A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption.

Contagious disease pandemics, such as COVID-19, can cause hospitals around the world to delay nonemergent elective surgeries, which results in a large surgery backlog. To develop an operational solution for providing patients timely surgical care with limited health care resources, this study proposes a stochastic control process-based method that helps hospitals make operational recovery plans to clear their surgery backlog and restore surgical activity safely. The elective surgery backlog recovery process is modeled by a general discrete-time queueing network system, which is formulated by a Markov decision process. A scheduling optimization algorithm based on the piecewise decaying [Formula: see text]-greedy reinforcement learning algorithm is proposed to make dynamic daily surgery scheduling plans considering newly arrived patients, waiting time and clinical urgency. The proposed method is tested through a set of simulated dataset, and implemented on an elective surgery backlog that built up in one large general hospital in China after the outbreak of COVID-19. The results show that, compared with the current policy, the proposed method can effectively and rapidly clear the surgery backlog caused by a pandemic while ensuring that all patients receive timely surgical care. These results encourage the wider adoption of the proposed method to manage surgery scheduling during all phases of a public health crisis.

Real-Time Prediction of Medical Demand and Mental Health Status in Ukraine under Russian Invasion Using Tweet Analysis.

Russia's invasion of Ukraine (February 24, 2022) has begun and there are concerns about the impact on health care supply and mental health. This study analyzed tweets in the Ukrainian language to capture the medical needs and mental health conditions in wartime Ukraine by focusing on ostensibly relevant words. The number of tweets containing the keywords and their overall proportion was compared before and after the Russian invasion of Ukraine. The survey period was divided into four phases-the pre-2022 Russian invasion, acute phase (4 weeks), subacute phase (12 weeks), and the chronic phase (8 weeks) up to August 10, 2022. The analysis targeted tweets sent in Ukrainian. The tweets were screened using a set of six classes with 75 key groups and 303 Ukrainian (204 original Japanese) keywords. Overall, 98,526,440 tweets were analyzed, with a pre-invasion and post-onset average of 1,096,976 and 3,328,243 tweets/week (a 3.0-fold increase), respectively. Of these, 3,197,443 tweets contained the keywords, with a pre-invasion and invasion average of 26,241 and 114,640 tweets/week (a 4.4-fold increase), respectively. The post-onset phase witnessed a considerable increase in all classes-medical services, treatment, medical resources, medical situations, and special situations-but not in the symptom class. Keywords related to psychological distress and anxiety immediately increased during the acute phase; those related to depression and post-traumatic stress reactions continued increasing as the invasion persisted, which may have reflected the mental state of those impacted. Analyzing tweets is useful for predicting people's real-time physical and mental health needs during wartime.

"Muscle Pics", a new body-checking behavior in muscle dysmorphia?

BACKGROUND AND AIMS: The internalization of ideal hypermuscular body and pro-muscularity media's influence have shown their importance in muscle dysmorphia development. The aim of the current study is to have a better understanding of links between specific body checking behaviors and muscle dysmorphia in social network context. METHODS: In total, 342 students practicing weightlifting at the university gym in Bordeaux answered to a survey with sociodemographic information and body checking symptoms including taking specific selfies of muscles and muscularity "Muscle Pics" and the MDDI (Muscle Dysmorphic Disorder Inventory). RESULTS: Muscle dysmorphia was prevalent in 18.7% of our population (64 students). We observed that muscle dysmorphia was correlated to "Muscle Pics", "Follow-up", "Message", "Selfie", and gym mirror checking with significant results (P<0.01). Also, « Muscle Pics ¼ were linked to APEDs use, pro-muscularity websites, fitness model comparison and gym mirror checking (P<0.01). For muscle dysmorphia, "Muscle Pics" have strong predictive results (OR=5.10, P=0.000) and (OR=4.08, P=0.000) for adjusted. "Follow up" (OR=4.76, P=0.000) and (OR=3.83, P=0.000) for adjusted, "Muscle Pics Selfie" (OR=11.20, P=0.000) and (OR=11.55, P=0.000) for adjusted, "Muscle Pics Message" (OR=4.49, P=0.001) and (OR=5.78, P=0.001) for adjusted. CONCLUSION: "Muscle Pics" showed several links with muscle dysmorphia for global score "drive for size", "functional impairment" but not for "appearance intolerance" dimension. Pro-muscularity websites, fitness model comparisons and gym mirror checking are linked to muscle dysmorphia and "Muscle Pics". Future research on "Muscle Pics" will help to provide a better understanding of muscle dysmorphia and its link with pro-muscularity influence websites.

