Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of the Word Similarity Measurement in English-Arabic Translation Memory Segment Retrieval Including an Inflectional Affix Intervention

The aim of this paper is to investigate the similarity measurement approach of translation memory (TM) in five representative computer-aided translation (CAT) tools when retrieving inflectional verb-variation sentences in Arabic to English translation. In English, inflectional affixes in verbs include suffixes only; unlike English, verbs in Arabic derive voice, mood, tense, number and person through various inflectional affixes e.g. pre or post a verb root. The research question focuses on establishing whether the TM similarity algorithm measures a combination of the inflectional affixes as a word or as a character intervention when retrieving a segment. If it is dealt with as a character intervention, are the types of intervention penalized equally or differently? This paper experimentally examines, through a black box testing methodology and a test suite instrument, the penalties that TM systems’ current algorithms impose when input segments and retrieved TM sources are exactly the same, except for a difference in an inflectional affix. It would be expected that, if TM systems had some linguistic knowledge, the penalty would be very light, which would be useful to translators, since a high-scoring match would be presented near the top of the list of proposals. However, analysis of TM systems’ output shows that inflectional affixes are penalized more heavily than expected, and in different ways. They may be treated as an intervention on the whole word, or as a single character change.
DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues

Recently, the Machine Translation (MT) community has become more interested in document-level evaluation especially in light of reactions to claims of “human parity”, since examining the quality at the level of the document rather than at the sentence level allows for the assessment of suprasentential context, providing a more reliable evaluation. This paper presents a document-level corpus annotated in English with context-aware issues that arise when translating from English into Brazilian Portuguese, namely ellipsis, gender, lexical ambiguity, number, reference, and terminology, with six different domains. The corpus can be used as a challenge test set for evaluation and as a training/testing corpus for MT as well as for deep linguistic analysis of context issues. To the best of our knowledge, this is the first corpus of its kind.
Comparative proteomic analysis of hybrid maize MR2008 and its parental lines

Abstract Maize is one of the three staple foods in the world. The white variety represents 60% of the maize importation with a world consumption of 1125 million tons in 2019/2020. Currently, new technologies could contribute to the analysis of this seed, supporting quality control and improvement. This study aims to carry out the morphological and proteomic comparison between the hybrid MR2008 and its parental lines LUG03 and CML491 through mass spectrometry and bioinformatics analysis. Herein, we identified that 34.8% of the hybrid proteome differs from the parental proteome. Also, ontological and morphological analyses determined that the hybrid exhibits more characteristics related to CML491 than LUG03, for example, metabolic pathways and enzymes, such as anthocyanidin 3-O-glucosyltransferase (UniProt P16166). This analysis allowed the identification of dominant characters, metabolic pathways and confirms the utility of this methodology in agricultural practices, mainly in processes of selection and quality control of a crop.
Effectiveness of a WHO self‐help psychological intervention for preventing mental disorders among Syrian refugees in Turkey: a randomized controlled trial

Refugees are at high risk of developing mental disorders. There is no evidence from randomized controlled trials (RCTs) that psychological interventions can prevent the onset of mental disorders in this group. We assessed the effectiveness of a self‐help psychological intervention developed by the World Health Organization, called Self‐Help Plus, in preventing the development of mental disorders among Syrian refugees experiencing psychological distress in Turkey. A two‐arm, assessor‐masked RCT was conducted in two Turkish areas. Eligible participants were adult Syrian refugees experiencing psychological distress (General Health Questionnaire ≥3), but without a diagnosis of mental disorder. They were randomly assigned either to the Self‐Help Plus arm (consisting of Self‐Help Plus combined with Enhanced Care as Usual, ECAU) or to ECAU only in a 1:1 ratio. Self‐Help Plus was delivered in a group format by two facilitators over five sessions. The primary outcome measure was the presence of any mental disorder assessed by the Mini International Neuropsychiatric Interview at six‐month follow‐up. Secondary outcome measures were the presence of mental disorders at post‐intervention, and psychological distress, symptoms of post‐traumatic stress disorder and depression, personally identified psychological outcomes, functional impairment, subjective well‐being, and quality of life at post‐intervention and six‐month follow‐up. Between October 1, 2018 and November 30, 2019, 1,186 refugees were assessed for inclusion. Five hundred forty‐four people were ineligible, and 642 participants were enrolled and randomly assigned to either Self‐Help Plus (N=322) or ECAU (N=320). Self‐Help Plus participants were significantly less likely to have any mental disorders at six‐month follow‐up compared to the ECAU group (21.69% vs. 40.73%; Cramer's V = 0.205, p<0.001, risk ratio: 0.533, 95% CI: 0.408‐0.696). Analysis of secondary outcomes suggested that Self‐Help Plus was not effective immediately post‐intervention, but was associated with beneficial effects at six‐month follow‐up in terms of symptoms of depression, personally identified psychological outcomes, and quality of life. This is the first prevention RCT ever conducted among refugees experiencing psychological distress but without a mental disorder. Self‐Help Plus was found to be an effective strategy for preventing the onset of mental disorders. Based on these findings, this low‐intensity self‐help psychological intervention could be scaled up as a public health strategy to prevent mental disorders in refugee populations exposed to ongoing adversities.
Methods for Asphalt Road Density Measurement: A Review

