Critical Objective Research Articles

Could We Prevent Tobacco Products in Young Populations?

The well-documented detrimental effects of tobacco on children and adolescents make it imperative to prevent tobacco use in young populations, which is a critical public health objective. Youth are particularly susceptible to nicotine’s addictive properties, and they are more susceptible to developing an addiction to tobacco products if they begin using them at a young age, according to research. The reduction of tobacco use among young people can be achieved by implementing evidence-based strategies, including increasing the minimum legal age for purchasing tobacco products, increasing taxation on these products, restricting marketing aimed at youth, and facilitating comprehensive school-based prevention programs, etc. Furthermore, prevention initiatives may be further bolstered by initiatives to encourage smoke-free environments and offer access to smoking cessation resources. In order to effectively prevent tobacco use in young populations and promote healthier outcomes for future generations, it is necessary to target various levels of intervention and involve multiple sectors, including healthcare professionals, educators, policymakers, and community leaders.

Trade Policy Ppace, Aid for Trade and, Intra-African and External African Manufactured Exports

Boosting intra-African manufactured exports is a critical objective of African leaders. The present analysis examines whether trade policy space and Aid for Trade (AfT) flows can contribute to achieving this objective. The analysis covers an unbalanced sample of 36 African countries over the period from 2004 to 2020, and uses estimators that allow obtaining the short-term (i.e., within-country effect) and long-term (i.e., between-country effect) effects of each regressor. The estimators are the combination of the feasible generalized least squares estimator on the one hand, and the Seemingly Unrelated Regression on the other hand, in the context of a hybrid model. The findings indicate that both trade policy space and AfT flows can help promote - including in a complementary way - African manufactured exports, especially intra-African manufactured exports. In this regard, trade policy space and AfT flows are instrumental in meeting African leaders’ objectives of promoting intra-African trade. The implications of the findings are discussed.

A hierarchical model of the information environment for railway power supply system critical objects control

It is proposed to consider the hierarchical model of the information environment as a structure according to its functions (implemented by the software part) and architecture (responsible for effective implementation of functional capabilities). It is proposed to improve the information management system of Ukrzaliznytsia’s critical power supply facilities, based on the developed hierarchical model of the information environment, which takes into account the presence of Ukrzaliznytsia’s own generation sources by integrating the appropriate control subsystem into the information system structure. The model emphasizes the integration of software and hardware components for data collection, pre-processing, and decision-making, contributing to a highly automated and intelligent control system. It proposes a multi-level structure incorporating artificial intelligence and advanced technologies to optimize electricity consumption and improve operational efficiency within the railway power supply system.

Threats to critical infrastructure objects of Ukraine under martial law

Threats to critical infrastructure objects of Ukraine under martial law

Land Use Optimization from the Perspective of Multiple Stakeholder Groups: A Case Study in Yongsheng County, Yunnan Province, China

With China’s rapid economic development in recent years, enhancing the sense of well-being among citizens has become a critical objective. However, the interests of various stakeholder groups are often overlooked in decision-making surrounding land use. In this study, Yongsheng County, Yunnan Province serves as a case study for land use scenario simulations. The equivalent factor method is combined with Participatory Rural Appraisal (PRA) to investigate the relationship between ecosystem multifunctionality (EMF) and the equity index of multiple stakeholder groups in various land use scenarios. We also explore whether an optimal combination of land use types exists. The results indicate that (1) The current ecosystem service value in Yongsheng County is primarily driven by climate regulation and biodiversity conservation, with a relatively high functional value index but a comparatively low equity index; (2) Different stakeholder groups mainly prioritize food production and ecosystem services impacting food production, such as water resource provision and climate regulation; (3) A land use allocation pattern of 20% farmland, 4% water bodies, 21% mixed forest, 20% coniferous forest, and 35% grassland appears to provide the optimal EMF index while simultaneously achieving the optimal equity index across stakeholder groups. This research may offer valuable insights for optimizing land use planning while taking into account the well-being of diverse stakeholder groups. It also may have practical implications for the formulation of innovative land use management strategies.

