Operational Tool Research Articles

Developing an assessment tool for the healthy lifestyles of the occupational population in China: a modified Delphi-analytic hierarchy process study

Lifestyle is an important factor affecting people's health. Evaluating and promoting healthy lifestyles among occupational population can not only improve work ability and efficiency, but also contribute to the prevention a variety of potential diseases. This study developed a preliminary index system based on literature review and group discussion. Two rounds of expert consultation were conducted on 12 experts using Delphi method. The questionnaire recovery rate of the two rounds of Delphi survey was over 85%, the expert authority coefficient was over 0.70, and Kendall W was 0.450 and 0.446, which were significant (P < 0.001). The weighting coefficient of each indicator was calculated using the hierarchical analysis method. Among them, the weighting coefficients of physical health lifestyle, mental health lifestyle, social adaptation lifestyle and occupational health lifestyle were 0.4133, 0.2922, 0.1078 and 0.1867, respectively. The consistency index CI = 0.024 and the consistency ratio CR = 0.027 of the first-level index judgment matrix. The weighting coefficient is acceptable and the indicators do not cause logical confusion. The healthy lifestyle assessment tool of the occupational population constructed in this study consists of 4 primary indicators, 13 secondary indicators and 45 tertiary indicators, which can provide a standardized and operable assessment tool for monitoring and evaluating the healthy lifestyle of the Chinese occupational population. At the same time, the weight analysis of various indicators through the analytic hierarchy process can also provide reference for the key areas of occupational health intervention.

Specifics of applying operational and combat measures and tools in counterintelligence operations under the conditions of the ongoing Russian military aggression

The Security Service of Ukraine (SSU) has faced the challange to sophisticate actual and fundamentally new approaches, methods and means of countering threats to national security under conditions of the ongoing full-scale invasion of Russian troops and occupation the territory of Ukraine. The imposition of martial law in Ukraine has considerably expanded the range of practical tasks performed by the bodies and departments of the Security Service of Ukraine. The conditions of the intelligence activities, specific features of counterintelligence activities during the war have been forming new approaches of counterintelligence operations. These factors set and manage new operational and combat tasks for the Security Service of Ukraine. Moreover, SSU is an authorized body in the sphere of counterintelligence activities, that performs tasks of preventing, detecting and analyzing threats to the national security of Ukraine and make challenges to the vital interests of the state. The accomplishment of thesre tasks is plausible on the basis of efficient and prompt identification and threats prevention to the national security of Ukraine, identifying the sources of threats, origin and consequences in order to successfully select methods and means to counter them. The application peculiarities of the operational and combat methods and means by the counterintelligence units of the Security Service of Ukraine during the execution of operational and combat missions in the conditions of the ongoing Russian military aggression against Ukraine are considered in the study. It has been determined that under martial law the counterintelligence units have developed a set of measures to counteract the Russian special services activities as a result of the special tasks that are under provisions of the Security Service of Ukraine. This was accomplished due to the efficiency and enemy goals identification, thoroughly selected methods and forms to prevent intelligence and subversive activities, using appropriate means to overcome the enemy. Mainly, the Security Service of Ukraine adjusts to modern realities, changes, supplements and improves its methods of activity. The Security Service of Ukraine develops a far-sighted strategy, amends its skills with new tactical techniques, creates and uses cutting-edge operational and combat tools and methods, which are transformed and sophisticated in the conditions of war. All these measures prevent threats to national security posed by the enemy. These methods are successfully applied by the counterintelligence units of the Security Service of Ukraine during the execution of operational and combat tasks, and require further theoretical justification.