Compaction process of the asphalt pavement is to reduce the air voids inside. It is important to monitor the quality of the compaction by density measurement. The thinner the asphalt pavement is, the more challenging the density measurement is. A conventional method for measuring density (i.e., collecting data) is to extract core samples by coring method and then conduct the in-lab tests. This method, so called destructive method, provides the highly accurate density measurements, but need to destroy the compacted pavement. Several Non-destructive (ND) methods for density measurement have also been developed. The term ND method refers to the method measuring density without destroying the pavements. They include nuclear density gauge (NDG), pavement quality indicator (PQI) and ground penetrating radar (GPR) . They are respectively based on the effects of the density on the nuclear (Gamma ray) and electromagnetic signals. A common problem of them is that their measurement results are affected by the factors other than density and they are very hard to be modelled or quantified. This paper is a review of the mentioned methods which are applied in measuring the density of the thin-layer asphalt pavement. Their operational theories, features and accuracies will be discussed respectively.
An Ensemble Method for the Heterogeneous Neural Network to Predict the Remaining Useful Life of Lithium-ion Battery

With the large-scale application of lithium-ion batteries (LIB), using deep neural networks to predict the remaining useful life (RUL) of LIB has gradually become a hotshot in recent years. RUL prediction method based on deep neural network can avoid studying electrochemical phenomena and manual extracting the features in battery. But single neural network has the different prediction accuracy and features extraction on different dataset. In this study, an ensemble method for the heterogeneous neural network is proposed, which integrates the prediction results of multiple heterogeneous neural networks with the adaptive weight. The weight of the neural network is higher with the closer correlation to the majority prediction results, vice versa. Furthermore, the weight of the neural network is adjusted via the predicting results for neural network on the different dataset, so that the computed weight of the neural network is adapted to the various dataset, and the effects of poor predictions of certain neural networks can be reduced sufficiently. The effectiveness of the ensemble method is verified on MIT-Stanford LIB degradation dataset, and the results show that the proposed method has higher accuracy than the existing ensemble methods for neural network.
Intelligent Search for Personalized Cancer Therapy Synthesis: an Experimental Comparison

CRC is one of the deadliest forms of tumor for adult humans, which often requires expensive and highly toxic treatments. In this work, we exploit a recent System Biology Markup Language (SBML) quantitative model of the tumor growth and its immune response to two immunotherapic drugs and define a non-linear, constrained optimization problem for the synthesis of personalized therapies. We compute a virtual population of physiologically admissible patients and define a parametric therapy model that enables the usage of standard parameter optimization algorithms. The whole approach is implemented with a novel methodology that requires a single tool, namely COPASI, for the therapy template modelling, the definition of the optimization problem and its solution. We present results of an experimental evaluation of our approach by comparing five search algorithms among those implemented in COPASI, in order to find out the most promising kind of algorithms for this problem. The results show that population-based search algorithms perform well and are able to compute personalized therapies within time bounds compatible with clinical practice.
A Novel Predictive Approach to Trajectory-aware Online Service Allocation in Mobile Edge Environment*

The mobile edge computing (MEC) paradigm places traditional digital infrastructure next to mobile networks and thus drives substantial improvements in performance and latency for mobile computing cases like gaming, video streaming, and IoT. However, it remains a great challenge to provide a effictive and performance guaranteed strategies for services offloading and migration in the MEC environment. Most existing solutions in this direction tend to consider task offloading as a offline decision making process by employing transient positions of users as model inputs. In this work instead, we consider a predictive-trajectory-aware task offloading strategy called PreMig. Simulations clearly demonstrate that our proposed strategy outperforms traditional ones in terms of effective service rate and migration overhead.
Evaluating the Effect of Weather on Tourist Revisit Intention using Natural Language Processing and Classification Techniques

