Adaptive Approach Research Articles

The Effect of Teaching Strategies on English Language Learning in Libya

This study examines the impact of teaching strategies on English language learning in Libya, focusing on the challenges Libyan teachers face in secondary schools. The research investigates the effectiveness of different teaching methods and explores innovative strategies, including interactive technologies, in the classroom. The primary aim is to assess how these strategies influence student engagement, language proficiency, and overall learning outcomes. A mixed-method approach was employed, incorporating a quantitative analysis of survey data from ten high school English instructors in Hoon. The results indicate that educators frequently employ supportive techniques, including offering comments and encouraging task completion. The study reveals substantial deficiencies in self-assessment and contemporary evaluation techniques, suggesting a dependence on conventional pedagogical methods. The absence of modern methodologies, such as student reflection and peer assessment, may restrict students' active engagement and impede their linguistic advancement. This research paper also identifies differences in teaching practices based on teacher demographics, including gender and years of experience. More experienced teachers were found to employ a broader range of strategies, suggesting the need for mentorship and professional development for newer educators. The study emphasizes integrating technology-enhanced tools and reflective assessment techniques to foster a more effective and engaging learning environment. The conclusions drawn from this research paper recommend that targeted professional development programs be implemented to support teachers in adopting more reflective and modern teaching methods. These programs should focus on enhancing teachers' skills in self-assessment techniques, peer evaluation, and technology integration in the classroom. By doing so, English language instruction in Libya can be significantly improved, contributing to better student outcomes and excellent English proficiency. Overall, the study underscores the need for a dynamic and adaptable approach to teaching English, where traditional methods are supplemented by innovative strategies that actively engage students and equip them with the necessary skills to succeed in a globalized world.

Effectuation Principles as an Entrepreneurial Strategy during a Crisis in the Hotel Industry in Indonesia

The principle of effectuation is a fascinating concept in entrepreneurship that emphasizes a flexible and adaptive approach to business creation. This approach is particularly beneficial in unpredictable contexts, encouraging entrepreneurs to leverage their current resources and networks. The aim of this study is to identify the entrepreneurial strategies employed by boutique hotels, grounded in five principles of effectuation, to navigate a crisis. This study utilizes semi-structured interviews as the primary method for collecting data on crisis management among business owners or senior executives. The respondents in this study comprised ten decision-makers from seven boutique hotels, including owners and top-level management. The findings indicate that boutique hotel entrepreneurs employed effectuation principles as an entrepreneurial strategy during a crisis, enabling the hotels to navigate the challenges effectively. This study's findings will enhance existing research in the hotel sector and elucidate entrepreneurial reactions to crises.

Translation and cross-cultural adaptation of the self evaluation of breathing questionnaire (SEBQ) into Danish

ABSTRACT Background and Purpose Dysfunctional breathing (DB) with or without an underlying medical condition is associated with impaired quality of life. DB-related symptoms can be measured with the 25-item Self Evaluation of Breathing Questionnaire (SEBQ). However, the SEBQ is not available in Danish.The aim of the present study was to translate and cross-culturally adapt the SEBQ into Danish and to assess the face validity of the Danish version of the questionnaire in individuals with DB-related symptoms. Materials and Methods The SEBQ was translated and cross-culturally adapted into Danish using an internationally acknowledged six-step forward-backward translation guideline in an interactive process with an expert committee of clinicians, translators, methodologists and the SEBQ developer. Face validity was explored through semi-structured interviews with 24 adult individuals with DB-related symptoms (age 20–70 years, female n = 14). Results The SEBQ was successfully translated and cross-culturally adapted into Danish. Three major modifications were made following the translation process and participant interviews. First, an introductory paragraph, including a recall period of the previous seven days, was added. Second, the administration of the questionnaire was changed from a paper to an electronic version. Finally, adaptations regarding semantic equivalence, especially concerning being ‘breathless’ and ‘short of breath’, were performed. The participants expressed that the final version of the SEBQ embraced their DB-related symptoms, was understandable, and easy to complete. Conclusion The SEBQ is the first available Danish questionnaire to measure DB-related symptoms, following an internationally acknowledged cross-cultural adaptation and face validity evaluation approach. This promising validation should be followed by an assessment of measurement properties in individuals with DB-related symptoms to investigate the adequacy of the SEBQ in a Danish context.

Enhancing Long-Term Robustness of Inter-Space Laser Links in Space Gravitational Wave Detection: An Adaptive Weight Optimization Method for Multi-Attitude Sensors Data Fusion

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively.

SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands

Objective.Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.Approach.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.Main results.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.Significance.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.

Analysis of Multivariable Sensor Responses to Multi-Analyte Gas Samples in the Presence of Interferents and Humidity.

