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Osteosarcoma is the most common type of primary malignant bone tumor. Due to the lack of selectivity and sensitivity of chemotherapy drugs to tumor cells, coupled with the use of large doses, chemotherapy drugs often have systemic toxicity. The use of modern sequencing technology to screen tumor markers in a large number of tumor samples is a common method for screening highly specific and selective anti-tumor drugs. This study aims to identify potential biomarkers using the latest reported gene expression signatures of oncogene-induced replication stress (ORS) in aggressive cancers, and potential anti-osteosarcoma drugs were screened in different drug databases. In this study, we obtained 89 osteosarcoma-related samples in the TARGET database, all of which included survival information. According to the median expression of each of six reported ORS gene markers (NAT10/DDX27/ZNF48/C8ORF33/MOCS3/MPP6), we divided 89 osteosarcoma gene expression datasets into a high expression group and a low expression group and then performed a differentially expressed gene (DEG) analysis. The coexisting genes of 6 groups of DEGs were used as replication stress-related genes (RSGs) of osteosarcoma. Then, key RSGs were screened using LASSO regression, a Cox risk proportional regression prognostic model and a tenfold cross-validation test. GSE21257 datasets collected from the Gene Expression Omnibus (GEO) database were used to verify the prognostic model. The final key RSGs selected were used in the L1000PWD and DGIdb databases to mine potential drugs. After further validation by the prognostic model, we identified seven genes associated with ORS in osteosarcoma as key RSGs, including transcription factor 7 like 2 (TCF7L2), solute carrier family 27 member 4 (SLC27A4), proprotein convertase subtilisin/kexin type 5 (PCSK5), nucleolar protein 6 (NOL6), coiled-coil-coil-coil-coil-helix domain containing 4 (CHCHD4), eukaryotic translation initiation factor 3 subunit B (EIF3B), and synthesis of cytochrome C oxidase 1 (SCO1). Then, we screened the seven key RSGs in two drug databases and found six potential anti-osteosarcoma drugs (D GIdb database: repaglinide, tacrolimus, sirolimus, cyclosporine, and hydrochlorothiazide; L1000PWD database: the small molecule VU-0365117-1). Seven RSGs (TCF7L2, SLC27A4, PCSK5, NOL6, CHCHD4, EIF3B, and SCO1) may be associated with the ORS gene signatures in osteosarcoma. Repaglinide, tacrolimus, sirolimus, cyclosporine, hydrochlorothiazide and the small molecule VU-0365117-1 are potential therapeutic drugs for osteosarcoma. [ABSTRACT FROM AUTHOR]

Inundation dynamics coupled with seasonal information is critical to study the wetland environment. Analyses based on remotely sensed data are the most effective means to monitor and investigate wetland inundation dynamics. For the first time, this study deployed an automated thresholding method to quantify and compare the annual inundation characteristics in dry and wet seasons in the Everglades, using Landsat imagery in Google Earth Engine (GEE). This research presents the long-term time series maps from 2002 to 2021, with a comprehensive spatiotemporal depiction of inundation. In this paper, we bridged the research gap of space-time analysis for multi-season inundation dynamics, which is urgently needed for the Everglades wetland. Within a GIS-based framework, we integrated statistical models, such as Mann–Kendall and Sen's Slope tests, to track the evolutionary trend of seasonal inundation dynamics. The spatiotemporal analyses highlight the significant differences in wet and dry seasons through time and space. The stationary or permanent inundation is more likely to be distributed along the coastal regions (Gulf of Mexico and Florida Bay) of the Everglades, presenting a warning regarding their vulnerability to sea level rise. [ABSTRACT FROM AUTHOR]

This paper first explores spatial distributions and patterns of COVID-19 case rates (cases/100,000 people) and mortality rates (deaths/100,000 people) and their disparities between urban and rural counties in the contiguous US. A county-level social vulnerability index was created using principal component analysis. Social vulnerability components were regressed against both county case and mortality rates. Results suggest that hotspots of case and mortality rates are clustered in Midwest and Upper-Midwest US. We found substantial disparities in case and mortality rates between urban and rural counties. County social vulnerability was positively correlated with both case and mortality rates suggesting counties with higher social vulnerability had higher case and mortality rates. Relationships between social vulnerability components and case and mortality rates vary across the conterminous US. Additionally, counties with increased racial and ethnic minorities, higher percentages of minors, and lower median household income are associated with higher COVID-19 case and mortality rates. [ABSTRACT FROM AUTHOR]

In this paper, an explainable artificial intelligence (AI) technique is employed to analyze the match style and gameplay of the national basketball association (NBA). A descriptive analysis on the evolution of the NBA gameplay is conducted by using clustering and principal component analysis. Supervised-learning based AI models (including the random forest and the feed-forward neural network) are applied to produce accurate predictions on NBA outcomes at a season-by-season and a month-by-month basis. To evaluate the interpretability of the established AI models, an explainable AI algorithm is utilized to deduce and assess the precise reasoning behind the model prediction based on the local interpretable model-agnostic explanation method. To illustrate its application potential, the method is applied to the open-source NBA data from 1980 to 2019. Experimental results demonstrate the effectiveness of the introduced explainable AI algorithm on predicting NBA outcomes with interpretation. [ABSTRACT FROM AUTHOR]

This article investigates the optimal control problem via reinforcement learning for a class of nonlinear discrete‐time systems. The nonlinear system under consideration is assumed to be partially unknown. A new learning‐based algorithm, T‐step heuristic dynamic programming with eligibility traces (T‐sHDP(λ)), is proposed to tackle the optimal control problem for such partially unknown system. First, the concerned optimal control problem is turned into its equivalence problem, that is, solving a Bellman equation. Then, the T‐sHDP(λ) is utilized to get an approximate solution of Bellman equation, and a rigorous convergence analysis is also conducted as well. Instead of the commonly used single step update approach, the T‐sHDP(λ) stores finite step past returns by introducing a parameter, and then utilizes these knowledge to update the value function (VF) of multiple moments synchronously, so as to achieve higher convergence speed. For implementation of T‐sHDP(λ), a neural network‐based actor‐critic architecture is applied to approximate VF and optimal control scheme. Finally, the feasibility of the algorithm is demonstrated by two illustrative simulation examples. [ABSTRACT FROM AUTHOR]

