Practical Cases Research Articles

Environmental Pollution and Extraction Methods of Extracellular Antibiotic Resistance Genes in Water

Antibiotics are widely used to treat diseases such as bacterial infections. However, the abuse of antibiotics has led to the spread of antibiotic resistant bacteria and intracellular and extracellular antibiotic resistance genes, making China one of the countries with the highest incidence of antibiotic resistance and thus threatening public health. Extracellular antibiotic resistance genes, as one of the novel environmental pollutants, could exist in water for a long time and could be transmitted between different bacteria through horizontal gene transfer, resulting in the spread of antibiotic resistance. At present, due to the limitation of enrichment and recovery methods, the in-depth studies of extracellular antibiotic resistance genes in water have been rarely reported. Thus, it is impossible to carry out effective supervision and risk assessments. Based on literature analysis and investigation, the pollution sources, current situations, and characteristics of extracellular antibiotic resistance genes in water are expounded. Meanwhile, the advantages and disadvantages of their enrichment and recovery methods are compared and analyzed and the enrichment and recovery methods are verified and discussed through practical cases. These provide theoretical reference for studies such as examining extracellular antibiotic resistance genes in water on their transmission and provide a technical basis for antibiotic resistance control and health risk assessments of extracellular antibiotic resistance genes.

LAMP: to shed light on AADL models

LAMP is an introspective analysis and processing framework for AADL. With LAMP, exploration, verification, transformation or any other processing rules are directly embedded inside the AADL model as annex subclauses. LAMP is based on the underlying LMP (Logic Model Processing) technology that itself leverages the Prolog language and a Prolog inference engine. This paper illustrates the use of LAMP by five practical use cases addressing some of the most important design issues in critical real-time software development.

Challenges and Potentials for “Supporting Supporters” on Education: A Practical and Rapid Case Study of the Centralized Information Portal Site in the 2024 Noto Peninsula Earthquake

Challenges and Potentials for “Supporting Supporters” on Education: A Practical and Rapid Case Study of the Centralized Information Portal Site in the 2024 Noto Peninsula Earthquake

Modeling the Urban Low-Altitude Traffic Space Based on the Land Administration Domain Model—Case Studies in Shenzhen, China

The urban low-altitude airspace is an integral part of urban space. As low-altitude utilization activities are being performed closer to the land surface, the management of the low-altitude space has become a focus of land administration. The management of the low-altitude airspace faces challenges such as cross-departmental coordination, fuzzy airspace boundaries, and complex spatial expressions. In 2020, the concept of “3D land administration” was introduced, marking the emergence of three-dimensional geospatial regulation in land management practices. Semantic models featuring the LADM (Land Administration Domain Model) as their core are updated iteratively to promote various applications related to 3D geographic regulation, but there is still a gap in the research on low-altitude utilization. Drawing upon Chinese regulations and policies, this paper employs the LADM framework to achieve semantic descriptions and expressions for managing areas in the low-altitude airspace: (1) relevant policies governing low-altitude spaces in China are analyzed, and the boundary between low-altitude and surface management is discussed; (2) the LADM structure is utilized to establish a comprehensive model for regulating low-altitude spaces; (3) and the capability of the LADM to support 3D low-altitude modeling is demonstrated through practical use cases in Shenzhen, China. Finally, the paper provides a comprehensive overview of the avenues for improvement and prospects.

Hollow amorphous N-doped TiO2 microspheres with uniform shell thickness and high carrier separation efficiency for photocatalytic of high-concentration Cr(VI)

Due to the low quantum efficiency, pure N-doped TiO2 (NTO) is still ineffective for catalyzing high concentrations of pollutants. In this study, a novel hollow amorphous NTO photocatalyst (AH-NTO) was synthesized by ultrasonic-assisted hydrothermal method and applied to photocatalysis of high-concentration chromium-containing wastewater. The hollow structure was constructed using a three-dimensional Fe3O4 template, and the shell layer thickness changed regularly compared to the stirring method, suggesting that ultrasound contributes to the formation of a uniform shell. Meanwhile, the low crystallization kinetics induced by the ultrasound-assisted low-temperature hydrolysis process enhance the generation and stability of amorphous NTO. Characterization of the system demonstrated that the hollow amorphous structure improved visible-light utilization efficiency (∼70% light transmittance) and specific surface area (182.95 m2·g-1). Furthermore, the optimal photocatalyst exhibited a high photogenerated carrier signal (0.231 μA·cm-2), low interface resistance (374.8 Ω), and a strong oxygen vacancy signal (∼0.38 a.u.), which provides favorable conditions for the separation and transfer of photogenerated carriers. Based on these properties, the photocatalyst effectively treated wastewater containing 0.274 g·L-1 of Cr(VI). The removal efficiency decreased by only 0.18% over five consecutive cycles, demonstrating excellent cycle stability and activity. Therefore, this work provides a practical case for treating high-concentration pollutants.

