Miracle-dz/UniFAR-FTUs huggingface dataset:
Grammar-based preprocessing for PPM compression and classification

The aim of this study is to investigate the efficiency of novel methods using context-free grammars and Prediction by Partial Matching (PPM) in order to build and evaluate the quality of compression models for text files such as English, Arabic, Persian, Welsh and Chinese. A further aim is then to apply these models to the problem of the classification of text to see how well they perform at this application. We apply grammar-based pre-processing prior to using the PPM compression algorithm. The methods achieve significantly better compression for different natural language texts compared with other well-known compression methods. Our method first generates a grammar based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in the text being compressed and then substitutes these sequences using the respective non-terminal symbols defined by the grammar in a pre-processing phase prior to compression. We describe further improvements using a two-pass scheme where grammar-based preprocessing is applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary Corpus and also achieve significantly improved results in compression when compared with other compression algorithms, including a grammar-based approach known as the Sequitur algorithm. Despite the advances of the PPM method in predicting upcoming symbols or words in the English language, more research is required to devise better compression methods for other languages, such as Arabic due to, for example, the rich morphological nature of Arabic text, in which a single word can take many different forms. In this dissertation, we propose a new method that achieves the best compression rates not only for Arabic language text but also for other languages that use Arabic script in their writing systems, such as Persian. Our word-based method (GRW-PPM) constructs a contextfree grammar for the text; this grammar is then encoded using PPM to achieve excellent compression rates. Finally, we investigate the classification of genre in English and Arabic text by using our new character-based text compression scheme (GRB-PPM). Experimental results on a parallel Arabic and English corpus show that our new method is very effective compared with traditional compression-based classification methods. We have also confirmed that good compression leads to good classification.
Cover crops and compost prevent weed seed bank buildup in herbicide‐free wheat–potato rotations under conservation tillage

Abstract Weeds are a major constraint affecting crop yields in organic farming and weed seed bank analysis can be an important tool for predicting weed infestation and assessing farming system sustainability. We compared the weed seed banks two and four years after transition from conventional to reduced tillage in organically managed winter wheat–potato cropping sequences in two replicated field trials. Experimental factors were either conventional (CT) with moldboard (25 cm) or reduced tillage (RT) with chisel ploughing (5–15 cm). Dead mulch (8–10 cm), consisting of rye–pea or triticale–vetch mixtures, was additionally applied to potatoes in the RT system. In both systems, one‐half of the plots received 5 t (ha/year) dry matter of a commercially sold yard waste compost as an organic amendment. Furthermore, subsidiary crops were grown in both systems, either as legume living mulches undersown in wheat or as cover crops sown after wheat. Prior to sowing the wheat and after potatoes, the soil seed bank from 0 to 12.5 and from 12.5 to 25 cm was sampled and assessed in an unheated glasshouse over nine months. The initial weed seed bank size in the topsoil was uniform (4,420 seedlings m−2). Two years later, wheat‐associated weeds, such as Galium aparine, Lamium spp. , and Myosotis arvensis, were 61% higher on average in RT than in CT. This was independent of subsidiary crops used. In contrast, Chenopodium album, a potato‐associated weed that depends on intensive tillage, was reduced by 15% in the mulched RT system compared to CT. When RT was combined with cover crops and compost application, the seed bank did not differ significantly from the CT system. We conclude that subsidiary crops, mulches, and potentially compost are important management tools that contribute to the success of RT in herbicide‐free cereal‐based systems in temperate climates.
Application of bone transgenic zebrafish in anti‐osteoporosis chemical screening

The zebrafish (Danio rerio) has recently been shown to be an ideal model to study bone disease including osteoporosis. The zebrafish osteoporosis model could be induced by glucocorticoid treatment with chemical staining for reflecting the level of bone mineralization. However, this methodology was unstable. Here, we developed a novel methodology to directly evaluate the bone mass and density.
A Scheduling for Slotted-CSMA-based Wireless Mesh Networks to Reduce Delivery Delay

In this paper, we propose a new scheduling problem for WMNs based on slotted-CSMA. Slotted CSMA is a mechanism that divides a single frequency channel into several time slots where CSMA functions. With a schedule that matches links and slots, each node transmits frames in the assigned slot to avoid collision of frames. CATBS (CSMA-Aware Time-Boundable Scheduling) is a slotted-CSMA-based WMN architecture with a scheduling algorithm. However, it suffers from large end-to-end delay due to its long slot time that allows transmitting several frames within a single slot. This paper extends the scheduling problem of CATBS to consider inter-slot collision to reduce the overhead of collision at slot boundaries even if using short slot time. Evaluation results shows that the proposed scheduling problem reduces the overhead that arises at slot boundaries, and improves communication performance when using short time slots.
Underwater place recognition using forward‐looking sonar images: A topological approach

