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GH4169 alloy, a nickel-based superalloy known for its excellent high temperature resistance, corrosion resistance, mechanical properties, and high-temperature tribological properties, is widely used in industrial applications, such as in gas turbines for space shuttles and rocket engines. This study addresses the issue of electrolyte product residue in the electrochemical machining process of a GH4169 alloy by utilizing a C6H5Na3O7-containing NaNO3 new mixed electrolyte. Comparative investigations of the electrochemical behavior and electrolyte product removal mechanisms at different concentrations of C6H5Na3O7 additive in NaNO3 solutions were conducted. The effects of additives, applied voltage, and the rotating speed of the cathode tool on the processing performance of micro-pit arrays on a GH4169 alloy were analyzed. The results indicate that the mixed solution containing C6H5Na3O7 significantly improves the localization and geometric morphology of the micro-pits compared to a single NaNO3 solution. The optimal electrochemical machining parameters were identified as 0.5 wt% C6H5Na3O7 + 10 wt% NaNO3 mixed electrolyte, 12 V applied voltage, and 0.1 r/min rotating speed of the cathode tool. Under these conditions, high-quality micro-pit arrays with an average diameter of 405.85 μm, an average depth of 87.5 μm, and an etch factor (EF) of 1.67 were successfully fabricated, exhibiting excellent morphology, localization, and consistency.

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and “micro-shifting” techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks.

GH4169 alloy/Inconel 718 is extensively utilized in aerospace manufacturing due to its excellent high temperature mechanical properties. Micro-structuring on the workpiece surface can enhance its properties further. Through-mask electrochemical micromachining (TMEMM) is a promising and potential processing method for nickel-based superalloys. It can effectively solve the problem that traditional processing methods are difficult to achieve large-scale, high-precision and efficiency processing of surface micro-structure. This study explores the feasibility of electrochemical machining (ECM) for GH4169 using roll-print mask electrochemical machining with a linear cathode. Electrochemical dissolution characteristics of GH4169 alloy were analyzed in various electrolyte solutions and concentrations. Key parameters including cathode sizes, applied voltage and corrosion time were studied in the roll-print mask electrochemical machining. A qualitative model for micro-pit formation on GH4169 was established. Optimal parameters were determined through experiments: 300 μm mask hole and cathode size, 10 wt% NaNO3 electrolyte, 12 V voltage, 6 s corrosion time. The results demonstrate that the micro-pits with a diameter of 402.3 μm, depth of 92.8 μm and etch factor (EF) of 1.81 show an excellent profile and localization.

Human society is facing the threat of various viruses. Proteases are promising targets for the treatment of viral infections. In this study, we collected and profiled 170 protease sequences from 125 viruses that infect humans. Approximately 73 of them are viral 3-chymotrypsin-like proteases (3CLpro), and 11 are pepsin-like aspartic proteases (PAPs). Their sequences, structures, and substrate characteristics were carefully analyzed to identify their conserved nature for proposing a pan-3CLpro or pan-PAPs inhibitor design strategy. To achieve this, we used computational prediction and modeling methods to predict the binding complex structures for those 73 3CLpro with 4 protease inhibitors of SARS-CoV-2 and 11 protease inhibitors of HCV. Similarly, the complex structures for the 11 viral PAPs with 9 protease inhibitors of HIV were also obtained. The binding affinities between these compounds and proteins were also evaluated to assess their pan-protease inhibition via MM-GBSA. Based on the drugs targeting viral 3CLpro and PAPs, repositioning of the active compounds identified several potential uses for these drug molecules. As a result, Compounds 1–2, modified based on the structures of Ray1216 and Asunaprevir, indicate potential inhibition of DENV protease according to our computational simulation results. These studies offer ideas and insights for future research in the design of broad-spectrum antiviral drugs.

Nanofiltration application for the separation of Mg2+-Li+ from salt-lake brines was attempted in the present work. Four different nanofiltration membranes identified in the manuscript as DL, DK, NF-270, and NF-90 were used to treat salt brine with a magnesium to lithium ratio (MLR) of 61, additionally contaminated by the other ions such as Na+, K+, Ca2+, etc. The effect of the dilution factor, operating pressure, circulation rate, and feed pH were assessed to identify the optimal operating conditions for each membrane based on the retention efficiency of each ion. The results showed an insignificant effect of Ca2+ on the retention performance of Mg2+-Li+. Na+ and K+ had a smaller hydration radius and larger diffusion coefficient, which competed with Li+ and altered the separation of Mg2+-Li+. Under the optimal conditions (dilution factor: 40; operating pressure: 1.2 MPa; circulation flow rate: 500 L/h; pH: 7), the retention efficiency of lithium was as low as 5.17%, separation factor (SF) was as low as 0.074, and the MLR in the permeate reduced to 0.088.

Straw return benefits soil nutrient circulation and avoids the environmental pollution caused by incineration. The straw return effect is determined by many factors, such as the returning mode and tillage method. To find the most suitable straw return mode in the hilly areas of south China, we conducted experiments with preceding maize straw in Nanchong (Sichuan Province, China) for three years. Five treatments were tested: (A) rotary tillage without straw return (RT), (B) crushed straw return with rotary tillage (CRT), (C) crushed straw return without rotary tillage (CSR), (D) whole straw return with rotary tillage (WRT), and (E) whole straw return without rotary tillage (WSR). The results indicated that CRT had the fastest decomposition rate, followed by CSR. Moreover, CRT had the fastest nutrient release rates for nitrogen, phosphorus, potassium, cellulose, hemicellulose, and lignin, as well as the highest maize yield (6.62% higher than RT). CRT increased the content of organic matter, total nitrogen, total phosphorus, and total potassium in the soil, as well as improved the soil pH. Furthermore, the numbers of bacteria, Actinomycetes, and fungi in the soil under CRT, CSR, and WSR treatments were higher than those under the other two treatments. This study has important reference value for exploring the most favourable straw return method for improving farmland fertility.