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg-Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the mixtures, with reliable identification and accurate quantification, even in the presence of various interferents. Concentration errors of approximately ±5% are obtained for mixtures containing up to 6 BTEX compounds (including chemical isomers) and up to 4 interferents. Additionally, the study investigates the impact of humidity on the polymer/plasticizer-coated shear-horizontal surface acoustic wave (SH-SAW) sensors, demonstrating accurate concentration estimation in a relative humidity range from dry nitrogen to 65%. This sensing-and-multivariate-signal-processing approach is a promising candidate for reliable environmental monitoring in real-world applications.

General Exceptions in the Digital Trade Environment: Challenges and Reforms under Article 20 of GATT and Article 14 of GATS

With the rapid development trend of digital trade today, this article introduces the legal interpretation of Article 20 of GATT and Article 14 of GATS. Firstly, it explains the application standards of this provision in the WTO. Secondly, it combines existing judgments and digital trade laws issued by international organizations to explain the current situation and shortcomings of the application of digital trade in the provision. The article also discusses how digital trade dynamics such as data flow, privacy concerns, and cross-border service delivery complicate the application of these established trade rules. Finally, the article proposes a series of recommendations aimed at refining the interpretation and development of trade laws to better accommodate the unique demands of the digital economy. These suggestions include the need for clearer definitions of key terms within the GATT articles, the establishment of a more robust framework for digital trade within the WTO, and the promotion of international cooperation to create harmonized standards that facilitate digital trade while protecting consumer and national interests. Through these measures, the article advocates for a more adaptive and forward-looking approach by the WTO to ensure that global trade regulations keep pace with technological advancements and market transformations.

Real-time autonomous control of a continuous macroscopic process as demonstrated by plastic forming.

To meet the need for more adaptable and expedient approaches in research and manufacturing, we present a continuous autonomous system that leverages real-time, in situ characterization and an active-learning-based decision-making processor. This system was applied to a plastic film forming process to demonstrate its capability in autonomously determining process conditions for specified film dimensions without human intervention. Application of the system to nine film dimensions (width and thickness) highlighted its ability to explore the search space and identify appropriate and stable process conditions, with an average of 11 characterization-adjustment iterations and a processing time of 19 minutes per width, thickness combination. The system successfully avoided common pitfalls, such as repetitive over-correction, and demonstrated high accuracy, with R2 values of 0.87 and 0.90 for film width and thickness, respectively. Moreover, the active learning algorithm enabled the system to begin exploration with zero training data, effectively addressing the complex and interdependent relationships between control factors (material supply rate, applied force, material viscosity) in the continuous plastic forming process. Given that the core concept of this autonomous process can, in principle, be transferred to other continuous material processing systems, these results have implications for accelerating progress in both research and industry.

Enhancing software quality assurance in ubiquitous learning environments through mutation testing and diverse test oracles

Ubiquitous learning enables software analysts to acquire knowledge and design test oracles from online resources including developer documentations, forums and collaborative platforms. Test oracles are primarily designed by humans and may contain quality defects. Mutation testing can facilitate the adaptability of ubiquitous learning to enhance software quality assurance. Unfortunately, mutation testing generates a large number of faulty versions known as mutants to analyze the quality of test oracles which is computationally expensive. In this paper, we proposed minimal path selection strategy to select fewer and non-trivial mutants to analyze diverse test oracles realizing ubiquitous learning environments. The proposed selection strategy explores the relationship between faulty conditions and output statements to identify feasible paths. To establish a ubiquitous learning setup, diverse test oracles are selected by incorporating user input, automated scripts and developer insights. This approach aims to enhance learner engagement and effectively design test oracles. The empirical evaluation and state-of-the-art comparison of various Java-built software programs demonstrated the potential of ubiquitous learning in software quality assurance by reducing 75% of the generated mutants. Furthermore, only 64%–85% of adequate test oracles were required to meet the desired quality assurance criteria. The study highlights the significance of personalized and adaptive approaches to assure software quality in ubiquitous learning environments.

A Developed Approach for Synthesizing Novel Fe3O4/FeO/BaCl2 Composites with Broadband and High-Efficiency Microwave Absorption Performance.

Designing high-performance microwave absorbing materials that are thin and exhibit strong absorption capabilities across a wide frequency range is critical for mitigating electromagnetic pollution through a simple, highly adaptable, and cost-effective approach. However, achieving these three targets remains a significant challenge. In this research a simple approach suitable for large-scale production of microwave absorbing materials, namely, Fe3O4/FeO/BaCl2 composites, is proposed, which includes the processes of chemical coprecipitation and calcination. The above approach can adjust the mass ratio of Fe3O4/FeO while prompt the formation of BaCl2 with mesoporous structure on the surface of Fe3O4/FeO, meeting the need for desirable microwave absorbing performance. Subsequently, the impacts of varying mass ratios of the Fe3O4/FeO/BaCl2 composites on microstructures, magnetic properties, and microwave absorption properties were examined. Based on this investigation, a mass ratio close to 3.5:5.5:1 was determined to be optimal. At this ratio, the Fe3O4/FeO/BaCl2 composites realize an effective absorption bandwidth of 6.70 GHz at only 1.16 mm thickness, covering the whole Ku-band, and the maximum reflection loss can be close to -46.8 dB at 1.4 mm. The robust microwave absorption performance of Fe3O4/FeO/BaCl2 composites can be attributed to heterostructured multi-interface structural design, the comprehensive effects of multiple reflections and dielectric/magnetic losses induced by BaCl2 with mesoporous structure as well as the aggregated Fe3O4/FeO particles. This work may offer insights into designing and preparing effective microwave absorption materials.

