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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This paper aims to illustrate the main changes during martial law for conducting financial intelligence (monitoring), in particular, in the system of combating legalisation (laundering) of illegally gained income.

The research methodology includes general scientific methods of cognition. The authors used it to research the theoretical, organisational and legal aspects of financial intelligence (monitoring) in normal situations and during martial law. The authors also use the doctrinal legal research method to analyse and describe the legislation connected to the financial intelligence (monitoring) during martial laws.

This paper is an original work written by authors that discusses financial intelligence (monitoring) during martial law. The following changes in the conduction of financial intelligence (monitoring) were highlighted: suspension of scheduled and unscheduled on-site inspections; postponement of the entry into force of the provisions of some regulations on the submission of reports in electronic form; and the norms amending to remove administrative barriers in matters of financial intelligence (monitoring) in martial law. The authors also noted that certain rules for the functioning of the financial intelligence (monitoring) system were being clarified. Thus, the financial intelligence (monitoring) regime is being weakened to some extent.

In connection with the introduction of martial law in the territory of Ukraine on 24 February 2022, the most important changes that have taken place in the banking and financial sectors of the economy should be mentioned. In particular, this applies to the issues of the regulation on the statutory and regulatory levels. Certain vital points regarding the mechanism and procedures for conducting financial intelligence (monitoring) also needed to be changed, on a need-to-know basis to adapt to the needs of Ukrainian society during the military aggression of the Russian Federation. This paper is devoted to the study of financial intelligence (monitoring) during martial law. The authors identified the main changes in the procedure for conducting financial intelligence (monitoring) by both the National Bank of Ukraine (one of the main entities) and the legislator (by amending the current regulatory framework).

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

The purpose of this article is to suggest that Fraby‐Perot optic sensor is a practical measurement gage to monitor the strain of great structures such as railway bridges.

A remote strain monitoring system based on F‐P optic fiber and virtual instrument is designed to monitor the strains of a railway bridge.

The application results show that the Fraby‐Perot optical fiber sensors can accurately measure strain and they are suitable for the long‐term and automatic monitoring. In addition, the system has several advantages over conventional structural instruments including fast response, ability of both static and dynamic monitoring, absolute measurement, immunity to interferences such as lightning strikes, electromagnetic noise and radio frequency, low attenuation of light signals in long fiber optic cables.

Health monitoring of structures is getting more and more recognition all over the world because it can minimize the cost of reparation and maintenance and ensure the safety of structures. A strain monitoring system based on F‐P optic fiber sensor was developed according to the health monitoring requirements of Wuhu Yangtze River Railway Bridge, which is the first cable‐stayed bridge with a maximum span of 312 m carrying both railway and highway traffic in China. It has run stably in the monitoring field more than two years and fulfilled the monitoring requirement very well. Now the system has been transplanted successfully to the Zhengzhou Yellow Railway Bridge for strain monitoring. So the work can be referenced by other similar health monitoring projects.

Long‐term, real‐time monitoring of strain using FP fiber optic sensors in railway bridge is an innovation. A remote strain data acquisition and real‐time processing are another character of the system. The work studied can be referenced by other structures monitoring, such as tunnel, concrete bridges, concrete and earth dams.

Corrosion monitoring techniques fall broadly into two categories: those which provide simple numeric data for control purposes, and those which offer a spectrum of information for diagnostic purposes. Corrosion monitoring can be carried out directly at locations susceptible to corrosion, or indirectly under conditions simulating susceptible but inaccessible points. The interpretation of the data can reflect the purpose of monitoring at the particular location. A consistent form of presentation, and comparative tabulation including statistical analysis can greatly facilitate correlation and trend spotting. Broad spectrum techniques may give an early indication of new problems. An effective internal corrosion monitoring programme can make a major contribution towards the control of plant operating costs.

The purpose of this paper is to review the recent advancements in the development of wearable sensors which can continuously monitor critical medical, assess athletic activity, watch babies and serve industrial applications.

The paper presents an in-depth review of a number of developments in wearable sensing and monitoring technologies for medical, athletic and industrial applications. Researchers and companies around the world were contacted to discuss their direction and progress in this field of medical condition and industrial monitoring, as well as discussions with medical personnel on the perceived benefits of such technology.