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation.

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

The association between mobile devices use and behavior problems among fourth grade children in Japan.

AIM: Previous studies showed mixed evidence on the association between duration of mobile devices usage and behavior problems among children. We aimed to examine this association using a large population-based sample of fourth grade children (i.e. aged 9-10 years old), stratified by gender. METHODS: Questionnaires with anonymous unique identifiers were distributed to 4th grade students (aged 9-10 years old) at all public schools in Adachi City, Tokyo (n = 4,105). Duration of mobile devices usage was self-reported, and behavior problems were assessed by the Strengths and Difficulties Questionnaire by caregivers. Multiple regression analyses were performed, adjusting for potential confounders such as socioeconomic status, family social capital, and number of friends. RESULTS: Among boys, U-shape association was found between duration of mobile devices usage and behavior problems; boys with usage time of less than 1 h showed 0.88 [95% confidence interval (CI) -1.50 to -0.27] point lower total difficulty score in comparison with boys who do not use mobile devices, and total difficulty score among boys with usage time of 1 h or more was not different with non-users. Among girls, dose-response positive association was found between duration of mobile devices usage and total difficulty score (P for trend: <0.001). CONCLUSION: Among children aged 9-10 years old, mobile devices usage time of less than 1 h was a protective factor for behavior problems in boys, while usage time of 1-h or more was a risk factor in girls. Further longitudinal study is warranted to confirm the findings.

Multifunctional Microparticles Incorporating Gold Compound Inhibit Human Lung Cancer Xenograft.

PURPOSE: Gold porphyrin (AuP) is a complex that has been shown to be potent against various tumors. A biocompatible interpenetrating network (IPN) system comprised of polyethyleneglycol diacrylate (PEGdA) and chemically-modified gelatin has been shown to be an effective implantable drug depot to deliver AuP locally. Here we designed IPN microparticles complexed with AuP to facilitate intravenous administration and to diminish systemic toxicity. METHODS: We have synthesized and optimized an IPN microparticle formulation complexed with AuP. Tumor cell cytotoxicity, antitumor activity, and survival rate in lung cancer bearing nude mice were analyzed. RESULTS: IPN microparticles maintained AuP bioactivity against lung cancer cells (NCI-H460). In vivo study showed no observable systemic toxicity in nude mice bearing NCI-H460 xenografts after intravenous injection of 6 mg/kg AuP formulated with IPN microparticles. An anti-tumor activity level comparable to free AuP was maintained. Mice treated with 6 mg/kg AuP in IPN microparticles showed 100% survival rate while the survival rate of mice treated with free AuP was much less. Furthermore, microparticle-formulated AuP significantly reduced the intratumoral microvasculature when compared with the control. CONCLUSION: AuP in IPN microparticles can reduce the systemic toxicity of AuP without compromising its antitumor activity. This work highlighted the potential application of AuP in IPN microparticles for anticancer chemotherapy.

A Multifunctional Hydrogel Delivers Gold Compound and Inhibits Human Lung Cancer Xenograft.

PURPOSE: Interpenetrating network system (IPN), consisting of polyethylene glycol (PEG) -diacrylate (PEGdA) and modified gelatin, is a biocompatible and biodegradable hydrogel and has been studied for the local delivery of bioactive molecules and drugs. Gold(III) porphyrin(AuP) is a stable metal compound in the development for anticancer application when administered systemically. The aim of this work is to develop a novel formulation for AuP based on IPN for local delivery. METHODS: IPN loaded with AuP hydrogel was optimized and synthesized. Drug release kinetics, cytotoxicity against tumor cells, and antitumor activity in lung cancer bearing nude mice were studied. RESULTS: AuP released from the IPN followed a first order kinetics in vitro. The AuP loaded IPN showed higher cytotoxicity against human lung cancer cell lines compared to IPN only. In mice bearing human lung cancer xenograft, AuP loaded IPN inhibited tumor growth and reduced angiogenesis. No sign of systemic toxicity was observed for all treatment groups. CONCLUSION: AuP loaded IPN provides an improved formulation over systemic delivery for tumor inhibition to complement surgical intervention. Graphical Abstract Injectable multifunctional matrix of polyethylene glycol and gelatin derivatives for the delivery of gold porphyrinto inhibit tumor growth.

