Interesting Task Research Articles

Imperfect refractive index matching in scanning laser optical tomography and a method for digital correction.

Scanning laser optical tomography (SLOT) is a volumetric multi-modal imaging technique that is comparable to optical projection tomography and computer tomography. Image quality is crucially dependent on matching the refractive indexes (RIs) of the sample and surrounding medium, but RI matching often requires some effort and is never perfect. Reducing the burden of RI matching between the immersion medium and sample in biomedical imaging is a challenging and interesting task. We aim at implementing a post processing strategy for correcting SLOT measurements that have errors caused by RI mismatch. To better understand the problems with poorly matched Ris, simulated SLOT measurements with imperfect RI matching of the sample and medium are performed and presented here. A method to correct distorted measurements was developed and is presented and evaluated. This method is then applied to a sample containing fluorescent polystyrene beads and a sample made of olydimethylsiloxane with embedded fluorescent nanoparticles. From the simulations, it is evident that measurements with an RI mismatch larger than 0.02 and no correction yield considerably worse results compared to perfectly matched measurements. RI mismatches larger than 0.05 make it almost impossible to resolve finer details and structures. By contrast, the simulations imply that a measurement with an RI mismatch of up to 0.1 can still yield reasonable results if the presented correction method is applied. The experiments validate the simulated results for an RI mismatch of about 0.09. The method significantly improves the SLOT image quality for samples with imperfectly matched Ris. Although the absolutely best imaging quality will be achieved with perfect RI matching, these results pave the way for imaging in SLOT with RI mismatches while maintaining high image quality.

A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit

AbstractCellular automata (CA) models are effective tools for simulating future urban expansion. With the widespread use of high‐resolution geospatial data for CA simulation, the computational intensity of CA models has increased. Additionally, due to the continuous development of CA modeling research, many scholars have made improvements to the models to enhance their simulation accuracy, resulting in an increasing computational complexity of the model. Consequently, the simulation task based on CA requires vast computing time and memory space. In recent years, deep learning (DL) has experienced rapid development. Many open‐source DL frameworks support graphic processing unit (GPU) parallel computing and provide efficient application programming interfaces (APIs) that can be easily called to handle tasks of interest. In this study, a high‐performance CA model was constructed based on the similarity between the neighborhood effect calculation process of the CA model and the convolutional process in a convolutional neural network (CNN). The convolution function in the DL library is used to calculate the neighborhood effect of the CA model to reduce the time and memory consumption of CA‐based simulation. The experimental results show that compared with the conventional CA model, the execution time of the GPU‐convolution‐CA model proposed in this study has been reduced by more than 98%.

METHODOLOGY OF TEACHING ELEMENTS OF COMBINATORICS AT THE COMPREHENSIVE SECONDARY SCHOOL

The object of the study is the elements of combinatorics at the comprehensive secondary school. The aim of the article is to improve the methodological system of teaching elements of combinatorics at the comprehensive secondary school. The article discusses methodological issues and problems of studying combinatorics elements in the process of teaching mathematics. The methodological features of studying combinatorics elements at the comprehensive secondary school are identified and characterized, their role in the course of mathematics from the point of view of improving the effectiveness of learning is considered. The methods and methodical techniques of teaching combinatorial material, which were previously tested in our educational process, are presented. The possibilities and expediency of their application in connection with the age peculiarities of students are substantiated. Methodological guidelines for teaching individual topics of combinatorics are given. Within the framework of teaching each topic, interesting tasks of applied significance are presented. Effective methods of their solution are given. The presented tasks have been tested for years in our teaching practice. In the process of teaching the elements of combinatorics, methods of cooperative learning, mathematical modeling, graphical modeling, coding, methods that promoting inter- and intra-subject connections were used.