• A numerical model for duct-ventilated embankments driven by temperature difference is developed. • The effects of duct structure on its cooling performance are analyzed. • Ducts can greatly reduce the effect of snow thermal insulation on the cooling performance of a crushed-rock layer. In pan-Arctic permafrost regions, longitudinal ventilation ducts are often installed in embankments to cool the underlying permafrost and thereby ensure road stability. However, the current design methods for embankments with longitudinal ventilation ducts (ED) normally simplify the conjugate heat transfer process between the duct wall and air in the duct. In this study, based on the chimney effect and the theory of fluid flow and heat transfer, we propose an improved numerical method for evaluating the cooling performance of the ED. Using this method, we conduct an assessment of the influences of duct configuration on the efficiency of ventilation duct cooling in winter. The results indicate that increasing the diameter of the duct and the height of the duct outlet can effectively improve the radial cooling range of the ventilation duct. However, the cooling performance of the ventilation duct becomes worse when the length of the duct is increased. To address the limited cooling performance of the ED, we design a crushed-rock interlayer embankment installed with longitudinal ventilation ducts (CED). It is found that the CED is more effective at maintaining the long-term thermal stability of the road in a typical pan-Arctic permafrost region when compared to the ED and the crushed-rock interlayer embankment. Furthermore, a cost analysis of the four different embankment designs reveals that the CED is a financially viable option for embankment construction in permafrost regions. [ABSTRACT FROM AUTHOR]

Background: Osteosarcoma (OS) is a highly metastatic primary bone malignancy, and the treatment options remain inadequate. Hence, exploring innovative natural medications is required. Prunetin (PRU) is an isoflavone that has been a proven anticancer agent in numerous cancer cell lines. However, the activity of PRU against OS remains uncertain. Materials and Methods: Here, we studied the anticancer activity of PRU (20 and 25 µM) on human OS cells MG-63 and investigated its latent mechanism. The PRU activity of MG-63 cells cytotoxicity, intracellular ROS, metastasis, apoptosis, anti-apoptotic proteins, MAPK/STAT-3, and AKT signaling pathways was assessed by MTT assay, DCFH-DA, DAPI, PI, AO/EB, cell adhesion, and RT-PCR analysis. Findings unveiled that PRU could constrain MG-63 cell viability and adhesion through elevated intracellular ROS and elicited apoptosis. Results: Likewise, PRU (20 and 25 µM) avert the MG-63 cell proliferation, which stimulates apoptosis by the enhancement of Bax and caspases, while it diminishes Bcl-2 in a dose-dependent way. Furthermore, PRU could reduce Pin-1, and anti-apoptotic elements, as well as trigger apoptotic signaling pathways. Our data established that PRU alleviates MG-63 cell proliferation and metastasis via ROS-mediated apoptosis, which triggers MAPKs/STAT3 and AKT pathways, suggesting that PRU is a promising natural remedy for OS. In order to comprehend the therapeutic target for cancer, we assessed the effect of PRU on the expression of Pin1, which is thought to be over-expressed in many human malignancies. According to our findings, PRU specifically suppressed Pin1 expression to reduce the expression of Akt, STAT3, P38, JNK, P65, and IL-6. We evaluated the impact of PRU on the expression of Pin1, which is allegedly over-expressed in many human malignancies, to better understand the therapeutic target for cancer. Researchers state that PRU inhibited the expression of Akt, STAT3, P38, JNK, P65, and IL-6 in particular, by suppressing Pin1 expression. Conclusion: Together, these results suggest that PRU may be an effective treatment for bone cancer in people by preventing Pin1 expression. [ABSTRACT FROM AUTHOR]

Field surveys focusing on the crack damages in paved embankment of the Qinghai-Tibet Highway were carried out at 17 sections. Some severe cracks were found in the road embankment constructed on the permafrost ground affecting the smooth operation of the highway. Road orientation, thaw settlement, crack width and length were measured at the typical crack sections. Besides, embankment slope surface temperatures were monitored at two road sections, and the temperature difference as high as 10 °C was observed in the cold winter time. Three scenarios were employed in the numerical modeling to assess the impact of climate warming, embankment slope surface temperature, and embankment itself on the underlying permafrost evolution process. Thereafter, a method was introduced to obtain the thaw settlement of the road embankment according to the simulated thawing front depth. The results indicate that the thaw settlement difference across the road embankment width is the main cause results in the crack damages. This uneven thaw settlement can be amplified due to the presence of the original ground slope. Additionally, climate warming intensified the underlying permafrost degradation and accelerated the thawing front penetrating into the subgrade permafrost. In summary, road orientation, original ground slope, climate warming, and underlying permafrost type are major but not the sole causes for the crack damages of the paved embankment in the Tibetan permafrost environments. It is recommended that steep slope and ice-rich permafrost ground should be avoided in the early design of the highway embankment. • Field surveys focusing on the crack damages of the Qinghai-Tibet Highway were carried out. • A 10 °C temperature difference between the embankment slopes was observed in the winter time. • Three scenarios were designed to assess the permafrost evolution underneath the road embankment. • A method was introduced to obtain the thaw settlement of the road embankment. [ABSTRACT FROM AUTHOR]

Abstract A laboratory experiment was performed to investigate the thermal effect of rainwater infiltration into a one-half scale replicated road embankment built on artificial permafrost subjected to four 15-day freeze-thaw cycles and several rainfall events during thaw periods in a cold environmental chamber. The ground temperature, water content, and heat flux were monitored at different depths at three key locations in the road subbase. Based on the experimental results, the rainwater infiltration has a significant heating effect on the shallow subbase layers down to a depth no more than 30 cm. The heat transfer in the road embankment is directly correlated to the amount of rainfall. Moreover, rainwater infiltration has different heating effects according to the locations in the road subbase. The most important heating effect is underneath the embankment slope, the second one in the embankment shoulder, and the third one underneath the concrete pavement. The changes in temperature, moisture, and heat flux in the road subbase due to the rainwater infiltration are delayed and attenuated with the increase in depth. Even if the road subbase was frozen at these depths, moisture changes in the road subbase as deep as 60 cm were observed. The volumetric unfrozen water content in the road subbase and subgrade depends on the ground temperature and exhibits hysteretic behavior during cooling and warming cycles. The observed variations in heat flux are related to the changes in temperature and moisture in the road subbase. For road embankments located in permafrost environments, the thermal effect of rainwater infiltration into the road subbase may lead to permafrost degradation at depth. Highlights • The rainwater has a significant heating effect on the shallow road subbase. • The heat transfer in the road embankment is directly correlated to the rainwater amount • Rainwater infiltration has different heating effects according to the locations in the road subbase. [ABSTRACT FROM AUTHOR]

Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today’s mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones. [ABSTRACT FROM AUTHOR]

