Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-strain disease dynamics on a metapopulation network

1 Many of the most impactful diseases that affect humans, livestock, and wildlife have clusters in their population2 genetic variability that we classify as strains. Importantly, host immunity to one of these strains is neither inde3 pendent from nor equivalent to immunity to related strains. This partial cross-protective immunity affects disease 4 dynamics across the population as a whole and can dramatically influence intervention strategies. While the study 5 of multi-strain diseases goes back decades, this work has not yet been generalized to a loosely connected collection of 6 subpopulations, i.e. a metapopulation. Starting from the strain theory of host-pathogen systems proposed by Gupta 7 (1998), we simulate multi-strain disease dynamics on a network of interconnected populations, characterizing the 8 effects of parameterization and network structures on these dynamics. We find that dynamics propagate through 9 the metapopulation network, even if parameters vary between populations. Moreover, in chains of connected pop10 ulations experiencing cyclical dynamics, the movement of (partially) immune individuals dampens the dynamics 11 of populations further along the chain. This work serves as an important first step in extending prior results on 12 multi-strain diseases to a generalized population structure. This extension is particularly apt in the case of livestock 13 production, where a system of mostly isolated populations (farms) is connected through the forced movement of 14 individuals. 15
Seismic performance of RC bridge piers reinforced through FRCM confinement

Corrosion of reinforced concrete (RC) structures is one of the main issues concerning existing buildings and especially of infrastructures. Corrosion phenomena, that is induced mainly by carbonatation and by high chlorides concentration, can significantly affect the performance of existing structures. In particular, the seismic performance can be significantly affected. Loss of transversal reinforcement in RC elements can reduce the concrete ductility while corrosion affecting bending reinforcement reduces the lateral load‐bearing capacity of RC elements. To enhance the performance of corroded elements and extend service life of deteriorated structures confinement is one of the most effective and commonly used techniques. This paper investigates the deterioration of the seismic performance of a RC bridge affected by corrosion and retrofitted through FRCM confinement. Different deterioration and retrofitting scenarios are combined and seismic fragility curves are computed for each case. Finally, time‐variant seismic reliability profiles are used to evaluate the best retrofitting scenario and intervention time to maximize reliability gains.
AN END-TO-END DEEP LEARNING SOUND CODING STRATEGY FOR COCHLEAR IMPLANTS

Cochlear implant (CI) users struggle to understand speech in noisy conditions. In this work, we propose an end-to-end speech coding and denoising sound coding strategy that estimates the elec-trodograms from the raw audio captured by the microphone. We compared this approach to a classic Wiener ﬁlter and TasNet to assess its potential beneﬁts in the context of electric hearing. The performance of the network is assessed by means of noise reduction performance (signal-to-noise-ratio improvement) and objective speech intelligibility measures. Furthermore, speech intelligibility was measured in 5 CI users to assess the potential beneﬁts of each of the investigated algorithms. Results suggest that the speech performance of the tested group seemed to be equally good using our method compared to the front-end speech enhancement algorithm.
Best Dispatching Rule Analysis for Dynamic Scheduling Problem with Periodical Demand*

—Dynamic scheduling can be used in scenarios in which jobs may arrive irregularly, and therefore, the schedule of jobs may need to be changed. In actuality, due to various reasons, demand for a service can suddenly change. In previous papers, various methods using genetic programming (GP) have been proposed. Using GP, an appropriate dispatching rule that determines the job sequence can be derived in a short amount of time, and derived rules are better than existing dispatching rules. However, the derived dispatching rules are not analyzed in previous papers. In this paper, dispatching rules that use GP are analyzed; the characteristics of the rules are described, and they are compared with previous dispatching rules.
1 Investigation of the Relationship between Adenoma Volume and Perioperative Hormone Levels in Patients with Acromegaly

Acknowledgments: We are grateful to our patients and their families for inspiring us. Conflict of interest: No financial or non financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article. The authors declare that they have no relevant conflict of interest. Funding: The authors declared that this study has received no financial support.
Scenario driven evaluation and interference mitigation proposals for Bluetooth and high data rate Bluetooth enabled consumer electronic devices

Previous research has focussed on residential coverage and achievable data rates using the Bluetooth personal area network (PAN) standard. To meet the bit rate needs of future consumer electronics devices, M-PSK modulation schemes have been proposed as likely candidates for high data rate extensions to Bluetooth. This paper investigates the effects of interference occurring between high data rate time-bounded and non-time-bounded Bluetooth enabled consumer electronic devices. Frequency hopping selection kernels for both the 79-hop and the 23-hop systems in Bluetooth were implemented in software. Using this model, collision statistics were obtained between a wanted Bluetooth user (in a piconet) and interferers (Bluetooth users in different piconets) operating under typical scenarios that were constructed within the test environment. These scenarios featured piconets operating in an office environment in both synchronous and asynchronous modes and with typical up and downlink loading factors. A detailed link level physical layer simulation was used to obtain packet error rate performance based on the collision statistics obtained. Finally, a state-of-the-art indoor space-time propagation modelling tool was used to analyse the data throughput performance capabilities for Bluetooth data medium (DM) rate and data high (DH) rate packets. Results highlight a clear need to mitigate interference by employing synchronous communication in future Bluetooth evolution networks.
Localisation, tracking, and navigation support for pedestrians in uninstrumented and unknown environments