Tourists’ revisit has significant monetary benefits to destinations because the cost of retaining existing visitors is less than attracting new visitors. Re-visit intention is often based on tourists experience and satisfaction at a destination. An important aspect that influences the relationship between satisfaction and intention to revisit is the weather conditions at a destination given the increased frequency of heatwaves that strike summer holiday destinations over the summer months. This work applies natural language processing and classification techniques to evaluate the impact of weather information on revisit intention utilizing reviews from TripAdvisor and online weather data. Information retrieval techniques (Doc2Vec) are applied on online reviews collected during the summer months between 2010-2019 from tourists that visited Cyprus. Reviews are labeled as "revisits" or "neutral" based on their textual content. The labelled reviews dataset is enhanced with weather information based on the reviews’ timestamp, such as temperature and humidity of tourists’ country of origin and Cyprus at the time of the visit to the hotel/destination. To account for the influence of hotel infrastructure and available services to deal with heatwaves (i.e., climate-controlled), the training dataset included hotel star rating as an additional parameter. An ensemble gradient boosting tree classifier is trained utilizing the compiled dataset to predict revisit intention. The classifier is evaluated against the area under the curve. To interpret the classifier’s inherent patterns, a popular machine learning interpretation technique is used, namely Shapley Additive Explanation (SHAP). Visualizations of the model using SHAP indicate that the heat index and weather difference between destination and country of origin influence revisit intention. Such preliminary insights are encouraging for further investigations with an end goal to develop a decision support system to assist destination managers during their target marketing campaigns.
An Intersectionality-Based Policy Analysis Examining the Complexities of Access to Wild Game and Fish for Urban Indigenous Women in Northwestern Ontario

The destruction of Indigenous food systems is a direct consequence of the settler-colonial project within Canada and has led to decreasing access to Indigenous foods, disproportionate rates of food insecurity and disconnection from Indigenous food systems and environments. We interviewed Indigenous women, non-Indigenous staff of Indigenous-serving organizations, and policymakers (i.e., those who develop, interpret, or implement wild food policy) to explore how the policy context has impacted Indigenous women and their communities’ experiences of accessing Indigenous foods in urban northwestern Ontario. We applied an Intersectionality-Based Policy Analysis (IBPA) Framework to shape our research questions and guide the thematic analysis of the data. We found that stakeholder groups had differing understandings of the issue of accessing wild foods and Indigenous food security and their actions either supported or disrupted efforts for access to wild food to promote food security or Indigenous Food Sovereignty. Policymakers cited necessary barriers to promote food safety and support conservation of wildlife. Staff of Indigenous-serving organizations approached the issue with consideration of both Western and Indigenous worldviews, while Indigenous women spoke about the ongoing impacts of colonial policy and government control over their lands and territories. The main policy areas discussed included residential school policy, food regulation, and natural resource regulation. We also investigated community-level strategies for improvement, such as a wild game license. Throughout, we tied the colonial control over ‘wildlife’ and the Western food safety discourse, with infringements on Indigenous Food Sovereignty, experiences of racism in food settings and on the land, as well as with broad control over Indigenous sovereignty in Ontario. This work contributes to an increased understanding of how Western discourses about health, food, and the environment are perpetuated through systemic racism in government policy and reiterated through policymakers' views and interpretations or actions. Government institutions must develop culturally safe partnerships with Indigenous leaders and organizations to facilitate a transfer of power that can support Indigenous Food Sovereignty.
Ectopic Expression of the Allium cepa 1-SST Gene in Cotton Improves Drought Tolerance and Yield Under Drought Stress in the Field

In some plants, sucrose: sucrose 1-fructosyltransferase (1-SST) is the first irreversible key enzyme in fructan biosynthesis. Studies have shown that fructan accumulation enhances abiotic stress tolerance of plants. To investigate the role of 1-SST in drought stress responses, a total of 37 cotton plants expressing a 1-SST gene from Allium cepa were developed by Agrobacterium-mediated transformation. Under drought stress in the field, compared with wild-type, ectopic expression of Ac1-SST in cotton resulted in significantly higher soluble sugars (especially 1-kestose), proline and relative water contents, as well as decreased malondialdehyde content, which contributed to maintaining intracellular osmoregulation and reducing membrane damage. In addition, ectopic expression of Ac1-SST in cotton significantly improved the photosynthesis rate, performance of PSII (including Pn, Fv/Fm, WUE, ΦPSII, and PItotal) and plant growth under drought stress. Furthermore, compared with the wild-type, under the droughted field, the yield loss per square meter of transgenic cotton was reduced by an average of 20.9% over two consecutive years. Our results indicate that the Ac1-SST gene can be used to improve drought tolerance and yield of cotton varieties, and might also be a promising drought-resistant gene for improving other crop varieties.
The Electricity Rebound Effect: Empirical Evidence From the Chinese Chemical Industry