Background Lung cancer leads to the largest number of cancer-related deaths worldwide and is usually accompanied with metastasis which is the primary cause of those death and correlated with poor prognosis. However, the mechanism of lung cancer metastasis is still lack of definition. Methods We compared the primary lung adenocarcinoma (AD) and its metastasis tissues induced by overexpression of Kras G12D and inactivation of P53 in mouse lungs by analyzing GSE40222 about the differentially expressed genes (DEGs), pathways and hub genes. And human lung AD databases are used to verify the conversed changes of identified key gene and then followed functional studies are performed to explore the functions of key gene. Results We identified 165 genes differentially expressed in lung AD metastasis compared to primary AD. Pathway analysis identified 649 GO biological processes and 8 KEGG pathways, such as ECM-receptor interaction. Biological network interaction identified the hub genes during lung adenocarcinoma metastasis, such as the up-regulated COL5A1 , a novel gene in AD metastasis. We found it's also increased in human AD and advanced stage. Knockdown of COL5A1 in human AD metastatic cells inhibited cell growth and invasion, and induced cell apoptosis. Notably, higher expression of COL5A1 was observed in the lung AD patients with recurrence and short survive. Conclusion By analyzing mouse lung AD and its metastases, we identified the potential key genes and pathways regulating lung AD metastasis, such as COL5A1 . The following analysis of COL5A1 in human AD database and cells explores its functions, holding the implications of target therapy in AD metastasis and also providing more clues for future studies. [ABSTRACT FROM AUTHOR]

Objective This study aimed to examine the alterations of cortical connectivity in first-episode schizophrenia (FES) with auditory hallucinations at early response of antipsychotics. Methods This was a nonexperimental control of medication study. We measured the cortical activity of 20 medicated patients with FES (medicated group), 19 nonmedicated patients with FES (nonmedicated group), and 22 healthy controls using electroencephalogram during eye-open resting state. Source reconstruction analysis was performed to determine the brain regions that showed significant group difference. A dynamic causal modelling (DCM) analysis was used to estimate the effective connectivity between sources. Result Both FES groups expressed increased activity in the right middle frontal gyrus (RMFG) and left/right superior temporal gyrus (L/RSTG) relative to that in the controls ( p < 0.05), and the nonmedicated group presented even higher activity than the medicated group ( p < 0.05). The effective connectivity from RMFG to LSTG was weaker in the nonmedicated group relative to that in the medicated group ( p < 0.01), although patients in the medicated group showed no difference with healthy controls in RMFG to L/RSTG connections. The Bayesian model selection analysis found modulatory lateralization in the nonmedicated group. Conclusion The patients with FES showed frontotemporal hyperactivity and disconnectivity. The effective connections accompanied with modulation were improved when hallucination diminished at early response of routine medication. Significance This study provided the first evidence of early drug response-related alterations in effective brain connectivity. [ABSTRACT FROM AUTHOR]

A complex luminous pattern showing square superlattice pattern (SSP) appearance on the edge and dot-line square superlattice pattern (DLSSP) appearance in the middle, which is called coexistence of SSP and DLSSP (CSSP), is observed in a dielectric barrier discharge (DBD) system. The spatio-temporal structure of CSSP obtained by using an intensified-charge coupled device (ICCD) indicates that CSSP is an interleaving of three kinds of square subpatterns in one half voltage, which is similar to that of SSP and DLSSP. Based on the activation-inhibition mechanism of surface charges, the influence of surface charges of prior discharge to the pattern formation is discussed by comparing the discharge characteristics of CSSP with those of SSP and DLSSP. The amount of surface charges corresponding to the prior discharges for SSP, DLSSP and CSSP is calculated respectively by using the time integration of the total current pulse to verify the above theoretical explanation. [ABSTRACT FROM AUTHOR]

Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning techniques have drawn ever-increasing research interests because of their inherent capability of overcoming the drawback of traditional algorithms dependent on hand-designed features. Deep learning approaches have also been found to be suitable for big data analysis with successful applications to computer vision, pattern recognition, speech recognition, natural language processing, and recommendation systems. In this paper, we discuss some widely-used deep learning architectures and their practical applications. An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different types of deep neural networks are surveyed and recent progresses are summarized. Applications of deep learning techniques on some selected areas (speech recognition, pattern recognition and computer vision) are highlighted. A list of future research topics are finally given with clear justifications. [ABSTRACT FROM AUTHOR]

• Theoretically formulated a hedging function consistent with the Budyko framework. • The derived function outperforms commonly-used rules. • The derived function depicts a general non-linear "supply–demand" relationship. Operating rules play an important role in enhancing the water utilization of reservoirs. Previous studies on hedging investigated the operating rules between water release (R) and available water (AW) based on minimizing the normalized shortage index, and applied the rules to optimization. However, seldom considered a realistic economic objective function to form a more general "supply–demand" relationship. To address this issue, this study formulated a theoretical function by a two-period reservoir operation model with the goal of minimizing the sum of economic loss from the current release and carryover storage targets. The derived function that guides the water supply was numerically validated to obtain the optimal time-varying hedging rules by non-dominated sorting genetic algorithm Ⅱ. The water-supply system of Dongwushi Reservoir in Hebei Province, China was selected as a case study. The results show that (1) The derived function depicts the relationship between "R-AW ratio" and "D-AW ratio", which is consistent with the Budyko "supply–demand" framework. (2) The optimized results using the derived Budyko hedging function outperform those using two-point hedging rule (TPH) and standard operating policy (SOP). (3) The derived function depicts a general non-linear "supply–demand" relationship rather than the linear one contained in commonly-used TPH. The derived function could be used for varied demands, indicating broader applications than TPH. [ABSTRACT FROM AUTHOR]

A new heuristic based on the Nawaz–Enscore–Ham algorithm is proposed in this article for solving a permutation flow-shop scheduling problem. A new priority rule is proposed by accounting for the average, mean absolute deviation, skewness and kurtosis, in order to fully describe the distribution style of processing times. A new tie-breaking rule is also introduced for achieving effective job insertion with the objective of minimizing both makespan and machine idle time. Statistical tests illustrate better solution quality of the proposed algorithm compared to existing benchmark heuristics. [ABSTRACT FROM AUTHOR]