The switching frequency of high-power inverters, such as those used in rail transit traction systems, is low, due to switching loss and heat dissipation limitations. This can result in considerable output voltage harmonics. This paper proposes an optimal SPWM strategy for traction inverters based on non-equally spaced carriers to address this issue. By dividing the fundamental wave cycle into regions according to the principle of three-phase, half-wave, and quarter-wave symmetry, the carrier width is symmetrically changed in each region, and a switching angle sequence is generated by comparing the fundamental wave with the non-isometric carrier. The proposed optimal modulation strategy has lower harmonic content than traditional strategies within a specific modulation range. Enhancing the inverter output waveform leads to increased torque accuracy, lowering additional losses in the motor and avoiding overheating. This results in improved performance, enhanced efficiency, and extended service life of the motor system. To further reduce voltage harmonics across the full-speed range, a multi-mode segmented synchronous modulation strategy is designed based on the optimal modulation strategy for different modulation ranges. Appropriate switching points are selected to improve the stability of the traction drive system across the full-speed range. The effectiveness of the proposed method is verified through simulation and experimental results.

The process of germanium–tannin complex is the oldest form of technology for extracting germanium from Ge-containing solutions. This reaction mechanism is relatively controversial as it imposes restrictions on ideas and methods for reducing the amount of tannin. In this paper, using SEM, TEM, FT-IR, XPS, NMR, TOF-SIMS and UV diffuse reflectance spectroscopy for a deep analysis of germanium–tannin complex, the mechanism of extracting germanium from Ge-containing solutions with tannins is investigated. The results show that the theoretical tannin complex mass is 30 times that of tannin mass, and the complex rate reaches 98.84%. The changes of -OH stretching band in FT-IR spectra, the emergence of C3H3GeO7, C2H3GeO6, and C8H7GeO7 in TOF-SIMS images, and the shift of Ge and O banding energy in XPS detail spectra provided definitive evidence for the germanium–tannin complex process, highlighting that the formed complexes of tannins with germanium involve six coordinate Ge-O bonds, which are obtained through orthophenol hydroxyl groups in tannins reacting with Ge4+. Furthermore, the complex mode of germanium–tannins is layer polymerization, which finally forms an agglomeration of complex flocs. The findings of this research is innovative, and can have a profound impact on the future introduction of various methods to reduce the mass of tannins.

This paper proposes a combined microwave and ultrasonic technique that aims to extract the refractory mineralogical properties of complex zinc ore. This technique consists of two steps: microwave-assisted phase transformation and ultrasonic-assisted complexation leaching. During the microwave-assisted phase transformation step, the refractory zinc phases transform into manageable phases using doping oxidation agents. In the ultrasonic-assisted complexation leaching step, the effect of NTA3 on Zn2+ species distribution is explored. The results show that microwave roasting with 20% Na2O2 at 600 °C for 15 min adequately transforms ZnS, ZnCO3 and Zn2SiO4 into ZnO and avoids the generation of Zn2SiO4. Further, 0.5 mol/L NTA3− (nitrilotriacetic acid) is especially effective as a complex additive for complexes with Zn2+ as Zn (NTA)24−. The ultrasonic technique can reduce the diffusion resistance and open the products in the leaching process, improving zinc leaching by 4.7%. The degree of zinc recovered from zinc ore can reach 91.4% when leach is assisted with 225 W ultrasonic force at 80 °C for 75 min. This paper shows great potential for the green extraction of non-traditional zinc-containing resources.

The recharge efficiency during artificial groundwater recharge (AGR) is reduced primarily by clogging that is triggered by suspended particles. However, there are loopholes in the current standards of recharge-water quality for clogging control during AGR, and the threshold values of turbidity to prevent clogging have not been reasonably determined. In this study, secondary effluents from wastewater treatment plants (WWTPs) were injected into saturated sand columns to simulate the process of AGR. Batch experiments under different turbidity conditions were conducted, and the numerical modeling of particle transport and deposition was performed to assess the clogging processes. Theories of single-collector contact and interfacial interaction energy were applied to elucidate possible microcosmic mechanisms. The results showed that the diluted secondary effluent (SE) with turbidities of 0.540 ± 0.050, 1.09 ± 0.050, and 1.84 ± 0.060 NTU caused considerable clogging in the porous media, which decreased the relative hydraulic conductivities (K/K0) by 13.2%, 17.6%, and 83.6%, respectively. The filtered SE with a turbidity of 0.160 NTU did not cause clogging, and K/K0 was reduced by only 1.70%. The clogging was attributed to the deposition of suspended particles in the sand matrix because they have a high collision efficiency (0.007–1.98) and attachment efficiency (0.029–0.589 kBT). Finally, this paper recommends that the turbidity of the recharge water should not exceed 0.500 NTU during AGR practices.

The spring steel for automotive stabilizer bars has a great responsibility in that its quality directly affects the stability, safety, and comfort of vehicle operation. The isothermal thermal compression behavior of a novel lean Si spring steel that was used to manufacture an anti-roll bar was investigated with a DIL805A/D quenching thermal dilatometer in this research. A hyperbolic sine type of constitutive model was established, and hot processing maps were produced to evaluate the experimental steel’s hot workability properties. The experimental results suggest that dynamic recrystallization (DRX) preferentially occurs at a low strain rate and high thermal processing temperature, while the processing maps of the experimental steel are susceptible to strain. The instability regions increase as the strain increases. The processing maps’ stable and instable domains should be decided upon comprehensive analysis of the instability criterion, power dissipation efficiency, and strain rate sensitivity index. The optimum parameters of hot processing for the experimental steel at various strains are that the deformation temperature of 1000–1150 °C and the strain rate of 0.1, approximately.

The hydrophilicity and inherent flammability of cotton textiles severely limit their usage. To solve these drawbacks, a superhydrophobic and flame-retardant (SFR) coating made of chitosan (CH), ammonium polyphosphate (APP), and TiO2-SiO2-HMDS composite was applied to cotton fabric using simple layer-by-layer assembly and dip-coating procedures. First, the fabric was alternately immersed in CH and APP water dispersions, and then immersed in TiO2-SiO2-HMDS composite to form a CH/APP@TiO2-SiO2-HMDS coating on the cotton fabric surface. SEM, EDS, and FTIR were used to analyze the surface morphology, element composition, and functional groups of the cotton fabric, respectively. Vertical burning tests, microscale combustion calorimeter tests, and thermogravimetric analyses were used to evaluate the flammability, combustion behavior, thermal degradation characteristics, and flame-retardant mechanism of this system. When compared to the pristine cotton sample, the deposition of CH and APP enhanced the flame retardancy, residual char, heat release rate, and total heat release of the cotton textiles. The superhydrophobic test results showed that the maximal contact angle of SFR cotton fabric was 153.7°, and possessed excellent superhydrophobicity. Meanwhile, the superhydrophobicity is not lost after 10 laundering cycles or 50 friction cycles. In addition, the UPF value of CH/APP@TiO2-SiO2-HMDS cotton was 825.81, demonstrating excellent UV-shielding properties. Such a durable SFR fabric with a facile fabrication process exhibits potential applications for both oil/water separation and flame retardancy.