Spatial Vulnerability of Network Systems under Spatially Local Hazards.

A hazard is often spatially local in a network system, but its impact can spread out through network topology and become global. To qualitatively and quantitatively assess the impact of spatially local hazards on network systems, this article develops a new spatial vulnerability model by taking into account hazard location, area covered by hazard, and impact of hazard (including direct impact and indirect impact), and proposes an absolute spatial vulnerability index (ASVI) and a relative spatial vulnerability index (RSVI). The relationship between the new model and some relevant traditional network properties is also analyzed. A case study on the spatial vulnerability of the Chinese civil aviation network system is conducted to demonstrate the effectiveness of the model, and another case study on the Beijing subway network system to verify its relationship with traditional network properties.

Intersection as key locations for bearded capuchin monkeys (Sapajus libidinosus) traveling within a route network.

There is evidence that wild animals are able to recall key locations and associate them with navigational routes. Studies in primate navigation suggest most species navigate through the route network system, using intersections among routes as locations of decision-making. Recent approaches presume that points of directional change may be key locations where animals decide where to go next. Over four consecutive years, we observed how a wild group of bearded capuchin monkeys used a route network system and Change Point locations (CPs) in the Brazilian ecotone of Cerrado-Caatinga. We built 200 daily routes of one wild bearded capuchin group. We used ArcGIS, the Change Point Test, Spatial Analysis in Macroecology (SAM), and statistical models to test the hypothesis that wild bearded capuchins use CPs located along routes in a different fashion than they use the CPs located at intersections of routes. A logistic regression model was used to determine the landscape variables affecting capuchins' directional changes at intersections or along routes. CPs at intersections were important points of travel path changes, whereas CPs along routes represented a zig-zag movement along the routes following the landscape features. CPs at intersections were associated with steeper terrains and shorter distances from important resources, along with better visibility of the home range. Our results support the hypothesis that intersections among routes in the route network system are located at points where monkeys have the best visibility available to make decisions on where to visit next.

Electrical Design and Evaluation of Asynchronous Serial Bus Communication Network of 48 Sensor Platform LSIs with Single-Ended I/O for Integrated MEMS-LSI Sensors.

For installing many sensors in a limited space with a limited computing resource, the digitization of the sensor output at the site of sensation has advantages such as a small amount of wiring, low signal interference and high scalability. For this purpose, we have developed a dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) (referred to as "sensor platform LSI") for bus-networked Micro-Electro-Mechanical-Systems (MEMS)-LSI integrated sensors. In this LSI, collision avoidance, adaptation and event-driven functions are simply implemented to relieve data collision and congestion in asynchronous serial bus communication. In this study, we developed a network system with 48 sensor platform LSIs based on Printed Circuit Board (PCB) in a backbone bus topology with the bus length being 2.4 m. We evaluated the serial communication performance when 48 LSIs operated simultaneously with the adaptation function. The number of data packets received from each LSI was almost identical, and the average sampling frequency of 384 capacitance channels (eight for each LSI) was 73.66 Hz.

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications.

Robot tactile sensation can enhance human-robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as "sensor platform LSI") as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated.

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities.

Self-Powering Gas Sensing System Enabled by Double-Layer Triboelectric Nanogenerators Based on Poly(2-vinylpyridine)@BaTiO3 Core-Shell Hybrids with Superior Dispersibility and Uniformity.

Current core-shell hybrids used in diverse energy-related applications possess limited dispersibility and film uniformity that govern their overall performances. Herein, we showcase superdispersible core-shell hybrids (P2VP@BaTiO3) composed of a poly(2-vinylpyridine) (P2VP) (5-20 wt %) and a barium titanate oxide (BaTiO3), maximizing dielectric constants by forming the high-quality uniform films. The P2VP@BaTiO3-based triboelectric nanogenerators (TENGs), especially the 10 wt % P2VP (P2VP10@BaTiO3)-based one, deliver significantly enhanced output performances compared to physically mixed P2VP/BaTiO3 counterparts. The P2VP10@BaTiO3-based double-layer TENG exhibits not only an excellent transferred charge density of 281.7 µC m-2 with a power density of 27.2 W m-2 but also extraordinary device stability (∼100% sustainability of the maximum output voltage for 54,000 cycles and ∼68.7% voltage retention even at 99% humidity). Notably, introducing the MoS2/SiO2/Ni-mesh layer into this double-layer TENG enables ultrahigh charge density of up to 1228 µC m-2, which is the top value reported for the TENGs so far. Furthermore, we also demonstrate a near-field communication-based sensing system for monitoring CO2 gas using our developed self-powered generator with enhanced output performance and robustness.