• In terms of detection algorithm: This paper uses the inner product principle to distinguish faulty feeder from healthy feeder, and a series of inner product sequences are obtained by inner product of each feeders zero-sequence current to the neutral point zero-sequence current. These inner product sequences have practical physical significance. The sign of faulty feeder sequence is opposite to that of healthy feeder sequence, and the amplitude of faulty feeder sequence is very large. The calculation principle is simple and helpful for engineering application. • In terms of detection information: The integrated inner product value is introduced through the inner product sum. The criterion is constructed by the integrated inner product value sign and amplitude. When the result of the sign judgment and amplitude judgment are consistent, the faulty feeder is output. The two information quantities of sign and amplitude are used to improve the accuracy of fault detection. This study aims to address the difficulty of detecting faulty feeders, especially for high impedance fault (HIF) in medium-voltage small resistance to ground systems. Based on the difference in the amplitude and phase of the zero-sequence current (ZSC) during HIF, this paper proposed a novel faulty feeder detection method based on the integrated inner product under HIF for a small resistance to ground system. The method was as follows: First, the inner product sequence was calculated for each feeder ZSC versus the neutral point ZSC. Second, the cumulative sum of the inner product sequence was calculated to obtain each feeder's inner product sum (IPS), and then each feeder's integrated inner product value (IIPV) was calculated through the IPS result. The IIPV expanded the error of the faulty feeder and healthy feeder and eliminated the effect of grounding resistance. Finally, the IIPV sign and amplitude were judged. If the IIPV sign of a certain feeder was opposite to the IIPV sign of other feeders and the IIPV amplitude of this feeder was greater than the IIPV amplitude of other feeders, then this feeder was judged to be a faulty feeder. A large number of simulations show that the method is accurate and stable. It can accurately detect faulty feeders under a signal-to-noise ratio (SNR) of 5 dB noise interference. Compared with other methods, this method has less calculation time and better judgment accuracy. [ABSTRACT FROM AUTHOR]

Large amounts of time series of spatial snapshot data have been collected or generated for the monitoring and modeling of environmental systems. Those time series of data also provide the opportunity to study the movements and dynamics of many different natural phenomena. While the snapshot organization is conceptually simple and straightforward, it does not directly capture or represent the dynamic characteristics of the phenomena. This study presents computational methods to identify dynamic events from time series of spatial snapshots. Events are represented as directed spatiotemporal graphs to characterize their initiation, development, movement, and cessation. Graph-based algorithms are used to analyze the dynamics of the events. The method is applied to time series of high-resolution radar reflectivity images during one of the deadliest storm outbreaks that impacted 15 states of southeastern United States between 23 and 29 April 2011. As shown in this case study, convective storm events identified using our methods are consistent with previous studies, and our analysis confirms that the left split/merger occurs more than right split/merger in those convective storm events, which confirms theory, numerical simulations, and other observed case studies. While this study does not differentiate between storm modes, the method shows potential for capturing a more detailed climatology of precipitation characteristics. [ABSTRACT FROM AUTHOR]

In this paper, the exponential stability problem is investigated for a class of Cohen–Grossberg neural networks with Markovian jumping parameter and mixed time-delays. The mixed time-delays under consideration consist of both the mode-dependent discrete time-delays and the mode-dependent distributed time-delays. By constructing a new Lyapunov–Krasovskii functional and employing the stochastic analysis techniques, sufficient conditions are proposed to guarantee that the addressed neural networks are exponentially stable in the mean square sense. It is shown that the developed stability criteria can be easily verified by using the standard numerical software. Finally, an illustrative example is provided to show the feasibility and usefulness of the developed results. [ABSTRACT FROM AUTHOR]

Urbanization and warming climate suggest that health impacts from extreme heat will increase in cities, thus locating vulnerable populations is pivotal. However, heat vulnerability indices (HVI) overwhelmingly interpret one model that may be inaccurate or methodologically flawed without considering how results compare with other HVI. Accordingly, this analysis applied a multimodal approach incorporating underrepresented health and adaptability measures to analyze heat vulnerability more comprehensively and better identify vulnerable populations. The Southeast Florida HVI (SFHVI) blends twenty-four physical exposure, sensitivity, and adaptive capacity indicators using uncommon statistical weights removing overlap, then SFHVI scores were compared statistically and qualitatively with ten models utilizing alternative methods. Urban areas with degraded physical settings, socioeconomic conditions, health, and household resources were particularly vulnerable. Rural and agricultural areas were also vulnerable reflecting socioeconomic conditions, health, and community resources. Three alternative models produced vulnerability scores not statistically different than SFHVI. The other seven differed significantly despite geospatial consistency regarding the most at-risk areas. Since inaccurate HVI can mislead decisionmakers inhibiting mitigation, future studies should increasingly adopt multimodal approaches that enhance analysis comprehensiveness, illuminate methodological strengths and flaws, as well as reinforce conviction about susceptible populations. [ABSTRACT FROM AUTHOR]

Battery degradation, caused by multiple coupled degradation mechanisms, severely affects the safety and sustainability of a battery management system (BMS). The battery state of health (SOH) is a commonly-adopted metric to evaluate a battery's degradation condition, which should be carefully modeled to facilitate the safety and reliability of a BMS. Recently, owing to the rapid progress of data science-related techniques, data-driven models for battery SOH estimation have attracted great attentions from both academia and industry communities. This paper aims to provide the scientists and engineers with a general overview of data-driven battery SOH estimation technology for BMSs. State-of-the-art models published during 2018–2022 are reviewed with care, including a) feature extraction and selection methods; b) benchmarks, variants and extensions of data-driven SOH estimation models; and c) publicly-available battery SOH datasets. Afterwards, experiments are conducted and analyzed on the Toyota & Stanford-MIT battery SOH datasets for benchmark study. Finally, existing challenges and feature trends are summarized. [ABSTRACT FROM AUTHOR]

Reservoirs lined with concrete slabs are very common in the northern cold regions of China. Failures such as the rotation and the dislocation of concrete lining slabs are usually found in those ice-covered reservoirs each year in winter. In this study, the interaction processes between water, ice, and lining when water level changes in ice-covered reservoirs are evaluated based on the theory of beams on elastic foundation. In general, ice cover can generate a rotation effect (bending moment) on concrete lining slabs when water level changes in ice-covered reservoirs. Under such conditions, the bending moment at the ice edge increases with the increase of the ice cover width and thickness. Once the tensile stress at the interface between concrete slab and its base layer exceeds the tensile strength of the interface itself, the concrete slab separates from its base layer and the support condition of the ice cover changes from fix-supported to hinge-supported. Then the rotational failure of the concrete slab may occur due to the rotation effect acted by ice cover. The calculated rotation angle of the concrete lining slab is in agreement with the angle observed at a reservoir in northwest China. In addition, prevention measures such as concrete lining slabs with hydrophobic coating on the surface, concrete lining slabs with dark surface, anchoring concrete slabs to reservoir slope, and integral large-scale lining modules are proposed to mitigate such rotational lining failures in ice-covered reservoirs. The results can help to improve the performance and service life of reservoirs in cold environments. [ABSTRACT FROM AUTHOR]

