Miracle-dz/UniFAR-FTUs huggingface dataset:
Using multi-tissue transcriptome-wide association study to identify candidate susceptibility genes for respiratory infectious diseases

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms. Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella. Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p < 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p < 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p < 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p < 0.05. Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases.
Automated Grey-Box Testing of Microservice Architectures

Microservices Architectures (MSA) have found large adoption in companies delivering online services, often in conjunction with agile development practices. Microservices are distributed, independent and polyglot entities – all features favouring black-box testing. However, for real-scale MSA, a pure black-box strategy may not be able to exercise the system to properly cover the interactions involving internal microservices.We propose a grey-box strategy (MACROHIVE) for automated testing and monitoring of (internal) microservices interactions. It uses combinatorial testing to generate valid and invalid tests from microservices specification. Tests execution and monitoring are automated by a service mesh infrastructure. MACROHIVE runs the tests and traces the interactions among microservices, to report about internal coverage and failing behaviour.MACROHIVE is experimented on TrainTicket, an open-source MSA benchmark. It performs comparably to state-of-the-art techniques in terms of edge-level coverage, but exposes internal failures undetected by black-box testing, gives detailed internal coverage information, and requires fewer tests.
Improved SIFT match for optical satellite images registration by size classification of blob-like structures

Due to high rate of false match and expensive computation cost, the existing scale-invariant feature transform (SIFT) operators are not efficient to register two optical satellite images that have a great spatial resolution difference. Some scale restriction schemes were proposed to reduce the false match of SIFT keypoints and computational cost. However, it is observed that many keypoints are still not correctly matched. This problem often leads to the failure of automatic registration in industrial applications. To solve this problem, the images being registered are normalized to adjust the scale of blob-like structures and to preclude useless blob-like structures. Then blob-like structures are classified according to their physical sizes, and keypoint matching is restricted to matching for the blob-like structures having the same physical sizes. The scale normalization and size classification significantly improve the correct match rate as well as computational cost. Experiments on two pairs of satellite images demonstrate the effectiveness of the proposed method.
NOMA-Based WPT-MEC Network System Cost Efficient Units Minimization

With the progress of the Internet of Things (loT) and 5G communication technology, the rapid development of the loT not only brings convenience to people's lives, but also brings problems such as insufficient computing power and environmen-tal pollution. On the one hand, Mobile Edge Computing (MEC) can coordinate the computing power of local and MEC servers, reduce computing latency and power consumption, and provide faster response for users. On the other hand, wireless power transmission (WPT) technology can effectively solve the pollution problem caused by traditional energy supply. On this basis, it is of great practical significance to study the architecture of the loT that combines MEC with WPT. In order to minimize the unit cost (UC) of WPT-MEC system, this paper proposes a computing offload a nd resource scheduling problem involving multiple users and a single server. We first describe t he problem a s a Mixed Integer Nonlinear Programming Problem(MINLP). Then, we propose an improved algorithm TRSA based on simulated annealing (SA) algorithm to obtain the optimal computing offload and resource allocation strategy to minimize the U C of the system. A large number of simulation results show that TRSA algorithm can significantly reduce the UC of WPT-MEC system in different scenarios compared with other baseline methods.
Defensive pessimism-like thinking in practice: the (dys)functional strategy for coping with risk uncertainty in the offshore oilfield?

Abstract UK Continental Shelf (UKCS) offshore oilfield drilling is recognised as a high-hazard occupation that occurs in one of the riskiest locations in the world. Dangerous machinery, combustible hydrocarbons, unpredictable weather conditions, rapidly shifting situational factors, rotating crews, and lengthy travel over rough seas by helicopter are just some salient risk-factors. At present, little scholarly research explores how regular offshore workers develop cognitive strategies to attempt to cope with ever-present risks. This research draws on an embedded ethnography of a remote offshore oilfield drilling platform in the UK North Sea. The risks of working offshore and the strategies workers developed and employed to deal with continual risk uncertainties were discussed in face-to-face interviews with thirty-five active drilling crew as they laboured on complex well-drilling operations. Notably, defensive pessimism (DP) like thinking was recurrently exhibited, with many workers recounting detailed narratives of mental role-play for ‘worst case’ offshore and helicopter travel scenarios. Often, thinking appeared interlinked with strengthening motifs for workers’ preparation and planning skills regarding occupational risk possibilities. These strategies are presented, as is a discussion of the effects such strategies had upon oilmen. The benefits of growing scholarly conversation surrounding DP-like thinking in practice are highlighted. alongside the methodological application of DP theory for further study surrounding practical development of coping strategies in similar high-risk workspaces.
Linearized least-squares training of multilayer feedforward neural networks