Features of creating a multi-level model of cyber security of critical infrastructure data: an interdisciplinary approach

The article examines the model of ensuring cyber security of Ukraine and documents the organizational and legal support for the protection of critical infrastructure objects from cyber attacks. The main concepts are described: multi-level data model, identification of cyber security, and critical infrastructure facilities. The levels of protection of critical infrastructure objects and measures at each level that ensure cyber security are highlighted and described. The stability of the critical infrastructure object is substantiated, and the formula for determining risks in critical infrastructure objects is described. The developed basic tuple model for the classification of critical information infrastructure objects by multi-level data models is highlighted. The prospects and risks of implementing multi-level data models in critical infrastructure facilities have been identified. It was concluded that multi-level data models are an important tool for ensuring cyber security in today's digital world and critical infrastructure enterprises in private. They allow for the effective protection of confidential information by isolating and controlling different levels of data access

Mix proportion design and life cycle assessment of ultra-high-performance lightweight concrete with hollow microspheres

Achieving high strength and low weight in cement-based composites remains a critical objective in material and structure engineering. This study presents the development of an ultra-high-performance lightweight concrete (UHPLC) composite with a density not exceeding 1950 kg/m³ and a compressive strength of at least 100 MPa. The UHPLC mix design was optimized using the central composite design (CCD) response surface methodology, and a life cycle assessment (LCA) was conducted for the first time on UHPLC from cradle to gate. The interactions between cement content, hollow microspheres, and their effects on workability, mechanical properties, and durability were analyzed. The results show that using the CCD response surface method for designing UHPLC mix proportions is feasible, allowing effective optimization for various objectives, such as balancing both strength and density, highlighting low density, or emphasizing ultra-high strength. the designed UHPLC exhibits mechanical and durability properties comparable to or better than UHPC. LCA results show higher energy consumption and environmental impact for UHPLC in the cradle-to-gate stage, mainly due to the microsphere production phase. Monte Carlo uncertainty analysis reveals that ADP, HTP, POCP, and EP are most sensitive to microsphere content, while GWP and AP are more affected by cement content. The optimized UHPLCs in this study can be selected depending on specific technical or environmental scenarios. Introducing HMs into the UHPC system for UHPLC production shows excellent performance, making it a promising material for advanced structural applications.

A Novel Pareto-Optimal Algorithm for Flow Shop Scheduling Problem

Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.

Digital Dreams and Dubious Dollars: The Challenge of Immersive Van Gogh

The purpose of this study is to discuss the popularity, questions, and controversies surrounding art-themed digital immersive exhibitions based on the life and paintings of Dutch painter Vincent Van Gogh (1853-1890). Since 2019, art-themed immersive exhibits have become extremely popular, with attendance that outstrips many museums showing the same artists, spawning controversy and challenging the fine art world. This study examines the rise of immersive art experiences, arguments for and against immersive spectacles as a way of experiencing artwork, and theoretical perspectives. Methodologies include research into historical, cultural, and commercial contexts including reviews, analyses, critiques, and participant observation. The study discusses components of the exhibition and presents specific arguments for and against them by producers, art critics, critical theorists, and spectators. Questions considered include: Are these exhibitions offering authentic art experiences, or are they exploiting great art for profit? Are they making art more accessible, or are they leading people away from seeing original paintings? Is the way people relate to art fundamentally changing in the age of social media, and if so, how will museums adjust? This study is novel in that it presents and evaluates diverging viewpoints on these controversies and proposes a more flexible approach that centers participant experiences over marketing claims and critical objections.

Platelet indices as a predictor in patients with aplastic anemia and immune thrombocytopenic purpura: a retrospective case–control study

Background and objectivesPlatelet indices are widely available and relatively cheap platelet parameters. The critical objective of this study is to assess the reliability of platelet indices as biomarkers for diagnosis and prognosis in adult thrombocytopenic patients.MethodsA retrospective case–control study, including 81 immune thrombocytopenia (ITP) cases, 50 aplastic anemia (AA) cases, and 150 participants as a control group. This study included patients admitted from 2016 to 2021 to the Clinical Hematology Unit, Department of Internal Medicine, University Hospital. The collected data included sociodemographic information, clinical data, laboratory data, and an assessment of the therapeutic response in the studied groups.ResultsFor the diagnosis of adult thrombocytopenic patients, platelet distribution width (PDW) showed the best diagnostic accuracy (85% for ITP and 91.9% for AA) at cutoff points of 14.9% and 17.2%, respectively. This was followed by mean platelet volume (MPV) with diagnostic accuracies of 77% for ITP and 89.3% for AA at a cutoff point of 9.4 fl. Platelet large cell ratio (PLCR) demonstrated insignificant accuracy in diagnosing either ITP or AA.ConclusionPlatelet indices can play a crucial influence in the diagnosis, not the prognosis, of adult thrombocytopenia.Trial registrationNCT05116033. https://classic.clinicaltrials.gov/ct2/show/NCT05116033

Toward Digital Periodontal Health: Recent Advances and Future Perspectives.