The Role of Artificial Intelligence Technology and Innovation in Disrupting Traditional Business Models and Startup Ecosystem

The rapid advancement of Artificial Intelligence (AI) is revolutionizing entrepreneurship by creating unprecedented opportunities and driving transformative change across various sectors. This abstract explores how AI is influencing entrepreneurial ventures, focusing on its role in enhancing data analytics, personalizing customer experiences, and advancing healthcare and automation. AI enables startups to leverage sophisticated tools for predictive analytics, tailored interactions, and efficient operations, which can disrupt traditional business models and accelerate growth. As AI technology continues to progress, it reshapes the competitive landscape, offering new avenues for innovation and strategic advantage. Understanding and harnessing AI’s potential is becoming crucial for entrepreneurs seeking to thrive in an increasingly digital and data-driven world. Keywords: Artificial intelligence technology, innovation, startup ecosystem, entrepreneurship

CAP, comprendre, agir et partager : une boite à outils made in Nouvelle Aquitaine pour accompagner les établissements dans leur transformation écologique

In the face of climate change, Health is mobilizing to find solutions and reduce its environmental impact. The CAP (Understand, Act and Share) sustainable hospital toolbox cited in the roadmap for the Ecological Planning of the French Health System offers 3 original, operational and complementary tools to (1) acculturate professionals in the sector (Plan Health Faire®), (2) build the establishment strategy (2030 Agenda), and (3) take action with healthcare professionals (The Sustainable Units program).

Tree density by development stage in the “El Carrizal” wathershed of Tapalpa, Jalisco

The Tapalpa forest presents an abundance of pine trees in the juvenile stage ofregeneration and sapling, but this amount progressively decreases in thesuccessive stages, when the trees show larger dimensions and take up more spaceto grow. However, it was unknown how much the loss was and how much itreached the maximum size. The objective was to quantify the grove, the individualspace, the separation between trees and their decrease. The stages were classifiedaccording to the normal diameter and the sites were classified as excessive,recommendable, sufficient and poor density. Frequency distribution, loss, spacing,and distance between trees were calculated using the Hart-Becking formula.During the development cycle, the loss was close to 90 % with a rate of 0.2 to 3%, presenting a cumulative of 60 % in the monte bravo and vardazcal stages. Therecommended class began with 1 300 to 2 700 trees ha⁻¹ in the juvenile phase,with their respective spacing of 2.07 to 2.98 m, while the mature stage culminatedwith 150 trees ha⁻¹ separated at 8.77 m. The distance between trees is consideredan auxiliary tool in thinning operations.

Dynamic traffic network representation model for improving the prediction performance of passenger flow for mass rapid transit

Accurate machine learning predictions of passenger flow data for mass rapid transit (MRT) systems can considerably improve operational efficiency by enabling better allocation of train and human resources. However, such predictions are challenging because MRT networks have complex structures with route dependence and transfer stations. Although the static state of an MRT network has been computed in previous studies, a comprehensive understanding of an MRT network requires characterizing its dynamics. Therefore, this paper proposes a dynamic traffic network representation (DTNR) model that captures station features from historical traffic flows and geographical information of MRT stations. Furthermore, a multilevel attention network (MLAN) model is proposed to predict MRT passenger flow as a downstream task following the pretraining of the DTNR model. The experimental results of this study indicate that the developed DTNR and MLAN models can accurately predict MRT passenger flow. These models are widely applicable to different MRT systems and passenger flow situations, making them a valuable tool for transportation planners and operators.

Machine-Learning-Accelerated DFT Conformal Sampling of Catalytic Processes.

Computational modeling of catalytic processes at gas/solid interfaces plays an increasingly important role in chemistry, enabling accelerated materials and process optimization and rational design. However, efficiency, accuracy, thoroughness, and throughput must be enhanced to maximize its practical impact. By combining interpolation of DFT energetics via highly accurate Machine-Learning Potentials with conformal techniques for building the training database, we present here an original approach (that we name Conformal Sampling of Catalytic Processes, CSCP), to accelerate and achieve an accurate and thorough sampling of novel systems by exporting existing information on a worked-out case. We use methanol decomposition (of interest in the field of hydrogen production and storage) as a test catalytic reaction. Starting from worked-out Pt-based systems, we show that after only two iterations of active-learning CSCP is able to provide reaction energy diagrams for a set of 7 diverse systems (Pd, Ni, Au, Ag, Cu, Co, Fe) leading to DFT-accuracy-level predictions. Cases exhibiting a change in adsorption sites and mechanisms are also successfully reproduced as tests of catalytic path modification. The CSCP approach thus offers itself as an operative tool to fully take advantage of accumulated information to achieve high-throughput sampling of catalytic processes.

