Long-term Operation Research Articles

The Role of Lockups in Stock Mergers

We document the frequent use of stock lockup agreements in mergers and acquisitions (M&As) paid in stock and examine the corporate determinants and consequences of the use and duration of lockups. Lockup agreements prohibit target shareholders from selling shares issued by the acquirer as a means of payment for a prespecified period. We find support for the hypothesis that target shareholders agree to lockups to precommit to hold on to the acquirer’s stock if they believe the merger’s long-term fundamentals are strong. Consistent with our hypothesis, lockups come with larger acquirer announcement returns, particularly when acquirer valuations are high; ex ante, lockup adoption likelihood increases with acquirers’ valuation. Lockups also come with higher deal completion likelihood, shorter merger negotiations, and higher long-term operating performance. We conclude the market interprets lockups as a signal of strong fundamentals, particularly when acquirers’ valuations are high. This paper was accepted by Bo Becker, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00993 .

Development of a Methodology for Obtaining Solid Models of Products That Are Objects of Reverse Engineering Using the Example of the Capstone Micro-GTU C 65

Currently, about a thousand micro gas turbine units of small and medium capacity are in operation in the Russian Federation, which are used as an autonomous power source at critical infrastructure facilities. During long-term operation, the component parts of the micro GTU may fail and require replacement or repair. The lack of spare parts and design documentation for their production makes it impossible to operate. As a way to solve the problem, the reverse engineering process can be used to produce components. One of the stages of reverse engineering is to determine the geometric parameters of the object. The fastest and most accurate way to obtain geometric characteristics in the reverse engineering process is 3D scanning. Three-dimensional scanning technology is used to obtain a solid 3D model of the prototype surface, based on which design documentation is subsequently developed. This article presents the results of a study of the influence of the parameters of the distance between polygonal grid points and the scanner exposure on the detailing of the outer surface and the geometric parameters of the resulting polygonal model. As a result of this study, the dependence of the final file size and the time spent on scanning and processing on the distance between the points of the polygonal grid and the model was established. Based on the dependence of the parameters, recommendations were obtained for choosing the distance between the points of the polygonal grid of laser 3D scanning. Also, after performing the stages of reverse engineering, the methodology for creating solid models and design documentation of parts of power equipment units using 3D scanning technology was improved.

Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems

Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value.

Non-monotonically changes in double Schottky barrier of MnCO3–doped ZnO varistors during DC aging

Abstract ZnO varistors play a crucial role as core component in metal oxide arresters due to their excellent nonlinear current density-electrical field (J–E) characteristics. After long-term operation under DC stress (DC aging), the J–E curves of ZnO varistors invented in the last decade cross with the original curve. Unexpectedly, the ‘crossover’ phenomenon cannot be explained by the vastly-accepted ion migration mechanism by which the barrier height monotonically declines after DC aging. In the present study, aging and recovery experiments were carried out on MnCO3-doped ZnO varistors. The symmetry of J–E characteristics in the same direction as and the opposite direction to the DC stress was compared, parameters of double Schottky barrier were analyzed, and evolution of point defects was deduced using dielectric spectra. It was found that, in addition to ion migrations, there are electrons ionized by zinc interstitials filling the interface states in the early stage of aging. Because these processes are dominant in different stages, the barrier height changes non-monotonically. MnCO3 can suppress the formation of zinc interstitials, so it simultaneously inhibits ion migrations and filling of the interface states by electrons. When 0.38 mol% MnCO3 is doped, J–E characteristics and the Schottky barrier of ZnO varistors show the smallest deviations during aging and recovery. The findings are helpful in updating criteria for the long-term stability of ZnO varistors and suggest that modifications to their formulae targeting the suppression of the filling the interface states by electrons are also expected to bolster the stability.

Quasi-static transverse mode degradation in an erbium–ytterbium co-doped fiber amplifier

Long-term stable operation of high-power fiber amplifiers is required to realize high-speed communication in non-terrestrial networks. Recently, quasi-static transverse mode degradation (QS-TMD) was found in ytterbium-doped fiber amplifiers on a few-hour time scale at 1064 nm. Here we demonstrate experimentally that QS-TMD also occurs in erbium–ytterbium co-doped fibers (EYDFs) that amplify light at 1550 nm. QS-TMD was observed by measuring the power of fundamental and higher-order modes separately. QS-TMD progressed as short-term transverse mode instability increased. A persistent long-period grating also formed in the EYDF. This work will aid design of stable high-power fiber amplifiers.