With the increasing demand for lithium, the shortage of resources has become increasingly apparent. In order to conserve resources and to improve recovery, the extraction of lithium from salt lakes has become mandatory for sustainable development. Porous metal-organic framework (MOF) materials have attracted extensive attention due to their high/tunable porosity, pore function, multiple pore structures/compositions, and open metal sites. Moreover, MOFs combine the advantages of other porous materials and have a wide range of applications, which have received significant interest from the scientific community. Therefore, the selection of MOFs materials, the optimization of preparation methods, and the research of lithium separators are key directions to improve the total yield of lithium resources in salt lakes in China. This study aims to improve the comprehensive utilization of resources after lithium extraction and strengthen the engineering technology research of lithium extraction from salt lakes. This study can help to achieve the goal of efficient, integrated, and sustainable utilization of salt lake resources. An attempt has been made to summarize the types and preparation methods of MOFs materials, as well as the separation mechanism of MOFs nanofiltration membranes, with reference to its application in lithium extraction from salt lake brine. Finally, the future development of MOFs nanofiltration membranes for lithium extraction from salt lakes is also proposed.

As an important secondary zinc resource, large-scale reserves of zinc oxide dust (ZOD) from a wide range of sources is of high comprehensive recycling value. Therefore, an experimental study on ultrasound-enhanced sulfuric acid leaching for zinc extraction from zinc oxide dust was carried out to investigate the effects of various factors such as ultrasonic power, reaction time, sulfuric acid concentration, and liquid–solid ratio on zinc leaching rate. The results show that the zinc leaching rate under ultrasound reached 91.16% at a temperature of 25 °C, ultrasonic power 500 W, sulfuric acid concentration 140 g/L, liquid–solid ratio 5:1, rotating speed 100 r/min, and leaching time 30 min. Compared with the conventional leaching method (leaching rate: 85.36%), the method under ultrasound increased the zinc leaching rate by 5.8%. In a kinetic analysis of the ultrasound-enhanced sulfuric acid leaching of zinc oxide dust, the initial apparent activation energy of the reaction was 6.90 kJ/mol, indicating that the ultrasound-enhanced leaching process was controlled by the mixed solid product layers. Furthermore, the leached residue was characterized by XRD and SEM-EDS, and the results show that, with ultrasonic waves, the encapsulated mineral particles were dissociated, and the dissolution of ZnO was enhanced. Mostly, the zinc in leached residue existed in the forms of ZnFe2O4, Zn2SiO4, and ZnS.

The superalloy FGH98 was successfully diffusion bonded (DB) with medium-entropy alloy (MEA) Al3Ti3(CrCoNi)94 using pure Ni as the interlayer at a temperature range of 1050–1170 °C for 1 h under 5 MPa. The microstructure and mechanical properties of joints were investigated. The diffusion bonding seam was composed of an interlayer zone (IZ) and two diffusion-affected zones (DAZ). The IZ and DAZ beside the FGH98 consisted of cubic Ni3(TiAl)-type γ′ phases due to the diffusion of Ti and Al atoms. Meanwhile, the DAZ adjacent to the MEA consisted of spherical γ′ phases. Both of the γ′ phases with different morphology kept the coherent relationship with the matrix. Moreover, increase of bonding temperature led to the morphology of interlayer γ′ phase to transform from sphere to cube. Due to the strengthening effect of a mass of γ′ phase distributed evenly in IZ and the DAZ beside the FGH98, the microhardness and Young’s modulus of these two zones were higher than that of DAZ near the MEA. The maximum shear strength of DB joint, 592 MPa, was achieved in the joint bonded by 1150 °C, which was the typical ductile fracture feature confirmed by the shear dimples.

This study focuses on the diffusion bonding of a CoCrNi-based medium-entropy alloy (MEA) to a DD5 single-crystal superalloy. The microstructure and mechanical properties of the joint diffusion-bonded at variable bonding temperatures were investigated. The formation of diffusion zone, mainly composed of the Ni3(Al, Ti)-type γ′ precipitates and Ni-rich MEA matrix, effectively guaranteed the reliable joining of MEA and DD5 substrates. As the bonding temperature increased, so did the width of the diffusion zone, and the interfacial microvoids significantly closed, representing the enhancement of interface bonding. Both tensile strength and elongation of the joint diffusion-bonded at 1110 °C were superior to those of the joints diffusion-bonded at low temperatures (1020, 1050, and 1080 °C), and the maximum tensile strength and elongation of 1045 MPa and 22.7% were obtained. However, elevated temperature produced an adverse effect that appeared as grain coarsening of the MEA substrate. The ductile fracture of the joint occurred in the MEA substrate (1110 °C), whereas the tensile strength was lower than that of the MEA before diffusion bonding (approximately 1.3 GPa).