Autonomous underwater vehicles are a prominent tool for underwater exploration because they can access dangerous places avoiding the risks for the human beings. However, the autonomous navigation still a challenge due to the characteristics of the environment that decrease the performance of the sensor and the robot perception. In this context, this paper proposes a loop closure detector addressed to the simultaneous localization and mapping problem at semistructured environments using acoustic images acquired by forward‐looking sonars. The images are segmented by an adaptative approach based on the acoustic beams analysis. A pose‐invariant topological graph is build to represent the relationship between image features. The loop closure detection is achieved using a graph comparison. The approach is evaluated in a real environment at a marina. The results reveal all loop closures of the data set are detected with a high precision and present an invariant to image rotation.
Intelligent support for exploration of data graphs

This research investigates how to support a user’s exploration through data graphs generated from semantic databases in a way leading to expanding the user’s domain knowledge. To be effective, approaches to facilitate exploration of data graphs should take into account the utility from a user’s point of view. Our work focuses on knowledge utility – how useful exploration paths through a data graph are for expanding the user’s knowledge. The main goal of this research is to design an intelligent support mechanism to direct the user to ‘good’ exploration paths through big data graphs for knowledge expansion. We propose a new exploration support mechanism underpinned by the subsumption theory for meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for adapting the subsumption theory for generating exploration paths is the automatic identification of Knowledge Anchors in a Data Graph (KADG). Several metrics for identifying KADG and the corresponding algorithms for implementation have been developed and evaluated against human cognitive structures. A subsumption algorithm which utilises KADG for generating exploration paths for knowledge expansion is presented and evaluated in the context of a semantic data browser in a musical instrument domain. The resultant exploration paths are evaluated in a controlled user study to examine whether they increase the users’ knowledge as compared to free exploration. The findings show that exploration paths using knowledge anchors and subsumption lead to significantly higher increase in the users’ conceptual knowledge. The approach can be adopted in applications providing data graph exploration to facilitate learning and sensemaking of layman users who are not fully familiar with the domain presented in the data graph.
Dhcr24 activates the PI3K/Akt/HKII pathway and protects against dilated cardiomyopathy in mice

24‐dehydrocholesterol reductase (Dhcr24) catalyzes the last step of cholesterol biosynthesis, which is required for normal development and anti‐apoptotic activities of tissues. We found that Dhcr24 expression decreased in the cTnTR141W dilated cardiomyopathy (DCM) transgenic mice. Therefore, we tested whether rescued expression of Dhcr24 could prevent the development of DCM and its possible mechanism.
Feasibility of accelerator generation to alleviate dark silicon in a novel architecture

This thesis presents a novel approach to alleviating Dark Silicon problem by reducing power density. Decreasing the size of transistor has generated an increasing on power consumption. To attempt to manage the power issue, processor design has shifted from one single core to many cores. Switching on fewer cores while the others are off helps the chip to cool down and spread power more evenly over the chip. This means that some transistors are always idle while others are working. Therefore, scaling down the size of the chip, and increasing the amount of power to be dissipated, increases the number of inactive transistors. As a result it generates Dark Silicon, which doubles every chip generation [63] One of the most effective techniques to deal with Dark Silicon is to implement accelerators that execute the most energy consumer software functions. In this way the CPU is able to dissipate more energy and reduce the dark silicon issue. This work explores a novel accelerator design model which could be interfaced to a Stack CPU and so could optimise the transistor logic area and improve energy efficiency to tackle the dark silicon problem based on heterogeneous multi-accelerators (co-processor) in stack structure. The contribution of this thesis is to develop a tool to generate coprocessors from software stack machine code. But it also employs up-to-date code optimisation strategies to enhance the code at the input stage. Analysis of the cores using key metrics, based on 65nm synthesis experiments and industry standard tool-sets. It further introduces a novel architecture to decrease the power density of the accelerator. In order to test these expectations, a large-scale synthesis translation experiment was conducted, covering widely recognised benchmarks, and generating a large number of cores (in the thousands). These were evaluated for a range of key metrics: silicon di-area, timing, power, instructions-per-clock, and power density, both with and without code optimisation applied. The results obtained demonstrate that one of two competing core models, ‘Wavecore’ (which is proposed in this thesis), delivers superior power density to the standard approach (which it refers to as Composite core), and that this is achieved without significant cost in terms of critical metrics of overall power consumption and critical path delays. Finally, to understand the benefit of these accelerators, these auto-generated cores are analysed in comparison to a standard stack-machine CPU executing the same code sequences. Both the cores generation work and the benchmark CPU assume a 65nm CMOS process node, and are evaluated with industry standard design tools. It is demonstrated that the generated cores achieve better power efficiency improvements over the relatively CPU core.
Building a mechanistic mathematical model of hepatitis C virus entry