Automated detection and removal of artifacts from sEMG signals based on fuzzy inference system and signal decomposition methods

Surface electromyography (sEMG) signal quality decreases when it is contaminated by different types of artifacts. Detection and removal of the contaminants from sEMG signals were a major and interesting task for several research works in recent years. In this paper, a novel automatic approach was designed for the detection and removal of artifacts from sEMG signals. The proposed method consists of a combined model that based on a fuzzy inference system (FIS), a signal decomposition method, and a denoising technique. Based on three spectral statistical features, which are calculated in frequency domain using power spectral density: composite multiscale entropy (CMSE), kurtosis (K), and skewness (S), FIS detects the artifactual epochs of the sEMG signal. Thereafter, those epochs are decomposed into their components by a signal decomposition. In this work, we have tested the performance of two decomposition methods namely stationary wavelet transform (SWT) and variational mode decomposition (VMD). A second FIS is used to select the contaminated components. Depending on the type of artifact, two procedures are considered, the first aims to denoise motion artifact (MOA), electrocardiography interference (ECG) and power line interference (PLI), whereas the second procedure aims to eliminate the additive white gaussian noise (AWGN). Finally, the denoised signal is reconstructed. The evaluation performance of the proposed method is compared with the performance of some existing methods using the following metrics: SNR improvement (ΔSNR), the reduction in artifact (λ(%)), improvement in signal to noise and distortion ratio (ΔSNDR) and improvement in the root mean square error (ΔRMSE). The results emphazed the superiority of our method over others in terms of all the used metrics and computational time.

SIF-TF: A Scene-Interaction fusion Transformer for trajectory prediction

Accurate pedestrian trajectory prediction is essential for the advancement of intelligent robot or autonomous vehicle, which is a challenging and interesting task. In this paper, a Scene-Interaction fusion Transformer (SIF-TF) for trajectory prediction is proposed, which takes into account three fundamental factors, i.e. social interaction, past trajectory, and semantic scene. A scene-social modeling method is added to the model to integrate social interaction and semantic scene. The proposed SIF-TF contains two critical components: the scene-social transformer and the temporal transformer. The scene-social transformer is tasked with capturing social interaction and semantic scene information, while the temporal transformer focuses on extracting temporal correlation information. Furthermore, the SIF-TF employs a two-stage trajectory prediction approach to jointly generate future trajectories. To evaluate the effectiveness, the comparative experiments were conducted on five widely-used public datasets. The experiments results, with an average evaluation metric of ADE/FDE of 0.23/0.47, significantly outperforms other state-of-the-art methods. These findings demonstrate that the proposed SIF-TF is capable of delivering more precise pedestrian trajectory predictions across diverse scene backgrounds.

Using generative AI to investigate medical imagery models and datasets

BackgroundAI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren’t yet known to experts. MethodsIn this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model’s predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier (“StylEx”); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e. what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g. whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries). FindingsTo demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities – retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown). InterpretationOur approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we plan to release code to enable researchers to train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes. FundingGoogle

Datasets and annotations for layout analysis of scientific articles

For a long time now, datasets containing scientific articles have been crucial to the analysis and recognition of document images. These document collections have frequently served as a testing ground for cutting-edge methods for optical character recognition, layout analysis, and document understanding in general. We thoroughly analyze and compare many datasets proposed for layout analysis of scientific documents, ranging from small collections of scanned papers to modern large-scale datasets containing digital-born papers, which have been proposed to train deep learning-based methods. Furthermore, we outline a detailed taxonomy of the annotation procedures used considering manual, automatic, and generative approaches, and we analyze their benefits and drawbacks. This survey is meant to provide the reader with a review of the most used benchmarks together with detailed information on data, annotations, and complexity, helping scholars to identify the most suitable dataset for their tasks of interest. We also discuss possible open problems to further enhance datasets to support research in the layout analysis of scientific articles.

ANALYTICAL DEFLECTIONS AND STRESS ANALYSIS IN SLENDER BAR FRAMED STRUCTURES SUBJECTED TO ANTISYMMETRIC LATERAL LOADS

Mechanical analysis of the lateral deflections in slender structures is an interesting task to model the mechanical response to transverse forces like seismic or wind forces. Special interest is focused on buildings, communication towers, or tower cranes, as well as any other kind of framed structures. A pure analytical solution for the lateral deflections and vertical displacements, stresses and bending moments of a framed structure under power-law lateral loads is presented in a non-dimensional form. The internal forces of the structure are stated by the force method which drives to a set of linear equations, which are analytically solved, thus reaching an exact, continuous, and differentiable solution. A parametric analysis is performed on a simplified case to show the influence of the dimensionless parameters on the solution. An explicit solution for deflections and bending moments is included for one and two structural levels. The solution is validated via numerical software, using STR and ABAQUS, upon a population of ten, building-like structures. Keywords: structure, deflections, force method, tower, building, lateral load, non-dimensional, antisymmetric

Evaluation of the electronic density at critical points of hydrogen bonds: PM7 a good alternative to ab‐initio methods

