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This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.

The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.

The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.

This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.

This study aims to design a facility network for the weavers to do direct business in the Indian handloom industry by using tourists as potential customers.

Data from 4,001 weavers of the handloom industry and 82 tourist locations of Varanasi city were collected. This data was analysed using the k-mean and elbow methods to determine the locations and the optimal number of collection centres, selling stores and warehouses to provide opportunities for the weavers to do direct business.

The study's findings showed that a greater opportunity could be provided with four optimal collection centres and selling stores, along with two warehouses in the handloom industry of Varanasi city. These results provide valuable guidance for policymakers to plan the Varanasi handloom network of facilities efficiently and effectively to improve the conditions of weavers.

Determining the optimal locations is crucial for designing a facility network. The proposed network will aid the government and policymakers in comprehending and pinpointing potential sites to establish new facilities in the handloom industry in Varanasi, a city with tourism potential.

This study seeks to explore the intricate relationship among supply chain transparency, digitalization and idiosyncratic risk, with a specific focus on newly public firms. The objective is to determine whether supply chain transparency effectively mitigates idiosyncratic risk within this context and to understand the potential impact of digitalization on this dynamic interplay.

The study utilizes data from Initial Public Offerings (IPOs) on China’s Growth Enterprise Board (ChiNext) over the last five years, sourced from the CSMAR database and firms’ annual reports. The research covers the period from 2009 to 2021, observing each firm for five years post-IPO. The final sample comprises 2,645 observations from 529 firms. The analysis employs the Hausman test, considering the panel-data structure of the sample and favoring fixed effects over random effects. Additionally, it applies the high-dimensional fixed effects (HDFE) estimator to address unobserved heterogeneity.

The analysis initially uncovered an inverted U-shaped relationship between supply chain transparency and idiosyncratic risk, indicating a delicate equilibrium where detrimental effects diminish and beneficial effects accelerate with increased transparency. Moreover, this inverted U-shaped relationship was notably more pronounced in newly public firms with a heightened level of firm digitalization. This observation implies that firm digitalization amplifies the impact of transparency on a firm’s idiosyncratic risk.

This study distinguishes itself by providing distinctive insights into supply chain transparency and idiosyncratic risk. Initially, we introduce and substantiate an inverted U-shaped correlation between supply chain transparency and idiosyncratic risk, challenging the conventional linear perspective. Secondly, we pioneer the connection between supply chain transparency and idiosyncratic risk, especially for newly public firms, thereby enhancing comprehension of financial implications. Lastly, we pinpoint crucial digital conditions that influence the relationship between supply chain transparency and idiosyncratic risk management, offering a nuanced perspective on the role of technology in risk management.

In the past 10 years, the scale of running events in China has increased dramatically, and the forms of running events have also become rich and diverse. Running is not only a social phenomenon but also a historical and cultural phenomenon as an organic part of human culture with its own sociological values in China. This chapter offers insight into the development of Chinese running culture and how this has emerged from ancient and modern Chinese running cultures based on Foucault's disciplinary power theory, biopower and the technologies of the self. This chapter argues that running culture in China constructs the subjectivity of the Chinese runners under the joint action of the technologies of power and the technologies of the self. The findings acknowledge how Chinese Runners present and express themselves by showing a ‘sense of presence’. Runners illustrate the implicit or explicit meaning and value of a particular way of life through running. Runners regard running as the technology of the self for self-expression and self-creation so that individuals can control their bodies and soul, thoughts, behaviours and ways of existence. Emerging technologies of power provide possibilities for the production of running culture in China, and the current policy under the technologies of power meets the needs of runners. In Chinese running culture, power was not oppressive but productive.

Owing to the finite nature of the boundary of the line (BOL), the conventional method, involving the strong matching of single-variety parts with storage locations at the periphery of the line, proves insufficient for mixed-model assembly lines (MMAL). Consequently, this paper aims to introduce a material distribution scheduling problem considering the shared storage area (MDSPSSA). To address the inherent trade-off requirement of achieving both just-in-time efficiency and energy savings, a mathematical model is developed with the bi-objectives of minimizing line-side inventory and energy consumption.

A nondominated and multipopulation multiobjective grasshopper optimization algorithm (NM-MOGOA) is proposed to address the medium-to-large-scale problem associated with MDSPSSA. This algorithm combines elements from the grasshopper optimization algorithm and the nondominated sorting genetic algorithm-II. The multipopulation and coevolutionary strategy, chaotic mapping and two further optimization operators are used to enhance the overall solution quality.

Finally, the algorithm performance is evaluated by comparing NM-MOGOA with multi-objective grey wolf optimizer, multiobjective equilibrium optimizer and multi-objective atomic orbital search. The experimental findings substantiate the efficacy of NM-MOGOA, demonstrating its promise as a robust solution when confronted with the challenges posed by the MDSPSSA in MMALs.

The material distribution system devised in this paper takes into account the establishment of shared material storage areas between adjacent workstations. It permits the undifferentiated storage of various part types in fixed BOL areas. Concurrently, the innovative NM-MOGOA algorithm serves as the core of the system, supporting the formulation of scheduling plans.

The spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).

First, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.

The quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.

This study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.