Research into localisation and tracking of pedestrians is a growing area, but developed techniques have tended to be infrastructure-based or to rely on accessto prior information such as floorplans and maps. There are situations, such assearch and rescue missions, where more robust and versatile self-contained localisation systems are desirable. To our knowledge, this a relatively unexplored area of research. Relative localisation, involving only objects in the vicinity of the target being tracked, offers a partial solution by not requiring any infrastructure. We demonstrate and discuss some techniques based on this approach, and develop an algorithm suitable for tracking highly mobile sensor networks. We also highlight its limitations and look for complementary oolutions. Pedestrian dead reckoning (PDR) based on foot-mounted inertial sensors is a promising method which we describe in detail, including its inherent flaws. We combine the dead reckoning and sensor node techniques to perform simultaneous localisation and mapping (SLAM) for pedestrians in indoor environments. Finally, we include our SLAM algorithm in a complete navigation solution which we evaluate in a virtual environment.This study allows us to offer insight into problems and opportunities offered by these technologies and their application in the field of pedestrian navigation in uninstrumented and unknown environments. We describe the potential offered by tracking and navigation once they are no longer dependent on infrastructure, predeployment, or prior knowledge of an area.
Proceedings of the Fourth Workshop on Computational Linguistics for Literature

Burrows’s Delta is the most established measure for stylometric difference in literary authorship attribution. Several improvements on the original Delta have been proposed. However, a recent empirical study showed that none of the proposed variants constitute a major improvement in terms of authorship attri-bution performance. With this paper, we try to improve our understanding of how and why these text distance measures work for author-ship attribution. We evaluate the effects of standardization and vector normalization on the statistical distributions of features and the resulting text clustering quality. Furthermore, we explore supervised selection of discriminant words as a procedure for further improving authorship attribution.
. (2017). Freudian

The use of deep networks has improved the state of the art in various domains of AI, making practical applications possible. At the same time, there are increasing calls to make learning systems more transparent and explainable, due to concerns that they might develop biases in their internal representations that might lead to unintended discrimination, when applied to sensitive personal decisions. The use of vast subsymbolic distributed representations has made this task very diﬃcult. We suggest that we can learn a lot about the biases and the internal representations of a deep network without having to unravel its connections, but by adopting the old psychological approach of analysing its slips of the tongue. We demonstrate in a practical example that an analysis of the confusion matrix can reveal that a CNN has represented a biological task in a way that reﬂects our understanding of taxonomy, inferring more structure than it was requested to by the training algo-rithm. In particular, we show how a CNN trained to recognise animal families, contains also higher order information about taxa such as the superfamily, parvorder, suborder and order for example. We speculate that various forms of psycho-metric testing for neural networks might provide us insight about their inner workings.
THE USE OF DATA AUGMENTATION AND EXPLORATORY DATA ANALYSIS IN ENHANCING IMAGE FEATURES ON APPLE LEAF DISEASE DATASET

ABSTRAK-Apples are an essential commodity produced in Batu City, Malang. In 2017, Batu City, Malang, produced 19.1 tons of apples, while in 2018, Batu City, Malang, produced 15.9 tons of apples. It can be concluded that the decline in the number of apple harvests in Batu City, Malang. With the influence of technology in agriculture, the influence of technology can be used to detect diseases on leaves to overcome the decrease in the number of harvests. With the Image Augmentation method used in this study, the existing dataset can have 6x more features. So that the healthy category, which previously had 516 image features, now has 3096 image features, the scab category, which previously had 592 image features, now has 3552 image features and the rust category, which previously had 622 image features, now has 3732 image features. With a dataset with 3000 image features, the model to be made can have a higher accuracy value. The model can be said to be sturdy/sturdy/good, or the model to be made can carry out the classification process with a good level of accuracy.
Atrazine toxicity in marine algae Chlorella vulgaris , in E. coli lux and gfp biosensor tests