2D/3D heterojunction carrier dynamics and interface evolution for efficient inverted perovskite solar cells

The 2D/3D heterojunction perovskites have garnered increasing attention due to their exceptional moisture and thermal stability. However, few works have paid attention to the influence of the subsequent change process of 2D/3D heterojunction PSC on the stability of PSCs. Moreover, the evolution of the interface and carrier dynamic behavior of the 2D/3D perovskite films with long-term operation has not been systematically developed before. In this work, the effects of 2D/3D heterojunction evolution on the interface of perovskite films and different carrier dynamics during 2D/3D evolution are systematically analyzed for the first time. The decomposition of 2D/3D heterojunction in the perovskite film will have a certain impact on the surface and carrier dynamics behavior of perovskite. During the evolution of 2D/3D heterojunction, PbI2 crystals will appear, which will improve the interfacial energy level matching between the electron transport layer and perovskite film. With a long evolution time, some holes will appear on the surface of perovskite film. The open circuit voltage (VOC) of PSCs increased from 1.14 to 1.18 V and the PCE increased to 23.21% after 300 h storage in the nitrogen atmosphere, and maintained 89% initial performance for with 3000 h stability test in N2 box. This discovery has a significant role in promoting the development of inverted heterojunction PSCs and constructing the revolution mechanism of charge carrier dynamic.

Continuous electrophoretic separation of submicron-microplastics from freshwater

Anthropogenic and natural weathering processes have produced submicron microplastics (MPs), an emerging contaminant. Due to small size, the treatment of submicron plastics particles by membrane processes requires small cutoff membranes, which necessitates great pressure gradient and suffers from clogging. The present study aims to develop an electrophoretic separation system for the separation of negatively charged submicron plastics particles from water. An electric field was supplied to produce an electrostatic force to counter the drag force in the permeation stream, thereby, preventing submicron plastics particles from entering the permeate. The critical electric field (Ec) for complete particles removal was estimated based on the dilute-to-influent flow rate ratio (qd), zeta potential, and size of submicron plastics particles. The result showed that at steady-state, particle removal could reach 99% at E > Ec at qd = 0.5. The distribution of plastics particles during electrophoretic separation was analyzed considering electrophoresis and particle deposition. The particle removal efficiency can be modelled by hydraulic condition and critical electric field. Finally, the engineering aspects such as long-term operation, electrode degradation and influence of coexisted constituents were evaluated. The operation cost of electrophoretic separation was calculated to be USD 0.48/m3, which is cost-effective at small scales compared to conventional membrane processes.

A deep learning method based on CNN-BiGRU and attention mechanism for proton exchange membrane fuel cell performance degradation prediction

The performance of proton exchange membrane fuel cells (PEMFCs) will gradually deteriorate during long-term operation. Accurate performance degradation prediction is crucial for extending the lifespan and improve the durability of fuel cells. This paper proposes a deep learning method (CNN-BiGRU-AM) that incorporates convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU) and attention mechanism (AM) for fuel cell degradation prediction. In the proposed method, CNN extracts complex features from the input data through convolutional operations, BiGRU models temporal information by considering both forward and reverse directions of the input sequence, and attention mechanism highlights key information in the input data through weight allocation. The proposed method is validated using long-term experimental data from fuel cells under steady-state and quasi-dynamic conditions. The results indicate that the absolute error of the proposed method is less than 1.2mV for 97.94% of the data samples under steady-state conditions and less than 1.2mV for 94.82% of the data samples under quasi-dynamic conditions. The prediction accuracy and stability of the proposed method are significantly improved compared to other deep learning prediction methods.

3D hydrogel-based salt resistant self-floating solar-driven interfacial evaporator with efficient water-lifting capacity for desalination

Solar desalination has been extensively researched as a promising way to address freshwater shortages. However, developing a salt resistant solar evaporator that can be easily extended and manufactured is still a challenge in practical applications. In this work, a 3D hydrogel-based salt resistant self-floating solar-driven interfacial evaporator with efficient water-lifting capacity was proposed using the prepared hydrogel with carbon black (CB) nanoparticles and extruded polystyrene (XPS) board as raw materials. The experimental results implied that the developed evaporator had the highest evaporation rate and evaporation efficiency at 0.05 g CB loading, which were 1.70 kg m-2h−1 and 92.8 % at a light intensity of 1 kW/m−2(−|-). Moreover, the evaporator also displayed prominent salt resistance in the evaporation of brine with high salinity and outstanding stability after 10 cycles. Significantly, the outdoor experiment demonstrated that 1 m2 evaporator can generate 5923.5 g freshwater per day, which was enough to meet the daily requirement of two adults for drinking water. In addition, the physical fields of the hydrogel with CB nanoparticles of the evaporator at evaporation equilibrium were simulated to analyze its long-term and high-performance operation capability. All in all, the 3D hydrogel-based salt resistant self-floating solar-driven interfacial evaporator with efficient water-lifting capacity presented an enormous application prospect in the field of solar desalination.

