A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.

Previous studies have reported inconsistent associations between low-carbohydrate diets (LCD) and plasma lipid profile. Also, there is little evidence on the role of the quality and food sources of macronutrients in LCD in cardiometabolic health. We investigated the cross-sectional associations between LCD and plasma cardiometabolic risk markers in a nationwide representative sample of the US population. Diet was measured through two 24-h recalls. Overall, healthy (emphasising unsaturated fat, plant protein and less low-quality carbohydrates) and unhealthy (emphasising saturated fat, animal protein and less high-quality carbohydrate) LCD scores were developed according to the percentage of energy as total and subtypes of carbohydrate, protein and fat. Linear regression was used to estimate the percentage difference of plasma marker concentrations by LCD scores. A total of 34 785 participants aged 18–85 years were included. After adjusting for covariates including BMI, healthy LCD was associated with lower levels of insulin, homoeostatic model assessment for insulin resistance (HOMA-IR), C-reactive protein (CRP) and TAG, and higher levels of HDL-cholesterol, with the percentage differences (comparing extreme quartile of LCD score) of −5·91, −6·16, −9·13, −9·71 and 7·60 (all P trend < 0·001), respectively. Conversely, unhealthy LCD was associated with higher levels of insulin, HOMA-IR, CRP and LDL-cholesterol (all P trend < 0·001). Our results suggest that healthy LCD may have positive, whereas unhealthy LCD may have negative impacts on CRP and metabolic and lipid profiles. These findings underscore the need to carefully consider the quality and subtypes of macronutrients in future LCD studies.

The southeastern Central Asian Orogenic Belt (CAOB) records the assembly process between several micro-continental blocks and the North China Craton (NCC), with the consumption of the Paleo-Asian Ocean (PAO), but whether the S-wards subduction of the PAO beneath the northern NCC was ongoing during Carboniferous–Permian time is still being debated. A key issue to resolve this controversy is whether the Carboniferous magmatism in the northern NCC was continental arc magmatism. The Alxa Block is the western segment of the northern NCC and contiguous to the southeastern CAOB, and their Carboniferous–Permian magmatism could have occurred in similar tectonic settings. In this contribution, new zircon U–Pb ages, elemental geochemistry and Sr–Nd isotopic analyses are presented for three early Carboniferous granitic plutons in the southwestern Alxa Block. Two newly identified aluminous A-type granites, an alkali-feldspar granite (331.6 ± 1.6 Ma) and a monzogranite (331.8 ± 1.7 Ma), exhibit juvenile and radiogenic Sr–Nd isotopic features, respectively. Although a granodiorite (326.2 ± 6.6 Ma) is characterized by high Sr/Y ratios (97.4–139.9), which is generally treated as an adikitic feature, this sample has highly radiogenic Sr–Nd isotopes and displays significantly higher K2O/Na2O ratios than typical adakites. These three granites were probably derived from the partial melting of Precambrian continental crustal sources heated by upwelling asthenosphere in lithospheric extensional setting. Regionally, both the Alxa Block and the southeastern CAOB are characterized by the formation of early Carboniferous extension-related magmatic rocks but lack coeval sedimentary deposits, suggesting a uniform lithospheric extensional setting rather than a simple continental arc.

We investigate various variable martingale Hardy spaces corresponding to variable Lebesgue spaces $\mathcal {L}_{p(\cdot )}$ defined by rearrangement functions. In particular, we show that the dual of martingale variable Hardy space $\mathcal {H}_{p(\cdot )}^{s}$ with $0<p_{-}\leq p_{+}\leq 1$ can be described as a BMO-type space and establish martingale inequalities among these martingale Hardy spaces. Furthermore, we give an application of martingale inequalities in stochastic integral with Brownian motion.

Automatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a geometric domain with complex shapes. This paper presents a self-learning element extraction system, FreeMesh-S, that can automatically acquire robust and high-quality element extraction rules. Two central components enable the FreeMesh-S: (1) three primitive structures of element extraction rules, which are constructed according to boundary patterns of any geometric boundary shapes; (2) a novel self-learning schema, which is used to automatically define and refine the relationships between the parameters included in the element extraction rules, by combining an Advantage Actor-Critic (A2C) reinforcement learning network and a Feedforward Neural Network (FNN). The A2C network learns the mesh generation process through random mesh element extraction actions using element quality as a reward signal and produces high-quality elements over time. The FNN takes the mesh generated from the A2C as samples to train itself for the fast generation of high-quality elements. FreeMesh-S is demonstrated by its application to two-dimensional quad mesh generation. The meshing performance of FreeMesh-S is compared with three existing popular approaches on ten pre-defined domain boundaries. The experimental results show that even with much less domain knowledge required to develop the algorithm, FreeMesh-S outperforms those three approaches in essential indices. FreeMesh-S significantly reduces the time and expertise needed to create high-quality mesh generation algorithms.

Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

The dependence of fishbone cycle on energetic particle intensity has been investigated in EAST low-magnetic-shear plasmas. It is observed that the fishbone mode growth rate, saturation amplitude as well as fishbone cycle frequency clearly increase with increasing neutral beam injection (NBI) power. Moreover, enhanced electron density and temperature perturbations as well as energetic particle loss were observed with greater injected NBI power. Simulation results using M3D-K code show that as the NBI power increases, the resonant frequency and the energy of the resonant particles become higher, and the saturation amplitude of the mode also changes, due to the non-perturbative energetic particle contribution. The relationship between the calculated energetic particle pressure ratio and fishbone cycle frequency is obtained as ${f_{\textrm{FC}}} = 2.2{(1000{\beta _{\textrm{ep,calc}}} - 0.1)^{5.9 \pm 0.5}}$. Results consistent with the experimental observations have been achieved based on a predator–prey model.

This paper proposes a task-related electroencephalogram research framework (tEEG framework) to guide scholars’ research on EEG-based cognitive and affective studies in the context of design. The proposed tEEG framework aims to investigate design activities with loosely controlled experiments and decompose a complex design process into multiple primitive cognitive activities, corresponding to which different research hypotheses on basic design activities can be effectively formulated and tested. Thereafter, existing EEG techniques and methods can be applied to analyse EEG signals related to design. Three application examples are presented at the end of this paper to demonstrate how the proposed framework can be applied to analyse design activities. The tEEG framework is presented to guide EEG-based cognitive and affective studies in the context of design. Existing methods and models are summarized, for the effective application of the tEEG framework, from the current literature spread in a wide spectrum of resources and fields.

Primary liver cancer is the third leading cause of cancer-related death worldwide. Most patients are diagnosed at late stages with poor prognosis; thus, identification of modifiable risk factors for primary prevention of liver cancer is urgently needed. The well-established risk factors of liver cancer include chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), heavy alcohol consumption, metabolic diseases such as obesity and diabetes, and aflatoxin exposure. However, a large proportion of cancer cases worldwide cannot be explained by current known risk factors. Dietary factors have been suspected as important, but dietary aetiology of liver cancer remains poorly understood. In this review, we summarised and evaluated the observational studies of diet including single nutrients, food and food groups, as well as dietary patterns with the risk of developing liver cancer. Although there are large knowledge gaps between diet and liver cancer risk, current epidemiological evidence supports an important role of diet in liver cancer development. For example, exposure to aflatoxin, heavy alcohol drinking and possibly dairy product (not including yogurt) intake increase, while intake of coffee, fish and tea, light-to-moderate alcohol drinking and several healthy dietary patterns (e.g. Alternative Healthy Eating Index) may decrease liver cancer risk. Future studies with large sample size and accurate diet measurement are warranted and need to consider issues such as the possible aetiological heterogeneity between liver cancer subtypes, the influence of chronic HBV or HCV infection, the high-risk populations (e.g. cirrhosis) and a potential interplay with host gut microbiota or genetic variations.

During the last glacial termination, a warming trend was generally interrupted by rapid millennium-scale cold reversals, such as the Greenland (Isotope) Stadial 1 (GS-1) and GS-2a events. To understand how glaciers on the Tibetan Plateau (TP) responded to these rapid climate events, this study constrained the timing and extent of three glacial events during the late-glacial period. Specifically, using a cosmogenic 10Be exposure dating method, we dated three prominent glacial moraines (PM1, PM2, PM3) back to 15,850 ± 980, 14,140 ± 880, and 12,430 ± 790 yr in the Pagele valley, southern TP, corresponding to GS-2a, Greenland Interstadial 1 (GI-1), and GS-1, respectively. By simulating glacial extents forced by different climate scenarios, the study constrained the temperature decreases relative to present to be 2.6°C–2.9°C, ~1.6°C, and 1.4°C–1.5°C during the GS-2a, GI-1, and GS-1 periods in the region, with precipitation values of 60%–80%, ~100%, and 80%–90% of present value, respectively. Considering information from oceanic and atmospheric circulation, the study suggested that on the TP, the glacial events during the last glacial termination were well connected with the millennium-scale climate events in the North Atlantic region through the westerlies, while the Indian summer monsoon played a positive role in sustaining the glaciers under the warming climate trend.

Information collection may affect the design quality and designer's performance through changing the structure of information and the way how information is searched and organized. Based on the theoretical analysis conducted by Wang et al., the present work continues to investigate the influence of designer's natural choice of information collection strategy on his/her mental stress both theoretically and empirically. Designers’ stresses are quantified from HRV data and are compared under different information collection strategies.

Design protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.

A miniaturized tri-passband power divider integrating a power divider with two tri-band bandpass filters is presented. The short- and open-stub-loaded resonators are used in this presented power divider to implement the bandpass-filtering response. Then, the presented power divider has not only the power-dividing/combining function, but also the tri-passband filtering response. When the central frequency of the second passband is fixed, the other two passband central frequencies of the tri-band power divider can be flexibly controlled by changing the lengths of the stubs. The even- and odd-mode equivalent circuits of the proposed tri-band power divider are analyzed, and the design equations are derived, which can be used to guide the design of the presented miniaturized tri-passband power divider. The measured results agree well with the simulated ones.