: Atrazine (ATR) is a widely used chlorinated herbicide from the s-triazine group. Due to the widespread use of ATR, it leaks into the environment and is detected in drinking water, exceeding the WHO-acceptable concentration of atrazine in drinking water, which is 2 μg/L. The aim of our study was to determine toxicity, protein degradation and genotoxicity of ATR at concentrations of 10; 1; 0.1; 0.01 mg/L on Chlorella vulgaris and with the application of E. coli bioluminescent biosensor strains. We measured the content of chlorophyll a , b , carotenoids in Chlorella vulgaris and the inhibition of this algae culture growth. E. coli RFM443 strains with gene constructs grpE:luxCDABE , lac:luxCDABE , recA:luxCDABE and E. coli strain MM294 trc:luxCDABE were used to determine toxicity, degradation of cellular proteins and genotoxicity. On the base of the obtained results, we concluded that ATR in the tested concentrations shows a toxic effect in relation to Chlorella vulgaris . ATR is toxic and genotoxic in E. coli RFM443 strains with grpE , lac , recA promoters and causes degradation of cellular proteins. Moreover, we have detected ATR toxicity toward the GFP protein in E. coli strain MM294-GFP. Taking into account the toxicity and genotoxicity of ATR documented in our research and in the experiments of other authors, we conclude that the presence of this herbicide in surface waters and drinking water is a serious threat to living organisms.
Analogue model of rift linkage and inversion with application to the Western Alps

Along-strike segmentation of orogens raises questions because its causes may predate orogeny in relation to structural inheritance. Here we focus on rift/margin linkage domains and their inversion by using analogue models with image analysis to extract the 3D strain field. Exten-sional models document, depending on the strike-perpendicular o ff set and the brittle-crust thickness, three types of rift linkage modes: (1) oblique linkage with early T-fault, (2) strongly-oblique linkage with R-fault network and, (3) transfer-linkage with late Y-strike-slip fault. Analogue model of inverted rift basins is used to analyse the misunderstood tectonic evolution of transition zones in the segmented Western Alpine belt.
Technical Update: Least-squares Temporal Diierence Learning

TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It has two major drawbacks: it may make ineecient use of data, and it requires the user to manually tune a stepsize schedule for good performance. For the case of linear value function approximations and = 0, the Least-Squares TD (LSTD) algorithm of Bradtke and Barto 5] eliminates all stepsize parameters and improves data eeciency. This paper updates Bradtke and Barto's work in three signiicant ways. First, it presents a simpler derivation of the LSTD algorithm. Second, it generalizes from = 0 to arbitrary values of ; at the extreme of = 1, the resulting new algorithm is shown to be a practical, incremental formulation of supervised linear regression. Third, it presents a novel and intuitive interpretation of LSTD as a model-based reinforcement learning technique. 1. Background We address the problem of approximating the value function V of a xed policy in a large Markov decision process 2, 10]. This is an important subproblem of several algorithms for sequential decision making, including optimistic policy iteration 2] and STAGE 3]. V (x) simply predicts the expected long-term sum of future rewards obtained when the process starts in state x and follows policy until termination. For simplicity we will assume that is proper (guaranteed to terminate), which implies that V is well-deened without the use of a discount factor; however, the algorithms presented here extend straightforwardly to the discounted case. For small Markov chains whose transition probabilities are all explicitly known, computing V is a trivial matter of solving a system of linear equations. However, in many practical applications, the transition probabilities of the chain are available only implicitly|either in the form of a simulation model or in the form of an agent's actual experience executing in its environment. In either case, we must compute V or an approximation thereof (denoted ~ V) solely from a collection of trajectories sampled from the chain. This is where the TD() family of algorithms applies. TD() was introduced in 11]; excellent summaries may now be found in several books 2, 10]. For each state on each observed trajectory, TD() incrementally adjusts the coeecients of ~ V toward new target values. The target values depend on
iSeg-WNet: Volumetric Segmentation of Infant Brain MRI Images

: Examination of infant brain development is extremely important in terms of early diagnosis of possible brain dysfunctions. Brain MRI structures are usually studied by segmentation into white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) tissues. Low-density contrast between tissues in infant brains complicates the segmentation process. It is seen that the segmentation process is done very well with the Deep Learning architectures that have been developed recently. In this study, an architecture called Deep Learning-based iSeg-WNet is proposed for segmentation of infant brain MRI images. Appropriate hyperparameters were determined by different studies and the performances of different architectures were compared. Performance comparison was made according to Dice metric. In experimental studies, it has been observed that the use of MRI images in T1w and T2w images together increases the segmentation performance. At the same time, high performance was obtained by using Dice Loss as a cost function and MinMax normalization as a data normalization process. When the segmentation performances of different architectures are examined, it is seen that the proposed architecture segments CSF (%91.8), GM (%89) and WM (%86) tissues with the highest success. The proposed architecture is available at https://github.com/GaffariCelik/iSeg-WNet.
Screening groundnut rhizobia for multiple plant growth promoting activities in Ethiopia