The Chinese government has adopted many policies to save energy and electricity in the chemical industry by improving technology and reforming its electricity market. The improved electricity efficiency and the electricity reform may indirectly reduce expected energy and electricity savings by decreasing the effective electricity price and the marginal cost of electricity services. To analyze the above issues, this paper employs the Morishima Elasticity of Substitution of the electricity cost share equation which is estimated by the DOLS method. The results show that: 1) There exists a rebound effect in the Chinese chemical industry, but it is quite large because the electricity price is being controlled by the government; 2) the reform of the electricity market reduces the rebound effect to 73.85%, as electricity price begins to reflect cost information to some extent; 3) there is still a lot of space for the reform to improve, and the rebound effect could be reduced further once the electricity price is adjusted to transfer the market information more correctly. In order to succeed in saving electricity and decreasing the rebound effect in the chemical industry, the policy implications are provided from perspectives of the improved energy efficiency and electricity pricing mechanism.
How Cues of Being Watched Promote Risk Seeking in Fund Investment in Older Adults

Social cues, such as being watched, can subtly alter fund investment choices. This study aimed to investigate how cues of being watched influence decision-making, attention allocation, and risk tendencies. Using decision scenarios adopted from the “Asian Disease Problem,” we examined participants’ risk tendency in a financial scenario when they were watched. A total of 63 older and 66 younger adults participated. Eye tracking was used to reveal the decision-maker’s attention allocation (fixations and dwell time per word). The results found that both younger and older adults tend to seek risk in the loss frame than in the gain frame (i.e., framing effect). Watching eyes tended to escalate reckless gambling behaviors among older adults, which led them to maintain their share in the depressed fund market, regardless of whether the options were gain or loss framed. The eye-tracking results revealed that older adults gave less attention to the sure option in the eye condition (i.e., fewer fixations and shorter dwell time). However, their attention was maintained on the gamble options. In comparison, images of “watching eyes” did not influence the risk seeking of younger adults but decreased their framing effect. Being watched can affect financial risk preference in decision-making. The exploration of the contextual sensitivity of being watched provides us with insight into developing decision aids to promote rational financial decision-making, such as human-robot interactions. Future research on age differences still requires further replication.
Specific Turbulence- and Chaoborus-Induced Morphotypes Affect the Streamlining Properties of Daphnia cucullata

Inducible defenses are a wide-spread defensive mechanism in Daphnia. For example, D. cucullata is known to form different adaptive morphologies under changing environmental conditions. In this species, predator presence elicits defensive helmets. Defended animals perform better and survive more frequently in predation attempts. Another peculiarity of D. cucullata is that they develop these defenses also in response to turbulence. The mechanisms that underly this defensive effect are so far unclear. Therefore, we investigated the three-dimensional morphology of the typical, the turbulence- and Chaoborus-exposed morph and analyzed their surface and volume alterations. We furthermore measured the swimming velocities of these morphs. With the determined shapes and the measured velocities, we simulated the drag force under different angles of attack and determined the energy necessary to move through the water. In the light of previous findings and hypotheses we here discuss biomechanical mechanisms that improve D. cucullata’s fitness in the investigated environments.
Emerging Role of LncRNA Regulation for NLRP3 Inflammasome in Diabetes Complications

Diabetes is a widespread metabolic disease with various complications, including diabetic nephropathy, retinopathy, cardiomyopathy, and other cardiovascular or cerebrovascular diseases. As the prevalence of diabetes increases in all age groups worldwide, diabetes and its complications cause an emerging public health burden. NLRP3 inflammasome is a complex of several proteins that play a critical role in inflammatory response and various diseases, including diabetes and its complications. Accumulating evidences indicate that NLRP3 inflammasome contributes to the development of diabetes and diabetic complications and that NLRP3 inflammation inactivation is beneficial in treating these illnesses. Emerging evidences suggest the critical role of long non-coding RNAs (lncRNAs) in regulating NLRP3 inflammasome activity in various diseases. LncRNAs are non-coding RNAs exceeding 200 nucleotides in length. Its dysregulation has been linked to the development of diseases, including diabetes. Recently, growing evidences hint that regulating lncRNAs on NLRP3 inflammasome is critical in developing and progressing diabetes and diabetic complications. Here, we discuss the role of lncRNAs in regulating NLRP3 inflammasome as well as its participation in diabetes and diabetic complications, providing novel insights into developing future therapeutic approaches for diabetes.