Dramatic progress is being made in continuous monitoring of many important body functions that indicate critical medical conditions that can be life-threatening, contribute to blindness or access activity. In the industrial arena, wearable devices bring remote monitoring to a new level.

Doctors will be able to replace one-off tests with continuous monitoring that provides a much better continuous real-time “view” into the patient’s conditions. Wearable monitors will help provide much better medical care in the future. Industrial managers and others will be able to monitor and supervise remotely.

An expert insight into advancements in medical condition monitoring that replaces the one-time “finger prick” type testing only performed in the doctor’s office. It is also a look at how wearable monitoring is greatly improved and serving athletics, the industry and parents.

This study aims to analyze the dynamic monitoring of deformation damage of steel structure buildings in long-term use. Although the steel structure building has the advantage of high structural strength, it will be deformed after being affected by factors such as corrosion and impact during long-term use, and which will affect building safety, especially the public building facilities. The dynamic monitoring of its security is an indispensable means.

This paper briefly introduced the principle of building information modeling (BIM)-based steel structure building information monitoring and the dynamic information monitoring system based on this principle. Then the monitoring system was used to analyze an operational steel structure suspension bridge in Xinxiang City, Henan Province, China, and compared it with the monitoring system based on back propagation (BP) neural network.

The results showed that the fitting degree of the dynamic deformation displacement data processed by BIM-based monitoring system was higher than that processed by BP-based monitoring system. Based on the comprehensive comparison of the dynamic data of all monitoring points, the BIM-based monitoring system had higher accuracy of deformation displacement monitoring and reliability of structural safety evaluation.

In summary, the BIM-based steel structure building monitoring system can effectively monitor the dynamic information of steel structure information.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

This study aims to examine the impact of board characteristics on debt choice.

The sample comprises of unique nonfinancial firms listed in the FTSE 350 over the period 2011–2018. This study uses Tobit and OLS regressions to check the impact of board characteristics on debt choice. The results are robust to the battery of robust checks.

This study finds that board size and board independence are positively associated with public debt. However, CEO duality and board meetings frequency are inversely associated with public debt. Overall, the findings are consistent with the “financial intermediation theory” that the firms with weak governance rely on bank financing, and firms with better corporate governance go for public debt.

This study offers significant insights for investors and policymakers.

This study offers new insights regarding the role of board characteristics in firms’ debt choice by showing the significant impact of board characteristics on debt choice. The findings indicate that the board’s efficient internal monitoring may substitute external monitoring by the bank.

This study aims to investigate the potential influence of organisational responses to conflicting institutional demands towards barriers to environmental performance (EP) monitoring of government green procurement (GGP) in Malaysia.

The paper used a qualitative methodology based on a single case study involving policymakers, procurement officials and a monitoring authority. The study data were analysed drawing on the perspectives of organisational responses to conflicting institutional demands.

The three key challenges that hindered EP monitoring of GGP in Malaysia were policy irregularities, knowledge asymmetry and communication gaps. These challenges are likely the consequences of the acquiescence, avoidance, compromise and defiance strategies commonly used in dealing with the institutional complexity faced in Malaysia’s public policy arena.

The government, at various institutional levels, may benefit from the theoretical and empirical findings of the case study. Knowledge of barriers can facilitate the policymakers in designing the monitoring process meticulously. Meanwhile, awareness of the influence of organisational responses to institutional complexity on GGP barriers can help redefine field actors’ interests and values in improving policy monitoring. In addition, reporting of the monitored EP bridges the institutional gaps between the macro-state level and the micro-organisational level of GGP, besides increasing the government’s transparency and accountability regarding green procurement.

Fewer challenges in the EP monitoring system contribute to an improved GGP policy. In turn, an improved policy may enhance public health and reduce environmental degradation.

The study contributes to the GGP monitoring and institutional theory by showing that barriers to EP monitoring culminate from the organisational response to the institutional demands faced in the policy environment. The authors argue that this is one of the few studies that have examined the barriers to EP monitoring of public policy explicated in the context of organisational responses to institutional demands.