Practical Problems Research Articles

Values, bias and replicability

The Value-free ideal of science (VFI) is a view that claims that scientists should not use non-epistemic values when they are justifying their hypotheses, and is widely considered to be obsolete in the philosophy of science. I will defend the ideal by demonstrating that acceptance of non-epistemic values, prohibited by VFI, necessitates legitimizing certain problematic scientific practices. Such practices, including biased methodological decisions or Questionable Research Practices (QRP), significantly contribute to the Replication Crisis. I will argue that the realizability of VFI is not a necessary condition for its validity. Then, I will show how some of the prominent proposals of value-laden science legitimize problematic scientific practices, provide real-world examples, and generalize the argument. Finally, I will show how value-laden methodological decisions contribute to the Replicability Crisis and discuss two strategies for realizing VFI.

Using social media platform X to enhance student medical English learning: an attempt based on design-based research (DBR) in a medical English for specific purposes (ESP) course

This study aimed to (1) elaborate on microblogging-based instruction, which was built on social media platform X and designed based on the four phases of the design-based research (DBR) framework (Amiel and Reeves in J Educ Technol Soc 11(4):29–40, https://www.jstor.org/stable/jeductechsoci.11.4.29, 2008); (2) put microblogging-based instruction into practice in a medical English for Specific Purposes (ESP) course; (3) examine students’ perceptions of the pedagogical value of X platform usage on their learning experiences; and (4) investigate how well microblogging-based instruction enhances the students’ specialised-language performance. On the basis of the four phases of the DBR framework, a study was conducted with students studying a medical ESP course to identify the problems in the teaching practices used in ESP courses. To improve the identified problems, following the second phase, social media platform X was used to supplement ESP face-to-face instruction. Three tasks were designed for the students to do on X. These tasks were implemented and assessed on 19 EFL first-year premedical undergraduate students over 16 weeks at a university in the Kingdom of Saudi Arabia. Data collected from the students included a copy version of the ESP final test, a questionnaire, and semi- structured interviews. The results revealed that X-based instruction improved students’ ability to use medicine-specialised language, offered opportunities for students to become more familiar with medicine-specific terms, perceived ability in using writing and reading skills, collaborative learning, and generated thoughtful discussions outside the confines of the classroom. However, the students’ inadequate knowledge of the specialised subject, the stress of making X mandatory, and log- in overload remain key challenges against the effective appropriation of X use in an ESP context.

Medical eponyms versus acronyms: what medical terminology is most beneficial to learn? A question of goals.

Appropriate use of medical terminology is one of the core conditions for successful communication in monolingual and multilingual healthcare communities. The modern scientific language is based on the descriptive terminology. However, it is often the case that the advantages of descriptive terminology are at odds with the ability to express complex concepts in just a few words. To solve this practicality problem it is customary to coin abbreviations and acronyms preferred to traditional eponyms. Today eponyms are considered ambiguous and non-descriptive, linked to the terminology of the past. The overview of this study demonstrates that the current habit of using acronyms can increase the scientific descriptive capacity compared to eponyms. On the other hand, acronyms remain ambiguous and more ephemeral than eponyms. Furthermore, eponyms are not as descriptive as acronyms, but they still carry important information for a medical student. If you truly believe in the importance of Medical Humanities in the medical curriculum, two aspects cannot be overlooked. First, eponyms bring students closer in an almost subliminal way to the history of medicine and the non-strictly technical-scientific field of medicine. Second, medicine is a complex science applied to humans and must strive to keep the patient at the center of its interests. Patients and their families preferably ask us to use eponyms. Which terminology to choose for medical students? The teachers have the last word.

The Interpretation of Watchman Nee’s Anthropology and Its Corresponding Ecclesiastical Influence in Contemporary Chinese Mainland Churches

The Interpretation of Watchman Nee’s Anthropology and Its Corresponding Ecclesiastical Influence in Contemporary Chinese Mainland Churches

Digitally Scaffolded Vulnerability: Facebook’s Recommender System as an Affective Scaffold and a Tool for Mind Invasion