Mapping the Kazakhstani STEM Education Landscape: A Review of National Research

The aim of this study was to map the current status of STEM education in Kazakhstan. The study encompasses 24 studies selected through a literature search in Google Scholar, ERIC, Web of Science, and Scopus. The descriptive characteristics of the reviewed studies reveal a significant increase in STEM education publications in Kazakhstan since 2019, indicating a growing emphasis on this field. The reviewed studies, spanning the years 2019 to 2023, included diverse formats such as journal articles, conference proceedings, book chapters, theses, and review articles. Notably, the reviewed studies involved participants from both K-12 and university levels, with a particular focus on female students in some studies. The thematically organized findings of the reviewed studies highlighted challenges faced by STEM education in Kazakhstan, including students’ perceptions about STEM subjects and careers, school environment and educational culture, and societal and gendered expectations. Creating interactive learning environments, addressing biases, dismantling gender stereotypes, and challenging traditional norms were identified as crucial steps to encourage the participation of young women in STEM disciplines. This study contributes to understanding STEM education in Kazakhstan and provides a foundation for future cross-country comparisons, emphasizing the necessity for adaptable approaches in designing and evaluating STEM initiatives in evolving educational contexts.

Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

Climate-Driven Sustainable Energy Investments: Key Decision Factors for a Low-Carbon Transition Using a Multi-Criteria Approach

Currently, the need for a clean transition has made the upscaling of sustainable energy investment projects imperative. This paper addresses the increasing importance of sustainable energy investment projects in the context of climate change and the urgent need for a global energy transition. Given the complexity of decision-making in this field, a multi-criteria decision-making (MCDM) approach is employed to assess the main criteria considered by project developers and financial institutions. Using the Analytic Hierarchy Process (AHP) method, eight criteria are identified and evaluated. Results highlight differing priorities between project developers and investors, emphasizing the need for adaptable approaches to accelerate sustainable energy investments. The study underscores the importance of understanding diverse stakeholder preferences and priorities in formulating effective strategies and managing associated risks to effectively promote sustainable energy projects. Future research should focus on real-life case studies and policy assessments to further enhance the understanding of sustainable energy investment dynamics.

Unsupervised multi-target domain adaptation for deforestation detection in tropical rainforest

Abstract. Geographic variability of the classes of interest, differences in sensor characteristics and changes in atmospheric conditions during image acquisition, among other factors, make it challenging to use a pre-trained deep learning classifier on new remote sensing data without a substantial drop in classification accuracy. This phenomenon occurs due to the so-called domain shift problem. Deep domain adaptation techniques have been used to mitigate the problem and thus avoid the time-consuming and costly collection of new labeled samples. Most recent domain adaptation approaches rely on single-source and single-target domains, refraining from exploiting other data distributions that are usually available. This work introduces a new unsupervised multi-target domain adaptation in the context of a change detection application, namely deforestation detection. The proposed approach addresses the substantial class imbalance typical of such application by applying unsupervised algorithms for selecting pseudo-labels in the target domain that will later serve as additional training references. We report results of experiments to evaluate the proposed method in four distinct sites of two Brazilian biomes using Sentinel-2 images. The results indicate that the proposed unsupervised domain adaptation method is a promising solution to reduce the effects of domain shift and to deal with the scarcity of labeled training data.

Domain Adaptive Urban Garbage Detection Based on Attention and Confidence Fusion

To overcome the challenges posed by limited garbage datasets and the laborious nature of data labeling in urban garbage object detection, we propose an innovative unsupervised domain adaptation approach to detecting garbage objects in urban aerial images. The proposed method leverages a detector, initially trained on source domain images, to generate pseudo-labels for target domain images. By employing an attention and confidence fusion strategy, images from both source and target domains can be seamlessly integrated, thereby enabling the detector to incrementally adapt to target domain scenarios while preserving its detection efficacy in the source domain. This approach mitigates the performance degradation caused by domain discrepancies, significantly enhancing the model’s adaptability. The proposed method was validated on a self-constructed urban garbage dataset. Experimental results demonstrate its superior performance over baseline models. Furthermore, we extended the proposed mixing method to other typical scenarios and conducted comprehensive experiments on four well-known public datasets: Cityscapes, KITTI, Sim10k, and Foggy Cityscapes. The result shows that the proposed method exhibits remarkable effectiveness and adaptability across diverse datasets.