The permafrost in the Qilian Mountains (QLMs), the northeastern margin of the Qinghai–Tibet Plateau, changed dramatically in the context of climate warming and increasing anthropogenic activities, which poses significant influences on the stability of the ecosystem, water resources, and greenhouse gas cycles. Yet, the characteristics of the frozen ground in the QLMs are largely unclear regarding the spatial distribution of active layer thickness (ALT), the maximum frozen soil depth (MFSD), and the temperature at the top of the permafrost or the bottom of the MFSD (TTOP). In this study, we simulated the dynamics of the ALT, TTOP, and MFSD in the QLMs in 2004–2019 in the Google Earth Engine (GEE) platform. The widely-adopted Stefan Equation and TTOP model were modified to integrate with the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) in GEE. The N-factors, the ratio of near-surface air to ground surface freezing and thawing indices, were assigned to the freezing and thawing indices derived with MODIS LST in considerations of the fractional vegetation cover derived from MODIS normalized difference vegetation index (NDVI). The results showed that the GEE platform and remote sensing imagery stored in Google cloud could be quickly and effectively applied to obtain the spatial and temporal variation of permafrost distribution. The area with TTOP < 0 °C is 8.4 × 104 km2 (excluding glaciers and lakes) and accounts for 46.6% of the whole QLMs, the regional mean ALT is 2.43 ± 0.44 m, while the regional mean MFSD is 2.54 ± 0.45 m. The TTOP and ALT increase with the decrease of elevation from the sources of the sub-watersheds to middle and lower reaches. There is a strong correlation between TTOP and elevation (slope = −1.76 °C km−1, p < 0.001). During 2004–2019, the area of permafrost decreased by 20% at an average rate of 0.074 × 104 km2·yr−1. The regional mean MFSD decreased by 0.1 m at a rate of 0.63 cm·yr−1, while the regional mean ALT showed an exception of a decreasing trend from 2.61 ± 0.45 m during 2004–2005 to 2.49 ± 0.4 m during 2011–2015. Permafrost loss in the QLMs in 2004–2019 was accelerated in comparison with that in the past several decades. Compared with published permafrost maps, this study shows better calculation results of frozen ground in the QLMs.

A method of conventional roasting pretreatment combined with ultrasonic enhanced leaching with ammonium acetate was proposed to solve the difficult problem of lead in electrolytic manganese anode mud. The effects of concentration, liquid−solid ratio, temperature, leaching time and rotating speed on the leaching process under conventional and ultrasonic conditions were studied, and the lead leaching rate can be as high as 93.09% under optimized process parameters. A leaching kinetic model under conventional and ultrasonic conditions was established to explore the restrictive links of the leaching process. The results show that the leaching process under both conventional and ultrasonic conditions is controlled by diffusion, and the activation energies are 29.40 kJ/mol and 26.95 kJ/mol for the conventional and ultrasound enhance leaching processes, respectively.

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 °C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, Chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000−2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 °C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate change was affected by regional climate change in Southwest China. Marked changes in land use have occurred in the TGRC, and they have resulted in changes in the LST and SINDVI. There was a significantly negative relationship between LST and SINDVI in most parts of the TGRC, especially in expanding urban areas and growing forest areas. Our study highlighted the importance of environmental protection, particularly proper management of land use, for sustainable development in the catchment.

At present, gold mines are increasingly scarce in the world. The yield of cyanidation tailing (CT) of refractory gold ores with a grade of 10 g/T is huge; however, the effective capitalization of the cyanidation tailing is a significant problem in the gold industry. In this work, a new treatment method, a microwave-roasting process developed. The effect of roasting temperature, calcium chloride concentration, holding time and mineral size on the recovery of Au researched under conventional and microwave conditions. It found that, under the same processing conditions, the Au recovery in the microwave field is much higher than that of conventional conditions. The preliminary reinforcing role of the microwave discussed in the recovery of gold. This might be because of the microwave absorption ability of CaCl2 is much better, heated quickly and the reactions intensified among CaCl2, O2, and H2O. As a result, the extraction of gold in CT drastically increased in the microwave field, and the microwave roasting technology shows the characteristics of environmental protection in terms of low energy consumption and high efficiency.

Recovery of zinc from low grade zinc oxide ore is attempted with ammonia and ammonium citrate solutions augmented by microwave roasting and ultrasound radiation. The influence of the ammonia-ammonium ratio, roasting temperature, ultrasound power, and leaching time were assessed on the recovery of zinc. A maximum zinc recovery of 88.57% could be achieved at a roasting temperature of 673 K, leaching temperature of 298 K, stirring speed of 300 rpm, total ammonia concentration of 5 mol/L with an ammonium citrate concentration of 1.2 mol/L, liquid to solid ratio of 5:1, the ultrasound power was 600 W and the leaching time was 120 min. The enhancement in recovery with increases in the roasting temperature up to 673 K was attributed to the conversion of ZnCO3 to ZnO. The phases of mineral samples and the reaction residues were characterized by X-ray diffraction (XRD).

Background: Hepatic iron overload is common in patients who have undergone hematopoietic cell transplantation (HCT) and may predispose to peri- and post-HCT toxicity. To better reveal more molecules that might be involved in iron overload-induced liver injury, we utilized proteomics to investigate differentially expressed proteins in iron overload-induced hepatocytes vs. untreated hepatocytes. Methods and Results: HH4 hepatocytes were exposed to ferric ammonium citrate (FAC) to establish an in vitro iron overload model. Differentially expressed proteins initiated by the iron overload were studied by two-dimensional liquid chromatography tandem mass spectrometry (2D-LC-MS) analysis. We identified 93 proteins whose quantity statistically significantly changes under excess hepatocyte iron conditions. Gene Ontology (GO) analysis showed that these differentially expressed proteins in HH4 cells are involved in various biological process including endocytosis, response to wounding, di-, trivalent inorganic cation homeostasis, inflammatory response, positive regulation of cytokine production, and etc. Meanwhile, proteomics data revealed protein level of TLR2 and IL6ST significantly increased 7 times and 2.9 times, respectively, in iron overloaded HH4 cells. Our subsequent experiments detected that FAC-treated HH4 cells can activate IL6 expression through TLR2-mediated inflammatory responses via the NF-κB pathway. Conclusions: In this study, we demonstrated that iron overload induced hepatocytes triggering TLR2-mediated inflammatory response via NF-κB signaling pathway in HH4 cells.

This paper considers a class of distributed online aggregative optimization problems over an undirected and connected network. It takes into account an unknown dynamic environment and some aggregation functions, which is different from the problem formulation of the existing approach, making the aggregative optimization problem more challenging. A distributed online optimization algorithm is designed for the considered problem via the mirror descent algorithm and the distributed average tracking method. In particular, the dynamic environment and the gradient are estimated by the averaged tracking methods, and then an online optimization algorithm is designed via a dynamic mirror descent method. It is shown that the dynamic regret is bounded in the order of O(T). Finally, the effectiveness of the designed algorithm is verified by some simulations of cooperative control of a multi-robot system.