The study focused on the predictive value of baseline metabolite ratios in bilateral hippocampus of first-episode schizophrenia by using proton magnetic resonance spectroscopy ( 1 H MRS). 1 H MRS data were acquired from 23 hallucination and 17 non-hallucination first-episode schizophrenia patients compared with 17 healthy participants. Clinical characteristics of patients were rated using the Positive and Negative Syndrome Scale (PANSS) before and after 3-month treatment. The schizophrenia patients showed lower NAA/Cr ratio than healthy participants respectively ( p = 0.024; p = 0.001), and non-hallucination patients had even lower NAA/Cr ratio than hallucination patients ( p = 0.033). After 3-month treatment, hallucination patients had greater improvement in negative symptoms than non-hallucination patients ( p = 0.018). The reduction of PANSS total score and negative factor score was positively correlated with the left NAA/Cr in both group patients ( p < 0.05). Given that the bilateral hippocampal baseline NAA/Cr had predictive value for the whole treatment response, and the left hippocampal NAA/Cr can predict the prognosis of negative symptoms during acute phase medication in first-episode schizophrenia. [ABSTRACT FROM AUTHOR]

We investigate the discharge characteristics of a penning ion source for a compact sealed neutron generator in DC mode. A measuring system consisting of console, vacuum gauges, and teslameter is established. By using the measuring system, the discharge current as a function of ion source voltage, gas pressure, and magnetic field is studied. The results show that the neutron generator can operate in a safe and steady state when the experimental parameters are as follows: ion source voltage of 1.2–2 kV, gas pressure of 4×10 −2 –8×10 −2 Pa, and magnetic field of 0.3–0.5 T. Within these ranges, the neutron yield of the generator can reach 2×10 8 n/s. [ABSTRACT FROM AUTHOR]

The blocking flow-shop scheduling problem, which is an NP-hard problem, has wide applications in industry. Many studies have focused primarily on single-objective optimization, such as minimizing the makespan or total flow time, which is insufficient for practical problems. In this study, a blocking flow-shop scheduling problem with bicriteria of the makespan and machine utilization is considered. A simple constructive heuristic, NEHCG, is proposed by developing new priority and tie-breaking rules based on the Nawaz–Enscore–Ham heuristic. In the new priority rule, the quartile deviation of job processing times is included for the first time, and the new tie-breaking rule is designed to break ties effectively in the insertion phase. Then, an improved constructive heuristic, PW_NEHCG, is presented by effectively combining NEHCG with an improved profile-fitting approach. Computational experiments show that the proposed constructive heuristics exhibit excellent performance compared to other well-known heuristics. [ABSTRACT FROM AUTHOR]

Static spiral patterns are observed in a dielectric barrier discharge system. It bifurcates from the concentric-ring pattern with the increase of the applied voltage. The formation of the static spiral patterns is investigated using an intensified-charge coupled device camera. It is found that the discharge filaments are the basic unit of the spiral patterns and they do not occur at the same location during successive half cycles. The uniform spiral arms, as seen with the naked eyes, are formed by the numerous discharge filaments located in different positions during successive acquisition intervals. The correlation coefficient between the discharges of successive half cycles is relatively low and is independent of the delay time between images. [ABSTRACT FROM AUTHOR]

We explore the effects of rice husk-based activated carbon (RHAC) with different pore structure and functional groups on the performance of lead-carbon batteries (LCB) in high-rate partial state of charge (HRPSoC) and its potential mechanisms. The results show that the performance of negative electrode is mainly affected by the rate and efficiency of RHAC on electrocatalytic of lead deposition. Notably, the reaction of phosphoric acid with silica in rice husk can significantly increase the content of phosphorus functional groups, which can enhance the electrocatalytic rate by complexing lead ions and inhibit hydrogen evolution reaction (HER). Micropore is more prone to hydrogen evolution in the medium-to-high polarization region (-1.25V∼-1.5 V vs Hg/Hg 2 SO 4) and hinder lead ion mass transfer, which leads to the general cycle performance of LCB containing high microporosity RHAC. In addition, RHAC only has a certain electric double layer capacitance (EDLC) contribution at the end of cycle, which is still on the premise that RHAC has high specific surface area (SSA) and mesoporosity. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. To ensure the stability of the AM process, it is of critical importance to obtain high-quality thermal images by using image processing techniques. In this paper, a novel image processing method is put forward with aim to improve the contrast ratio of the thermal images for image segmentation. To be specific, an image-enhancement generative adversarial network (IEGAN) is developed, where a new objective function is designed for the training process. To verify the superiority and feasibility of the proposed IEGAN, the thermal images captured from an AM process are utilized for image segmentation. Experiment results demonstrate that the developed IEGAN outperforms the original GAN in improving the contrast ratio of the thermal images. [Display omitted] • An image enhancement generative adversarial network is proposed for improving the contrast ratio of the image. • A designed penalty term is introduced in the loss function of the generative adversarial network. • The contrast improvement index is utilized for performance evaluation. • The proposed algorithm is successfully employed in thermal imaging analysis. [ABSTRACT FROM AUTHOR]

Due to the ever-increasing amount of various information provided by the internet, recommendation systems are now used in a large number of fields as efficient tools to get rid of information overload. The content-based, collaborative-based and hybrid methods are the three classical recommendation techniques, whereas not all real-world problems (e.g. the food recommendation problem) can be best addressed by such classical recommendation techniques. This paper is devoted to solving the food recommendation problem based on many-objective optimization (MaOO). A novel recommendation approach is proposed by transforming the original recommendation problem into an MaOO one that contains four different objectives, i.e., the user preferences, nutritional values, dietary diversity, and user diet patterns. The experimental results demonstrate that the designed recommendation approach provides a more balanced way of recommending food than the classical recommendation methods that only consider individuals' food preferences. [ABSTRACT FROM AUTHOR]

In wire arc additive manufacturing (WAAM), the electric arc is an essential part of the welding equipment, which serves as the heat source and is directed by the current and voltage. The working status of the electric arc is an important factor in determining the quality of the fabricated components. During the welding process, the current and voltage may change abruptly due to some abnormalities in the operating conditions, which may affect the working status and thereby affect the quality of products. Such abnormal changes in the current and voltage can be treated as outliers. In order to identify outliers in current and voltage to further improve the welding process, in this paper, a novel deep-transfer-learning-embedded outlier detection approach is developed for WAAM. A new domain adaptation strategy is designed where the cross-domain discrepancies of the marginal distribution and conditional distribution are minimized. Specifically, two separate coefficients are introduced to adjust the conditional domain discrepancies of normal instances and outliers with the purpose of alleviating the data imbalance problem. The particle swarm optimizer is employed to adjust the hyper-parameters. The developed deep transfer learning framework is exploited in designing a new outlier detector with application to WAAM. The proposed approach is exploited in real-world industrial data collected through the WAAM process. Experimental results demonstrate that the proposed outlier detection approach outperforms the standard deep-learning-based outlier detector approach and the standard transfer-learning-embedded outlier detection approach in terms of detection accuracy. [ABSTRACT FROM AUTHOR]