Reconstructing transient pressures in pipe networks from local observations by using physics-informed neural networks.

Reconstructing transient states presents significant challenges, particularly within complex pipe networks. These challenges arise due to nonlinear behaviours, inherent uncertainties in the system, and limitations in data availability. This work proposed a novel approach employing Physics-Informed Neural Networks (PINN) to reconstruct transient states in pipe networks, even with limited sensor data. To integrate the complex topology of pipe network systems into neural networks, the method integrates the PINN framework with an efficient elastic water column (EWC) model which can be simply formulated across diverse pipe network configurations. The results showed the proposed PINN method can accurately reconstruct the pressure and flow variation at unmonitored locations, even provided with noisy data at a limited number of locations. One of its advantages lies in its ability to effectively capture extreme values that hold potential significance for pipe infrastructure, providing a promising avenue for pipe failure analysis and enhanced safety management. Laboratory experiments have also been conducted to validate the efficacy and reliability of this method, thus further underlining its potential for real-world applications.

Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China.

The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, using an urban stormwater model as necessary. Most studies have used urban hydrological models to assess flood risk; however, due to limited flow pipeline data, the calibration and the validation of the models are difficult. This study applied the MIKE URBAN model to build a drainage system model in the Beijing Future Science City of China, where the discharge of pipelines was absent. Three methods, of empirical calibration, formula validation, and validation based on field investigation, were used to calibrate and validate the parameters of the model. After the empirical calibration, the relative error range between the simulated value and the measured value was verified by the formula as within 25%. The simulated runoff depth was consistent with a field survey verified by the method of validation based on field investigation, showing the model has good applicability in the study area. Then, the rainfall scenarios of different return periods were designed and simulated. Simulation results showed that, for the 10-year return period, there are overflow pipe sections in northern and southern regions, and the number of overflow pipe sections in the northern region is more than that in the southern region. For the 20-year return period and 50-year return period, the number of overflow pipe sections and nodes in the northern region increased, while for the 100-year return period, the number of overflow nodes both increased. With the increase in the rainfall return period, the pipe network load increased, the points and sections prone to accumulation and waterlogging increased, and the regional waterlogging risk increased. The southern region is prone to waterlogging because the pipeline network density is higher than that in the northern region and the terrain is low-lying. This study provides a reference for the establishment of rainwater drainage models in regions with similar database limitations and provides a technical reference for the calibration and validation of stormwater models that lack rainfall runoff data.

Network prediction of surgical complication clusters: a prospective multicenter cohort study.

Complicated relationships exist in both occurrence and progression of surgical complications, which are difficult to account for using a separate quantitative method such as prediction or grading. Data of 51,030 surgical inpatients were collected from four academic/teaching hospitals in a prospective cohort study in China. The relationship between preoperative factors, 22 common complications, and death was analyzed. With input from 54 senior clinicians and following a Bayesian network approach, a complication grading, cluster-visualization, and prediction (GCP) system was designed to model pathways between grades of complication and preoperative risk factor clusters. In the GCP system, there were 11 nodes representing six grades of complication and five preoperative risk factor clusters, and 32 arcs representing a direct association. Several critical targets were pinpointed on the pathway. Malnourished status was a fundamental cause widely associated (7/32 arcs) with other risk factor clusters and complications. American Society of Anesthesiologists (ASA) score ⩾3 was directly dependent on all other risk factor clusters and influenced all severe complications. Grade III complications (mainly pneumonia) were directly dependent on 4/5 risk factor clusters and affected all other grades of complication. Irrespective of grade, complication occurrence was more likely to increase the risk of other grades of complication than risk factor clusters.

Detecting service chains and feature interactions in sensor-driven home network services.

Sensor-driven services often cause chain reactions, since one service may generate an environmental impact that automatically triggers another service. We first propose a framework that can formalize and detect such service chains based on ECA (event, condition, action) rules. Although the service chain can be a major source of feature interactions, not all service chains lead to harmful interactions. Therefore, we then propose a method that identifies feature interactions within the service chains. Specifically, we characterize the degree of deviation of every service chain by evaluating the gap between expected and actual service states. An experimental evaluation demonstrates that the proposed method successfully detects 11 service chains and 6 feature interactions within 7 practical sensor-driven services.