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Green innovation and natural resource efficiency: The role of environmental regulations and resource endowment in Chinese cities

Achieving natural resource utilization efficiency (NRUE) is a critical objective in addressing the pressing challenges of climate change and the risks associated with the finite availability of mineral resources. Enhancing NRUE can mitigate environmental impacts and support sustainable development for future generations. In this context, green innovation emerges as a pivotal driver of environmental sustainability and resource conservation. However, its potential role in advancing the NRUE has not been fully explored. To fill this gap, this study conducts an empirical analysis of 268 Chinese cities from 2010 to 2022, examining the impact of green innovation on NRUE. This study also incorporates the mediating role of environmental regulations in shaping this impact. The findings demonstrate that green innovation significantly enhances NRUE, primarily by facilitating industrial structural upgrading and reducing resource dependence. Additionally, environmental regulations strengthen the positive effects of green innovation, further amplifying its contribution to NRUE. Moreover, geographical location and differences in resource endowments among cities lead to variations in the effectiveness of green innovation on NRUE. These findings underscore the complex interplay between green technological progress, proactive government policies, and NRUE, offering valuable insights for policymakers and researchers alike.

Self-assembled ROS-triggered Bletilla striata polysaccharide-releasing hydrogel dressing for inflammation-regulation and enhanced tissue-healing

The antimicrobial and pro-healing properties remain critical clinical objectives for skin wound management. However, the escalating problem of antibiotic overuse and the corresponding rise in bacterial resistance necessitates an urgent shift towards an antibiotic-free approach to antibacterial treatment. The quest for antimicrobial efficacy while accelerating wound healing without antibiotic treatment have emerged as innovative strategies in skin wound treatment. Here, a dual-function hydrogel with antimicrobial and enhanced tissue-healing properties was developed by utilizing cyclodextrin, ferrocene, polyethyleneimine (PEI), and Bletilla striata polysaccharide (BSP), through multiple non-covalent interactions, which can intelligently release BSP by recognizing the wound inflammatory microenvironment through the cyclodextrin-ferrocene unit. Moreover, the porosity (65 % - 85 %), Young's modulus (400 KPa - 140 KPa), and DPPH scavenge rate (18 % - 40 %) of the hydrogel are modulated by varying the BSP content. The hydrogel exhibits outstanding antibacterial properties (98.3 % reduction of Escherichia coli observed after exposure to HTFC@BSP-20 for 24 h) and favorable biocompatibility. Furthermore, in a rat full-thickness skin wound model, the dual-function hydrogel significantly accelerates wound healing, increased CD31 expression promotes vascular regeneration, reduced TNF-α express and inhibited the inflammation. This multifunctional ROS responsive hydrogel provides a new perspective for antibiotics-free treatment of skin injuries.

Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

CONTROL ALGORITHM FOR IMPLEMENTING A MATHEMATICAL MODEL FOR ASSESSING THE EFFECTIVENESS OF SCENARIO MANAGEMENT AS A TOOL FOR ENSURING THE SECURITY OF A STRATEGIC FACILITY

Ensuring the safety of critical infrastructure facilities and other strategic objects is one of the main tasks of Ukraine as a state defending itself against military aggression. To accomplish this task, the authors developed a mathematical model of scenario management as a tool to ensure a strategic object’s security. However, the problem arises that, due to the nonlinearity of the processes under consideration, the theoretical solutions rely on assessing various controlling influences and, if necessary, their corrections to obtain the planned result. This article aims to develop a control algorithm for implementing a mathematical model to assess the effectiveness of scenario management as a tool for ensuring the security of a strategic object. For this, it is necessary to complete the following tasks: to consider a mathematical model for assessing the effectiveness of scenario management as a tool for ensuring the safety of a strategic object; develop the structure of the control algorithm for the implementation of this mathematical model; analyse the structure of the algorithm and make recommendations about the need to create the basic procedures for its implementation. A proposed control algorithm for implementing a mathematical model for assessing the effectiveness of scenario management as a tool for ensuring the security of a strategic object is a hierarchical structure of nine blocks (or modules) located at seven hierarchical levels, connected by direct and feedback links. It assesses the effectiveness of solving private tasks to ensure the security of a strategic object under protection, adjusting control influences aimed at implementing the selected scenario management option. At the first hierarchical level is the module for determining the parameters of systematisation of critical infrastructure conglomeration security data. The second level includes the object conglomeration module, which consists of submodules. The third hierarchical level contains the module for collecting data by parameters. The fourth level has two modules. The first is for performing calculations, and the second is for selecting evaluation parameters. It also aims to supplement or reject duplicate data. The fifth hierarchical level also contains two modules. The first is evaluating results according to particular criteria, and the second is the selection of evaluation criteria. The sixth level is the results interpretation module. This module provides the final assessment of the facility’s safety. The seventh hierarchical level is the module for making decisions on adjusting control actions. For the correct, practical application of this control algorithm to assess the effectiveness of solving private tasks to ensure the security of strategic and critical infrastructure objects under protection, it is necessary to develop the procedures for its application in detail. Keywords: critical infrastructure object, scenario management, algorithm, mathematical model, emergency.

