Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Explainability Methods in Recurrent Neural Network Architectures for Financial Time Series Data

Statistical methods were traditionally primarily used for time series forecasting. However, new hybrid methods demonstrate competitive accuracy, leading to increased machine-learning-based methodologies in the financial sector. However, very little development has been seen in explainable AI (XAI) for financial time series prediction, with a growing mandate for explainable systems. This study aims to determine if the existing XAI methodology is transferable to the context of financial time series prediction. Four popular methods, namely, ablation, permutation, added noise, and integrated gradients, were applied to a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) network trained on S&P 500 stocks data to determine the importance of features, individual data points, and specific cells in each architecture. The explainability analysis revealed that GRU displayed the most significant ability to retain long-term information, while the LSTM disregarded most of the given input and instead showed the most notable granularity to the considered inputs. Lastly, the RNN displayed features indicative of no long-term memory retention. The applied XAI methods produced complementary results, reinforcing paradigms on significant differences in how different architectures predict. The results show that these methods are transferable in the financial forecasting sector, but a more sophisticated hybrid prediction system requires further confirmation.
The real effects of bank lobbying: Evidence from the corporate loan market

Using a large sample of corporate loan facilities and hand-matched information on bank lobbying, we show that borrower performance improves after receiving credit from lobbying banks. This especially holds for opaque borrowers about which a bank possesses valuable information, as well as for borrowers with strong corporate governance. We also find that credit from lobbying banks funds corporate capital expenditures that increase the scope of firm operations, thereby leading to sales growth. Our findings are consistent with the information-transmission theory that political lobbying provides regulators with valuable borrower information, which results in improved bank-lending supervisory decisions and corporate borrower performance.
Storage Environment Effect On Pinto Bean Quality And Resistance To Airflow Of Pinto And Navy Beans

A literature review identified known storage recommendations. A survey of pinto bean handlers, processors, and growers determined current practices and parameters expected to affect bean quality in storage. The storage research conducted showed that it is extremely important to store pinto beans at cool temperatures in a dark environment to maintain color and cooking quality. The resistance to airflow of both pinto and navy beans is similar to that of soybeans.
Retrospective study of post-operative care in pandemic in tertiary care centre

Background: COVID-19 pandemic has unfortunately changed the surgical interventions and the post-operative management worldwide. Most of the medical institutes in India had formulated their own protocols from the beginning of the pandemic in March 2020 to stop all elective surgical procedures as soon as possible. However emergencies and all types of obstetric as cases were not stopped as it was unavoidable conditions.Methods: This retrospective observational study was done in the department of obstetrics and gynaecology, at Sri Venkateshwaraa Medical College, Ariyur, Puducherry, during the period April 2020-December 2020. Data was collected from the case sheets and operative notes and retrospective analysis was done.Results: On analysing we have realised that LSCS rate was higher and induction for trial of labour was kept minimal. We had 3 infective morbidity in the form of wound infection and 1 patient was returned to the operation theatre for reexploration. Arranging the blood during the pandemic was also a tedious task.Conclusions: In the present era of pandemic, where the patient load is high, we can rely on post graduate students to look after the post-operative patients through effective communication with the available consultants.
Dietary Orange Pulp and Organic Selenium Effects on Growth Performance, Meat Quality, Fatty Acid Profile, and Oxidative Stability Parameters of Broiler Chickens

In this study, orange pulp (OP) and/or organic Se were fed to broilers in order to investigate their effects on the performance, behavior, breast meat quality, and oxidative stability. A total of 240 chicks were allocated to four groups: a control group; an OP group, fed with OP at 50 g/kg of diet; a Se group, fed with organic Se at 0.15 ppm; and an OP + Se group, fed with OP and organic Se at 50 g/kg and 0.15 ppm, respectively. The selenium and OP + Se groups showed improved meat oxidative stability during frozen storage from 90 to 210 days (p < 0.05), whereas the performance and meat quality were unaffected by the dietary treatments (p > 0.05), apart from a reduction in the meat pH and the dressing percentage in the OP-supplemented groups (p < 0.05). A synergistic action between OP and Se was observed for the meat oxidative stability. The polyunsaturated fatty acid (FA) and α-linolenic acid (ALA) contents in the breast meat lipid fractions were increased in the OP groups (p < 0.05). Dietary intervention did not affect the feeding or drinking behaviors of the broilers (p > 0.05). The dietary supplementation of broiler chickens with the citrus industry byproduct orange pulp at 50 g/kg, along with organic Se at 0.15 ppm, beneficially improves the meat oxidative stability and the meat nutritional value, with no negative side effects on the performance or the meat quality.
Inhibitor of Hyaluronic Acid Synthesis 4-Methylumbelliferone Suppresses the Secretory Processes That Ensure the Invasion of Neutrophils into Tissues and Induce Inflammation