Supercritical carbon dioxide (CO2) has extremely important applications in the extraction of unconventional oil and gas, especially in fracturing and enhanced oil recovery (EOR) technologies. It can not only relieve water resource wastage and environmental pollution caused by traditional mining methods, but also effectively store CO2 and mitigate the greenhouse effect. However, the low viscosity nature of supercritical CO2 gives rise to challenges such as viscosity fingering, limited sand–carrying capacity, high filtration loss, low oil and gas recovery efficiency, and potential rock adsorption. To overcome these challenges, low–rock–adsorption thickeners are required to enhance the viscosity of supercritical CO2. Through research into the literature, this article reviews the solubility and thickening characteristics of four types of polymer thickeners, namely surfactants, hydrocarbons, fluorinated polymers, and silicone polymers in supercritical CO2. The thickening mechanisms of polymer thickeners were also analyzed, including intermolecular interactions, LA–LB interactions, hydrogen bonding, and functionalized polymers, and so on.

Supercritical CO2 has wide application in enhancing oil recovery, but the low viscosity of liquid CO2 can lead to issues such as poor proppant-carrying ability and high filtration loss. Therefore, the addition of thickening agents to CO2 is vital. Hydrocarbon polymers, as a class of green and sustainable materials, hold tremendous potential for acting as thickeners in supercritical CO2 systems, and PVAc is one of the best-performing hydrocarbon thickeners. To further improve the viscosity enhancement and solubility of PVAc, here we designed a novel polymer structure, PVAO, by introducing CO2-affine functional groups to PVAc. Molecular dynamics simulations were adopted to analyze viscosity and relevant solubility parameters systematically. We found that PVAO exhibits superior performance, with a viscosity enhancement of 1.5 times that of PVAc in supercritical CO2. While in the meantime, PVAO maintains better solubility characteristics than PVAc. Our findings offer insights for the future design of other high-performance polymers.

Biogenic volatile organic compounds (BVOCs) are key compounds in atmospheric chemistries, but difficult to measure directly. In this study, a pre-concentration unit combined with gas chromatography-mass spectrometry (GC-MS) was developed for the quantitative analysis of 18 BVOCs in ambient air. The analytes are trapped on an empty silonite-coated tube, which is cooled by a thermoacoustic cooler to cryotrap at −150 °C, and then desorbed by rapid heating to 200 °C. The set-up involves neither the exchange of solid adsorbents nor any further condensation or refocusing steps. Reliable operation is ensured by the thermoacoustic cooler, which neither contains a liquid refrigerant nor requires refilling a cryogen. The pre-concentration unit parameters such as water removal temperature, desorption temperature and desorption time were optimized. All compounds had correlation coefficients that were better than 0.95, and the detection limits were 0.005–0.009 ppbv when the injection volume is 400 mL. The repeatability ranges were 0.9–5.8%. The recoveries were ranged from 81.8% to 93.2%. This new method was applied for the first time to measure ambient BVOCs in suburb Guangzhou in summer 2022. Isoprene concentrations ranged from 0.375 ppbv to 2.98 ppbv. In addition, several extremely low-level monoterpenes (e.g., α-pinene, β-pinene, and D-limonene) were also detected by the method.

Climate change significantly impacts the livelihoods of farmer households. Particularly vulnerable areas, both economically and environmentally, face significant threats from climate change. This study developed a framework to assess household-level vulnerability to climate change by integrating the Exposure-Sensitivity-Resilience Analysis (ESRA) and Sustainable Livelihoods Analysis (SLA) frameworks. Using Gui-Zhou Province as the study area, the study examined whether livelihood vulnerability differs among various types of farmer households in economically and environmentally vulnerable areas and identified the main factors contributing to vulnerability. Results indicate significant differences in livelihood vulnerability among the three household types, with pure agricultural households (PAH) being the most vulnerable due to high exposure, sensitivity, and low adaptive capacity. Further analysis revealed minor differences in sensitivity but significant differences in adaptive capacity among the three farmer categories. In terms of sensitivity, all three farmer household categories exhibit high sensitivity to water, housing, and agricultural production. Regarding adaptive capacity, significant differences in human and financial capital exist among the three household types, with off-farm households (OFH) possessing the highest adaptive capacity due to their substantial human and financial capital. Further research identified high exposure and low adaptive capacity as the primary causes of livelihood vulnerability, noting no significant difference in the main contributing factors among the three types of farmer households. Common factors contributing to the livelihood vulnerability of farmer households include agricultural cooperatives, labor capacity, temperature changes, drought frequency changes, precipitation changes, agricultural insurance, and losses in agricultural production. Overall, the proposed livelihood vulnerability framework offers guidance for analyzing the vulnerability of farmer households in areas with both economic and environmental vulnerabilities under climate change. Concurrently, tailored measures to reduce farmer households’ livelihood vulnerability should be developed for different household types, considering the local climatic, geographic, and socioeconomic conditions.

Deployable structures based on origami are widely used in the application of actuators. In this paper, we present a novel family of origami-based deployable structures with constant curvature. Two categories of non-flat-foldable and non-developable degree-4 vertices (NFND degree-4 vertices) are introduced. Pyramid structures are constructed based on the NFND degree-4 vertices. Doubly symmetric and singly symmetric spherical origami tubular cells (SOTCs) are established based on pyramid structures. To construct deployable origami modules using SOTCs, linking units are introduced. The mobility of the SOTCs and origami modules is analyzed using constraint screws. To realize the construction and simulation of deployable structures, kinematic and geometric analyses of the doubly symmetric and singly symmetric SOTCs are performed. Finally, we introduce four cases for deployable structures in spherical actuators based on the combination of multiple origami modules. These case studies demonstrate the potential of these deployable origami structures in the design of spherical actuators.

Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY–170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP.