AbstractI aim to illustrate how the recommender systems of digital platforms create a particularly problematic kind of vulnerability in their users. Specifically, through theories of scaffolded cognition and scaffolded affectivity, I argue that a digital platform’s recommender system is a cognitive and affective artifact that fulfills different functions for the platform’s users and its designers. While it acts as a content provider and facilitator of cognitive, affective and decision-making processes for users, it also provides a continuous and detailed amount of information to platform designers regarding users’ cognitive and affective processes. This dynamic, I argue, engenders a kind of vulnerability in platform users, structuring a power imbalance between designers and users. This occurs because the recommender system can not only gather data on users’ cognitive and affective processes, but also affects them in an unprecedentedly economic and capillary manner. By examining one instance of ethically problematic practice from Facebook, I specifically argue that rather than being a tool for manipulating or exploiting people, digital platforms, especially by their underlying recommender systems, can single out and tamper with specific cognitive and affective processes as a tool specifically designed for mind invasion. I conclude by reflecting how the understanding of such AI systems as tools for mind invasion highlights some merits and shortcomings of the AI Act with regards to the protection of vulnerable people.

Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges

Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder representations from transformers (BERT) leads to a robust encoder for the transformer model that can understand the broader context and deliver state-of-the-art performance across various NLP tasks. This has inspired researchers and practitioners to apply BERT to practical problems, such as information retrieval (IR). A survey that focuses on a comprehensive analysis of prevalent approaches that apply pretrained transformer encoders like BERT to IR can thus be useful for academia and the industry. In light of this, we revisit a variety of BERT-based methods in this survey, cover a wide range of techniques of IR, and group them into six high-level categories: (i) handling long documents, (ii) integrating semantic information, (iii) balancing effectiveness and efficiency, (iv) predicting the weights of terms, (v) query expansion, and (vi) document expansion. We also provide links to resources, including datasets and toolkits, for BERT-based IR systems. Additionally, we highlight the advantages of employing encoder-based BERT models in contrast to recent large language models like ChatGPT, which are decoder-based and demand extensive computational resources. Finally, we summarize the comprehensive outcomes of the survey and suggest directions for future research in the area.

Best practices for teaching the concept of the derivative: Lessons from experienced calculus instructors

Much research has reported on difficulties exhibited by students when working with the concept of the derivative in first-semester university calculus. In an effort to generate and share a resource on effective instructional practices related to the teaching of the concept of the derivative, a 12-item questionnaire was administered to experienced calculus instructors in the United States. Most of the 10 experts who participated in this study considered providing ample examples and practice problems, using graphing utilities such as Desmos and GeoGebra, and employing problem solving strategies in the teaching of the concept of the derivative to be effective in supporting students learn about the concept of the derivative, among other things. These experts further remarked on representations of the concept of the derivative and real-world contexts they typically use in their teaching of the concept, in addition to providing rationales for the choice of real-world contexts they typically use in their teaching of the concept. Recommendations for instruction are included.

Networked Evolutionary Game-Based Demand Response via Feedback Controls

In this paper, the demand response considering interactive decision making between residential users and utility companies is modelled and controlled using networked evolutionary game (NEG) theory. The NEG is achieved via a widely used mathematical tool, namely the semi-tensor product (STP) of matrices, through which, feedback controls can be implemented to dynamically regulate the game-based networks. Firstly, the dynamic interactions between utility companies who provide various energy consumption packs and residential users who can intelligently choose preferable packs are modelled in the state-space form, in which both sides are consistently pursuing their maximum payoffs. Then, a quantitive analysis of such system’s equilibria is conducted using rigorous mathematical derivations, followed by the design of a profile feedback controller when the existing equilibria are not satisfactory towards the required energy consumption. Finally, an illustrative example is demonstrated, where the dynamic gaming between utility companies and users is illustrated and the effectiveness of the proposed feedback control is validated. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses a very practical engineering problem, which is the demand response in the modern smart grid. Different from general optimization approaches in the existing works, the method proposed by this paper improves the demand response via feedback control of networked systems, which is based on the rigorous matrix approaches via state space equations. In addition, the work in this paper considers very practical factors in actual engineering (i.e., users with different action choices and decision logics being all together), which also provides much value to practitioners than the relevant works with similar theoretical approaches. The method proposed in this paper provides an effective regulation of the demand response in practice and can be flexibly adjusted according to actual situations.