Flexible regulation of airport air-conditioning systems: Impact of cooling load variations on temperature stability

The integration of renewable energy sources has led to notable supply-demand imbalances due to their intermittent nature. Air-conditioning systems, as significant energy consumers with considerable flexibility, play a crucial role in integrating renewable energy and regulating power systems. This paper delves into the capability of air-conditioning systems in airports to flexibly adjust, particularly examining how varying cooling loads impact indoor temperatures. The energy flexibility of the air-conditioning system is evaluated based on three fundamental cooling load variations (incremental, translational, and discrete), and several evaluation parameters are defined. For areas with smaller cooling load fluctuations, there is a greater adjustment margin, but long adjustment periods should be avoided. For areas with larger fluctuations, the original cooling load curve should be considered in the adjustment strategy. The simulation results show that advancing or delaying the supply by 1 hour during peak cooling load periods can ensure indoor temperature stability, and step-type cooling load curves do not adversely affect indoor comfort levels. Practical case studies further demonstrate that utilizing water storage as an active regulation strategy can reduce operational costs by 26%, and with flexible adjustments, this reduction can increase to 35-54%. The study offers a framework to optimize air-conditioning flexibility in airports, facilitating better renewable energy integration and grid stability, which holds significant potential to reduce costs and improve energy efficiency, thus contributing to sustainable energy management.

Sustainable pavement maintenance and rehabilitation planning using the quantum cognitive trust network

Selecting appropriate pavement maintenance strategies can enhance resource efficiency, reduce environmental pollution, prolong road lifespan, and improve societal sustainability. Currently, most models, which selecting pavement maintenance options, exhibit high subjectivity and low reliability. This study employs an innovative probabilistic linguistic multi-attribute decision-making model basing on the quantum cognitive theory. Therefore, the model focuses on expert decision-making groups for pavement maintenance strategies. It considers the trust networks within the decision-making process, as well as the sequential and interference effects arising from experts' belief biases and behavioral interactions. By doing so, it facilitates a more scientific selection of optimal strategies among multiple maintenance options. The proposed method has been validated through practical case studies of road segments, and its scientific validity and effectiveness have been further corroborated through sensitivity analysis.

Family Relations of Muslims in the Post-Soviet Space: Main Features and Approaches to Psychological Counseling

The family relations of Muslims under present-day conditions are undergoing a signifi cant transformation. The increase in the number of divorces makes it necessary to look for the ways to resolve family confl icts and to harmonize marital relations. In this regard, the requirements for the qualifi cation of family psychologists consulting Muslim couples and families are increasing. It is not enough to master one or another method of family counseling for working with Muslim families. The most important role is played by the formation of the specialist's intercultural competence, as well as by mastering the knowledge and understanding the ethnic, religious and regional specifi cs of the clients. The culture of Muslim ethnic groups is collectivistic, which aff ects the relationships both within a married couple and within an extended family, whose role in Muslim communities is still important. In the article, this thesis is illustrated by the analysis of the author's practical case of family counseling. The author, based on the consideration of ethnic and religious values underlying Muslim family relations, examines the most eff ective psychotherapeutic approaches in working with Muslim families and couples.

Optimal planning for hybrid renewable energy systems under limited information based on uncertainty quantification

Developing hybrid renewable energy systems (HRES) is crucial for ensuring affordable, sustainable, and reliable energy access in remote rural and island regions. Rich and detailed data play a pivotal role in the optimal design of HRES. However, remote areas often face data limitations due to inadequate metering infrastructure, which further increases uncertainties in HRES design. This paper proposes a planning optimization method that quantifies multiple uncertainties, requiring only limited information for the optimal HRES design. First, uncertainties in HRES planning are classified into different types based on intrinsic randomness, data availability, and impact level. Then, by integrating uncertainty quantification with credibility theory, knowledge-based scenario generation models are developed to leverage limited available information to generate extreme and typical operating conditions for a tailored HRES design. Finally, a fuzzy credibility-constrained stochastic-robust optimization model is proposed to optimize the design of HRES. A practical case facing significant data scarcity and gaps is used to validate the effectiveness of the proposed method. The proposed approach reduces the loss of load probability for HRES to 0.02% and lowers the levelized cost of energy to $0.16/kWh—33% lower than that of the deterministic method and 79% lower than that of the traditional stochastic method.

Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications

This paper is dedicated to solving the problem of concept drift in industrial plants using artificial intelligence methods. For this purpose, methodological approaches and procedures are considered and analyzed. Based on the findings, reference architectures were developed at different abstraction levels that can be used in an industrial environment and enable continuous machine learning. Continuous machine learning offers the possibility of adapting to dynamic changes in the production environment, which are reflected in constantly changing data sets. Through a combination of machine learning techniques, a novel and practical framework for continuous learning, also known as lifelong learning, is presented. The integration of problem-focused machine learning methods is advancing in production, e.g., predictive maintenance, process optimization or fault detection. Thereby, fully or semi-automated adaptations to changing environments requiring continuous improvements are less often explored, although practical use cases often require adaptive capabilities as the physical data distribution may change over time. In this paper, the application was continuously improved based on case studies and empirical results, and finally validated with a quality assurance application. Various methods and approaches for detecting concept and data deviations, retraining, packaging and model updating had to be investigated, which led to the question of what a real industry-oriented implementation could look like. The result is a reference architecture that can run on cloud and edge computing resources. This reference architecture is validated in real-world application in the parquet production sector, proving its feasibility and efficiency.

Dynamics and structure of HIV infection cases in forensic practice

Dynamics and structure of HIV infection cases in forensic practice

Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tradeoff solutions is sought under tight evaluation budgets. In this article, we introduce a novel concept of inverse transfer in multiobjective optimization. Inverse transfer stands out by employing Bayesian inverse Gaussian process models to map performance vectors in the objective space to population search distributions in task-specific decision space, facilitating knowledge transfer through objective space unification . Building upon this idea, we introduce the first Inverse Transfer Evolutionary Multiobjective Optimizer (invTrEMO). A key highlight of invTrEMO is its ability to harness the common objective functions prevalent in many application areas, even when decision spaces do not precisely align between tasks. This allows invTrEMO to uniquely and effectively utilize information from heterogeneous source tasks as well. Furthermore, invTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand based on user preferences. Empirical studies on multi- and many-objective benchmark problems, as well as a practical case study, showcase the faster convergence rate and modeling accuracy of the invTrEMO relative to state-of-the-art evolutionary and Bayesian optimization algorithms. The source code of the invTrEMO is made available at https://github.com/LiuJ-2023/invTrEMO .

An accelerated Benders decomposition method for distributionally robust sustainable medical waste location and transportation problem

This study addresses the sustainable medical waste location and transportation (SMWLT) problem from the viewpoint of social risk, environmental impact, and economic performance, where model uncertainty includes risk and transportation costs. In practice, it is usually hard to obtain the exact probability distribution of uncertain parameters. To address this challenge, this study first constructs an ambiguity set to model the partial distribution information of uncertain parameters. Based on the constructed ambiguity set, this study develops a new multi-objective distributionally robust chance-constrained (DRCC) model for the SMWLT problem. Subsequently, this study adopts the robust counterpart (RC) approximation method to reformulate the proposed DRCC model as a computationally tractable mixed-integer linear programming (MILP) model. Furthermore, an accelerated Benders decomposition (BD) enhanced by valid inequalities is designed to solve the resulting MILP model, which significantly improves the solution efficiency compared with the classical BD algorithm and CPLEX solver. Finally, a practical case in Chongqing, China, is addressed to illustrate the effectiveness of our DRCC model and the accelerated BD solution method.

On the importance of discharge observation uncertainty when interpreting hydrological model performance

Abstract. For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations. Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations. Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.

GRASP with Evolutionary Path Relinking for the conditional [formula omitted]-Dispersion problem

In this paper, we propose a new heuristic method that hybridizes GRASP with Path Relinking to solve the conditional p-Dispersion problem. Given n elements, from which q<n have been already selected, this problem seeks to select p<n additional unselected elements to maximize the minimum dissimilarity among them. The conditional p-dispersion problem models a facility location problem motivated by a real situation faced in many practical settings arising when some facilities have been already located. The algorithm includes a novel proposal based on an efficient interplay between search intensification and diversification provided by the Path Relinking component, and it also incorporates an intelligent way to measure the diversity among solutions. An extensive computational experimentation is carried out to compare the performance of our heuristic with the state of the art method. The comparison shows that our proposal is competitive with the existing method, since it is able to identify 17 best-known values. Additionally, our experimentation includes a real practical case solved for a Spanish company in its expansion process. This case illustrates both the applicability of the conditional p-dispersion model, and the suitability of our algorithm to efficiently solve practical instances.