GP-Marker facilitates the analysis of intact glycopeptide quantitative data at different levels.

Protein glycosylation is a highly heterogeneous post-translational modification that has been demonstrated to exhibit significant variations in various diseases. Due to the differential patterns observed in disease and healthy populations, the glycosylated proteins hold promise as early indicators for multiple diseases. With the continuous development of liquid chromatography-mass spectrometry (LC-MS) technology and spectrum analysis software, the sensitivity for the decipher of the tandem mass spectra of the glycopeptides carrying intact glycans, i.e., intact glycopeptides, enzymatic hydrolyzed from glycoproteins has been significantly improved. From quantified intact glycopeptides, the difference of protein glycosylation at multiple levels, e.g., glycoprotein, glycan, glycosite, and site-specific glycans, could be obtained for different samples. However, the manual analysis of the intact glycopeptide quantitative data at multiple levels is tedious and time consuming. In this study, we have developed a software tool named "GP-Marker" to facilitate large-scale data mining of spectra dataset of intact N-glycopeptide at multiple levels. This software provides a user-friendly and interactive interface, offering operational tools for machine learning to researchers without programming backgrounds. It includes a range of visualization plots displaying differential glycosylation and provides the ability to extract multi-level data analysis from intact glycopeptide data quantified by Glyco-Decipher.

Identification and application of quality elements, indicators, and criteria for assessment of impact on habitats in marine Natura 2000 areas

The EU Habitat Directive adopted in 1992, requires member states of the European Union to protect species and habitats considered to be of ‘Community Interest’ and listed in annexes to the directive. The appropriate environmental assessment of “plans and projects” is an important part of the conservation process. Despite several amendments and guidelines supporting the implementation of the Habitat Directive, science based operational procedures, indicators, and impact criteria for assessing potential negative impacts on marine Natura 2000 areas are still lacking. The lack of a generic and operational methodology complicates the management of plans and projects with potential impact on marine Natura 2000 areas. In this study, generic methods for the assessment of marine aquaculture in the inner Danish waters in relation to Natura 2000 areas was developed and applied for assessment of nine existing marine fin fish farms, in accordance with the latest methodological guidance on the provisions of Article 6(3) and (4) of the Habitat Directive. The applied methodology is based on high resolution 3D hydrodynamic- and ecosystem modelling (MIKE by DHI), that describes the dynamical physical, chemical, and biogeochemical processes and changes of marine ecosystems in time and space. To our knowledge, this is the first study that formulates operational biological quality elements, key indicators, concrete and generic impact criteria, and assessment procedures for operational assessment across several distinct marine habitat types. The method represents a generic, operational, transparent, and science-based assessment tool, that simplifies management, and is widely applicable for quantification of environmental impacts from various marine activities and eutrophication related pressures across geographical zones and different marine habitat types in marine Natura 2000 areas.

Research on the Impact of Equity Pledge on Company Value

This article aims to provide a literature review on the impact of equity pledges on corporate value, and to explore in depth the application of equity pledges as a financial tool in corporate governance and capital operation, as well as its multidimensional impact on corporate value. By reviewing and analyzing relevant literature both domestically and internationally, this article first defines the basic concept of equity pledges and then elaborates on the impact mechanism of equity pledges on company value from both positive and negative perspectives. In terms of positive impact, this article explores how equity pledges can promote corporate financing, optimize capital structure, and enhance the control of major shareholders over the company. In terms of negative impacts, the possible control risk, market risk, and potential damage to the interests of small and medium-sized shareholders brought about by equity pledges were analyzed. Furthermore, this article also discusses the differences in the impact of equity pledges on company value in different scenarios and proposes corresponding policy recommendations and research prospects.

Are we Ready for Real-Time Continuous Glucose Monitoring in the Hospital Setting? Benefits, Challenges, and Practical Approaches for Implementation : Case Vignette: Remote Real-Time Continuous Glucose Monitoring for Hospitalized Care in Quincy Koala.

