Current research on heterogeneous advanced oxidation processes (HAOPs) predominantly emphasizes catalyst iteration and innovation. Significant efforts have been made to regulate the electron structure and optimize the electron distribution, thereby increasing the catalytic activity. However, this focus often overshadows an equally essential aspect of HAOPs: the adsorption effect. Adsorption is a critical initiator for triggering the interaction of oxidants and contaminants with heterogeneous catalysts. The efficacy of these interactions is influenced by a variety of physicochemical properties, including surface chemistry and pore sizes, which determine the affinities between contaminants and material surfaces. This disparity in affinity is pivotal because it underpins the selective removal of contaminants, especially in complex waste streams containing diverse contaminants and competing matrices. Consequently, understanding and mastering these interfacial interactions is fundamentally indispensable not only for improving process efficiency but also for enhancing the selectivity of contaminant removal. Herein, we highlight the importance of adsorption-driven interfacial interactions for fundamentally elucidating the catalytic mechanisms of HAOPs. Such interactions dictate the overall performance of the treatment processes by balancing the adsorption, reaction, and desorption rates on the catalyst surfaces. Elucidating the adsorption effect not only shifts the paradigm in understanding HAOPs but also improves their practicality in water treatment and wastewater decontamination. Overall, we propose that revisiting adsorption-driven interfacial interactions holds great promise for optimizing catalytic processes to develop effective HAOP strategies.
Nonlinear variations in the coordinate time series of global navigation satellite system (GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects, including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.
Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades. Despite the high sensitivity of push-broom hyperspectral imagers, they experience limited swath and wavelength coverage. In this study, we report the development of a push-broom airborne multimodular imaging spectrometer (AMMIS) that spans ultraviolet (UV), visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) wavelengths. As an integral part of China’s High-Resolution Earth Observation Program, AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads. It has been mounted on aircraft platforms such as Y-5, Y-12, and XZ-60. Since 2016, AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data. This study describes the system design, calibration techniques, performance tests, flight campaigns, and applications of the AMMIS. The system integrates UV, VNIR, SWIR, and TIR modules, which can be operated in combination or individually based on the application requirements. Each module includes three spectrometers, utilizing field-of-view (FOV) stitching technology to achieve a 40° FOV, thereby enhancing operational efficiency. We designed advanced optical systems for all modules, particularly for the TIR module, and employed cryogenic optical technology to maintain optical system stability at 100 K. Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data. The AMMIS features more than 1400 spectral bands, with spectral sampling intervals of 0.1 nm for UV, 2.4 nm for VNIR, 3 nm for SWIR, and 32 nm for TIR. In addition, the instantaneous fields of view (IFoVs) for the four modules were 0.5, 0.25, 0.5, and 1 mrad, respectively, with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode. This study reports on land-cover surveys, pollution gas detection, mineral exploration, coastal water detection, and plant investigations conducted using AMMIS, highlighting its excellent performance. Furthermore, we present three hyperspectral datasets with diverse scene distributions and categories suitable for developing artificial intelligence algorithms. This study paves the way for next-generation airborne and spaceborne hyperspectral payloads and serves as a valuable reference for hyperspectral sensor designers and data users.