Dual-loop Segmented Piston Trajectory Control of Free Piston Linear Generator based on sliding mode control

Free piston linear generator (FPLG) can be applied as range extender for electrical vehicles to reduce greenhouse gas emissions attributed to its fuel flexibility and ultimate freedom of piston motion. However, FPLG still faces several challenges in control such as misfire and collision due to the eliminate of mechanical crankshaft. To fully utilize the potential of the ultimate freedom of piston motion, this paper presents a novel piston trajectory control strategy which employs the piston trajectory on the entire operation process of FPLG as the control objective. Considering disturbances and high coupling of FPLG, a sliding mode controller is employed to track reference piston trajectory. Additionally, a segmented control strategy is implemented to decouple the control of combustion pressure force and electromagnetic force. In order to ascertain the effectiveness of the proposed controller, a prototype of single-piston FPLG and a corresponding numerical model are constructed. The experimental and simulation results demonstrate the proposed controller shows fast response, precise tracking performance, and good robustness at various reference piston trajectories. Furthermore, the experimental results illustrate that the system can achieve cold start through the seamless transition between operation states and long-term stable operation by the controller.

The contradictory impact of ESG performance on corporate competitiveness: Empirical evidence from China's Capital Market

In recent years, with the increasing awareness of sustainable development, corporate ESG (Environmental, Social, and Governance) performance has garnered widespread attention from investors and has had a sustained impact on long-term business operations. The mechanisms by which ESG performance affects corporations primarily include the "governance effect" and the "window-dressing effect." Aimed at high-quality development goals, what kind of impact does ESG performance have on corporate competitiveness? This paper attempts to explore the economic relationship between corporate ESG performance and corporate competitiveness in China's capital market through theoretical analysis and empirical testing. The research results show that ESG performance significantly promotes corporate competitiveness, particularly through the "social" dimension (S) and "governance" dimension (G). ESG performance significantly enhances corporate competitiveness by increasing corporate philanthropic donations and improving total factor productivity, demonstrating the "governance effect" of ESG. However, in the "environmental" dimension (E), ESG performance instead leads to an increase in corporate carbon emissions, which may indicate that corporate efforts in environmental areas are more for superficial appearance, exhibiting the "window-dressing effect" of ESG. This study reveals the crucial role of ESG practices in enhancing corporate competitiveness, providing theoretical support and policy implications for promoting corporate competitiveness and achieving high-quality development goals.

An image analysis-based method to determine the vanadium electrolyte contents during the capacity recovery process with a facile chemical oxidation strategy

Repairing and regenerating the unbalanced electrolytes is critical for the long-term operation of vanadium redox flow batteries (VRFBs). In this work, we propose a simple strategy to repair the unbalanced electrolytes for capacity recovery through chemical oxidation with the V(V) electrolyte and develop a method based on image analysis to obtain the electrolytes’ V(IV) ion contents. When the V(V) electrolyte with the same concentration as that of the unbalanced electrolyte is added to the unbalanced electrolyte, V(V) ions gradually oxidize V(III) ions to V(IV) ions, and the color of the electrolyte changes significantly. By extracting the RGB information of the electrolyte image, it is found that the B channel value presents the highest sensitivity to the V(IV) ion content. Thus, the relationship between them is fitted, and the R2 of the fitted curve reaches 0.9978. Furthermore, the image analysis-based method is applied to predict the V(V) electrolyte volumes needed to repair the unbalanced electrolytes. Results show that the deviation of the B channel values between the recovered and standard V(IV) electrolytes is within 5%, demonstrating the effectiveness of the proposed repair strategy and image analysis-based method, which show great promise for practical VRFB applications.