AbstractHydrogen bonds are of vital importance in the formation and functioning of organic molecules; their characterization with theoretical tools is a task of interest to the scientific community. Commonly, the electron density at the bond critical point is used to characterize bonds. Generally this task is carried out with ab‐initio methods. In this study, the PM7 semi‐empirical approach is used to evaluate the quality of the electron density at the hydrogen bond critical points (HBCPs). The research takes into account dimers, nucleobases and alpha‐helices bonded by hydrogen bonds: , , , , , and . The results are compared with second‐order Møller–Plesset and density functional theory. The findings demonstrate that PM7 is a reliable alternative for explaining the electron density trends at the HBCPs.

Modelling, control design, and analysis of the inner control's loops intended for single‐phase voltage‐controlled inverter‐based microgrid

AbstractIn voltage‐controlled voltage source inverters (VSIs)‐based microgrids (MGs), the inner control is of prime interest task for guaranteeing safe and stable operation. In this paper, an in‐depth investigation of the modelling, control design, and analysis of the voltage and current inner control loops intended for single‐phase voltage‐controlled VSIs is established. The main objective of this work is to provide a comprehensive study of the mathematical modelling, control design, and performance evaluation of the inner control's loops considering different proportional‐integral (PI) controller types with and without compensation, and to determine the optimal scheme that can offer better performance in terms of implementation simplicity, robustness, and transient and steady‐state responses. Thus, the mathematical closed‐loop models of designed outer voltage and inner current control schemes based on PI, P, and feedforward controllers with and without compensation are, first, derived. Following this, a systematic and effective control design for tuning the different PI controllers’ parameters is proposed. Furthermore, an analysis revealing the performance of the designed voltage and current control schemes is provided. This analysis enables us to choose a P controller and PI feedforward controller for the current control loop and the voltage control loop, respectively. The chosen P and PI controllers should be simple; meanwhile, they should offer a wide bandwidth. A simulation study is carried out in MATLAB/Simulink software to assess the performance of the adopted inner control scheme for both linear and non‐linear loads. In addition, an experimental setup, based on a TMS320F2837xD μC, of a single‐phase VSI supplying linear and non‐linear loads is built to verify the effectiveness and the robustness of the adopted inner controller. The results demonstrated: (1) the necessity of introducing the compensation term, which is responsible for offering control improvement against voltage perturbation, (2) the high tracking performance of the chosen controller in terms of dynamic and steady‐state responses as well as its simplicity of implementation.

Solving Nurse Rostering Optimization Problem using Reinforcement Learning - Simulated Annealing with Reheating Hyper-heuristics Algorithm

Nurse rostering is the process of scheduling hospital nurses for a certain period of time. This is a complex and interesting task that involves multiple perspectives, including those of the hospital, nurses, and patients. In addition, nurse scheduling must consider existing constraints to obtain a schedule that is close to optimal. Common constraints include the number of available nurses, their abilities, labor regulations, and hospital policies. The numerous constraints in nurse scheduling lead to a problem known as the Nurse Rostering Problem (NRP), which is difficult to solve conventionally and is considered an NP-hard problem. Many researchers have attempted to solve NRP computationally, as this approach reduces the time required to create nurse schedules. The less time needed to create nurse schedules, the more time can be allocated to improving medical services, which ultimately impacts the quality of healthcare in the hospital's surrounding area. Several approaches have been used to solve NRP, including hyper-heuristic methods, which are search methods used to solve optimization problems. The aim of this study is to employ a hyper-heuristic approach using Reinforcement Learning - Simulated Annealing with Reheating algorithm to solve the nurse scheduling problem in hospitals in Norway. The results show that the Reinforcement Learning - Simulated Annealing with Reheating algorithm can produce schedules with an 82% improvement in solutions. This algorithm also outperforms the comparison algorithms, with a 3% to 47% improvement over the Simple Random - Hill Climbing algorithm, a 2% to 12% improvement over the Reinforcement Learning - Hill Climbing algorithm, and a 0.7% to 7% improvement over the Reinforcement Learning - Simulated Annealing algorithm.