While preliminary evidence for use of real-time continuous glucose monitoring (rtCGM) in the hospital setting is encouraging, challenges with currently available devices and technology will need to be overcome as part of real-world integration. This paper reviews the current evidence and guidelines regarding use of rtCGM in the hospital and suggests a practical approach to implementation. There is now a considerable body of real-world evidence on the benefits of reducing dysglycemia in the hospital using both traditional point-of-care (POC) glucose testing and rtCGM. Benefits of rtCGM include decreased frequency of hypo- and hyperglycemia with reduced need of frequent POC checks and it is both feasible and well-accepted by nursing staff and providers. If expansion to additional sites is to be considered, practical solutions will need to be offered. Recommendations for an operational workflow and tools are described to guide implementation in the non-ICU setting. Further testing in randomized controlled trials and real-world dissemination and implementation designs is needed, together with industry and technology collaborations, to further streamline the integration into health systems.

Vinegar injections can be used safely to control outbreaks of crown-of-thorns starfish Acanthaster cf. solaris during the peak spawning season

Concerns are mounting over the effects of crown-of-thorns starfish (COTS) (Acanthasteridae) outbreaks, prompting the need for integrated management strategies. Although direct control methods are short-term and localized, they remain one of the few operational tools that can be easily implemented by locals. Vinegar injections have recently emerged as a highly effective method; however, their impact on reproductive behavior remains untested. Here, we investigated the short-term spawning response of mature COTS to double injections of household vinegar. First, COTS abundances and reproductive status were monitored during a massive outbreak affecting New Caledonia’s reefs. In situ and laboratory experiments were then conducted to determine whether injected COTS would trigger synchronized spawning among mature individuals in close proximity. Our results indicated that injections had no significant effect on spawning behavior, even in densely populated aggregations (&gt;4000 COTS ha-1). In the field, starfish exhibited ripe gonads with high gamete content (up to 35% of body weight) 3 d after conspecifics were injected. In the laboratory, mature COTS that were held with injected, decaying individuals in a confined volume did not expel their gametes after 2 d. This suggests that vinegar injections could be used at any time, even during peak spawning, without risking synchronized spawning in the affected areas.

Sizing Methodology of Dynamic Wireless Charging Infrastructures for Electric Vehicles in Highways: An Italian Case Study

The necessity of pushing the road mobility towards more sustainable solutions has become of undeniable importance in last years. For this reason, both research and industry are constantly investigating new technologies able to make the usage of battery electric vehicles(BEV) as accessible and usable as traditional internal combustion engine vehicles (ICEV). One of the most limiting issues concerns the short range of electric vehicles, which complicates their use for long distances, such as for highway travels. A promising solution seems to be the “charge-while-driving” approach, by exploiting the inductive dynamic wireless power transfer (DWPT) technology. Nevertheless, such systems show different issues, first of all, high investment and maintenance costs. Furthermore, it is not clear how extensive a potential dynamic wireless charging infrastructure needs to be to make a real advantage for electric vehicle drivers. As a consequence, the aim of this paper is to introduce a new methodology to estimate the number and length of wireless charging sections necessary to allow the maximum number of electric vehicles to travel a specific highway without the need to stop for a recharge at a service area. Specifically, the methodology is based on a algorithm that, starting by real traffic data, simulates vehicle flows and defines the basic layout of the wireless charging infrastructure. This simulator can provide a decision support tool for highway road operators.