Tidal flats (non-vegetated area) are soft-sediment habitats that are alternately submerged and exposed to the air by changeable tidal levels. The tidal flat dynamics research mainly utilizes the cell-level comparisons between the consecutive snapshots, but the in-depth study requires more detailed information of the dynamic activities. To better track, represent, and analyze tidal flats' dynamic activities, this study proposes an integrated approach of a three-level Geographic Information Science (GIS) framework and a graph model. In the three-level GIS framework, the adjacent cells are assembled as the objects, and the objects on different time steps are linked as lifecycles by tracking the predecessor–successor relationships. Furthermore, eleven events are defined to describe the dynamic activities throughout the lifecycles. The graph model provides a better way to represent the lifecycles, and graph operators are utilized to facilitate the event analysis. The integrated approach is applied to tidal flats' dynamic activities in the southwest tip of Florida Peninsula from 1984 to 2018. The results suggest that the integrated approach provides an effective way to track, represent, and analyze the dynamic activities of tidal flats, and it offers a novel perspective to examine other dynamic geographic phenomena with large spatiotemporal scales. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neighborhood mutation based memetic differential evolution is proposed for multimodal optimization. In the proposed method, an adaptive neighborhood mutation (ANM) strategy is devised to allow the individuals to conduct a diverse search at the early stage of evolution and then gradually switch to an intensive search at the later stage of evolution. Further, the ANM is devised such that encouraging promising individuals for exploitation while unpromising individuals for exploration during evolution to appropriate search the multimodal space. In addition, an adaptive Gaussian based local search strategy, which considers the difference between the offspring and their paired individuals with successful replacements, is developed to appropriately improve promising individuals during evolution. The proposed method has been extensively accessed on a suite of twenty multimodal benchmark functions and the performance of which is compared with sixteen related multimodal algorithms. The results clearly demonstrate the superiority of proposed method as well as the merit of devised strategies. [ABSTRACT FROM AUTHOR]

In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of the United States (US) has been limited. In this study, we developed a composite community disaster resilience index (CCDRI) for the coastal communities of the conterminous US that considers different dimensions of disaster resilience. The resilience variables used to construct the CCDRI were justified by examining their influence on disaster losses using ordinary least squares (OLS) and geographically weighted regression (GWR) models. Results suggest that the CCDRI score ranges from −12.73 (least resilient) to 8.69 (most resilient), and northeastern communities are comparatively more resilient than southeastern communities in the study area. Additionally, resilience components used in this study have statistically significant impact on minimizing disaster losses. The GWR model performs much better in explaining the variances while regressing the disaster property damage against the resilience components (explains 72% variance) than the OLS (explains 32% variance) suggesting that spatial variations of resilience components should be accounted for an effective disaster management program. Moreover, findings from this study could provide local emergency managers and decision-makers with unique insights for enhancing overall community resilience to disasters and minimizing disaster impacts in the study area. [ABSTRACT FROM AUTHOR]

Urban expansion is one of the most dramatic forms of land transformation in the world and it is one of the greatest challenges in achieving sustainable development in the 21st century. Previous studies analyzed urbanization patterns in areas with rapid urban expansion while urban areas with low to moderate expansion have been overlooked, especially in developed countries. In this study, we examined the spatiotemporal dynamics of urban expansion patterns in South Florida, United States (US) over the last 25 years (1992–2016) using Remote Sensing and GIS techniques. The main goal of this paper was to investigate the degree and spatiotemporal patterns of urban expansion at different administrative level in the study area and how spatiotemporal variance in different explanatory factors influence urban expansion in this region. More specifically, this research quantifies the rates, types, intensity, and landscape metrics of urban expansion in Miami-Fort Lauderdale-Palm Beach, Florida Metropolitan Statistical Area (Miami MSA) which is the 7th largest MSA and 4th largest urbanized area in the US using remote sensing (satellite imageries) data from National Land Cover Datasets (NLCD) and Coastal Change Analysis Program (C-CAP) at 30 m spatial resolution. We further investigated the urban growth patterns at the county and city areas that are located within this MSA to portray the local 'picture' of urban growth in this region. Urban expansion in this region can be divided into two time periods: pre-2001 and post-2001 where the former experienced rapid urban expansion and the later had comparatively slow urban expansion. Results suggest that infilling was the dominant type of urban expansion followed by edge-expansion and outlying. Results from landscape metrics represent that newly developed urban lands became more aggregated and simplified in form as the time progressed in the study region. Also, new urban lands were generated away from the east coast and historic cities which eventually created new urban cores. We also used correlation analysis and multiple linear stepwise regression to address major explanatory factors of spatiotemporal change in urban expansion during the study period. Although the influence of factors on urban expansion varied temporally, Population and Distance to Coast were the strongest variables followed by Distance to Roads and Median Income that influence overall urban expansion in the study area. [ABSTRACT FROM AUTHOR]

Recently, deep learning techniques have been widely applied to fault diagnosis due to their outstanding feature extraction abilities. The success of deep-learning-based fault diagnosis methods is highly dependent on the quantity and quality of the training data. In practical scenarios, it is challenging to obtain sufficient high-quality training data for fault diagnosis tasks due to complex environments, which would affect the effectiveness of the deep learning methods. In this paper, a new fault diagnosis method is proposed for motor bearing fault diagnosis under small samples. The Siamese neural networks (SNNs) are employed to extract the fault features. A multi-stage training strategy is proposed to train the SNNs with the aim to prevent the training stagnation problem and handle the small sample problem. A multi-source feature fusion network is developed to make full use of the multi-source sensor data by fusing the extracted fault features for further fault diagnosis. The proposed method is applied to motor bearing fault diagnosis on two real-world datasets. Experimental results demonstrate the effectiveness and feasibility of the introduced method for motor bearing fault diagnosis under small samples. [ABSTRACT FROM AUTHOR]

Optimization is an important research field, especially in engineering, physical sciences, and economics. The main purpose of optimization is to maximize the profit and minimize the cost of production as well as the loss of the system. Evolutionary computation algorithms, such as the genetic algorithm and the particle swarm optimization (PSO) algorithm have been successfully employed in solving various optimization problems. Owing to its application potential and promising performance in discovering the optimal solution, the PSO algorithm has been recognized as a powerful optimization technique and attracted an ever-increasing interest in the evolutionary computation community. In this article, a novel $N$ -state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain. The performance of the proposed NS-MJPSO algorithm is evaluated via some widely used mathematical benchmark functions. The experimental results demonstrate that the developed NS-MJPSO algorithm outperforms some currently popular PSO algorithms on the widely used benchmark functions. [ABSTRACT FROM AUTHOR]