The authors develop a linearized least-squares formulation for estimating the weight coefficients of a neural network. Linearization of the nonlinear network about the most recent weight estimates leads to a conditional least-squares criterion which may be solved recursively in time. The resulting coefficient update equations resemble those of the recursive least-squares solution in adaptive filtering, much as the update equations for linearized stochastic gradient descent (backpropagation) resemble those of the least mean squares solution in adaptive filtering. Simulations on small logic mapping problems indicate a three- to tenfold increase in training efficiency for this technique as compared to gradient descent. <<ETX>>
A Comparative Study on Bengali Speech Sentiment Analysis Based on Audio Data

Sentiment analysis is one of the most researched areas for every language. Due to the rise of AI, the use of speech in every sector is rapidly growing so is the importance of Speech Sentiment Analysis. Despite being the seventh most spoken language in the world, Bengali speech sentiment analysis studies are not much enriched. This study compared the Bengali speech sentiment analysis using machine learning and CNN, LSTM, and Bi-LSTM models. We have used the SUBESCO and BanglaSER datasets for training our models where the KNN model outperformed other models with an accuracy of 90%. Later, we evaluated the performance of the models with our custom-made test dataset. Experimental results show that AdaBoost and Bi-LSTM model performed best with 45% accuracy. Moreover, to understand the feature effect on the output, we used the interpretable SHAP model in the ML model outcomes as they provide the best results allowing us to have an explainable advantage to determine the results.
The effect of geological biases on our perception of early land plant radiation

The Silurian–Devonian plant radiation was a critical development in the evolution of early terrestrial ecosystems. Characterizing the diversity dynamics of this radiation has been a focus of numerous studies. However, little is known about the impact of geological bias on our perception of this biodiversification. Here, we use a new, comprehensive compilation of plant occurrences from North America, together with a Macrostrat lithological dataset, to elucidate the relationships between the palaeobotanical and geological records of early land plants. Results show that observed raw diversity patterns at both species and genus rank are significantly correlated with fluctuations of sedimentary rock volume, especially of non‐marine fossiliferous deposits. The lack of terrestrial sedimentary deposits before the Emsian (Early Devonian) makes it difficult to obtain an accurate depiction of the pre‐Emsian plant diversification in North America. However, complementary analyses reveal that sampling‐standardized diversity patterns partially correct the raw trajectories, especially at the genus‐level if enough preserved non‐marine sediments are available for sampling. Our findings highlight that geological incompleteness remains a fundamental bias for describing early plant diversification. This indicates that, even when sampling is extensive, observed diversity patterns potentially reflect the heterogeneity of the rock record, which blurs our understanding of the early history of land vegetation.
Markov processes whose steady state distribution is matrix-exponential with an application to the GI/PH/1 queue

This paper is concerned with a bivariate Markov process {Xt, Nt ; t ≧ 0} with a special structure. The process Xt may either increase linearly or have jump (downward) discontinuities. The process Xt takes values in [0,∞) and Nt takes a finite number of values. With these and additional assumptions, we show that the steady state joint probability distribution of {Xt, Nt ; t ≧ 0} has a matrix-exponential form. A rate matrix T (which is crucial in determining the joint distribution) is the solution of a non-linear matrix integral equation. The work in this paper is a continuous analog of matrix-geometric methods, which have gained widespread use of late. Using this theory, we present a new and considerably simplified characterization of the waiting time and queue length distributions in a GI/PH/1 queue. Finally, we show that the Markov process can be used to study an inventory system subject to seasonal fluctuations in supply and demand.
Time interval from diagnosis to treatment of brain metastases with stereotactic radiosurgery is not associated with radionecrosis or local failure