Sensing Technology Applications in the Mining Industry-A Systematic Review.

Introduction Industry 4.0 has enhanced technological development in all fields. Currently, one can analyse, treat, and model completely different variables in real time; these include production, environmental, and occupational variables. Resultingly, there has been a significant improvement in the quality of life of workers, the environment, and in businesses in general, encouraging the implementation of continuous improvement measures. However, it is not entirely clear how the mining industry is evolving alongside this industrial evolution. With this in mind, this systematic review aimed to find sensing technology applications within this sector, in order to assist the mining industry in its goal to evolve digitally. METHODOLOGY: The research and reporting of this article were carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS AND DISCUSSION: A total of 29 papers were included in the study, with sensors being applied in several fields, namely safety, management, and localisation. Three different implementation phases were identified regarding its execution: prototype, trial, and (already) implemented. The overall results highlighted that many mechanisms are in need of improvement in underground settings. This might be due to the fact that underground mining has particular safety challenges. CONCLUSIONS: Ventilation and mapping are primary issues to be solved in the underground setting. With regard to the surface setting, the focus is directed toward slope stability and ways of improving it regarding monitoring and prevention. The literature screening revealed a tendency in these systems to keep advancing in technologically, becoming increasingly more intelligent. In the near future, it is expected that a more technologically advanced mining industry will arise, and this will be created and sustained by the optimisation of processes, equipment, and work practices, in order to improve both the quality of life of people and the health of the environment.

Neural networks during delay discounting as trans-disease marker: A meta-analytical review.

Delay discounting reflects a devaluation of delayed long-term benefits but pursuing immediate rewards. Higher discounting rates (h-DR) are found ubiquitous in many diseases and unhealthy conditions, particularly in addiction disorder (AD), attention-deficit/hyperactivity disorder (ADHD), and obesity. Thus, h-DR was considered to be a common benchmark across many diseases facilitating to understand one disease to relevant others, which was called trans-disease process. However, the common and specific neural biomarkers associated with this process has not yet been studied well. We performed a voxel-wise task-related neuroimaging meta-analysis to clarify the neural pattern of trans-disease process across AD, ADHD and obesity. We recruited 19 eligible papers, including 9 AD papers (154 patients), 6 ADHD papers (106 patients) and 4 obesity studies (94 patients). Neuroimaging meta-analysis demonstrated the presence of neural biomarkers of trans-disease process: these patients showed inadequate brain response in caudate, ventromedial and dorsolateral prefrontal cortex (dlPFC) than do of healthy controls (HCs). Disease-specific neural patterns were also found, with prominent hypoactivation in parahippocampal-striatum network for AD, hyperactivation in dopamine-projection striatum network for ADHD and decreased activity in dorsal anterior cingulate cortex and dlPFC for obesity. This study provided robust evidence to reveal the neural substrates of trans-disease process, as well further promoted the triple brain network model in favor of the theoretical developments of these neuropsychiatric disorders.

Industrial three-division network system in China: efficiencies and their impact factors.

The industrial production system can be divided into energy consumption (EC), wastewater treatment (WWT), and waste gas treatment (WGT) stages. Based on three stages, this paper presents an empirical investigation on China's industrial efficiencies and the impact factors between 2011 and 2015. Specifically, we apply the network slacks-based measure (SBM) model to evaluate the industrial eco-efficiency, and calculate the division efficiencies via efficiency decomposition approach. Furthermore, the factors affecting the industrial efficiencies are explored through Tobit regression. We find that (1) there is a great potential to improve the eco-efficiency, and for most provinces, the EC efficiencies are highest, followed by the WGT efficiencies, and the WWT efficiencies are the lowest. (2) The efficiencies present obvious area disparities, the eco-efficiency of the eastern area is the highest except for 2012 and 2013, and the eastern area behaves best at the EC stage, while the western area at the WGT stage and the central area fluctuate greatly at the WWT stage. (3) Technological innovation and urbanization level hinder the improvements of eco-efficiency, while economic structure, industrial structure, and economic development level are positive impact factors, especially the industrial structure. Moreover, environmental regulation insignificantly affects the eco-efficiency but exerts a positive effect on the WGT efficiency.