Zero-shot learning is a promising technique for diagnosing mechanical faults in complex and uncertain environments. However, when diagnosing mechanical faults across different severities using zero-shot learning, the impact of insensitive features should be minimized due to the susceptibility of zero-shot prototypes and the vulnerability of vibration signals. To accomplish this, an Insensitive Feature Removal Network (IFRN) with an entire attention mechanism (EAM) module and a denoise autoencoder module is proposed to remove insensitive features hierarchically by dividing them into two categories: common insensitive features (CIF) and private insensitive features (PIF), each with different properties depending on their corresponding sub-labels presence in mechanical faults. Concretely, EAM removes insensitive features that the classifier cannot differentiate with the help of the entire attention weight comparison by attention generation, attention comparison, and attention limitation parts. Then, the denoise autoencoder module with long short memory term (LSTM) is utilized to remove the insensitive features that are not completely removed, especially for the insensitive features that independently arise. The IFRN's effectiveness is demonstrated through comparative experiments and ablation studies using the Case Western Reserve University (CWRU) dataset, where the experimental result shows that IFRN outperforms conventional zero-shot learning methods. Furthermore, an analysis with prototype distance and sample aggregation is presented to further justify the effectiveness of the proposed method in reducing the prototype shift and improving classification accuracy by removing insensitive features. • IFRN with EAM is proposed to hierarchically alleviate the effect of insensitive features. • EAM can effectively eliminate insensitive features that the classifier cannot differentiate. • IFRN performance is verified on the CWRU rolling bearing dataset. [ABSTRACT FROM AUTHOR]

In this paper, a modified collaborative filtering (MCF) algorithm with improved performance is developed for recommendation systems with application in predicting baseline data of Friedreich's Ataxia (FRDA) patients. The proposed MCF algorithm combines the individual merits of both the user-based collaborative filtering (UBCF) method and the item-based collaborative filtering (IBCF) method, where both the positively and negatively correlated neighbors are taken into account. The weighting parameters are introduced to quantify the degrees of utilizations of the UBCF and IBCF methods in the rating prediction, and the particle swarm optimization algorithm is applied to optimize the weighting parameters in order to achieve an adequate tradeoff between the positively and negatively correlated neighbors in terms of predicting the rating values. To demonstrate the prediction performance of the proposed MCF algorithm, the developed MCF algorithm is employed to assist with the baseline data collection for the FRDA patients. The effectiveness of the proposed MCF algorithm is confirmed by extensive experiments and, furthermore, it is shown that our algorithm outperforms some conventional approaches. [ABSTRACT FROM AUTHOR]

The permutation flowshop scheduling problem is a vital production management concern of industries such as electronic product manufacturing and integrated circuit fabrication. In many studies, jobs are prioritized independently according to their features or properties. This article develops a new perspective by introducing the self-attention mechanism into scheduling for the first time. The job similarities are characterized by the dot-product of processing time matrices and taken as job priorities. Based on this idea, a new priority rule denoted by PRSAT is proposed, and a heuristic named NEHLJP2 is presented with makespan minimization. The computational results with the Taillard and VRF benchmarks and 640 random instances demonstrate that the new priority rule and heuristic dominate the existing ones at a nominal cost of computation time. [ABSTRACT FROM AUTHOR]

Background Reconstruction of thoracolumbar structural stability is a formidable challenge for spine surgeons after vertebral body tumor resection. Various disadvantages of the currently used expandable or nonexpandable cages have limited their clinical applications. We sought to develop a novel prosthesis for clinical use and assess its preliminary clinical outcome in reconstruction of thoracolumbar structural stability after spinal tumor resection. Methods Using data obtained from a retrospective analysis of the morphological characteristics of the thoracolumbar vertebrae and endplates in previously reported studies, we modified the nano-hydroxyapatite/polyamide-66 (n-HA/PA66) strut into a novel height-adjustable vertebral body. A retrospective study was performed of 7 patients who had undergone reconstruction of thoracolumbar structural stability with this novel prosthesis from August 2016 to January 2017. Results A novel height-adjustable vertebral body (AHVB) composed of n-HA/PA66 with 2 separate components with a 163° contact surface at each end was manufactured. The height-adjustable range was 28–37 mm. No significant implant-related complications were observed in the process of operation. All patients experienced a significant reduction in pain, with the visual analog scale score decreasing from 7.9 to 4.0. Neurological improvement was assessed using the Frankel grading system after surgery. Postoperative radiographic and computed tomography/magnetic resonance imaging findings indicated that the operated segment was stable, the outcome of kyphosis correction was good, and no prosthesis subsidence or dislocation was observed. Conclusion This novel prosthesis has many advantages in the reconstruction of height, lordosis, and alignment after thoracolumbar spinal tumor resection and has a favorable prospect for clinical application. Highlights • A novel AHVB composed of n-HA/PA66 was designed. • This novel prosthesis' interfaces match the morphology of thoracolumbar endplates. • The material used is similar to that of natural bones; no additional bone grafts are needed. • Good performance was found in structural stability reconstruction and deformity correction. [ABSTRACT FROM AUTHOR]

A uniform discharge seen with naked eyes is observed with an asymmetrical dielectric barrier discharge (DBD) plasma actuator. By using the optical emission spectrum method, the plasma parameters at different gas pressures are investigated. The molecular vibration temperatures are calculated by the spectra lines of N 2 second positive band system (C 3 Π u → B 3 Π g ), the electron excitation temperatures are calculated by the relative intensity ration of spectral lines of Ar I 763.51 nm (2P 6 → 1S 5 ) and Ar I 772.42 nm (2P 2 → 1S 3 ), and the electron densities are compared by comparing the broadenings of Ar I (2P 2 → 1S 5 ) spectra lines, respectively. It is found that the molecular vibration temperature and the electron excitation temperature decrease when the gas pressure is increased, while the electron density increases. [ABSTRACT FROM AUTHOR]

Abstract: Cerebrotendinous xanthomatosis (CTX) is an autosomal recessive, lipid storage disorder which is extremely rare in the Chinese population. It is characterized by progressive neurologic dysfunction and enlargement of tendon xanthomas, and is often accompanied with neuropsychiatric symptoms. Few reports are available regarding depression and antidepressant medication in CTX patients. Here, we report a Chinese case of CTX associated with fluoxetine-responsive major depression. [Copyright &y& Elsevier]

In this paper, a hybrid learning approach, which combines the extreme learning machine (ELM) with a new switching delayed PSO (SDPSO) algorithm, is proposed for the problem of the short-term load forecasting (STLF). In particular, the input weights and biases of ELM are optimized by a new developed SDPSO algorithm, where the delayed information of locally best particle and globally best particle are exploited to update the velocity of particle. By testing the proposed SDPSO-ELM in a comprehensive manner on a tanh function, this approach obtain better generalization performance and can also avoid adding unnecessary hidden nodes and overtraining problems. Moreover, it has shown outstanding performance than other state-of-the-art ELMs. Finally, the proposed SDPSO-ELM algorithm is successfully applied to the STLF of power system. Experiment results demonstrate that the proposed learning algorithm can get better forecasting results in comparison with the radial basis function neural network (RBFNN) algorithm. [ABSTRACT FROM AUTHOR]