Introduction Brain metastases are the most common intracranial tumor diagnosed in adults. In patients treated with stereotactic radiosurgery, the incidence of post-treatment radionecrosis appears to be rising, which has been attributed to improved patient survival as well as novel systemic treatments. The impacts of concomitant immunotherapy and the interval between diagnosis and treatment on patient outcomes are unclear. Methods This single institution, retrospective study consisted of patients who received single or multi-fraction stereotactic radiosurgery for intact brain metastases. Exclusion criteria included neurosurgical resection prior to treatment and treatment of non-malignant histologies or primary central nervous system malignancies. A univariate screen was implemented to determine which factors were associated with radionecrosis. The chi-square test or Fisher’s exact test was used to compare the two groups for categorical variables, and the two-sample t-test or Mann-Whitney test was used for continuous data. Those factors that appeared to be associated with radionecrosis on univariate analyses were included in a multivariable model. Univariable and multivariable Cox proportional hazards models were used to assess potential predictors of time to local failure and time to regional failure. Results A total of 107 evaluable patients with a total of 256 individual brain metastases were identified. The majority of metastases were non-small cell lung cancer (58.98%), followed by breast cancer (16.02%). Multivariable analyses demonstrated increased risk of radionecrosis with increasing MRI maximum axial dimension (OR 1.10, p=0.0123) and a history of previous whole brain radiation therapy (OR 3.48, p=0.0243). Receipt of stereotactic radiosurgery with concurrent immunotherapy was associated with a decreased risk of local failure (HR 0.31, p=0.0159). Time interval between diagnostic MRI and first treatment, time interval between CT simulation and first treatment, and concurrent immunotherapy had no impact on incidence of radionecrosis or regional failure. Discussion An optimal time interval between diagnosis and treatment for intact brain metastases that minimizes radionecrosis and maximizes local and regional control could not be identified. Concurrent immunotherapy does not appear to increase the risk of radionecrosis and may improve local control. These data further support the safety and synergistic efficacy of stereotactic radiosurgery with concurrent immunotherapy.
Development of a balanced test image for visual print quality evaluation

A test image for color still image processes was developed. The image is based on general requirements on the content and specific requirements arising from the quality attributes of interest. The quality attributes addressed in the study include sharpness, noise, contrast, colorfulness and gloss. These were chosen based on visual relevance in studies of the influence of paper in digital printing. Further requirements such as arising from the use cases of the image are discussed based on eye tracking data and self-report of the usefulness of different objects for quality evaluation. From the standpoint of being sufficiently sensitive to quality variations of the imaging systems to be measured the reference test image needs to represent quality maxima in terms of the relevant quality parameters. As for different viewing times, no object should be exceedingly salient. The paper presents the procedure of developing the test image and discusses its merits and shortcomings from the standpoint of future development.
Assessment and management of pain/nociception in patients with disorders of consciousness or locked-in syndrome: A narrative review

The assessment and management of pain and nociception is very challenging in patients unable to communicate functionally such as patients with disorders of consciousness (DoC) or in locked-in syndrome (LIS). In a clinical setting, the detection of signs of pain and nociception by the medical staff is therefore essential for the wellbeing and management of these patients. However, there is still a lot unknown and a lack of clear guidelines regarding the assessment, management and treatment of pain and nociception in these populations. The purpose of this narrative review is to examine the current knowledge regarding this issue by covering different topics such as: the neurophysiology of pain and nociception (in healthy subjects and patients), the source and impact of nociception and pain in DoC and LIS and, finally, the assessment and treatment of pain and nociception in these populations. In this review we will also give possible research directions that could help to improve the management of this specific population of severely brain damaged patients.
High Andean Steppes of Southern Chile Contain Little-Explored Peltigera Lichen Symbionts

Peltigera lichens can colonize extreme habitats, such as high-elevation ecosystems, but their biodiversity is still largely unknown in these environments, especially in the southern hemi- sphere. We assessed the genetic diversity of mycobionts and cyanobionts of 60 Peltigera lichens collected in three high Andean steppes of southern Chile using LSU, β-tubulin, COR3 and ITS loci for mycobionts, and SSU and rbcLX loci for cyanobionts. We obtained 240 sequences for the different mycobiont markers and 118 for the cyanobiont markers, including the first report of β-tubulin sequences of P. patagonica through modifying a previously designed primer. Phylogenetic analyses, ITS scrutiny and variability of haplotypes were used to compare the sequences with those previously reported. We found seven mycobiont species and eleven cyanobiont haplotypes, including considerable novel symbionts. This was reflected by ~30% of mycobionts and ~20% of cyanobionts haplotypes that yielded less than 99% BLASTn sequence identity, 15 new sequences of the ITS1-HR, and a putative new Peltigera species associated with 3 Nostoc haplotypes not previously reported. Our results suggest that high Andean steppe ecosystems are habitats of unknown or little-explored lichen species and thus valuable environments to enhance our understanding of global Peltigera biodiversity.
Electromagnetic scattering from bounded or infinite subsurface bodies