Artificial intelligence-enabled profiling of overlapping retinal disease distribution for ocular diagnosis

<p>Eyesight, an invaluable gift profoundly impacts our daily lives. In a rapidly evolving healthcare landscape, the preservation and enhancement of ocular health stand as critical objectives. This research endeavors to analyze the two retinal fundus multi-disease image datasets (RFMiD) one containing 3200 images and the other containing 860 fundus images. The primary objective of this study is to scrutinize these datasets, discern variations in the frequency of labeled diseases within and across them, and explore common combinations of labels. These findings hold important implications for the field of retinal image analysis, as they provide valuable insights into the distribution and co-occurrence of defects.</p>

Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton.

Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human-machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified.

Application of Artificial Intelligence Technologies in Security of Cyber-Physical Systems

Cybersecurity questions of cyber-physical systems (CPS) have become ever more vital in recent times. Advances in technology and digitization have posed new weaknesses in CPS and cyber attackers take advantage of it. Viruses, malware, and sophisticated forms of cyberattacks have become a dangerous reality for critical infrastructure CPS. Lately, artificial intelligence (AI) technology has been extensively applied in the struggle against cyber threats in CPS. AI may improve system security by providing tools to quickly detect cyberthreats and automatically resolve them. Digitization of critical infrastructures (energy distribution networks, smart systems, oil and gas industry, water infrastructure, etc.) has increased their efficient management and at the same time, the number of cyber attackers on sensors, actuators, network and control equipment has also increased. Cyber security of critical objects can be ensured through AI. This article explores the theoretical foundations, application fields, and AI conceptual models. It analyzes the benefits and shortcomings of applying AI technologies to the security of the abovementioned systems. Mechanisms for detecting cyber threats in cyber-physical systems with the help of AI and predicting and preventing security threats are proposed.

Drivers’ situational awareness of surrounding vehicles during takeovers: Evidence from a driving simulator study

This study aimed to understand the influence of surrounding vehicle configuration, driving lane, and traffic density on drivers’ situational awareness (SA), takeover performance, and eye-tracking behaviors in conditionally automated driving. An experiment was conducted with the participation of 40 university students using a fixed-base driving simulator configured to simulate SAE Level 3 automation. During the experiment, participants were engaged in playing Tetris on a tablet as a non-driving related task in automated driving mode. Upon hearing an auditory takeover request, participants were instructed to take control of the vehicle, and then complete a scene reconstruction task to report their SA after transferring control back to the automated driving system. Our findings showed that drivers often neglected vehicles at their sides and rear during the takeover, which was associated with higher collision risks. Higher oncoming traffic density led to drivers’ worse SA of surrounding vehicles but more cautious driving behavior. Driving in the right lane generally resulted in smoother takeovers with lower collision risks. Interestingly, while SA did moderate the impacts of driving conditions on safety margins, a higher level of SA did not consistently relate to improved performance, especially in complex scenarios. This suggests the need for support systems that guide drivers to focus on safety–critical objects rather than simply amplifying SA in general. These insights have significant implications for the design of driver monitoring and support systems in automated vehicles.

Wind farm control using distributed economic MPC scheme under the influence of wake effect

As wind farms (WFs) expand in scale, there is a growing need for active power control to track the reference power benchmark issued by the grid dispatch center and also the imperative to reduce the fatigue load on key components of each wind turbine (WT). The presence of the wake effect causes a decrease in power generation for downstream WTs and an increase in the fatigue load. Consequently, the suppression of the wake effect has emerged as a critical control objective for WFs. For tackling the challenge, this article designs a hierarchical WF control framework. The upper-level controller employs a sequential convex programming (SCP) approach to maximize the WF's captured wind energy function and determine the optimal induction factors for the WTs. The lower-layer controller uses a distributed economic model predictive control (DEMPC) scheme to control the WT locally to achieve reference power tracking while reducing the fatigue load on each WT. Finally, the effectiveness of the designed algorithm was verified by conducting the simulation on a wind farm containing nine WTs.