Rhizobia could exhibit plant growth-promoting mechanisms besides nitrogen fixation. In search of efficient rhizobia with multiple PGP traits, 72 groundnut nodulating rhizobia isolates were screened in vitro for their plant growth promoting traits like phosphate solubilization, production of indole acetic acid, ammonia, hydrogen cyanide, production of different hydrolytic enzymes and antifungal activity. About 72% of the isolates produced IAA, varying from 7.4 to 78.8 μg. ml −1 and 23.6% of the isolates were able to solubilize tri-calcium phosphate. Even if all isolates could produce ammonia, two isolates were strongly produced. Only two isolates were able to produce hydrogen cyanide. The enzymatic production study revealed that 50 and 48% of the tested isolates showed protease, and cellulase activities respectively. Only 13.8% of the isolates were found to be inhibitory against the test pathogen, Fusarium oxysporum , but the maximum inhibition potential was exhibited by GNR-07. The present study also demonstrated that 100% of the isolates exhibited multiple PGP (plant growth promotion) properties, with isolates GNR-37 and GNR-28 being superior, acquiring the highest number of PGP properties (87.5% each). These isolates can be potential candidates as a PGPR inoculant after evaluation of their performance under greenhouse and field conditions.
Primary prevention of hepatic encephalopathy post-TIPS: A systematic review and meta-analysis

Background: Transjugular intrahepatic portosystemic shunt (TIPS) can be an effective treatment for cirrhotic patients who develop variceal bleeding and ascites. However, TIPS placement is associated with an increased risk of developing hepatic encephalopathy (HE). Recently, there have been efforts to use the typical medical therapies prophylactically in patients undergoing TIPS placement to prevent post-TIPS HE. Methods: We conducted literature searches in MEDLINE, Embase, CINAHL, Scopus, and Cochrane to examine studies that use prophylactic medical therapy for preventing post-TIPS HE. A narrative synthesis and grading of recommendations assessment assessment were done for all studies. Meta-analysis was performed for eligible studies using the Mantel-Haenszel method random-effects model. Nine hundred twenty-one articles were screened and 5 studies were included in the study after 2 levels of screening. The medications studied were rifaximin, lactulose, lactitol, L-Ornithine-L-aspartate (LOLA), albumin, and combination therapies. Results: Narrative results showed that lactulose, lactitol, LOLA and albumin prophylaxis were not associated with reduction in HE occurrence or mortality. A combination of rifaximin and lactulose was found to be associated with lower occurrence of HE, and the results were not different when LOLA was added. Meta-analysis (n = 3) showed that rifaximin treatment was not associated with changes in HE occurrences. Conclusion: In conclusion, a vast majority of medications were not found to be effective post-TIPS HE prophylaxis when used alone. A rifaximin and lactulose combination therapy may be beneficial. Overall, there is significant limitation in the current data and more studies are needed to yield more robust meta-analysis results in the future.
Treatment of glenohumeral internal rotation deficit in the general population with shoulder pain: An open single-arm clinical trial

Background: Maladaptation can provoke important alterations in the arthrokinematics such as an internal rotation reduction in the dominant shoulder compared with the nondominant shoulder known as glenohumeral internal rotation deficit (GIRD). Though the number of studies investigating GIRD in athletic population, there are not studies reporting the efficacy of the GIRD treatment in the nonathlete population, a kind of study required to improve our understanding of patient care with this pathology. This study aimed to describe the efficacy of the GIRD treatment in nonathlete population with shoulder pain. Methods: An open single-arm trial with 35 patients was adopted for evaluating the efficacy of GIRD treatment in patients with shoulder pain. All patients with shoulder pain who attended the consultation, accepted, and agreed to participate in the study between October 2020 and March 2021 were included. A treatment sequence including joint manual therapy techniques and soft tissue release techniques was applied in the consultation. Then, patients were instructed to adapt the daily active biological stimulus at home. The IR before (IR0) and after (IR1) the treatment was considered the outcome measure. The GIRD was calculated as the difference between the IR of the non-painful shoulder and the IR of the painful shoulder before (GIRD0) and after treatment (GIRD1). A paired Student t test was used to compare the GIRD of each patient before and after the treatment. Results: Treatment of the patients significantly increased the IR of the painful shoulder in all the patients (P-value < .0001) So, the mean IR0 was 26.09 ± 14. 46º (23.64–28.53), and after the treatment the mean IR1 was 67.98 ± 15. 03º (65.48–70.52). The mean difference after the treatment (IR1–IR0) was 41.89 ± 14. 74º (39.4–44.39). The treatment also significantly reduced GIRD (P-value < .0001). So, the mean GIRD0 was 42.95 ± 16. 26º (40.2–45.7), and after the treatment the mean GIRD1 was –1.05 ± 17. 18º (–3.96 to 1.85). Conclusions: The treatment administrated in this study significantly increased the internal rotation of the treated and painful shoulder and reduced the GIRD from the first consultation. Level of evidence: Level 3.