The direct scattering problem from a stack of two rough interfaces or from a buried object with a rough interface above it is addressed. The two boundaries are separating three homogeneous and isotropic media. The profiles are assumed to be invariant in one (and the same) direction, and the probing field is time harmonic. The problem is solved with a rigorous integral method leading to a single boundary integral equation. This equation is converted into a linear system of equations, thanks to a boundary finite element method. To improve the accuracy, especially when the two boundaries are very close to each other, the singularity of the kernel is isolated and its integration is performed analytically. As shown through several examples, this versatile tool is well suited for the investigation of physical phenomena, especially the competition between surface and subsurface scattering.
Near-Field Radiative Heat Transfer between $\beta-$GeSe monolayers: An ab initio study

ABSTRACT We present a theoretical study of the near-field radiative heat transfer (NFRHT) between two -GeSe monolayers, each at a different temperature. (This is a relevant 2D material with superior electron transport and optical properties compared to black-phosphorus monolayers). The required optical conductivity of the monolayer is calculated using density functional theory including spin-orbit coupling, and using the Perdew-Burke-Erzenhof parametrization. Both the intra and interband transitions are taken into account, as well as the contribution of the optical phonons. This allows us to obtain more realistic predictions of the NFRHT between two monolayers of GeSe. The role of the electron doping concentration and the plasma relaxation frequency is investigated, showing a non-monotonic dependence on the radiative heat transfer with increasing doping, and having an optimal doping where the heat flux is maximize. A strong optical anisotropy in the electric conductivity is obtained from the contribution of both electrons and ions This anisotropy is explored, showing that the relative rotation of two monolayers results in modulation of the NFRHT much larger than previously found for similar 2D materials, like -GeSe. As the angle of rotation between the monolayers increases the total heat transfer decreases. Our analysis demonstrates the relevance of properly taking into account the materialelectronic and ionic contributions.
Spiking Convolutional Vision Transformer

This paper introduces the design of a Spiking Convolutional Vision Transformer (SCvT) by using the biological-inspired learning rules including spike-timing-dependent plasticity (STDP) and reward–modulated STDP (R-STDP) for a classification task. We describe the structure of the SCvT simulated with deep spiking convolutional neural networks (DSCNN) and give a detailed description of the classification strategy.
TECS: A Trust Model for VANETs Using Eigenvector Centrality and Social Metrics

Vehicular Ad Hoc Networks (VANETs) rely heavily on trustworthy message exchanges between vehicles to enhance traffic efficiency and transport safety. Although cryptography-based methods are capable of alleviating threats from unauthenticated attackers, they can not prevent attacks from those legitimate network participants. This paper proposes a trust model to deal with attackers from the latter case, who can tamper with their received messages and deliberately decrease the trust value of benign vehicles. The trust evaluation process is formed by two stages: (i) the local trust evaluation at vehicles and (ii) trust aggregation on Road Side Units (RSUs). In the local trust evaluation stage, vehicles detect attacks and calculate the trust value for others in a distributed manner. Also, the social metrics of vehicles are calculated based on interaction records and trajectories. In the trust aggregation stage, each RSU collects local data from nearby vehicles and derives aggregation weights from the eigenvector centrality of the local trust network and social metrics. Then the RSU broadcasts the aggregated trust value towards vehicles in proximity. These vehicles can thus obtain a more accurate and comprehensive view. Vehicles with trust value below a preset threshold will be considered malicious. Extensive simulations based on the ONE simulator show that the proposed model (TECS) outperforms another benchmark model (IWOT-V) regarding the malicious vehicle detection and the delivery rate of authentic messages.
The Effect of Perceptions of Parents' Democratic Parenting on Online Game Addiction

The development of technology gives its users both positive and negative impacts. The role of parents is crucial in educating children. Teenagers will find their identity, get along with their environment, and become interested in internet games. When it happens without parental supervision, it will have the impact of becoming addicted. This study aimed to determine the effect of democratic parenting on parents with online game addiction. Quantitative methods are used in this study. This study involved the population of UKM X at University X with a saturated sampling technique obtained by respondents in 30 subjects. Data collection was obtained using a democratic parenting and online game addiction questionnaire. The results obtained are Ha is rejected, the hypothesis is rejected (p > 0.05) there is no effect of online game addiction with democratic parenting of parents. The findings of this study draw on previous research on the relationship between parenting and online game addiction. Further research the analysis using other factors, such as the type of game played due to the emergence of various online games so that players will be motivated to continue playing these online games.