Thermodynamic analysis of ternary coupled system based on combined heating and power unit, steam ejector and compressed carbon dioxide energy storage

The flexible transformation of coal-fired units is the primary task of thermal power generation technology. Therefore, how to transform the functions of coal-fired power plants has become a new challenge. Three improvement methods are proposed in this paper (Method 1: exhaust steam from medium pressure cylinder; Method 2:high-pressure cylinder exhaust steam driving low-pressure cylinder exhaust steam; SPE). The compressed carbon dioxide energy storage system (CCES) and steam ejectors (SE) are integrated into the coal-fired power plant. The technical potential of CHP-SE-CCES ternary coupling system (CSC) is discussed by establishing thermodynamic model. We first analyzed the feasibility of the operation of the CSC system. Then we evaluated the advantages of the proposed method compared with the basic method from the perspective of energy utilization and exergy, and carefully studied the impact of key parameters on the performance of the CSC system. Finally, the characteristics of the coupled system under different running scheduling conditions are analyzed through a practical case. The results show that the integration of SE and CCES enhances the operational flexibility of the coupled system. At the rated heat load, method 2 reduces power load by 68.56 MW and method 3 by 50.56 MW, expanding the operational area. The energy efficiency of method 2 is 75.74%, which is 10.24% and 14.95% higher than other methods, respectively. It meets power and heating needs perfectly and achieves deep decoupling. It also saves 424.76 and 508.06 tons of standard coal daily compared to other systems.

Research on Intelligent Screening of International Communication Information and National and Regional Communication Strategies Based on Deep Learning Algorithm

In the context of globalization, the importance of international communication has become increasingly prominent. However, in the face of massive international communication information, how to effectively select and formulate communication strategies has become a big challenge. The traditional manual screening method is not only time-consuming and labor-intensive but also easy to be affected by subjective factors. This study aims to address the efficiency and accuracy issues in current international communication information screening and dissemination strategy formulation through the application of deep learning algorithms. It is of great theoretical and practical value to use deep learning algorithms to conduct intelligent screening of international communication information and research on national and regional communication strategies. To explore the application of deep learning algorithms in the intelligent screening of international communication information and the research of national and regional communication strategies, first, we will introduce the basic principles and methods of deep learning, as well as its applications in natural language processing (NLP), image recognition, and other fields. Then, we will explore how to use deep learning algorithms for intelligent screening of international dissemination information, including information classification, clustering, topic modeling, etc. Combined with practical cases, the application of deep learning algorithms in national and regional communication strategies will be studied, including communication content generation, communication channel selection, and communication effect evaluation.

A case for mindfulness-based strengths reading practice for the gifted

ABSTRACT Gifted students often experience social and emotional differences, such as heightened empathy, perfectionism, and asynchronous development. If unaddressed, these challenges can lead to isolation, depression, and underachievement. This paper explores the integration of Mindfulness-Based Strengths Practice (MBSP) and bibliotherapy as interventions to support the holistic development of gifted children. MBSP combines mindfulness with character strengths identified through the Values in Action (VIA) Inventory, promoting self-awareness, emotional regulation, and resilience. By merging MBSP with bibliotherapy in what is termed mindfulness-based strengths reading, this approach leverages narratives and characters to reflect gifted students' emotional complexities and strengths. This dual strategy addresses emotional challenges while fostering personal growth, flourishing, and prosocial behavior. The combined method provides a balanced and structured approach to meeting the unique social and emotional needs of gifted students, emphasizing both healing and development.

Research on the Deep Integration of CDIO and Maker Education to Promote the Cultivation of Innovative Talents in Emerging Engineering Disciplines

"First-class practical projects" constitute one of the four fundamental elements of the Undergraduate Education Teaching Reform Pilot Work Plan (Plan 101), aiming to cultivate future "revolutionary" and "disruptive" talents in emerging engineering disciplines. Both CDIO engineering education and maker education are committed to nurturing students' innovative and practical abilities, serving as robust pillars for the cultivation of talents in emerging engineering fields. Based on a review of relevant literature on CDIO engineering education and maker education from both domestic and international sources, this paper analyzes the differences between the two in terms of educational philosophy, curriculum system and content, teaching faculty, teaching resources, teaching methodologies, and evaluation methods. It proposes the concept of deeply integrating CDIO with maker education, outlining methods for such integration: constructing a curriculum system that merges CDIO with maker education, compiling textbooks for an engineering practice system that incorporates maker projects within the CDIO framework, establishing a makerspace through school-enterprise collaboration that integrates engineering practice with maker activities, building a practical teaching model that fuses CDIO with maker education, formulating a diversified evaluation scale that combines qualitative and quantitative assessments, and organizing competition activities that integrate CDIO with maker education. Furthermore, a comprehensive practical case study of the deep integration of CDIO and maker education is provided.

Mastering Data Structures Programing: Practical Examples for Beginners and Beyond

This article provides a comprehensive guide to mastering data structures in JavaScript, focusing on practical implementations and applications. Starting with foundational structures like arrays and linked lists, it progresses to advanced topics including stacks, queues, trees, and graphs. Each section explores the theoretical basis of the data structures, their practical use cases, and code examples to facilitate understanding. Additionally, the article includes decision-making strategies for selecting the appropriate data structure, optimization techniques for writing efficient code, and best practices for maintainable and readable implementations. Designed for both beginners and intermediate developers, the guide concludes with suggestions for further learning and a call to action for applying these concepts to real-world problem-solving.