Educating the Younger Generation of Polish Female Refugees in Postwar Britain

Abstract This study explores the education of young Polish female refugees in postwar Britain, and the implications of the establishment of the Committee for the Education of Poles, a body brought into being on April 1, 1947, as a consequence of the passage of the Polish Resettlement Bill in March 1947 (the first ever British legislation dealing with mass immigration). It examines the newly introduced grammar schools for girls, the challenges raised for these new educational establishments, and the strategies introduced by the British authorities in order to cope with the growing numbers of Polish adolescents entering the country after World War II. Based on archival records and supported by oral interviews, the sample case studies presented herein highlight one way in which the young Polish community was rehabilitated in exile at the start of their path towards civic integration. They highlight the key importance of education to that pathway. The studies presented here provide an insight into the experiences of, and challenges faced by, young Polish women during the Process of their education and resettlement. Thanks to extant school reports, it has been possible to ascertain not only the practical and financial problems that impacted the schools presented in the paper, which operated under the auspices of the Committee for the Education of Poles, but also their slow, but steady improvement over time. The paper provides clear evidence that it was thanks to the Committee that a significant proportion of the younger generation of Poles managed to acquire the qualifications needed to secure their first jobs and ultimately a career in industry or business and so play a useful part in the economic life of Great Britain and her overseas territories.

Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning

This paper studies a multi-objective scheduling problem in a continuous annealing operation of the steel industry, which is to simultaneously determine the production batch sizes for coils and their schedules so as to minimize the cost of setup, earliness, and tardiness. A large batch can reduce the setup costs but lead to a cost increase in earliness and tardiness. Thus, the conflict objectives of minimizing setup cost, earliness, and tardiness can be formulated separately. In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL) for effectively obtaining the Pareto solutions. In the proposed AMODE-RDL, DRL is integrated into the MODE algorithm as a controller, which can adaptively select mutation operators and parameters according to different search domains. Computational results on randomly generated instances and the practical problem instances show that DRL can effectively guide MODE to select mutation operators and parameters. The proposed algorithm can obtain better solutions compared to other powerful multi-objective evolutionary and adaptive MODE algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Setup usually leads to a decrease in production capacity and an increase in production costs. Similarly, earliness and tardiness costs are also crucial. These costs correspond to production, customer demand, and inventory, which are common objectives in production systems. However, the three objectives are generally conflicting, and it is very hard for practitioners to make appropriate decisions with manual experience. The multi-objective optimization methods can provide different scheduling for practitioners who may choose the suitable one according to current working conditions. Accordingly, the proposed model and algorithm can extend to other fields with similar characteristic problems.

A Physics-Guided MFL Deformed Defect Recovery Method

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a sparse factor is applied to reduce redundant features. The proposed method not only utilizes deep features from deep networks, but also increases the guidance of physical features, so that the algorithm is more robust and accurate. The experiment results show that our proposed method is more effective than the comparative methods on MFL simulated and measured defects. In a word, the proposed method has strong theoretical research and practical value.

EV Charging Scheduling Under Demand Charge: A Block Model Predictive Control Approach

This paper studies the online scheduling of electric vehicle charging by a service provider subject to a demand charge in a distribution system. Demand charge imposes a penalty on the peak power consumption over each billing period, representing a substantial cost for the service provider with a large number of clients. Because the demand charge is calculated at the end of the billing period, it poses challenges in real-time scheduling when energy demand forecasts are inaccurate, resulting in either overly conservative power consumption or substantial demand charge. We propose a block model predictive control approach that decomposes the demand charge into a sequence of stage costs. Optimality conditions on demand patterns are also presented and analyzed. Numerical simulations demonstrate the efficacy of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses a significant practical problem of minimizing the demand charge on the real-time scheduling of deferrable demands. In particular, we consider a setting where a commercial electric vehicle (EV) charging service provider has to manage the online scheduling of a large number of arriving EVs at a charging facility subject to a maximum charging power constraint and a tariff with the demand charge. A major practical challenge is to balance the tradeoff between maximizing profit in scheduling as much EV charging as possible and the need to minimize penalty on the peak charging power. We propose a model predictive control strategy that decomposes the overall demand charge into a sequence of terminal costs. Also addressed is the practical constraint arising from the mismatched EV charging decision period and the power measurement period used to compute the demand charge. Using real data collected at the Adaptive Charging Network (ACN) testbed in simulations, the proposed approach yields 8-12% improvement in operational profit over existing benchmarks, while it has yet been tested in actual charging systems. In the future research, we will address the charging scheduling under demand charge over multiple charging stations.