Precise coseismic displacements in earthquake/tsunamic early warning are necessary to characterize earthquakes in real time in order to enable decision-makers to issue alerts for public safety. Real-time global navigation satellite systems (GNSSs) have been a valuable tool in monitoring seismic motions, allowing permanent displacement computation to be unambiguously achieved. As a valuable tool presented to the seismic community, the GSeisRT software developed by Wuhan University (China) can realize multi-GNSS precise point positioning with ambiguity resolution (PPP-AR) and achieve centimeter-level to sub-centimeter-level precision in real time. While the stable maintenance of a global precise point positioning (PPP) service is challenging, this software is capable of estimating satellite clocks and phase biases in real time using a regional GNSS network. This capability makes GSeisRT especially suitable for proprietary GNSS networks and, more importantly, the highest possible positioning precision and reliability can be obtained. According to real-time results from the Network of the Americas, the mean root mean square (RMS) errors of kinematic PPP-AR over a 24 h span are as low as 1.2, 1.3, and 3.0 cm in the east, north, and up components, respectively. Within the few minutes that span a typical seismic event, a horizontal displacement precision of 4 mm can be achieved. The positioning precision of the GSeisRT regional PPP/PPP-AR is 30%–40% higher than that of the global PPP/PPP-AR. Since 2019, GSeisRT has successfully recorded the static, dynamic, and peak ground displacements for the 2020 Oaxaca, Mexico moment magnitude (Mw) 7.4 event; the 2020 Lone Pine, California Mw 5.8 event; and the 2021 Qinghai, China Mw 7.3 event in real time. The resulting immediate magnitude estimates have an error of around 0.1 only. The GSeisRT software is open to the scientific community and has been applied by the China Earthquake Networks Center, the EarthScope Consortium of the United States, the National Seismological Center of Chile, Institute of Geological and Nuclear Sciences Limited (GNS Science Te P Ao) of New Zealand, and the Geospatial Information Agency of Indonesia.
Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR). This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications, and its formation cause, morphology, and negative influence have been deeply investigated. However, this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation. In this paper, a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed. Firstly, sublook processing is beneficial for recovering the scattered stripes from a single-look complex image; the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image. Secondly, the amplitude spectrum density function (SDF) is estimated from the amplitude stripe pattern. Thirdly, a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs. In addition, another key parameter, the scintillation index, can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index. The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes. Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines. Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System (GPS) measurements. The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution.
Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable urban development. Regrettably, there exists a significant gap in detailed and consistent data on 3D building space structures with global coverage due to the challenges inherent in the data collection and model calibration processes. In this study, we constructed a global urban structure (GUS-3D) dataset, including building volume, height, and footprint information, at a 500 m spatial resolution using extensive satellite observation products and numerous reference building samples. Our analysis indicated that the total volume of buildings worldwide in 2015 exceeded 1 × 1012 m3. Over the 1985 to 2015 period, we observed a slight increase in the magnitude of 3D building volume growth (i.e., it increased from 166.02 km3 during the 1985–2000 period to 175.08 km3 during the 2000–2015 period), while the expansion magnitudes of the two-dimensional (2D) building footprint (22.51 × 103 vs 13.29 × 103 km2) and urban extent (157 × 103 vs 133.8 × 103 km2) notably decreased. This trend highlights the significant increase in intensive vertical utilization of urban land. Furthermore, we identified significant heterogeneity in building space provision and inequality across cities worldwide. This inequality is particularly pronounced in many populous Asian cities, which has been overlooked in previous studies on economic inequality. The GUS-3D dataset shows great potential to deepen our understanding of the urban environment and creates new horizons for numerous 3D urban studies.
Reconfigurable parallel mechanisms were first discovered in response to the growing demand for flexible and adaptive systems in various fields. Unlike traditional mechanisms, which are designed for specific tasks and have fixed topology and mobility characteristics, a reconfigurable parallel mechanism can be adapted to different situations by changing its structure, motion, and function. This adaptability enables a single mechanism to perform a wide range of tasks, reducing the need for multiple dedicated systems. This paper presents a comprehensive review of reconfigurable parallel mechanisms. The characteristics of their designs, analyses of their properties, and challenges they face are reported. The beginning of this paper features an introduction of reconfigurable parallel mechanisms and their classification into different types. Methods for synthesizing reconfigurable parallel mechanisms are discussed. A performance evaluation index related to reconfigurability, workspace, singularity, stiffness, and dynamics, among other indices, is presented. This review covers the challenges faced in the creation of systematic design theories, unified performance analyses, evaluation index systems, and in the implementation of reconfigurable parallel mechanisms, such as the development of efficient control strategies and integration with other technologies. The paper concludes with a discussion of future research directions for reconfigurable parallel mechanisms.