Integrin-dependent adhesion of neutrophils to tissue, accompanied by the development of neutrophil-induced inflammation, occurs both in the focus of infection and in the absence of infection in metabolic disorders such as reperfusion after ischemia, diabetes mellitus, or the development of pneumonia in patients with cystic fibrosis or viral diseases. Hyaluronic acid (HA) plays an important role in the recruitment of neutrophils to tissues. 4-methylumbilliferon (4-MU), an inhibitor of HA synthesis, is used to treat inflammation, but its mechanism of action is unknown. We studied the effect of 4-MU on neutrophil adhesion and concomitant secretion using adhesion to fibronectin as a model for integrin-dependent adhesion. 4-MU reduced the spreading of neutrophils on the substrate and the concomitant secretion of granule proteins, including pro-inflammatory components. 4-MU also selectively blocked adhesion-induced release of the free amino acid hydroxylysine, a product of lysyl hydroxylase, which can influence cell invasion by modifying the extracellular matrix. Finally, 4-MU inhibited the formation of cytonemes, the extracellular membrane secretory structures containing the pro-inflammatory bactericides of the primary granules. The anti-inflammatory effect of 4-MU may be associated with the suppression of secretory processes that ensure the neutrophil invasion and initiate inflammation. We suggest that HA, due to the peculiarities of its synthesis, can promote the release of secretory carriers from the cell and 4-MU can block this process.
A novel network traffic combination prediction model

Network has become an indispensable part of public life. To improve network utilization, network performance, network quality, and enhance network security, precise prediction of network traffic is an indispensable method and basis for solving the above problems. In order to accurately predict the network traffic, a novel combination prediction model for network traffic is proposed. In this model, local mean decomposition (LMD), bidirectional long short‐term memory (BiLSTM), and Bayesian optimization algorithm are combined. First, the LMD method decomposes the network traffic time series to obtain several product function (PF) components and a residual by LMD. Then, each PF component and residual is predicted with BiLSTM model. Meanwhile, the Bayesian optimization algorithm is introduced to optimize the hyperparameters of BiLSTM. Finally, the predicted value of each PF component and residual is linearly superimposed to obtain the final predicted value. Through the study of two groups of actual network traffic datasets and compared with a variety of state‐of‐the‐art prediction models, the proposed model has a preferable prediction results by comparison of the results.
Relationship between Work-Family Balance and Job Performance of Academics: A Study on Selected National Universities in Sri Lanka

Work-family balance is a challenging issue for many working adults. As far as Sri Lanka is concerned employees’ awareness and perception about work-family balance is very limited in comparison to Western countries. Academics in Sri Lanka are no exceptions and they receive very little or no family-friendly support from universities as they struggle to achieve a balance between work and family with their never-ending workload. The main research objective of this study was to investigate the relationship between university academics’ work-family balance and job performance. Further this study aimed to find the dimensional impact of work-family balance on job performance of university academics. With the purpose of accomplishing the aforesaid objectives, a self-administered survey was carried out among 200 university academics that belong to the University of Sri Jayewardenapura, University of Colombo, University of Kelaniya and the University of Ruhuna. The sample was selected based on simple random sampling from the management faculties of the respective universities. This research study has found a statistically significant positive relationship between work-family balance and job performance among academics. It was also found that a significant positive relationship existed between work-family balance dimensions and job performance. Multiple regression analysis revealed satisfaction balance as the major contributor to job performance. These results are useful to the university administration to make policies to increase academics’ work life and family life through adopting supportive work environment.
Introducing a Novel Method for Smart Expansive Systems’ Operation Risk Synthesis