IMPROVING MECHANISMS FOR MANAGING COMMUNICATIVE CREATIVITY OF HIGHER EDUCATION INSTITUTION LEADERSHIP PERSONNEL

This study explores the critical role of communicative creativity among leadership personnel in higher education institutions and proposes mechanisms for its enhancement. In an era of rapid change and complex challenges in the education sector, effective and innovative communication from institutional leaders is paramount. Through a comprehensive survey of students across various higher education institutions, this research identifies key issues in leadership communication, including limited visibility of leaders, inconsistent engagement with students, and challenges in conflict resolution. The study reveals a gap between student expectations and current leadership communication practices, highlighting the need for more adaptive and student-centered approaches. Drawing on these findings and existing literature, the article proposes a set of ten mechanisms to improve communicative creativity in highereducation leadership. These mechanisms include leadership visibility programs, communication skills training, feedback loops, regular open forums, and creative problem-solving initiatives. The proposed framework aims to foster a more responsive, transparent, and collaborative educational environment. This research contributes to the growing body of literature on higher education leadership by offering practical, implementable strategies to enhance communicative creativity. The findings have significant implications for leadership development in higher education and suggest directions for future research in assessing the long-term impact of these communication enhancement mechanisms.

Adaptive Two-Stage Stochastic Programming with an Analysis on Capacity Expansion Planning Problem

Problem definition: Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expansion planning problem with different generation resources including renewable energy. We demonstrate the value of adopting adaptive two-stage approach against the existing policies under limited flexibility and highlight the efficiency of the proposed heuristics along with practical implications on the studied problem. Funding: This work was supported by the National Science Foundation [Grant 1633196] and the Office of Naval Research [Grant N00014-18-1-2075]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0157 .

Predicting the Impact of Climate Change on Corylus Species Distribution in China: Integrating Climatic, Topographic, and Anthropogenic Factors.

This study investigates the impact of climate change on the distribution of Corylus species in China using the MaxEnt model. Key environmental variables, such as Bio6 (mean temperature of the coldest month) and human footprint, emerged as significant determinants of habitat suitability. The study reveals substantial shifts in suitable habitats due to global warming and increased precipitation, with notable expansion towards higher latitudes. Species like Corylus heterophylla Fisch. ex Bess. and Corylus mandshurica Maxim. demonstrate resilience in extreme conditions, highlighting the importance of specific ecological traits for conservation. Future projections under various SSP scenarios predict continued habitat expansion, emphasizing the need for targeted conservation strategies to address the critical role of human activities. This research highlights the complex interplay between climatic, topographic, and anthropogenic factors in shaping Corylus habitats, advocating for integrated adaptive management approaches to ensure their sustainability amid ongoing climate change.

Bearing-Constrained Leader-Follower Formation of Single-Integrators With Disturbance Rejection: Adaptive Variable-Structure Approaches.

This article studies the problem of stabilizing a leader-follower formation specified by a set of bearing constraints while disturbed by some unknown uniformly bounded disturbances. A set of leaders are positioned at their desired positions while each follower is modeled by a single integrator with an additive time-varying disturbance. Adaptive variable-structure control laws using displacements or only bearing vectors are applied to stabilize the desired formation. Thanks to the adaptive mechanisms, the proposed control laws require neither information on the bearing Laplacian nor the directions and upper bounds of the disturbances. It is further proved that when the leaders are moving with the same bounded uniformly continuous velocity, the moving target formation can be achieved under the proposed control laws. Simulation results are provided to support the stability analysis.

Rolling bearing fault diagnosis based on acoustic-vibration data fusion and mode decomposition combined with the crested porcupine optimization algorithm

Variational Mode Decomposition (VMD) is extensively utilized in the domain of rotating machinery fault diagnosis. Nevertheless, the reliance on empirical parameter tuning and the limitations inherent in traditional single-signal (either vibration signals or acoustic signals) fault diagnosis methods present substantial challenges. These include incomplete information and high sensitivity to noise. To address these shortcomings, a new method is introduced that integrates multi-modal sensor data and implements an adaptive VMD approach, enhanced through the Crested Porcupine Optimization Algorithm (CPO) to automatically optimize the key parameter. Then, uses optimized VMD to decompose the time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of data. Finally, the decomposed acoustic-vibro signal components are fed into the CNN-BiGRU-AT network, which learns the spatio-temporal dependencies to enhance the accuracy of fault classification. The experimental results conducted across diverse high-noise environments demonstrate that this method’s superior noise robustness compared to single-modality sensor approaches， highlighting the method's potential for machinery fault detection in noisy conditions.