The mechanism by which hepatitis C virus (HCV) gains entry into cells is a complex one, involving a broad range of host proteins. Entry is a critical phase of the viral lifecycle, and a potential target for therapeutic or vaccine-mediated intervention. However, the mechanics of HCV entry remain poorly understood. Here we describe a novel computational model of viral entry, encompassing the relationship between HCV and the key host receptors CD81 and SR-B1. We conduct experiments to thoroughly quantify the influence of an increase or decrease in receptor availability upon the extent of viral entry. We use these data to build and parameterise a mathematical model, which we then validate by further experiments. Our results are consistent with sequential HCV-receptor interactions, whereby initial interaction between the HCV E2 glycoprotein and SR-B1 facilitates the accumulation CD81 receptors, leading to viral entry. However, we also demonstrate that a small minority of virus can achieve entry in the absence of SR-B1. Our model estimates the impact of the different obstacles that viruses must surmount to achieve entry; among virus particles attaching to the cell surface, 20-35% accumulate sufficient CD81 receptors, of these 4-8% then complete the subsequent steps to achieve productive infection. Furthermore, we make estimates of receptor stoichiometry; between 3 and 6 CD81 receptors are likely to be required to achieve viral entry. Our model provides a tool to investigate the entry characteristics of HCV variants and outlines a framework for future quantitative studies of the multi-receptor dynamics of HCV entry.
Temporal Expectancies Affect Accuracy in Standard-Comparison Judgments of Duration, but Neither Pitch Height, Nor Timbre, Nor Loudness

Presenting a stimulus at the most expected point in time should benefit its perceptual processing (Jones, 1976; Large & Jones, 1999). For example, accuracy decreases when comparing the pitch of two tones separated by a sequence of temporally regular distractors if the final tone is shifted away from the expected time (Jones, Moynihan, MacKenzie, & Puente, 2002). However, recent research could not replicate this effect (Bauer, Jaeger, Thorne, Bendixen, & Debener, 2015), so we explored possible explanations. First, we varied the size and probability of timing shifts of the comparison tone in 7 experimental combinations (N = 16 in each). Second, we strengthened temporal expectancies by using a rhythmically rich distractor sequence, either repeating the standard tone at the end of the sequence (N = 26) or not (N = 28). Third, we had listeners compare either the timbre (N = 55) or the loudness (N = 24) instead of pitch. No effects of temporal expectancy (nor interactions with musical training) emerged in these experiments; however, they did occur when participants judged the relative duration of time intervals (N = 38). That is, a temporal expectancy profile was only observable in the context of a temporal task, and did not generalize to other domains.
Delaying Change: Examining How Industry and Managerial Turbulence Impact Structural Realignment

This paper examines when firms pursue structural realignment through business unit reconfiguration, specifically by recombining business units. Our results refine and extend contingency theory and studies of organization design by drawing on theories of decision avoidance and delay to describe environmental conditions when firms pursue or postpone structural realignment. Our empirical analysis of 46 firms from 1978 to 1997, operating within the U.S. medical device and pharmaceutical sectors, demonstrates that while decision makers initiate structural recombination during periods of industry growth (i.e., munificence), they reduce their recombination efforts during periods of industry turbulence (i.e., dynamism), and managerial turbulence (i.e., growth in top management team size). We also find evidence that firms delay realignment and bide their time for better environmental conditions of declining turbulence and industry growth before pursuing more structural realignment. Together, these findings suggest that decision makers often delay initiating structural recombination until they can effectively process information and assess how structural changes will help them realign the organization to the environment.
“Everyone Needs a Breath of Fresh Air”

Background Europe continues to have among the highest worldwide prevalence of adult smoking (28%) and the highest among females (19%). Nurses’ rates of smoking in the region are comparable or higher than the general female population. Nurses who smoke are less likely to intervene with patients who smoke; therefore, supporting nurses’ efforts to quit is critical to promoting nurses’ well-being and strengthening the profession’s impact on prevention of tobacco-induced diseases. Objective The aim of this study was to explore nurses’ perceptions of hospital workplace factors that influence nurses’ smoking and quitting behaviors in Central and Eastern Europe. Methods Each country had a project director involved in the recruitment of participants and the translation of instruments. Using a moderator guide, focus groups (N = 9) about smoking and quitting were conducted in 5 countries (Czech Republic, Hungary, Romania, Slovakia, Slovenia) among 82 nurses who self-reported as current or former smokers. Recorded transcripts were translated and analyzed using content analysis methods. Results The majority of nurses were female (94%) and currently smoking (65%). Four major themes were identified that describe workplace factors influencing nurses’ smoking behaviors and efforts to quit: (1) taking breaks, (2) effect of smoking on patient interactions, (3) perceived collegial support for quitting, and (4) impact of workplace policies. Conclusions Workplace factors influence nurses smoking and quitting behaviors. Implications for Practice Changes in healthcare systems and policies are needed to support nurses’ quit efforts. Additional education is needed to ensure that nurses understand issues related to smoking and interactions with patients.
Optimizing Screening, Brief Intervention, and Referral to Treatment (Sbirt) Training for Nurses and Social Workers: Testing the Added Effect of Online Patient Simulation