High-power direct current fast charging (DC-HPC), particularly for megawatt-level charging currents (≥ 1000 A), is expected to significantly reduce charging time and improve electric vehicle durability, despite the risk of instantaneous thermal shocks. Conventional cooling methods, which separately transmit current and heat, struggle to achieve both flexible maneuverability and high-efficiency cooling. In this study, we present a synergetic cooling and transmission strategy using a gallium-based liquid metal flexible charging connector (LMFCC), which efficiently dissipates ultra-high heat flux while simultaneously carrying superhigh current. The LMFCC exhibits exceptional flexible operability (bending radius of 2 cm) and transmission stability even under significant deformation owing to the excellent liquidity and conductivity of liquid metal (LM). These properties are markedly better than those of solid metal connector. A compact induction electromagnet-driven method is optimized to significantly increase the LM flow rate and the active cooling capacity, resulting in sudden low temperature (< 16 °C at 1000 A). This synergetic cooling and charging strategy are expected to enable ultrahigh-heat-flux thermal management and accelerate development of the electric vehicle industry.
Controlled intracellular delivery of biomolecular cargo is critical for developing targeted therapeutics and cell reprogramming. Conventional delivery approaches (e.g., endocytosis of nano-vectors, microinjection, and electroporation) usually require time-consuming uptake processes, labor-intensive operations, and/or costly specialized equipment. Here, we present an acoustofluidics-based intracellular delivery approach capable of effectively delivering various functional nanomaterials to multiple cell types (e.g., adherent and suspension cancer cells). By tuning the standing acoustic waves in a glass capillary, our approach can push cells in flow to the capillary wall and enhance membrane permeability by increasing membrane stress to deform cells via acoustic radiation forces. Moreover, by coating the capillary with cargo-encapsulated nanoparticles, our approach can achieve controllable cell-nanoparticle contact and facilitate nanomaterial delivery beyond Brownian movement. Based on these mechanisms, we have successfully delivered nanoparticles loaded with small molecules or protein-based cargo to U937 and HeLa cells. Our results demonstrate enhanced delivery efficiency compared to attempts made without the use of acoustofluidics. Moreover, compared to conventional sonoporation methods, our approach does not require special contrast agents with microbubbles. This acoustofluidics-based approach creates exciting opportunities to achieve controllable intracellular delivery of various biomolecular cargoes to diverse cell types for potential therapeutic applications and biophysical studies.
Acoustic-vortex (AV) tweezers ensure stable particle trapping at a zero-pressure center, while particle assembly between two vortex cores is still prevented by the high-potential barrier. Although a one-dimensional low-pressure attractive path of particle assembly can be constructed by the interference between two independent cylindrical Bessel beams, it remains challenging to create two-dimensional (2D) neighboring vortexes using a source array in practical applications. In this paper, a three-step phase-reversal strategy of 2D particle assembly based on the synchronized evolution of a centrosymmetric array of M off-axis acoustic vortexes (OA-AVs) with a preset radial offset is proposed based on a ring array of planar sources. By introducing initial vortex phase differences of −2π/M and +2π/M to the vortex array, low-pressure patterns of an M-sided regular polygon and M-branched star are formed by connecting the vortex cores and the field center before and after the tangent state of adjacent OA-AVs. Center-oriented particle assembly is finally realized by a central AV constructed by coincident in-phase OA-AVs. The capability of particle manipulation in the lateral and radial directions is demonstrated by low-pressure patterns with acoustic radiation forces pointing to the field center during a synchronized central approach. The field evolution is certified by experimental field measurements for OA-AVs with different vortex numbers, initial vortex phase differences, and radial offsets using a ring array of 16 planar sources. The feasibility of particle assembly in two dimensions is also verified by the accurate manipulation of four particles using the low-pressure patterns of a four-sided polygon, a four-branched star, and a central AV in experiments. The three-step strategy paves a new way for 2D particle assembly based on the synchronized evolution of centrosymmetric OA-AVs using a simplified single-sided source array, exhibiting excellent potential for the precise navigation and manipulation of cells and particles in biomedical applications.