In this paper, the recent studies of laboratory astrophysics with strong magnetic fields in China have been reviewed. On the Shenguang-II laser facility of the National Laboratory on High-Power Lasers and Physics, a laser-driven strong magnetic field up to 200 T has been achieved. The experiment was performed to model the interaction of solar wind with dayside magnetosphere. Also the low beta plasma magnetic reconnection (MR) has been studied. Theoretically, the model has been developed to deal with the atomic structures and processes in strong magnetic field. Also the study of shock wave generation in the magnetized counter-streaming plasmas is introduced.

Objective: To study the relationship of Nε-(carboxymethyl)-lysine level (CML) with microstructure changes of white matter (WM), and cognitive impairment in patients with type 2 diabetes mellitus (T2DM) and to discuss the potential mechanism underlying T2DM-associated cognitive impairment. Methods: The study was performed in T2DM patients (n=22) with disease course ≥5 years and age ranging from 65 to 75 years old. A control group consisted of 25 sex- and age-matched healthy volunteers. Fractional anisotropy (FA) of several WM regions was analyzed by diffusion tensor imaging scan. Plasma CML levels were measured by enzyme-linked immunosorbent assay, and cognitive function was assessed by Mini-Mental State Examination and Montreal cognitive assessment (MoCA). Results: The total Mini-Mental State Examination score in the patient group (25.72±3.13) was significantly lower than the control group (28.16±2.45) (p<0.05). In addition, the total MoCA score in the patient group (22.15±3.56) was significantly lower than the control group 25.63±4.12) (p<0.01). In the patient group, FA values were significantly decreased in the corpus callosum, cingulate fasciculus, inferior fronto-occipital fasciculus, parietal WM, hippocampus, and temporal lobes relative to corresponding regions of healthy controls (p<0.05). Plasma CML level was negatively correlated with average FA values in the global brain (r=−0.58, p<0.01) and MoCA scores (r=−0.47, p<0.05). Conclusions: In T2DM, WM microstructure changes occur in older patients, and elevations in CML may play a role in the development of cognitive impairment.

This study aimed to explore whether the presence of a Y chromosome azoospermia factor (AZF) microdeletion confers any adverse effect on embryonic development and clinical outcomes after intracytoplasmic sperm injection (ICSI) treatment. Fifty-seven patients with AZF microdeletion were included in the present study and 114 oligozoospermia and azoospermia patients without AZF microdeletion were recruited as controls. Both AZF and control groups were further divided into subgroups based upon the methods of semen collection: the AZF-testicular sperm extraction subgroup (AZF-TESE, n = 14), the AZF-ejaculation subgroup (AZF-EJA, n = 43), the control-TESE subgroup (n = 28) and the control-EJA subgroup (n = 86). Clinical data were analyzed in the two groups and four subgroups respectively. A retrospective case–control study was performed. A significantly lower fertilization rate (69.27 versus 75.70%, P = 0.000) and cleavage rate (89.55 versus 94.39%, P = 0.000) was found in AZF group compared with the control group. Furthermore, in AZF-TESE subgroup, the fertilization rate (67.54 versus 74.25%, P = 0.037) and cleavage rate (88.96 versus 94.79%, P = 0.022) were significantly lower than in the control-TESE subgroup; similarly, the fertilization rate (69.85 versus 75.85%, P = 0.004) and cleavage rate (89.36 versus 94.26%, P = 0.002) in AZF-EJA subgroup were significantly lower than in the control-EJA subgroup; however, the fertilization rate and cleavage rate in AZF-TESE (control-TESE) subgroup was similar to that in the AZF-EJA (control-EJA) subgroup. The other clinical outcomes were comparable between four subgroups (P > 0.05). Therefore, sperm from patients with AZF microdeletion, obtained either by ejaculation or TESE, may have lower fertilization and cleavage rates, but seem to have comparable clinical outcomes to those from patients without AZF microdeletion.

Synthesis of well-defined sodium yttrium fluoride (NaYF4) nanocrystals has been achieved in nonpolar solvents, but these nanocrystals possess a hydrophobic surface and need to be surface-modified for various biological applications. Development of facile aqueous solution method to synthesize one-dimensional NaYF4 with a hydrophilic surface still remains challenging. Herein, we demonstrate a simple route to prepare hydrophilic NaYF4 nanorods by using hydrophobic NaYF4 nanospheres as precursor. It is interesting to find that hydrothermal treatment of oleic acid-capped NaYF4 nanocrystals can not only induce anisotropic growth of these nanocrystals but also change their surface properties. The hydrophilic NaYF4 nanorods synthesized in this work has been well characterized and possible formation mechanism has also been discussed.