China’s urbanization has received as much attention as its economic growth in recent years. While rapid urbanization has promoted modernization and wealth creation, it has also brought many problems and challenges. So, since 2014, the Chinese government has implemented a new urbanization policy nationwide. However, few researchers have examined whether China’s new urbanization policies promote sustainable urbanization. This paper evaluates the sustainable urbanization of 19 urban agglomerations in China using the elastic coefficient. The results indicate a significant increase in the number of cities achieving sustainable urbanization since the implementation of China’s new urbanization policy. The number has risen from 46 in the period of 2010–2015 to 71 in the period of 2015–2021. Considering the impact of the new coronavirus on economy, there would have been even more cities entering sustainable urbanization during the latter period. This indicates that the new urbanization policy has a positive influence on the sustainable urbanization of China’s urban agglomerations. The sustainable urbanization status is not static and changes with different policies that are implemented in different time periods. Therefore, it is important to find useful policies that can guide and enhance the urbanization process in these cities. In addition, cities that have a sustainable urbanization status are not perfect. To address this issue, it is important to categorize and implement targeted policies for cities experiencing both sustainable and non-sustainable urbanization. Changes in a city’s size or urbanization rates are not related to the city’s sustainable urbanization status. A larger city size does not indicate sustainable urbanization, and, similarly, cities with faster urbanization rates do not exhibit a higher level of urbanization sustainability.

Water-level fluctuation (WLF) can destroy soil aggregates and induce soil organic carbon (SOC) loss, potentially triggering impacts on the concentration of atmospheric carbon dioxide. However, responses of soil aggregate content and aggregate-associated organic carbon to WLF have not been well studied, especially in the water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir (TGR). Therefore, samples from different elevations (145 m, 155 m and 165 m) in the WLFZ of the TGR were collected for experiments. The wet sieving method was used to divide soil into silt and clay (0.25 mm). The K2Cr2O7-H2SO4 oxidation method was used to measure total SOC content in different soil aggregates. A modified Walkley and Black method was used to measure labile carbon in different soil aggregates. Results showed that macro-aggregate content substantially decreased, while micro-aggregate content remained stable and silt and clay fraction accumulated with a decrease in water-level elevations. Moreover, total SOC content and labile carbon in macro-aggregate were obviously higher than those in the micro-aggregate and the silt and clay fraction. Macro-aggregate contributed the most to SOC sequestration, while micro-aggregate contributed the least, and the contribution of macro-aggregate increased with a decrease in water-level elevations. We concluded that the macro-aggregate was the most active participant in the SOC sequestration process, and preferentially increasing the macro-aggregate content of the lowest water-level elevation was conducive to an improvement in soil carbon sequestration potential and would mitigate climate change.

The colorless and odorless ethylene glycol is prone to unknowingly causing poisoning, making preventive monitoring of ethylene glycol necessary. In this paper, scandium (III) trifluoromethanesulfonate was used as a catalyst to successfully prepare covalent organic framework (COF) nanospheres linked by imines at room temperature. The COF nanospheres were characterized by XRD, SEM, TEM, FT-IR, UV-Vis and BET. The results show that COF nanospheres have rough surfaces and a large number of mesoporous structures, which greatly increase the active sites on the surface of the sensing material and enhance the gas sensing performance. The sensing results showed that the prepared imine-conjugated COF nanospheres exhibited a good response–recovery ability for 10 consecutive response–recovery cycles for ethylene glycol at room temperature and had a theoretical detection limit of 40 ppb. In addition, the responses of COF nanospheres to nearly 20 interfering gases, including HCl, HNO3, phenol, formaldehyde and aniline, are relatively low compared to the response to ethylene glycol, indicating that the COF nanospheres have high selectivity towards ethylene glycol. The COF nanospheres show good sensitivity and selectivity for the detection of ethylene glycol, which should be attributed to the large specific surface area, hydrogen bonding interactions, and high defects. This work provides an effective method for the detection of ethylene glycol and expands the application field of COF materials.

Despite the significant impact of network hyperparameters on deep learning car-following models, there has been relatively little research on network hyperparameters of deep learning car-following models. Therefore, this study proposes a car-following model that combines particle swarm optimization (PSO) and gated recurrent unit (GRU) networks. The PSO-GRU car-following model is trained and tested using data from the natural driving database. The results demonstrate that compared to the intelligent driver model (IDM) and the GRU car-following model, the PSO-GRU car-following model reduces the mean squared error (MSE) for the speed simulation of following vehicles by 88.36% and 72.92%, respectively, and reduces the mean absolute percentage error (MAPE) by 64.81% and 50.14%, respectively, indicating a higher prediction accuracy. Dataset 3 from the drone video trajectory database of Southeast University and NGSIM’s I-80 dataset are used to study the car-following model’s cross-dataset adaptability, that is, to verify its transferability. Compared to the GRU car-following model, the PSO-GRU car-following model reduces the standard deviation of the test results by 60.64% and 32.89%, highlighting its more robust prediction stability and better transferability. Verifying the ability of the car-following model to produce the stop-and-go phenomenon can evaluate its transferability more comprehensively. The PSO-GRU car-following model outperforms the GRU car-following model in creating stop-and-go sensations through platoon simulation tests, demonstrating its superior transferability. Therefore, the proposed PSO-GRU car-following model has higher prediction accuracy and cross-dataset adaptability compared to other car-following models.

Multiple automatic guided vehicles are widely involved in industrial intelligence. Path planning is crucial for their successful application. However, achieving robust and efficient path planning of multiple automatic guided vehicles for real-time implementation is challenging. In this paper, we propose a two-layer strategy for multi-vehicle path planning. The approach aims to provide fast computation and operation efficiency for implementation. The start–destination matrix groups all the vehicles, generating a dynamic virtual leader for each group. In the first layer, the hybrid A* algorithm is employed for the path planning of the virtual leaders. The second layer is named leader–follower; the proposed Weight-Leader-Vicsek model is applied to navigate the vehicles following their virtual leaders. The proposed method can reduce computational load and achieve real-time navigation by quickly updating the grouped vehicles’ status. Collision and deadlock avoidance is also conducted in this model. Vehicles in different groups are treated as dynamic obstacles. We validated the method by conducted simulations through MATLAB to verify its path-planning functionality and experimented with a localization sensor.