This study aims to address the issues of limited pipe diameter adaptability and low inspection efficiency of current pipeline inspection robots, a new type of pipeline inspection robot capable of adapting to various pipe diameters was designed.

The diameter-changing mechanism uses a multilink elastic telescopic structure consisting of telescopic rods, connecting rods and wheel frames, driven by a single motor with a helical drive scheme. A geometric model of the position relationships of the hinge points was established based on the two extreme positions of the diameter-changing mechanism.

A pipeline inspection robot was designed using a simple linkage agency, which significantly reduced the weight of the robot and enhanced its adaptive pipe diameter ability. The analysis determined that the robot could accommodate pipe diameters ranging from 332 mm to 438 mm. A static equilibrium equation was established for the robot in the hovering state, and the minimum pressing force of the wheels against the pipe wall was determined to be 36.68 N. After experimental testing, the robots could successfully pass a height of 15 mm, demonstrating the good obstacle capacity of the robot.

This paper explores and proposes a new type of multilink elastic telescopic variable diameter pipeline inspection robot, which has the characteristics of strong adaptability and flexible operation, which makes it more competitive in the field of pipeline inspection robots and has great potential market value.

The robot is characterized by the innovative design of a multilink elastic telescopic structure and the use of a single motor to drive the wheel for spiral motion. On the basis of reducing the weight of the robot, it has good pipeline adaptability, climbing ability and obstacle-crossing ability.

The purpose of this study is to investigate the interplay between fiscal dominance and monetary policy in South Africa from 1960 to 2023.

The study employs a structural vector autoregression (SVAR) medel to analyze the relationship between fiscal dominance and monetary policy. Short-term and long-term shocks of government borrowing and deficits are examined to understand their impact on inflation dynamics.

Fiscal dominance has a significant effect both in the short and long run. There is evidence that government debt and deficits increase inflation, overriding the effects of monetary policy aimed at maintaining price stability. On the other hand, the study reveals that money supply shocks have a greater effect in reducing fiscal dominance compared to interest rate shocks. The variance movement on inflation is significantly explained by government debt and deficits. This emphasizes the persistence of inflationary pressures associated with fiscal dominance, highlighting the importance of effective policy interventions to mitigate inflationary risks.

This study contributes to the existing literature by providing insights into the dynamics of fiscal dominance in South Africa. Moreover, this study extends the theoretical framework of the fiscal theory of the price level (FTPL) and the government budget constraint. This study contributes valuable insights into the dynamics of fiscal dominance in South Africa and offers guidance for policymakers in formulating strategies to safeguard economic stability.

The purpose of this study is to examine the differential impact of qualitative and quantitative informational signals within the merger and acquisition (M&A) press releases on deal completion and duration. A significant percentage of deals by emerging market acquirers get abandoned before completion, and those that are completed have a longer duration. The limited information about the operations of acquirers from emerging markets creates suspicion among the stakeholders involved in deal resolution, hindering the completion of deals. Thus, using the signal-feedback paradigm, authors investigate how informational signals in the M&A press release impact the deal resolution.

The study employs content analysis on M&A press releases announced by firms from five emerging economies: Brazil, Russia, India, China and South Africa. The technique is applied based on the exploration-exploitation framework developed by March (1991) to categorize the announced deal motives (qualitative information). Next, the authors identify the percentage of relevant quantitative information disclosed in the press release, following which results are obtained using logistic and ordinary least square regressions.

The study reports that deals with declared exploratory motives take longer to complete. Additionally, deals disclosing higher percentage of quantitative disclosure exhibit lower completion rate and increased deal duration.

This is the first study to provide evidence that familiarity bias impacts deal duration as relative to exploitation deals that are familiar to the stakeholders; exploratory deals take longer to conclude. Further, our analysis indicates that a greater percentage of quantitative disclosure may not always reduce information risk but rather be interpreted negatively in the form of the acquirer’s overconfidence in the deal’s potential.

This paper aims to unravel the relationship between market-driven impacts of climate change and firms’ deployment of renewable energy (RE) innovation. The purpose is to understand how market-related forces, influenced by uncertainty, shape firms’ behaviour in response to climate change challenges.

Drawing on the behavioural theory of the firm (BTOF), the paper develops a conceptual model to decode the relationship between each category of market-driven impacts and the resulting RE innovation within firms. The model takes into account the role of uncertainty and differentiates between multinational enterprises (MNEs) and domestic firms.

The analysis reveals five key sources of market-driven impacts: investor sentiment, media coverage, competitors’ adoption of ISO 14001, customer satisfaction and shareholder activism. These forces influence the adoption of RE innovation differently across firms, depending on the level of uncertainty and the discrepancy between environmental performance and aspiration level.

This paper contributes to the literature in four ways. Firstly, it emphasises the importance of uncertainty associated with market-driven impacts, which stimulates different responses from firms. Secondly, it fills a research gap by focusing on the proactivity of firms in adopting RE innovation, rather than just operational strategies to curb emissions. Thirdly, the paper extends the BTOF by incorporating the concept of uncertainty in explaining firm behaviour. Finally, it provides insights into the green strategies of MNEs in the face of climate change, offering a comprehensive model that differentiates MNEs from domestic firms.