Investigating the thermal performance of a pipe-encapsulated movable PCM wall and its impact on reducing indoor heat gain during summer

The utilization of phase change materials (PCM) in building envelopes is considered to be an effective technology for reducing energy consumption during building operations. In this study, a novel approach is presented for integrating PCM into walls by encapsulating it using pipes, allowing for its mobility through the use of compressed air. Four experimentally validated thermal models, pipe-encapsulated movable PCM (PEM-PCM) wall (Model 1), wall with PCM fixed to the exterior (Model 2), wall with PCM fixed to the interior (Model 3), and wall without PCM (Model 4), were proposed to compare their energy efficiency in summer. Different upper threshold limits (Ttr,up) and lower threshold limits (Ttr,low) were employed to explore the impact of various threshold combinations control strategies on the thermal performance of the wall. The results show that the most effective operating scheme is Ttr,up of 27 °C and Ttr,low of 25 °C. In terms of long-term operation, cumulative summer indoor heat gain of Model 1 is 10.65 %, 10.85 % and 11.4% lower than that of Model 2,3 and 4. Notably, September demonstrates the greatest potential for energy savings, with Model 1 reducing the monthly cumulative indoor heat gain by 54.12% compared to Model 4.

Experimental study on corrosion compatibility of laser-welded ODS steel with liquid lead lithium

The liquid lead lithium blanket is considered to be one of the most promising blanket concepts. Oxide Dispersion Strengthened (ODS) steel probably emerge as an important candidate for blanket structure material for its substantial enhancement of neutron radiation resistance and mechanical strength at high temperature. However, during the long-term operation of fusion reactor, it’s inevitable for structural material and its welded joints to experience adverse corrosion from liquid lead lithium. In this paper, the self-developed rotating corrosion apparatus was used to conduct a corrosion test of laser-welded ODS steel exposed to liquid lead lithium for thousands of hours. The influence of corrosion time on the corrosion of ODS steel laser welding was analyzed. The results show that the corrosion rates of ODS steel and its welded joints are 76 and 88.6 μm⋅year -1, respectively. After 5000 h of exposure, various pits and gullies are observed on the surface of weld zone, while penetrating cracks are clearly visible in the cross-section. Corrosion occurs preferentially from austenite grain boundaries, and the corrosion mechanism is mainly the dissolution of Fe and Cr alloying elements. Meanwhile, a decrease in the hardness of the welded joints with the increase of corrosion time is observed.

A Bayesian-interference approach to quantify degradation parameters in a water-cooled variable speed screw compressor chiller

Abstract Variable-speed screw chillers can provide effective capacity modulations in commercial buildings to reduce energy consumption, match the load requirements, and contribute to lower equivalent carbon emissions. However, variable-speed operation can also yield to degradation phenomena inside the compressor. Assessing the real-time health status of chillers and detecting anomalies are important aspects to ensure long-term operation and improve reliability. To this end, an automated accelerated life test (ALT) procedure was developed and applied to a 513 kW (145.9 RT) water-cooled variable-speed screw chiller in a laboratory test facility to experimental assess performance degradation over time. After every 1,000 operating hours of ALT, steady-state performance tests at 30%, 50%, 75% and 100% load were conducted according to AHRI Standard 550/590. Additional operating conditions were included while conducting the tests to provide a more complete assessment of the degradation trends compared to the initially measured baseline. The experimental data set was then used to quantify the performance degradation based on the impact on the screw compressor operation. A Bayesian-inference identification approach has been developed to identify changes of sensitive parameters that statistically provide evidence for the likelihood of a performance degradation of the compressor. The information was used to implement the parameters in a dynamic model which was used to perform studies to predict the degradations of a chiller. The outcomes of this study help to improve the maintenance schedule of a compressor and even the whole chiller so that the system can be operated more economically and unplanned downtime can be minimized.

Vermicomposting organic waste with Eisenia fetida using a continuous flow-through reactor: Investigating five distinct waste mixtures

In this study, the vermicomposting of source-separated organic solid waste was investigated using Eisenia fetida in a continuous flow-through reactor. This novel approach is capable of simulating the complex interactions and dynamics of a full-scale vermicomposting system, offering valuable insights into the optimization of organic waste management. The primary objective of the research was to evaluate the quality of vermicompost produced in the reactor and determine its compliance with regulatory standards. For this purpose, food waste, yard waste, and pruning waste were mixed in different proportions to create five waste mixtures, which were then subjected to an 8-week pre-composting process. Subsequently, vermicompost was produced from the pre-composted waste mixtures using a continuous flow-through reactor that allows for long-term operation without disturbing the worms. While the total Kjeldahl nitrogen (TKN) content of the vermicomposts ranged from 2.04% to 2.48%, the total organic carbon (TOC) content of the vermicomposts was between 25.68% and 32.64%. Therefore, the carbon/nitrogen (C/N) ratio was calculated to be between 12.60 and 13.66. The quality of the final compost product showed that it met the limitations suggested in the European Fertilizer Regulation for the main physico-chemical parameters and heavy metals. It is concluded that vermicomposting in a continuous flow-through system reactor would be applicable to different organic waste streams.