With the development of deep learning, convolutional neural networks (CNNs) and Transformer-based methods have become key techniques for medical image classification tasks. However, many current neural network models have problems such as high complexity, a large number of parameters, and large model sizes; such models obtain higher classification accuracy at the expense of lightweight networks. Moreover, such larger-scale models pose a great challenge for practical clinical applications. Meanwhile, Transformer and multi-layer perceptron (MLP) methods have some shortcomings in terms of local modeling capability and high model complexity, and need to be used on larger datasets to show good performance. This makes it difficult to utilize these networks in clinical medicine. Based on this, we propose a lightweight and efficient pure CNN network for medical image classification (Eff-PCNet). On the one hand, we propose a multi-branch multi-scale CNN (M2C) module, which divides the feature map into four parallel branches along the channel dimensions by a certain scale factor and carries out a deep convolution operation using different scale convolution kernels, and this multi-branch multi-scale operation effectively replaces the large kernel convolution. This multi-branch multi-scale operation effectively replaces the large kernel convolution. It reduces the computational cost of the module while fusing the feature information between different channels and thus obtains richer feature information. Finally, the four feature maps are then spliced along the channel dimensions to fuse the multi-scale and multi-dimensional feature information. On the other hand, we introduce the structural reparameterization technique and propose the structural reparameterized CNN (Rep-C) module. Specifically, it utilizes multiple linear operators to generate different feature maps during the training process and fuses all the participants into one through parameter fusion to achieve fast inference while providing a more effective solution for feature reuse. A number of experimental results show that our Eff-PCNet performs better than current methods based on CNN, Transformer, and MLP in the classification of three publicly available medical image datasets. Among them, we achieve 87.4% Acc on the HAM10000 dataset, 91.06% Acc on the SkinCancer dataset, and 97.03% Acc on the Chest-Xray dataset. Meanwhile, our approach achieves a better trade-off between the number of parameters; computation; and other performance metrics as well.

Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people.

Inverse synthetic aperture ladar (ISAL) can achieve high-resolution images for long-range moving targets, while its performance is affected by atmospheric turbulence. In this paper, the dynamic evolution of atmospheric turbulence is studied by using an infinitely long phase screen (ILPS), and the atmospheric coherent time is defined to describe the variation speed of the phase fluctuation induced by atmospheric turbulence. The simulation results show that the temporal decoherence of the echo induced by turbulence causes phase fluctuation and introduces an extra random phase, which deteriorates the phase stability and makes coherent synthesis impossible. Thus, we evaluated its effects on ISAL imaging and found a method to mitigate the impact of turbulence on ISAL images. The phase compensation algorithm could correct the phase variation in different pulses instead of that within the same pulse. Therefore, the relationship between the atmospheric coherent time and pulse duration time (rather than that between the atmospheric coherent time and ISAL imaging time) ultimately determines the ISAL imaging quality. Furthermore, these adverse effects could be mitigated by increasing the atmospheric coherent time or decreasing the pulse duration time, which results in an improvement in the ISAL imaging quality.

Electrocatalytic water splitting is a crucial area in sustainable energy development, and the development of highly efficient bifunctional catalysts that exhibit activity toward both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is of paramount importance. Co3O4 is a promising candidate catalyst, owing to the variable valence of Co, which can be exploited to enhance the bifunctional catalytic activity of HER and OER through rational adjustments of the electronic structure of Co atoms. In this study, we employed a plasma-etching strategy in combination with an in situ filling of heteroatoms to etch the surface of Co3O4, creating abundant oxygen vacancies, while simultaneously filling them with nitrogen and sulfur heteroatoms. The resulting N/S-VO-Co3O4 exhibited favorable bifunctional activity for alkaline electrocatalytic water splitting, with significantly enhanced HER and OER catalytic activity compared to pristine Co3O4. In an alkaline overall water-splitting simulated electrolytic cell, N/S-VO-Co3O4 || N/S-VO-Co3O4 showed excellent overall water splitting catalytic activity, comparable to noble metal benchmark catalysts Pt/C || IrO2, and demonstrated superior long-term catalytic stability. Additionally, the combination of in situ Raman spectroscopy with other ex situ characterizations provided further insight into the reasons behind the enhanced catalyst performance achieved through the in situ incorporation of N and S heteroatoms. This study presents a facile strategy for fabricating highly efficient cobalt-based spinel electrocatalysts incorporated with double heteroatoms for alkaline electrocatalytic monolithic water splitting.

The quantitative defect detection of wire rope is crucial to guarantee safety in various application scenes, and sophisticated inspection conditions usually lead to the accurate testing of difficulties and challenges. Thus, a magnetic flux leakage (MFL) signal analysis and convolutional neural networks (CNNs)-based wire rope defect recognition method was proposed to solve this challenge. Typical wire rope defect inspection data obtained from one-dimensional (1D) MFL testing were first analyzed both in time and frequency domains. After the signal denoising through a new combination of Haar wavelet transform and differentiated operation and signal preprocessing by normalization, ten main features were used in the datasets, and then the principles of the proposed MFL and 1D-CNNs-based wire rope defect classifications were presented. Finally, the performance of the novel method was evaluated and compared with six machine learning methods and related algorithms, which demonstrated that the proposed method featured the highest testing accuracy (>98%) and was valid and feasible for the quantitative and accurate detection of broken wire defects. Additionally, the considerable application potential as well as the limitations of the proposed methods, and future work, were discussed.

Egg size is a crucial indicator for consumer evaluation and quality grading. The main goal of this study is to measure eggs’ major and minor axes based on deep learning and single-view metrology. In this paper, we designed an egg-carrying component to obtain the actual outline of eggs. The Segformer algorithm was used to segment egg images in small batches. This study proposes a single-view measurement method suitable for eggs. Experimental results verified that the Segformer could obtain high segmentation accuracy for egg images in small batches. The mean intersection over union of the segmentation model was 96.15%, and the mean pixel accuracy was 97.17%. The R-squared was 0.969 (for the long axis) and 0.926 (for the short axis), obtained through the egg single-view measurement method proposed in this paper.