Parsimonious streamflow forecasting system based on a dynamical systems approach

Operational hydrologic forecasting often relies on the use of physically-based computer models at the catchment scale. However, our understanding of streamflow generation is still evolving and incomplete, and oftentimes linearization assumptions must be made to make physically-based hydrologic and hydraulic models tractable. In addition to uncertainty in the physical runoff-generation mechanisms, many physically-based approaches do not formally assimilate observed streamflow data. Streamflow is their output, deterministically simulated from observed inputs (such as precipitation, temperature, etc.). Divergences between observed and simulated streamflow may be due to input uncertainty, calibration issues, or problems with the inherent physical process representation. Thus, data-driven approaches that are responsive to observed streamflow by including assimilation as an essential feature are promising in operational hydrology. In this study, we use a novel forcings-weighted k-Nearest Neighbor (kNN) nonlinear timeseries forecasting method developed in the context of the modeling of nonlinear dynamical systems. The rainfall-runoff transform is assumed to be a low-dimensional system of (unknown) nonlinear differential equations; the deterministic state space is reconstructed based on the streamflow timeseries; and a forecast is generated based on how “similar” the current hydrograph is to historical events encoded within the reconstructed state space, weighted by the similarity of forcings. We tested the model in a long-term test period with no real data assimilation, and in forecast mode with daily data assimilation. When used for forecasting, we also illustrate two different uncertainty quantification approaches: forecast postprocessing and internal-to-the-model ensemble generation. The method is conceptually and computationally simple, but offers promising results. We tested the model in three different scenarios. First, investigating a long-term test period with no real data assimilation in a rainfall-dominated research catchment at the Coweeta Hydrologic Lab in North Carolina, USA. The simulation exhibited an NSE of 0.73. Next, we included a conceptual snowmelt model to drive the long term simulation and studied boreal-temperate transition zone catchments in northern Minnesota, USA. The study area included a small (<0.1 mi2) reference research catchment at the Marcell Experimental Forest, and a large river basin (>19 k mi2) with incomplete forcings information. The kNN method yielded NSEs from 0.28 to 0.50. And finally, we conducted a pseudo-operational forecasting test for a large river basin in northern Minnesota, USA with a focus on performance forecasting a flood-of-record on the Rainy River. The test involved reforecasting, data assimilation, and implementation of ensemble and postprocessing bias correction uncertainty-quantification schemes. The technique demonstrated good performance capturing a flood-of-record on a large catchment (−5.7 to −3.3 % crest error with 1-day of lead time). For the entire test period in forecast mode, the 1-day lead time NSE was 0.99, going down to 0.79 for the 7-day lead time, showing its general utility as an operational forecasting tool.

A note on the telegraph type differential equation with time involution

In the present paper, the initial value problem for the telegraph type involutory in t second order linear partial differential equation with damping term is investigated. The equivalent initial value problem for the fourth order partial differential equations to the initial value problem for this second order linear partial differential equations with involution and damping term is obtained. Applying the operator tools, the stability estimates for the solution and its first and second order derivatives of this problem are established.

From Battlefield to Border: The Evolving Use of Drones in Surveillance Operations

In recent decades, drone technology has undergone rapid advancements, making it a vital tool in various surveillance operations. Initially limited to the battlefield, the use of drones has now expanded to various civilian applications, including border monitoring, law enforcement, and environmental surveillance. This shift is driven by enhancements in drone capabilities, including extended range, endurance, and sensor technology. This study employs a qualitative approach using case studies to analyze the evolution of drone usage in surveillance operations. Data were collected through literature reviews, interviews with industry experts, and analysis of reports from security and defense agencies. The study also compares the effectiveness of drone-based surveillance operations with traditional methods through statistical analysis and field operational evaluations. The findings indicate that drone usage significantly enhances the efficiency and effectiveness of surveillance operations. Drones enable wider area coverage at lower costs and reduced risks compared to conventional methods. Additionally, drones equipped with advanced sensors facilitate more accurate and real-time data collection, which is crucial in critical security situations. The study also identifies major challenges in drone usage, including regulatory issues, privacy concerns, and the integration of technology with existing systems. The evolving use of drones in surveillance operations shows great potential in enhancing security and efficiency across various sectors. However, a balanced approach between technological innovation and regulatory frameworks is necessary to address existing challenges and maximize the benefits of this technology.

Integration of Artificial Intelligence (AI) within SmartLynx Airlines to Increase Operational Efficiency

This article investigates the current and potential future applications of AI in the SmartLynx airlines. The airline industry is a complex system that requires efficient and effective management of various operations, including revenue management, flight operations, customer service, and baggage handling. The use of Artificial Intelligence (AI) has now emerged as a promising solution to address the challenges faced by many other airlines. The aim of this research is to analyze SmartLynx Airlines' operational efficiency in flight operations segments of the Latvian aviation industry and to develop recommendations for improving the current operational strategy for the company through the integration of AI-supported tools with conventional flight operation tools. Research tasks are: (1) to conceptualize theoretical aspects of the use of AI in the aviation industry; (2) to perform empirical research regarding current operational issues and study the use of AI in SmartLynx Airlines to improve these issues; (3) to work out recommendations. The current research employs the quantitative approach – a survey of SmartLynx employees of various departments.

Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi-arid conditions

AbstractThermal remote sensing indicators of crop water status can help to optimize irrigation across time and space. The Crop Water Stress Index (CWSI), calculated from thermal data, has been widely used in irrigation management as it has a proven association with evapotranspiration ratios. However, different approaches can be used to calculate the CWSI. The aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS). To that end, three different methods were used to estimate the CWSI: Jackson’s model (CWSIj), Wet Artificial Reference Surface (WARS) method (CWSIw), and the Bellvert approach (CWSIb). A simpler indicator calculated as the difference between canopy and air temperature (Tc–Ta) was the benchmark to beat. The water status of a vine cultivar with anisohydric behavior (Merlot) in a vineyard in central Spain was assessed for two years with different agroclimatic conditions. Canopy temperature (Tc) was obtained from UAS flights at 9:00 h and 12:00 h solar hour over eight days during the irrigation period (June–August), and from vines under five different irrigation treatments. Stem water potential (SWP), stomatal conductance (gs), and leaf temperature (TL) were recorded at the time of the flights and compared with the thermal indices (CWSIj, CWSIw, CWSIb) and the benchmark indicator (Tc–Ta). Results show that the simpler indicator of water stress, Tc–Ta, performed better at identifying varying levels of crop hydration than CWSIb or CWSIw at 12:00 h. Under conditions of extreme aridity, the latter indices were less accurate than the physically-based CWSIj at 12:00 h, which had the highest correlation with SWP (r = 0.84), followed by the benchmark index Tc–Ta (r = 0.70 at 12:00). Considering the current climatic trends towards aridification, the CWSIj emerges as a useful operational tool, with robust performance across days and times of day. These results are important for irrigation management and could contribute to improving water use efficiency in agriculture.

OIL DUST INDICATOR

An oil dust indicator is a crucial component in maintaining the efficiency and longevity of machinery and engines. It serves the dual purpose of monitoring both oil level and contamination levels, particularly from dust and particulate matter. The indicator typically consists of a sensor, a processor unit, and a display or alert mechanism. The sensor, often a combination of optical and pressure sensors, continuously monitors the oil. clarity and level. When dust or other contaminants accumulate in the oil, the sensor detects changes in clarity and triggers an alert. The processor unit interprets these signals and activates the display or alert mechanism, informing operators of the need for maintenance. In addition to indicating the need for oil replacement or cleaning, advanced oil dust indicators can also provide data on the severity of contamination, allowing for proactive maintenance planning. This capability can significantly reduce downtime and maintenance costs while optimizing the performance and lifespan of machinery and engines. Oil dust indicators find applications in various industries, including automotive, manufacturing, and power generation. They play a vital role in ensuring the smooth operation of critical machinery and equipment, making them indispensable tools for maintenance professionals and operators alike. This study proposes an advanced oil dust indicator that incorporates sensors for total dissolved solids (TDS), color, and pH levels to provide comprehensive monitoring of oil quality. Keywords: oil dust detector can sense dust in oil.

Inaccuracies and Uncertainties for Harmonic Estimation in Distribution Networks

The proliferation of electronic loads has led to a substantial increase in harmonic emissions within low-voltage distribution networks. The accurate estimation of the expected levels of harmonics in a network is a daunting task for network operators. Stochastic-based harmonic estimation models can offer a comprehensive assessment of the expected levels of harmonics in the presence of existing and future loads, including electric vehicles and smart-grid-enabled devices. Such models offer a valuable tool for network operators to assess the potential impact of harmonics on future networks and to create sustainable design solutions to meet the increasing demand for electricity while achieving net zero targets. However, several variables associated with these estimations models involve a level of uncertainty due to their stochastic nature, leading to inaccuracies in the estimations. This paper aims to provide a more realistic estimate of these uncertainties in order to improve the outcomes of harmonic estimation models for the development of sustainable distribution networks.