Background Social workers and nurses are critical to the amelioration of substance misuse, making their training in evidence-based practices such as screening, brief intervention, and referral to treatment (SBIRT) particularly pertinent. Online patient simulation (OPS) is one training modality that allows students to develop and practice SBIRT skills that they might not obtain through didactic instruction, but it can be time and resource intensive. The aim of this study was to test the effect of OPS, over and above in-person training, on students’ SBIRT attitudes, knowledge, and perceived skills. Methods Social work and nursing students (N = 308) were recruited from a college in the northeastern United States. Students in the study were randomly assigned to either training as usual (TAU), which included pre-coursework videos, in-person didactic instruction, and role-plays, or the experimental condition (EXP), consisting of TAU plus access to self-paced SBIRT skills practice using OPS by SIMmersion. The SBIRT Attitudes, Self-perception of Skills, and Knowledge (AKS) survey was delivered at baseline, immediately post-training, and at 30-day follow-up (post-30) to assess overall changes as well as changes in the specific domains of SBIRT confidence, importance, and attitudes. Paired t tests were conducted to determine differences in mean scores between time points for the entire sample. Independent-samples t tests were conducted to test differences between EXP and TAU on AKS scores at each time point and to test differences between high and low OPS use. Results Results showed a significant difference from pre- to post-training on composite AKS scores. There were no significant differences between TAU and EXP in composite scores or by AKS domain, and no differences within the EXP group for those with high and low use. Conclusions Participants in EXP did not have significantly increased AKS scores, demonstrating that access to OPS did not produce an additive effect on the acquisition of self-perceived SBIRT knowledge, attitudes, and skills.
Testosterone‐dependent facial and body traits predict men's sociosexual attitudes and behaviors

Strategic Pluralism Theory contends that human mating strategies are calibrated toward short‐term (ST) or long‐term (LT) mating according to the expression of condition‐dependent traits and characteristics of the social and physical environment. Traits reflecting the effects of testosterone have been considered condition‐dependent traits that provide information about the calibration of male mating strategy. We investigated the relationship of muscle mass and facial masculinity with attitudes and behaviors reflecting ST and LT mating tactics.
Analysis of efficacy of the self‐assembling peptide‐based remineralization agent on artificial enamel lesions

The objective of this in vitro study was to analyze and compare the biomimetic remineralizing efficacy of the self‐assembling peptide (P11‐4) with agents containing casein phoshopeptide‐amorhous calcium phosphate fluoride (CPP‐ACFP) and sodium fluoride (NaF) on artificial caries lesions using DIAGNOdent and micro‐computed tomography (μCT). Artificial enamel lesions were prepared on extracted impacted sound mandibular third molars. The samples were randomly allocated to four groups (n = 8): Group 1, P11‐4 (Curodont Repair, Credentis AG, Switzerland); Group 2, CPP‐ACFP (MI Varnish, GCCo., Japan); Group3, NaF (Duraphat Varnish, Colgate, Colgate‐Palmolive, NY, USA); Group 4, artificial saliva (control). The agents were applied to demineralized surfaces according to manufacturers' instructions; all specimens were stored in artificial saliva for 1 month. Demineralization and remineralization on enamel surfaces were analyzed and quantified by DIAGNOdent (KaVo, Germany) and μCT (SkyScan1174, Belgium) for lesion depth/area/volume/mineral density (MD). The remineralization efficacy of the agents was evaluated by DIAGNOdent on 1st, 7th, 30th days and by μCT on 30th day. Data were statistically analyzed by ANOVA, Kruskal–Wallis, T test, and Wilxocon tests. The highest remineralization efficacy findings in all periods were determined in Group 1, followed by Groups 2, 3, and 4. The remineralization findings for fluorescence, MD, lesion depth in Group 1 were found significantly higher (p < 0.01) than Group 3; and no significant differences (p > 0.05) were found between Groups 1–2 and Groups 2–3. The area and volume change values in Groups 1, 2, and 3 have shown no significancy (p > 0.05). A significant correlation (p < 0.01) was found between μCT and DIAGNOdent methods. The data of this study have demonstrated that P11‐4 has showed the best remineralization efficacy, followed by CPP‐ACFP and NaF. It is concluded that self‐assembling peptide‐based remineralization agent can be used successfully for biomimetic remineralization of enamel subsurface lesions.