Inspired by the layered structure of dental enamel in the human body, a superhydrophobic coating with an elastic gradient was developed and placed on the inner wall of a gas transmission pipeline to reduce erosion and corrosion. The coating comprises a hard bionic superhydrophobic top coating and a hydrogel layer underneath for buffering and self-repair. To improve the impact resistance of the top coating, layered structures with different viscoelasticities were constructed by controlling the content of lauric acid (LA)@TiO2 particles and carbon nanotubes (CNTs). The amylose hydrogel underlayer not only acts as a shock absorber but also restores potential damage in the top layer, bringing an additional benefit to the corrosion resistance of the coating. Thanks to these three cooperative approaches, the coating exhibits excellent mechanical durability (800 cycles with 600-mesh sandpaper under a 49 kPa load) and corrosion resistance (with a corrosion potential of −0.21 V). Moreover, it maintains its superhydrophobicity after sanding, bending, soaking, and scratching, demonstrating its potential for application to protect transmission pipelines from erosion and corrosion.
Quasi-zero-stiffness (QZS) metamaterials have attracted significant interest for application in low-frequency vibration isolation. However, previous work has been limited by the design mechanism of QZS metamaterials, as it is still difficult to achieve a simplified structure suitable for practical engineering applications. Here, we introduce a class of programmable QZS metamaterials and a novel design mechanism that address this long-standing difficulty. The proposed QZS metamaterials are formed by an array of representative unit cells (RUCs) with the expected QZS features, where the QZS features of the RUC are tailored by means of a structural bionic mechanism. In our experiments, we validate the QZS features exhibited by the RUCs, the programmable QZS behavior, and the potential promising applications of these programmable QZS metamaterials in low-frequency vibration isolation. The obtained results could inspire a new class of programmable QZS metamaterials for low-frequency vibration isolation in current and future mechanical and other engineering applications.
Humans achieve cognitive development through continuous interaction with their environment, enhancing both perception and behavior. However, current robots lack the capacity for human-like action and evolution, posing a bottleneck to improving robotic intelligence. Existing research predominantly models robots as one-way, static mappings from observations to actions, neglecting the dynamic processes of perception and behavior. This paper introduces a novel approach to robot cognitive learning by considering physical properties. We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body (P-body), a cognition-body (C-body), and a behavior-body (B-body). Each body engages in physical dynamics and operates within a closed-loop interaction. Significantly, three crucial interactions connect these bodies. The C-body relies on the P-body’s extracted states and reciprocally offers long-term rewards, optimizing the P-body’s perception policy. In addition, the C-body directs the B-body’s actions through sub-goals, and subsequent P-body-derived states facilitate the C-body’s cognition dynamics learning. At last, the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body, which leads to the next interactive step. These interactions foster the joint evolution of each body, culminating in optimal design. To validate our approach, we employ a navigation task using a four-legged robot, D’Kitty, equipped with a movable global camera. Navigational prowess demands intricate coordination of sensing, planning, and D’Kitty’s motion. Leveraging our framework yields superior task performance compared with conventional methodologies. In conclusion, this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body, C-body, and B-body, while considering physical properties. Our framework’s successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.
The enzymatic depolymerization of polyethylene terephthalate (PET) offers a sustainable approach for the recycling of PET waste. Great efforts have been devoted to engineering PET depolymerases on the substrate binding cleft and the surrounding loops/α-helices on the surface. Here, we report the systematic engineering of whole β-sheet regions in the core of IsPETase (a PETase from Ideonella sakaiensis) via a fluorescent high-throughput screening assay. Twenty-one beneficial substitutions were obtained and iteratively recombined. The best variant, DepoPETase β, with an increase in the melting temperatures (Tm) of 22.9 °C, exhibited superior depolymerization performance and enabled complete depolymerization of 100.5 g of untreated post-consumer PET (pc-PET; 0.26% Wenzyme/WPET enzyme loading) in liter-scale bioreactor at 50 °C within 4 d. Crystallization and molecular dynamics simulations revealed that the improved activity and thermostability of DepoPETase β were due to enhanced hydrogen bonds and salt bridges in the β-sheet region, a more tightly packed structure of the core sheets and the surrounding helix, and improved binding of PET to the active sites. This study not only demonstrates the importance of engineering strategy in the β-sheet region of PET hydrolases but also provides a potential PET depolymerase for large-scale PET recycling.