The rapid loss of Arctic Sea ice cover and thickness diminishes the surface albedo, which increases the ocean’s absorption of solar heat and exacerbates the Arctic amplification effect. According to the most recent research from the Intergovernmental Panel on Climate Change, the Sixth Assessment Report (IPCC, AR6), the extent of summer sea ice is anticipated to decrease below 1 million km2 by the 2050s as a result of the extreme climate. Nevertheless, past and future changes in sea ice albedo radiative forcing and the resulting economic cost remain to be explored in systematic and multi-disciplinary manners. In this study, we first analyze the evolution of Arctic sea ice radiative forcing (SIRF) from 1982 to 2100 using a radiative kernel method based on albedo data from the Polar Pathfinder-Extent (APP-x) and Coupled Model Intercomparison Project 5 (CMIP5). Then, the SIRF is converted to CO2 equivalent emissions via the Dynamic Integrated Model of Climate and Economy (DICE) model. Finally, the associated costs are calculated using the substitute cost method, based on the social cost of carbon to achieve the Paris Agreement targets. The results show that the average Arctic SIRF was −0.75 ± 0.1 W·m−2 between 1982 and 2020, and increased by 0.12 W·m−2 during this period. The SIRF in April–June accounts for nearly 77% of the average annual value, with a maximum absolute value of –3.2 W·m−2 in May. Through model transformation, it is shown that the Arctic SIRF rising leads to global warming comparable to the effect of an increase of 34.5 Gt of CO2 in the atmosphere relative to pre-industrialization, and results in a loss of 24.4–48.8 trillion USD for climate regulation service (CRS). From 2020 to 2100, in the representative concentration pathway (RCP) 8.5, the Arctic SIRF is projected to increase by 0.31 W·m−2. Combined with the discount rate, the estimated average annual cost over the period ranges from 6.7–13.3 trillion USD. These findings provide a systematic understanding of the radiative effect of Arctic sea ice change on the global climate and the corresponding economic cost.

Ultra-wideband (UWB) is a promising indoor position technology with centimetre-level positioning accuracy in line-of-sight (LOS) situations. However, walls and other obstacles are common in an indoor environment, which can introduce non-line-of-sight (NLOS) and deteriorate UWB positioning accuracy to the meter level. This paper proposed a succinct method to identify NLOS induced by walls and mitigate the error for improved UWB positioning with NLOS. First, NLOS is detected by a sliding window method, which can identify approximately 90% of NLOS cases in a harsh indoor environment. Then, a delay model is designed to mitigate the error of the UWB signal propagating through a wall. Finally, all the distance measurements, including LOS and NLOS, are used to calculate the mobile UWB tag position with ordinary least squares (OLS) or weighted least squares (WLS). Experiment results show that with correct NLOS indentation and delay model, the proposed method can achieve positioning accuracy in NLOS environments close to the level of LOS. Compared with OLS, WLS can further optimise the positioning results. Correct NLOS indentation, accurate delay model and proper weights in the WLS are the keys to accurate UWB positioning in NLOS environments.

Land surface temperature (LST) is a key parameter in the study of surface energy balance and climate change from local through to global scales. Vegetation has inevitably influenced the LST by changing the surface properties. However, the thermal environment pattern in the Yangtze River Basin (YRB) still remains unclear after the implementation of large-scale ecological restoration projects. In this study, the temporal and spatial variation characteristics of LST were analyzed based on the Theil–Sen estimator, Mann–Kendall trend analysis and Hurst exponent from 2003 to 2021. The relationships between vegetation and LST were further revealed by using correlation analysis and trajectory-based analysis. The results showed that the interannual LST was in a state of fluctuation and rise, and the increasing rate at night time (0.035 °C·yr−1) was faster than that at day time (0.007 °C·yr−1). An obvious cooling trend could be identified from 2007 to 2012, followed by a rapid warming. Seasonally, the warming speed was the fastest in summer and the slowest in autumn. Additionally, it was found that autumn LST had a downward trend of 0.073 °C·yr−1 after 2015. Spatially, the Yangtze River Delta, Hubei province, and central Sichuan province had a significant warming trend in all seasons, except autumn. The northern Guizhou province and Chongqing showed a remarkable cooling trend only in autumn. The Hurst exponent results indicated that the spring LST change was more consistent than the other three seasons. It was found by studying the effect of land cover types on LST changes that sparse vegetation had a more significant effect than dense vegetation. Vegetation greening contributed 0.0187 °C·yr−1 to the increase in LST in winter, which was spatially concentrated in the central region of the YRB. For the other three seasons, vegetation greening slowed the LST increase, and the degree of the effect decreased sequentially in autumn, summer, spring and winter. These results improve the understanding of past and future variations in LST and highlight the importance of vegetation for temperature change mitigation.

This paper presents the development of a compact, three-electrode electrochemical device functionalized by a biocompatible layer of hyaluronic acid methacrylate (HAMA) hydrogel for the adsorptive removal of detrimental lead (Pb(II)) ions in aqueous solutions. An adsorption mechanism pertaining to the observed analytical performance of the device is proposed and further experimentally corroborated. It is demonstrated that both the molecular interactions originating from the HAMA hydrogel and electrochemical accumulation originating from the electrode beneath contribute to the adsorption capability of the device. Infrared spectral analysis reveals that the molecular interaction is mainly induced by the amide functional group of the HAMA hydrogel, which is capable of forming the Pb(II)–amide complex. In addition, inductively coupled plasma mass spectrometric (ICP-MS) analysis indicates that the electrochemical accumulation is particularly valuable in facilitating the adsorption rate of the device by maintaining a high ion-concentration gradient between the solution and the hydrogel layer. ICP-MS measurements show that 94.08% of Pb(II) ions present in the test solution can be adsorbed by the device within 30 min. The HAMA hydrogel-modified electrochemical devices exhibit reproducible performance in the aspect of Pb(II) removal from tap water, with a relative standard deviation (RSD) of 1.28% (for n = 8). The experimental results suggest that the HAMA hydrogel-modified electrochemical device can potentially be used for the rapid, on-field remediation of Pb(II) contamination.

Numerous path-planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning approaches and decision-making strategies and presents the path-planning algorithms for various types of robots, including aerial, ground, and underwater robots. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. The decision-making strategies mainly consist of centralized and decentralized approaches. The trend of the decision-making system is to move towards a decentralized planner. Finally, the new challenge in multi-robot path planning is proposed as fault tolerance, which is important for real-time operations.