Unveiling embedded features in Wav2vec2 and HuBERT msodels for Speech Emotion Recognition

Speech Emotion Recognition (SER) is a very interesting task that allows the machine to identify and recognize the different emotional states from human speech using new technologies. The SER can be represented by two main steps, namely feature extraction and emotion classification. Our contribution to the SER field will focus on these two phases. This paper seeks to investigate the influence of embedded features in Wav2vec2 and HuBERT models on SER, two variants per module are implemented, including Wav2vec2 base, Wav2vec2 large, HuBERT large and HuBERT X-large. In addition, we adopt a linear Support Vector Machine (SVM) as a downstream model to recognize emotions. The proposed approach relying on the combination of HuBERT X-large features with the SVM model led to the highest recognition rate of 82.6% on the RAVDESS database. Moreover, the results obtained are promising and in compliance with the current SER state-of-the-art. Hence, the embedded features of HuBERT X-large model have shown significant results for SER.

История развития культурно-исторической теории и ее современное восприятие: ответы на вопросы и вопросы к ответам

Background. The identification of key periods of development in culturalhistorical theory is not just a task of historical interest. It may help to reconstruct the logics and the moving forces in the development of Vygotsky’s theoretical approaches at different periods and may thereby help to improve understanding of key concepts and principles. The area of dialogue to which this article contributes is contemporary perception of cultural-historical theory. Objective. The aim is to analyse and reconstruct the evolution of theoretical thought of L.S. Vygotsky. Methods. The research involved analysis of texts by L.S. Vygotsky, relating to different stages of his scientific path, biographical materials, analytical publications of authors studying the works of L.S. Vygotsky. Results. As literature shows, there is a tendency to highlight some periods of Vygotsky’s theoretical path while underestimating others. In some cases, this limits the development of a holistic evolutionary approach to cultural-historical theory. The article begins with a description of the key stages of Vygotsky’s theoretical evolution with an emphasis on continuity and discontinuity. Then it presents and critically discusses two examples of perception of Vygotsky’s legacy, which are Gonzales Rey’s (2011) identification of “defining moments in Vygotsky’s work”, and Engeström’s (1987, 1990) account of “three generations of CHAT”. Conclusions. Cultural-historical theory is a powerful living and developing theory which provides rich and strong theoretical and experimental tools for contemprary generations of researchers. New dialogues are required: however, a holistic evolutionary approach to the history of cultural-historical theory should remain on the agenda.

Experience in field research and collecting work in the Sinhalese environment in the 21st century

The Sinhalese people, the main population of Sri Lanka, have a rich and original culture, the study of which remains an interesting and urgent task. The author focuses on the experience of her field research in the Sinhalese milieu in the 1970s–80s and, in more detail, in the 21st century, when she made three expedition trips to Sri Lanka (in 2009, 2013 and 2014). Most interesting for her it seemed to uncover the possible innovations in everyday life, in material culture, in people’s mood. In addition, the author (partly in collaboration with colleagues from the Russian Ethnographic Museum and the State Hermitage) achieved some specific goals. In 2013, it was the search for the latest data on the status of the Veddas, the descendants of the island’s autochthons; in 2014 — a thorough acquaintance with the Sinhalese traditional handicraft production. This task was facilitated by the appearance in Sri Lanka of certain novelties interesting for the ethnographer — special original locations for studying and popularizing the Sinhalese folk culture and life, in particular, in the form of the “Ape Gama” Museum (Our village) and the ecological and ethnographic center “Gama Gedara” (Village House). The author also notes the significant assistance of her scholarly Sinhalese colleagues (historians, archaeologists, philologists) and some Buddhist monks. In the final part of the article, she shows how traditions and innovations coexist in the modern Sinhala craft (as visualized in the exhibits donated by her to the Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) RAS).

Impact Of Learning Approaches On Career Interest And Task Persistence Of Woodwork Technology Students In Nigerian Universities