A mixotrophic denitrification composite for treatment of nitrate-contaminated water: Performance, microbial community and functional genes

A mixotrophic denitrification composite was prepared with sulfur, pyrite, sawdust and calcium carbonate (SPSC) for the treatment of nitrate-contaminated wastewater. As a homogeneous integrated composite, fast mass transfer, convenient operation and transportation and easy attachment for microorganisms were obtained in the SPSC-based denitrification system. The long-term operation result showed that SPSC bioreactor exhibited strong resistance to the fluctuations of influent nitrate load, and 98.3% of the nitrate in the influent was reduced even under low hydraulic retention time (HRT). The SPSC system achieved an average nitrate removal rate of 4.59 mg-N/L/h, which was nearly triple the rate of sulfur autotrophic denitrification (SAD) system that utilized sulfur as its sole electron donor. The sulfate yield of the SPSC system (4.52mg SO42-/ mg NO3--N) was significantly lower than those of the SAD system (9.36mg SO42-/ mg NO3--N). Heterotrophic and autotrophic denitrifiers coexisted in the SPSC system, which was beneficial for the denitrification process. Metagenomic analysis indicated that the SPSC composite enriched the functional genes, which encoding key enzymes (nitrate reduction, sulfur oxidation and energy generation) involved in nitrogen, sulfur and carbon metabolism. Collectively, the SPSC composite synthesized in this study has provided a high efficiency, low-cost and promising technology for the treatment of nitrate-contaminated water.

Hybrid model of multimodal based on data enhancement and lumped reaction kinetics: Applying to industrial ebullated-bed residue hydrogenation unit

Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation unit (EBRH), However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes Kernel Principal Component Analysis-Fuzzy C-Means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.

Optimizing hydrogen storage in a heterogeneous aquifer considering hysteresis and dissolution

Hydrogen (H2) is a rising solution for carbon-neutral fuel transition. Saline aquifer, ubiquitous underground, is a competitive candidate for large-scale underground hydrogen storage (UHS). This study proposed to use a heterogeneous aquifer formation for probing the functioning of UHS and optimizing its operation. By integrating geological information with lithofacies from the Ordos Basin in China, a heterogeneous triplet aquifer model was built, upon which compositional simulations of UHS operations that consider two-phase flow, relative permeability hysteresis, hydrogen and water phase behavior, rock compressibility, and dissolution mechanisms were conducted. Orthogonal array L25 (56) with mixed levels was designed to optimize the H2 recovery. Results substantiate the feasibility of storing millions of cubic meters of H2 with over 95% recovery. Ignoring dissolution and relative permeability hysteresis will result in an overestimation of the hydrogen recovery factor, with the effect of relative permeability hysteresis being more significant. Provided equal cycle length, a lower injection-withdrawal rate is suitable for long-term operation of hydrogen storage, while a higher rate can achieve a higher recovery factor during the early cycles. Measures of lowering the skin factor and pre-extracting brine could improve storage capacity and reduce water production during injection and withdrawal. The optimized case is expected to achieve an average H2 recovery of 98.8% for 30 operation cycles with moderately low saline water production. These findings provide valuable insights for developing large-scale and cost-effective UHS in saline aquifers.

Dynamic Analysis of the Effectiveness of Emergency Collaboration Networks for Public Health Emergencies from a Systems Thinking Perspective

In recent years, public health emergencies have become frequent worldwide. In response to these complex and evolving emergencies, the organizations involved are increasingly collaborating with each other. From a systems thinking perspective, greater attention should be given to the long-term development and continuous operation of emergency collaboration systems. By time slicing the development of the COVID-19 epidemic in Wuhan, the different phases of emergency collaboration networks can be respectively established. A new method for identifying key organization nodes and different network attack strategies is proposed, assessing network effectiveness from two dimensions: efficiency and resilience. The results indicate that, compared to random attack strategies, the efficiency and resilience of these networks are significantly affected by deliberate attack strategies, underscoring the network’s sensitivity to high-importance nodes. Based on the variations in network efficiency and resilience, the effectiveness of different forms of networks are classified into four types. The pre-emergency network is categorized as resilience-focused, the mid-emergency network as efficiency-oriented, the post-emergency network as efficient-resilient, and the overall emergency network as inefficient-fragile. Analyzing forms of network effectiveness at different phases offers a deeper understanding of the operational characteristics, dynamic changes, and existing issues within emergency collaboration networks. This study provides a vital theoretical basis and practical guidance for emergency management departments and decision-makers on how to effectively improve collaboration mechanisms between different organizations.