This article puts forward a two-stage integrated method to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). At the first stage, a convolutional neural network (CNN) is employed to preliminarily estimate the cycle life of each testing LIB, where the network structure of the CNN is carefully designed to extract the discharge capacity features. By analyzing the cycle lives, an LIB which has the most similar degradation mode to each testing LIB is chosen from the training dataset. The capacities of the selected LIB are identified based on a double exponential model (DEM). At the second stage, the identified DEM is utilized as the initial mean function of the Gaussian process regression (GPR) algorithm. The GPR algorithm is then applied to early RUL prediction of each testing LIB in a personalized manner. To verify the efficacy of the proposed method, four LIBs with long-term cycle lives are selected as the testing dataset. Experimental results show the superior performance of the proposed method over the standard CNN-based RUL prediction method and the standard GPR-based RUL prediction method. [ABSTRACT FROM AUTHOR]

Automatic generation of multi-scale representations from the same spatial data source has been the research focus in map generalization for a long time. Based on the Fourier technique, this paper proposes a continuous, multi-scale representation model for progressive transformation of cartographic curves on the Internet. In our method, all the curves, whether closed or open, are depicted as periodical functions which are further expressed as Fourier series. The convergence degrees of the Fourier series are explored for different kinds of curves, and truncating frequencies are derived based on the similarity between the original and reconstructed curves. Using information theory and the Radical Law in cartography, the relationship between map scales and Fourier frequencies is established. Based on the proposed multi-scale model, we also introduce the principles and implementation of a progressive transmission method. Our method is evaluated using the contours from a topographic map. The results show that our model is a valid approach to multi-scale representation of cartographic curves. [ABSTRACT FROM AUTHOR]

Objective: Previous studies on osteoporotic vertebral fractures are usually based on the neutral posture of spine; however, the fractures are usually associated with the flexion posture of spine. Therefore, we aimed to ascertain the relationship between vertebral compression fractures and thoracolumbar hyperflexion Cobb angles (TLHCobb) and determine the clinical cut‐off of the TLHCobb angle. Methods: In this retrospective case–control study, TLHCobbs were collected from 154 postmenopausal women (67.45 ± 6.68 years) with vertebral compression fractures (study group) and 310 postmenopausal women (66.57 ± 8.22 years) without vertebral compression fractures (control group) from June 2017 to July 2019. Demographic data, clinical data, and quantitative computed tomography (QCT) findings were compared between the groups. Chi‐squared tests, unpaired t‐tests, and Mann Whitney U were used to assess the group characteristics and proportions. Logistic regression was used to examine the association between vertebral compression fractures and TLHCobb. The cut‐off of the TLHCobb was determined by ROC curve and Youden's index. Results: Fracture prevalence was higher in the higher TLHCobb study group than that in the control group [OR = 2.81 (2.15–3.67)] after adjusting for age, BMI, and QCT findings. TLHCobbs at and >20.05° were associated with an increased fracture prevalence and ORs of 2.79 (1.82–4.27) and 4.83 (3.24–7.20), respectively. TLHCobb, disk height (semiquantitative grading score) and QCT values differed between the study and control groups (p < 0.001 for all three). There were no significant differences in body mass index (BMI), or coronal TLCobb between the two groups. Conclusion: There was an association between the prevalence of vertebral compression fractures and TLHCobbs in postmenopausal women, and a TLHCobb > 20.05° can be an indicator of vertebral fracture. [ABSTRACT FROM AUTHOR]

Background: The surgical efficacy and prognostic outcomes of patients with unspecific malignant bone tumors (UMBTs) remain unclear. The study is to address: 1) What are the clinicopathological features and prognostic determinants for patients with UMBTs? 2) Can a nomogram be developed for clinicians to predict the short and long-term outcomes for individuals with UMBTs? 3) Does surgery improve outcomes for UMBT patients who received radiotherapy or chemotherapy after balancing the confounding bias?Methods: 400 UMBT patients were filtrated from the Surveillance, Epidemiology, and End Results database to assess the clinicopathological features, treatments, and factors affecting prognosis. The optimal cutoff values of continuous variables were identified by the x-tile software. Kaplan-Meier method and multivariate Cox proportional hazard modeling were performed to evaluate the independent prognostic factors. Nomogram was further developed by using R software with rms package. The surgical efficacy was further assessed for patients receiving radiotherapy or chemotherapy after performing propensity score matching.Results: The enrolled cohort included 195 (48.8%) female and 205 (51.2%) male patients. The 2- and 5-year cancer-specific survival (CSS) and overall survival (OS) rate were 58.2 ± 3.0%, 46.8 ± 3.2%, and 46.5 ± 2.6%, 34.4 ± 2.5%, respectively. Nomogram was finally developed for CSS and OS according to the identified independent factors: age, tumor extent, primary tumor surgery, tumor size, and pathology grade. For UMBT patients who received radiotherapy or chemotherapy, surgical intervention was associated with better CSS (pr = 0.003, pc = 0.002) and OS (pr = 0.035, pc = 0.002), respectively.Conclusions: Nomogram was developed for individual UMBT patient to predict short and long-term CSS and OS rate, and more external patient cohorts are warranted for validation. Surgery improves outcomes for UMBT patients who received either radiotherapy or chemotherapy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integrated design of green and grey infrastructure with real-time control rules. • System failures were considered while optimizing the renovation scheme. • More investment does not necessarily increase system resilience to failures. • Resilience to failures significantly increased while maintaining other objectives. The mutual benefits of real-time control and green and grey infrastructure for urban drainage system (UDS) renovations have drawn great attention. However, risks emerged due to equipment and structural failures, and no studies considered such failures while optimizing the renovation scheme. To address this issue, this study proposed a multi-criteria optimization method for the integrated design of green and grey infrastructure with real-time control rules, where the economic cost and system performance under normal and exceptional conditions were optimized simultaneously. Failure probabilities of equipment and structure were quantified using homogeneous Poisson process models, and failure losses were estimated with the aid of a machine learning-based surrogate model. This approach was tested with a combined UDS in China. Results indicate that: (1) More investment does not necessarily increase system resilience to failures. Equipment and structural failures can significantly lower the effectiveness of grey infrastructure and real-time control. Therefore, solutions with more investments in grey infrastructure, which indicate higher costs, experience greater failure losses. (2) System resilience to failures can be significantly improved while maintaining other objectives when compared with the traditional design scheme. The proposed method allows for a new perspective in addition to the cost-and-resilience two-objectives design of UDS renovations, especially for systems threatened by various failures. [ABSTRACT FROM AUTHOR]