Among the aims of woodwork technology in universities is to prepare graduates with the requisite skills and competencies in woodworking in order to enable them become productive and contribute to national development. Unfortunately, woodwork technology has been plagued with gross under achievement by students. It has also been plagued with poor implementation processes and structures across the tertiary institutions leading to poor career interest and low task persistence of students. The purpose of the study was to determine the impact of learning approaches on career interest & task persistence of woodwork technology students in Nigerian Universities. This study adopted a correlation research design. The population for this study is 76. The population comprised all the 300-level and 400-level students of woodwork technology education programme in the six universities that offer the programme in South-East, Nigeria. The data required for this study was collected using 20-item structured questionnaires. The data collected from the research questions was answered using Pearson Moment correlation coefficient to answer each of the questions. In determining the strength of the relationship Cohen (1988), guidelines were used to interpret the result. They are as follows: Small r =.10 to .29, Medium r =.30 to .49, and Large r =.50 to 1.0 respectively. Hypotheses 1-10 were tested at a 0.05 level of significance using ANOVA output from the Linear Regression Analysis. There is a largely positive relationship between students’ learning approach and career interest of students. There is a significant relationship between students’ learning approach and career interest of woodwork students. There is a strong positive relationship between students’ learning approach and task persistence of woodwork Students. There is a significant relationship between students’ learning approach and task persistence of woodwork students. Based on the findings of the study, the students’ learning approach has a significant influence on career interest of woodwork technology students. There is a significant relationship between students’ learning approach and career interest of woodwork students. The students’ learning approach has a significant influence on task persistence of woodwork technology students. There is also a significant influence of students’ learning approach on task persistence of woodwork students.

Monofluoromethylation of N-Heterocyclic Compounds.

The review focuses on recent advances in the methodologies for the formation or introduction of the CH2F moiety in N-heterocyclic substrates over the past 5 years. The monofluoromethyl group is one of the most versatile fluorinated groups used to modify the properties of molecules in synthetic medical chemistry. The review summarizes two strategies for the monofluoromethylation of N-containing heterocycles: direct monofluoromethylation with simple XCH2F sources (for example, ICH2F) and the assembly of N-heterocyclic structures from CH2F-containing substrates. The review describes the monofluoromethylation of pharmaceutically important three-, five- and six-membered N-heterocycles: pyrrolidines, pyrroles, indoles, imidazoles, triazoles, benzothiazoles, carbazoles, indazoles, pyrazoles, oxazoles, piperidines, morpholines, pyridines, quinolines and pyridazines. Assembling of 6-fluoromethylphenanthridine, 5-fluoromethyl-2-oxazolines, C5-monofluorinated isoxazoline N-oxides, and α-fluoromethyl-α-trifluoromethylaziridines is also shown. Fluoriodo-, fluorchloro- and fluorbromomethane, FCH2SO2Cl, monofluoromethyl(aryl)sulfoniummethylides, monofluoromethyl sulfides, (fluoromethyl)triphenylphosphonium iodide and 2-fluoroacetic acid are the main fluoromethylating reagents in recent works. The replacement of atoms and entire functional groups with a fluorine atom(s) leads to a change and often improvement in activity, chemical or biostability, and pharmacokinetic properties. The monofluoromethyl group is a bioisoster of -CH3, -CH2OH, -CH2NH2, -CH2CH3, -CH2NO2 and -CH2SH moieties. Bioisosteric replacement with the CH2F group is both an interesting task for organic synthesis and a pathway to modify drugs, agrochemicals and useful intermediates.

Milestones on the Quantum Utility Highway: Quantum Annealing Case Study

We introduce quantum utility , a new approach to evaluating quantum performance that aims to capture the user experience by considering the overhead costs associated with a quantum computation. A demonstration of quantum utility by the quantum processing unit (QPU) shows that the QPU can outperform classical solvers at some tasks of interest to practitioners, when considering the costs of computational overheads. A milestone is a test of quantum utility that is restricted to a specific subset of overhead costs and input types. We illustrate this approach with a benchmark study of a D-Wave annealing-based QPU versus seven classical solvers for a variety of problems in heuristic optimization. We consider overhead costs that arise in standalone use of the D-Wave QPU (as opposed to a hybrid computation). We define three early milestones on the path to broad-scale quantum utility. Milestone 0 is the purely quantum computation with no overhead costs and is demonstrated implicitly by positive results on other milestones. We evaluate the performance of a D-Wave Advantage QPU with respect to milestones 1 and 2: For milestone 1, the QPU outperformed all classical solvers in 99% of our tests. For milestone 2, the QPU outperformed all classical solvers in 19% of our tests, and the scenarios in which the QPU found success correspond to cases where classical solvers most frequently failed. This approach of isolating subsets of overheads for separate analysis reveals distinct mechanisms in quantum versus classical performance, which explain the observed differences in patterns of success and failure. We present evidence-based arguments that these distinctions bode well for annealing quantum processors to support demonstrations of quantum utility on ever-expanding classes of inputs and with more challenging milestones in the very near future.