Multirate Mixture Probability Principal Component Analysis for Process Monitoring in Multimode Processes

In the multirate sampling processes, the process data are usually collected from various operating conditions and display multimodal characteristics. To monitor these multirate multimode processes, a multirate mixture probability principal component analysis model is proposed for process modeling and fault detection. In this model, the local multirate models are built first for each mode and all of them are subsequently fused with the mixture modeling approach. Such model is able to deal with multirate data with various amount of sampling rates, contributing to a remarkable fault detection and mode identification performance by utilizing all the available measurements even if some variables are unobserved. Then the expectation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> maximum algorithm is utilized to estimate all the model parameters in the probabilistic framework and the corresponding monitoring method is also developed based on the constructed models. Finally, the effectiveness of the proposed method is demonstrated through a PRONTO benchmark and a real multimode ammonia synthesis process. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motivated by the practical problem of ununiform sampling intervals in multimode processes, this paper proposes a novel multirate mixture probability principle component analysis model for processes modeling and monitoring. In this model, all the available observations with different sampling rates can be incorporated, which contributes greatly to capturing the multimodal characteristics within the industrial processes. Such ability is the key to realize multimode process monitoring, evaluation, fault diagnosis, and process optimization. In addition, although this paper only focuses on the continuous multirate data in industry, it is equally applicable to other forms of multirate data, such as images and videos.

Mathematical Lens on Administrative Services: A mathematical perspective on the administrative services of certain staffers in certain institution

The application of mathematics in administrative services is multifaceted, enhancing decision-making, operational efficiency, and strategic planning. Mathematical techniques are crucial in formulating and solving both theoretical and practical problems across various fields, including business and management. This paper explores the complex issues of unfairness or bureaucracy within the administrative services of certain staffers of a certain institution. By employing advanced mathematical concepts, we model and analyze these problems to gain deeper insights into their causes and potential solutions. The paper introduces novel mathematical frameworks to quantify and assess the impact of these issues on the overall functioning of the institution.

National cross-disciplinary research ethics and integrity study: methodology and results from Estonia

While empirical studies of research ethics and integrity are increasingly common, few have aimed at national scope, and even fewer at current results from Central and Eastern Europe. This article introduces the results of the first national research integrity survey in Estonia, which included all research-performing organisations in Estonia, was inclusive of all disciplines and all levels of experience. A web-based survey was developed and carried out in Estonia with a call sent to all accredited Estonian research institutions. The results indicate that the vast majority (89%) of respondents consider research ethics and integrity issues important and view falsification, fabrication, and plagiarism (FFPs) as the most severe forms of misconduct. Self-reporting of FFPs is generally comparable to levels published in other studies (6.2%). Gift authorship (41%) and hampering the work of a colleague (32%) were problematic practices most noticed among colleagues. At the same time, two of the noticed questionable research practices (QRPs) – salami-slicing and misuse of research funding – were seen as less severe, hinting at the existence of counter-norms that career advancement rules and structural factors like funding policies may encourage. The availability of research ethics and integrity guidelines was considered good. Ethical aspects of studying potentially stigmatising data in a very small research community are discussed in the article and results are analysed through counter-norms and normative dissonance frames.

Scientific and Theoretical Basis for Training Future Communication Specialists in Higher Military Education Institutions

Since 2014 Ukraine has been under the military influence of the Russian aggressor and in February 2022, it immediately turned into a full-scale war. Equipping the Armed Forces of Ukraine with sophisticated weapons, expanding the range of tactical techniques for conducting combat operations in various conditions necessitated a broad scientific search for ways to develop and improve the training (combat) process, including physical training, which has always been considered the most important factor in the high combat capability of the Armed Forces of Ukraine and the Security and Defense Sector of Ukraine. According to the results of the analysis of scientific, methodological and specialised literature, it has been established that the system of training future communication specialists for military and professional activities needs a scientifically based transformation in accordance with the requirements of today. The purpose to analyse the scientific, methodological and special literature on the training of future communication specialists for military and professional activities by means of special physical training. Research methods: analysis, synthesis, comparison, systematization of scientific and special sources. Conclusions. As a result of the analysis of scientific, methodological and specialised literature, it is determined that the system of training future communication specialists for military professional activities needs a scientifically based transformation in accordance with the new requirements of today. The article also analyses various approaches of modern scholars to the training of future specialists for professional activities using special physical exercises. In addition, in the context of theoretical research, we have identified an insufficient number of scientific papers devoted to solving the practical problem of training future communication specialists for military professional activities by means of special physical training, which emphasises the relevance and practical component of the chosen area of scientific research.