Phototrophy and chemotrophy are two dominant types of microbial metabolism. However, to date, the potential of the ubiquitous and versatile mechanical energy as a renewable energy source to drive the growth of microorganisms has remained unknown and not utilized. Here, we present evidence in favor of a previously unidentified metabolic pathway, in which the electronic energy produced from mechanical energy by the piezoelectric materials is used to support the growth of microorganisms. When electroactive microorganism Rhodopseudomonas palustris (R. palustris; with barium titanate nanoparticles) was mechanically stirred, a powerful biohybrid piezoelectric effect (BPE) enabled sustainable carbon fixation coupled with nitrate reduction. Transcriptomic analyses demonstrated that mechanical stirring of the bacteria–barium titanate biohybrid led to upregulation of genes encoding functions involved in electron and energy transfer in R. palustris. Studies with other electroactive microorganisms suggested that the ability of microbes to utilize BPE may be a common phenomenon in the microbial world. Taken together, these findings imply a long-neglected and potentially important microbial metabolic pathway, with potential importance to microbial survival in the energy-limited environments.
With extensive attention being paid to the potential environmental hazards of discarded face masks, catalytic pyrolysis technologies have been proposed to realize the valorization of wastes. However, recent catalyst selection and system design have focused solely on conversion efficiency, ignoring economic cost and potential life-cycle environmental damage. Here, we propose an economic–environmental hybrid pre-assessment method to help identify catalysts and reactors with less environmental impact and high economic returns among various routes to convert discarded face masks into carbon nanotubes (CNTs) and hydrogen. In catalyst selection, it was found that a widely known Fe–Ni catalyst exhibits higher catalytic activity than a cheaper Fe catalyst, potentially increasing the economic viability of the catalytic pyrolysis system by 38%–55%. The use of this catalyst also results in a carbon reduction of 4.12–10.20 kilogram CO2 equivalent for 1 kilogram of discarded face masks, compared with the cheaper Fe catalyst. When the price of CNTs exceeds 1.49 × 104 USD·t–1, microwave-assisted pyrolysis is the optimal choice due to its superior environmental performance (in terms of its life-cycle greenhouse gas reduction potential, eutrophication potential, and human toxicity) and economic benefits. In contrast, conventional heating pyrolysis may be a more economical option due to its good stability over 43 reaction regeneration cycles, as compared with a microwave-assisted pyrolysis catalyst with a higher conversion efficiency. This study connects foundational science with ecological economics to guide emerging technologies in their research stage toward technical efficiency, economic benefits, and environmental sustainability.
Wearable electronics incorporating proteins for biocompatibility have garnered significant research attention, given their potential applications in biocompatible medical devices, artificial skin, humanoid robots, and other fields. However, a notable challenge exists, as many wearable electronics currently lack those essential properties due to issues such as non-biological compatibility, as well as insufficient mechanical and conductive performance. Here, we have developed a hybrid keratin (KE) hydrogel by incorporating a liquid metal (LM, eutectic gallium-indium alloy) to design a wearable electronic device with excellent biocompatibility, enhanced conductivity, and good mechanical properties. The resulting keratin liquid metal (KELM) hydrogel demonstrates favorable mechanical characteristics, including good tensile strength (166 kPa), impressive stretchability (2600%), and long-term stability. Furthermore, it exhibits good conductivity (6.84 S∙m−1) and sensitivity as a sensing material (gauge factor (GF) = 7.03), rendering it suitable for constructing high-performance strain sensors. Notably, the KELM hydrogel-based wearable electronics extend their functionality to monitoring marine inhabitants’ health. This innovative application provides new insights for designing the next generation of biomimetic electronic devices, with potential applications in human-machine interfaces, electronic skin, artificial intelligence, and health monitoring.