The Role of Teachers and Learners in TBLT

This paper deals with the implementation of TBLT in the classroom. This research discussed the role of teachers and learners in TBLT to improve their ability. The problem statement in this research is how the writing abilities of the students before and after giving Task-Based Language Teaching (TBLT) work among the students with different phases of task activities. Task-Based Language Teaching (TBLT) is an approach that uses tasks as the primary element of planning and instructing in language teaching. In recent years, there has been an increase in interest in Task-Based Language Teaching (TBLT) because TBLT is an interesting task activity that enhances students’ learning process. Learning is a method of active learning that involves multiple levels of acquaintance at the same time. Task-based language teaching is a learner-centred approach. TBLT advocates the shift from teacher-dependence to learner independence and provides authentic materials.

안전권에 대한 헌법적 고찰

‘Freedom’ can be defined as the absence of physical and psychological coercion, and ‘safety’ can be defined as the absence of danger and risk. Danger and risk can lead to undesirable and forcible restrictions on personal freedom. In this respect, security can be said to be a prerequisite for individual freedom. The fundamental right to safety can be said to be an unwritten fundamental right that is recognized even if it is not explicitly stipulated in the Constitution. However, if it is not stipulated in the Constitution, it may be difficult for the Constitutional Court to make a decision on the right to security as a fundamental right. Public safety is an important legal interest and national task that the state must protect. Regarding the legal nature of safety, there are views that view it as a ‘public interest’ and views that view it as a ‘national task.’ Even if the legal nature of safety is viewed as a public interest or national task, it is not a reason to deny the fundamental right to safety. In a situation where security is threatened, the fundamental right as an effective right specified in the Constitution can no longer be understood as an effective right, and may be subordinated to the ‘fundamental right to security’, which is abstract and has the upper hand in balancing test.
 Freedom and security are compatible only when the legislator regulates the content of the law as clearly as possible and ensures legal stability in accordance with the pro- visions of each pre-structured national legal order. From this perspective, the principle of clarity in the rule of law can guarantee legal stability and properly perform the function of guaranteeing freedom when the offender has predictability and substantive and content clarity. In this case, a constitutional balance between freedom and safety can be achieved.
 However, in areas where uncertainty reigns, such as science and technology law and communications law, pre-structured substantive and content laws seek to protect the current legal status by preventing danger and risks. As open legal norms such as the Science and Technology Act and the Communications Act continue to develop, they lose control of legal norms because they use legal concepts and legal terms that are not defined in existing laws. Unclearness of the law in terms of substance and content causes the absence of standards for the content of the law, weakens its binding force, and inevitably leads to uncertainty about the existence of the law. In situations of uncertainty in relation to these areas, it follows that freedom can no longer be guaranteed by safety. This is because safety guarantees based on content-related laws always face limitations.

Optimal design of a simulated-moving-bed separation process for economical production of xylitol from bamboo-hydrolysis byproducts

Xylitol, which is one of industrially useful sugar-alcohols, has received constant attention from various industries. For the eco-friendly and economical production of xylitol, the byproducts from bamboo hydrolysis treatments for bioethanol-production purposes can be utilized as the source for xylitol. Since such bamboo-hydrolysis byproducts were reported to consist of several polyols such as xylitol, xylose, and glycerol, one essential requirement for ensuring a large-scale production of xylitol will be to secure a highly-efficient process for separation of xylitol from the above-mentioned polyol mixture. To address this issue, we aimed to establish an optimal simulated-moving-bed (SMB) process that can accomplish the xylitol-separation task of interest in a continuous mode while maintaining the best performances. It was found first that the use of an ion-exchange resin of styrene–divinylbenzene copolymer in lanthanum form at 60 °C was most suitable as the adsorbent conditions of the xylitol-separation SMB. Under such adsorbent conditions, the intrinsic parameters of the polyol mixture components were determined and then applied to the optimization of the xylitol-separation SMB with a standard type of configuration (2–2–2–2), which was carried out based on the standing wave design method. It was confirmed that the optimized process was successful in keeping the front and rear of each solute band within their corresponding zones under the maximized degree of adsorbent utilization, which suggested that the yield of xylitol and the removal rates of non-products could be maintained at sufficiently high levels while maximizing the SMB throughput. Further comprehensive optimizations revealed that compared to the standard configuration-based SMB, the 5–2–7–2 configuration-based SMB can more than double the xylitol product concentration as well as almost halve the desorbent usage, while maintaining the same throughput and separation capability.