. The pressure with the robot arm was verified from the FE results under all conditions. This study indicates that the sensor array has promising potential to reduce the trial and error with implants for various surgical procedures, including multi-level artificial cervical disk replacement and ACDF, which may help clinicians to reduce pain, suffering, and costly follow-up procedures.

It is essential to understand the spatial equity of healthcare services to achieve the Sustainable Development Goals. Spatial and non-spatial factors affect access to healthcare, resulting in inequality in the hierarchical medical treatment system. Thus, to provide a comprehensive equity evaluation, it is indispensable to investigate the extent to which spatial accessibility to healthcare services varies due to various factors. This study attempted to analyze the determinants of healthcare accessibility under multi-trip modes and integrate them into Theil index, as a demand index to evaluate spatial equity in the system. The results reveal an inadequate and inequitable distribution of healthcare resources. While access to primary hospitals is limited (47.37% of residential locations cannot access them on foot), 96.58% of residential locations can access general and tertiary hospitals via public transport or driving. Furthermore, inequitable access to the three-tiered medical system was evaluated on a more granular scale, with primary hospitals being closest to achieving equity (inequitable for only 48.83% of residential locations), followed by general and tertiary hospitals (82.01% and 89.20%, respectively). The unequal residential locations brought on by an abundance of medical resources are far from those with a shortage of resources (66.86% > 5.34%). It is thus suggested that services be expanded or resources be transferred to move toward a more equitable system. Our findings provide policymakers with insights into how to increase accessibility to public health.

Two-dimensional van der Waals (vdW) heterostructures are potential candidates for clean energy conversion materials to address the global energy crisis and environmental issues. In this work, we have comprehensively studied the geometrical, electronic, and optical properties of M2CO2/MoX2 (M = Hf, Zr; X = S, Se, Te) vdW heterostructures, as well as their applications in the fields of photocatalytic and photovoltaic using density functional theory calculations. The lattice dynamic and thermal stabilities of designed M2CO2/MoX2 heterostructures are confirmed. Interestingly, all the M2CO2/MoX2 heterostructures exhibit intrinsic type-II band structure features, which effectively inhibit the electron-hole pair recombination and enhance the photocatalytic performance. Furthermore, the internal built-in electric field and high anisotropic carrier mobility can separate the photo-generated carriers efficiently. It is noted that M2CO2/MoX2 heterostructures exhibit suitable band gaps in comparison to the M2CO2 and MoX2 monolayers, which enhance the optical-harvesting abilities in the visible and ultraviolet light zones. Zr2CO2/MoSe2 and Hf2CO2/MoSe2 heterostructures possess suitable band edge positions to provide the competent driving force for water splitting as photocatalysts. In addition, Hf2CO2/MoS2 and Zr2CO2/MoS2 heterostructures deliver a power conversion efficiency of 19.75% and 17.13% for solar cell applications, respectively. These results pave the way for exploring efficient MXenes/TMDCs vdW heterostructures as photocatalytic and photovoltaic materials.

With the growing popularity of microgrids for alternative energy management, there is demand for tools that allow us to study the effect of microgrids in distributed power systems. Popular methods involve software simulation and prototype validation with physical hardware. Simulations often do not capture the complex interactions, and combinations of software simulations with hardware testbeds promise to give a more accurate picture. These testbeds, however, usually aim at the validation of hardware for industrial-scale use, which makes them expensive and not readily accessible. To fill the gap between full-scale hardware and software simulation, we propose a modular lab-scale grid model at a 1:100 power scale over residential single-phase networks with 12 V AC and 60 Hz grid voltage. We present different modules—power sources, inverters, demanders, grid monitors, and grid-to-grid bridges—that can be assembled into distributed grids of almost arbitrary complexity. The model voltage poses no electrical hazards, and microgrids can readily be assembled with an open power line model. Unlike a prior DC-based grid testbed, the proposed AC model allows us to examine additional aspects, such as frequency, phase, active and apparent power, and reactive loads. Grid metrics, including the discretely sampled voltage and current waveforms, can be collected and sent to higher-tier grid management systems. We integrated the modules with Beagle Bone micro-PCs, which in turn connect any such microgrid with an emulation platform built on CORE (Common Open Research Emulator) and the Gridlab-D power simulator, thereby allowing hybrid software/hardware simulations. Our grid modules were shown to fully operate in this environment. Through the CORE system, multitiered control and even remote grid management is possible. However, we also found that the AC waveform poses design challenges that require us to balance accurate emulation (most notably with respect to harmonic distortion) with per-module costs.

Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person’s spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.

The unequal allocation of healthcare resources raises many fundamental problems, one of which is how to address inequity in population health. This paper focuses on disparities in public transport healthcare accessibility, with a special focus on an expanding subway system. Based on a vulnerability index, including factors that are likely to limit healthcare opportunities, a two-step floating catchment area method was used to assess the distribution of supply and demand for healthcare. Quantity, quality, and walking distance accessibility were aggregated into hexagonal grids. The Theil index was used to measure inequity and understand the influence of subways on spatial disparities in healthcare accessibility. The ongoing construction of the subway has heterogeneous impacts on healthcare accessibility for different parts of the city and exacerbates spatial inequity in many areas. In an environment where people in peri-urban areas are excluded from healthcare access because of low subway coverage, the results suggest that the potential for subways to address inaccessibility is limited. The findings highlight the requirement of efficient public transport services and are relevant to researchers, planners, and policymakers aiming to improve accessibility to healthcare, especially for populations who dwell in winter cities.

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.