Geniposide, the principal active iridoid glucoside ingredient in Fructus gardeniae used in numerous traditional Chinese clinical prescriptions, has been shown to cause herbal hepatotoxicity because of its glycone metabolite genipin. This study explored the role of gut microbiota in alleviating geniposide hepatotoxicity with isoflavones in soy products. Metabolic profiling using ultra high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS) revealed two metabolic pathways and six main forms of geniposides in vivo. Enzyme inhibitor experiments have shown that isoflavones alter geniposide metabolism by mediating specific enzymes, including β-glucosidase (β-GC) and sulfotransferase (SULT), in an established pseudo-sterile rat model. Isoflavones pretreatment by gavage for three weeks optimized the structure of the gut microbiota was linked to the regulation of key metabolic enzymes. Furthermore, experiments involving fecal microbiota transplantation (FMT) established the direct contribution of the gut microbiota to the regulation of enzyme activities and geniposide metabolism. This study demonstrated that isoflavones in soy products regulated the metabolic enzymes of geniposode dependent on gut microbiota, especially Lactobacillus spp., which was further verified in our clinical trials analyzed using 16S ribosomal RNA (rRNA) and metagenomic sequencing, thus regulating geniposide metabolism. Furthermore, as dominant beneficial bacterium, Lactobacillus spp. were discovered to be promising microbial targets for the better management of geniposide hepatotoxicity. These findings provide valuable insights for the prevention and intervention of drug-induced liver injury.
With the accelerated expansion of the platform economy, the supply chain has evolved into a new stage of the platform supply chain (PSC), which is deeply integrated with the platform economy. Logistics engineering management plays a crucial role in ensuring the efficient operation of PSCs and contributes to the construction of a global economic system. Given its importance to the efficiency of PSCs, the choice of logistics service strategy in logistics engineering management has attracted considerable scholarly attention. However, the current research is fragmented and lacks systematic analysis and synthesis. This paper provides a comprehensive overview of logistics engineering management in PSCs from the perspective of logistics service strategy selection from January 2005 to September 2024. To this end, we first review the research related to self-built logistics (SBL) and third-party logistics (3PL) in PSCs due to the complete independence of these two logistics service strategies. The results show that the following two topics are of great interest to researchers. One is the choice of the optimal logistics service strategy for the members of PSCs, while the other is the impact of factors related to logistics services on PSCs, including the channel selection, platform entry, sales model, and so forth. Next, we summarize the determinants influencing the choice between SBL and 3PL for the members of PSCs. The results indicate that the influencing factors are the service cost and service level, followed by the channel, brand, market potential, and competition. Then, on the basis of the themes of logistics service sharing (LSS), we review the research on LSS in PSCs, as LSS often emerges as an innovative model after a certain stage of development in SBL and 3PL. We find that LSS is regarded as an important complement to SBL and 3PL, with key research hotspots, including the channel, partner selection, and service competition. Service cost is a major factor influencing LSS, with competition, consumers’ logistics preference, and market potential being secondary factors. Finally, this paper outlines several important and promising directions for future research. This paper has important management implications for building a modern logistics system and promoting the transformation of PSCs.
Machine learning (ML) has been increasingly adopted to solve engineering problems with performance gauged by accuracy, efficiency, and security. Notably, blockchain technology (BT) has been added to ML when security is a particular concern. Nevertheless, there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security, making the traditional ML process vulnerable to off-chain risks. Therefore, the research objective is to develop a novel ML on blockchain (MLOB) framework to ensure both the data and computational process security. The central tenet is to place them both on the blockchain, execute them as blockchain smart contracts, and protect the execution records on-chain. The framework is established by developing a prototype and further calibrated using a case study of industrial inspection. It is shown that the MLOB framework, compared with existing ML and BT isolated solutions, is superior in terms of security (successfully defending against corruption on six designed attack scenario), maintaining accuracy (0.01% difference with baseline), albeit with a slightly compromised efficiency (0.231 second latency increased). The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands. This finding can alleviate concerns regarding the computational resource requirements of ML–BT integration. With proper adaption, the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