Of Hearth and Home: Professional Wellness After Hours

Burnout is a prevalent problem in the contemporary practice of medicine. Defined by the Agency for Healthcare Research and Quality as, “a long-term stress reaction marked by emotional exhaustion, depersonalization, and a lack of sense of personal accomplishment,” this multifactorial condition has significant implications for the clinicians who suffer it, their patients, and families. Neurologists suffer some of the highest rates of burnout. Burnout research on interventions often focus on the work environment. In this article, we will focus on burnout’s effects on home life and features of home life that can impact resiliency, specifically sleep hygiene.

"Sandpaper. Yeah.”: Educators’ embodied insights into comprehensive sexual health education pedagogy

In this arts-informed inquiry, I examine Canadian sex educators’ embodied sense-making of comprehensive sexual health education (CSHE). I seek to understand how educators use their bodies to negotiate contested pedagogical terrain in order to gain insights into conflicting patterns observed in the literature, as well as to challenge how educators’ personal pedagogies may be implicated in uneven enactments of CSHE. Using sensory ethnographic principles, I focus inquiry on educators’ embodied experiences of contentious pedagogical practices, including the over-reliance on institutionalized forms of knowledge. I conceptualize their experiences as “interchange”— the sense of simultaneous bodily gaining and giving in response to the social-political demands of teaching CSHE. I analyze two focal experiences of interchange—namely, Feeling Right(s) and Be/ing Schooled—to highlight paradoxical frictions of educators’ personal and pedagogical anti-oppressive aims. Last, I utilize Maclaren’s concept of “unfreedom” to discuss addressing problematic CSHE practices as an intersubjective project.

Replica analysis of overfitting in regression models for time to event data: the impact of censoring

We use statistical mechanics techniques, viz. the replica method, to model the effect of censoring on overfitting in Cox’s proportional hazards model, the dominant regression method for time-to-event data. In the overfitting regime, Maximum Likelihood (ML) parameter estimators are known to be biased already for small values of the ratio of the number of covariates over the number of samples. The inclusion of censoring was avoided in previous overfitting analyses for mathematical convenience, but is vital to make any theory applicable to real-world medical data, where censoring is ubiquitous. Upon constructing efficient algorithms for solving the new (and more complex) Replica Symmetric (RS) equations and comparing the solutions with numerical simulation data, we find excellent agreement, even for large censoring rates. We then address the practical problem of using the theory to correct the biased ML estimators without knowledge of the data-generating distribution. This is achieved via a novel numerical algorithm that self-consistently approximates all relevant parameters of the data generating distribution while simultaneously solving the RS equations. We investigate numerically the statistics of the corrected estimators, and show that the proposed new algorithm indeed succeeds in removing the bias of the ML estimators, for both the association parameters and for the cumulative hazard.

Fast prototype and rapid construction of three-dimensional and multi-scaled pitcher for controlled drainage by systematic biomimicry

Biomimetic materials that use natural wisdom to solve practical problems are developing rapidly. The trend for systematic biomimicry is towards in-situ characterization of natural creatures with high spatial resolutions. Furthermore, rapid reconstruction of digital twin models with the same complex features as the prototype is indispensable. However, it faces bottlenecks and limits in fast characterization and fabrication, precise parameter optimization, geometric deviations control, and quality prediction. To solve these challenges, here, we demonstrate a state-of-the-art method taking advantage of micro-computed tomography and three-dimensional printing for the fast characterization of the pitcher plant Nepenthes x ventrata and fabrication of its biomimetic model to obtain a superior drainage controller with multiscale structures with precise surface morphology optimization and geometric deviation control. The film-rupture-based drainage dynamic and mechanisms are characterized by x-ray and high-speed videography, which determines the crucial structures for unique directional drainage. Then the optimized artificial pitchers are further developed into sustained drainage devices with novel applications, such as detection, reaction, and smoke control.