In different areas of human activity, the need to choose optimal (rational) options for actions from the proposed alternatives inevitably arises. In the case of retrospective statistical data, risk analysis is a convenient tool for solving the problem of choice. However, when planning the growth and development of complex systems, a new approach to decision-making is needed. This article discusses the concept of risk synthesis when comparing alternative options for the development of a special class of complex systems, called smart expansive systems, by the authors. “Smart” in this case implies a system capable of ensuring a balance between its growth and development, considering possible external and internal risks and limitations. Smart expansive systems are considered in a quasi-linear approximation and in stationary conditions of problem-solving. In general, when the alternative to comparison is not the object itself, but some scalar way of determining risks, the task of selecting the objects most at risk is reduced to assessing the weights of factors affecting the integral risk. As a result, there is a complex task of analyzing the risks of objects, solved through the amount by which the integral risk can be minimized. Risks are considered as anti-potentials of the system development, being retarders of the reproduction rate of the system. The authors give a brief description of a smart expansive system and propose approaches to modeling the type of functional dependence of the integral risk of functioning of such a system on many risks, measured, as a rule, in synthetic scales of pairwise comparisons. The solution to the problem of reducing the dimension of influencing factors (private risks) using the vector compression method (in group and inter-scale formulations) is described. This article presents an original method for processing matrices of incomplete pairwise comparisons with indistinctly specified information, based on the idea of constructing reference-consistent solutions. Examples are provided of how the vector compression method can be applied to solve practical problems.
The persistence and spatial transformations of traditional Joglo house according to hamemayu hayuning bawana philosophy

This research aimed to explain the conceptual persistence of spatial transformations in the Joglo house nDalem Puspodiningratan based on Hamemayu Hayuning Bawana philosophy. This philosophy is the public wisdom directed towards maintaining a harmonious relationship between humans and each other, nature, and God. This philosophy is applied in several dimensions of the Javanese life such as the architecture which is meaningfully designed. Moreover, the Hamemayu Hayuning Bawana is observed to be playing an important role in the spatial configuration of the traditional Joglo house. It is important to note that nDalem Puspodiningratan is a traditional Joglo house with a well-maintained condition and which undergoes a spatial transformation due to the inhabitation of alternating families. This research was, therefore, conducted using visual observation and documentation, literature review, interviews, graphic method, and comparison to explore the Joglo transformation data and background. The results showed the Joglo nDalem Puspodiningratan persisted in preserving the Hamemayu Hayuning Bawana's philosophical principles despite the spatial transformations. Meanwhile, the transformations discovered to be violating the traditional house rules are believed to be reducing the Hamemayu Hayuning Bawana practice while those observed to be following the rules maintain the practice. There is a need for further research on the transformations of traditional buildings in other cases of the traditional Javanese architecture to show the existence of spatial transformations and conceptual persistence related to Hamemayu Hayuning Bawana.
Biliary Diseases from the Microbiome Perspective: How Microorganisms Could Change the Approach to Benign and Malignant Diseases

Recent evidence regarding microbiota is modifying the cornerstones on pathogenesis and the approaches to several gastrointestinal diseases, including biliary diseases. The burden of biliary diseases, indeed, is progressively increasing, considering that gallstone disease affects up to 20% of the European population. At the same time, neoplasms of the biliary system have an increasing incidence and poor prognosis. Framing the specific state of biliary eubiosis or dysbiosis is made difficult by the use of heterogeneous techniques and the sometimes unwarranted invasive sampling in healthy subjects. The influence of the microbial balance on the health status of the biliary tract could also account for some of the complications surrounding the post-liver-transplant phase. The aim of this extensive narrative review is to summarize the current evidence on this topic, to highlight gaps in the available evidence in order to guide further clinical research in these settings, and, eventually, to provide new tools to treat biliary lithiasis, biliopancreatic cancers, and even cholestatic disease.
Injury Patterns after Falling down Stairs—High Ratio of Traumatic Brain Injury under Alcohol Influence

Falling down a staircase is a common mechanism of injury in patients with severe trauma, but the effect of varying fall height according to the number of steps on injury patterns in these patients has been little studied. In this retrospective study, prospectively collected data from a Level 1 Trauma Center in Germany were analyzed regarding the injury patterns of patients admitted through the trauma room with suspicion of multiple injuries following a fall down a flight of stairs between January 2016 and December 2019. In total 118 patients were examined which where consecutively included in this study. More than 80% of patients suffered a traumatic brain injury, which increased as a function of the number of stairs fallen. Therefore, the likelihood of intracranial hemorrhage increased with higher numbers of fallen stairs. Fall-associated bony injuries were predominantly to the face, skull and the spine. In addition, there was a high coincidence of staircase falls and alcohol intake. Due to a frequent coincidence of staircase falls and alcohol, the (pre-)clinical neurological assessment is complicated. As the height of the fall increases, severe traumatic brain injury should be anticipated and diagnostics to exclude intracranial hemorrhage and spinal injuries should be performed promptly to ensure the best possible patient outcome.
Machine Learning Based Low-Cost Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature.
Emergence of SARS-CoV-2 Variants in the World: How Could This Happen?

The COVID-19 pandemic has had a significant global impact, with more than 280,000,000 people infected and 5,400,000 deaths. The use of personal protective equipment and the anti-SARS-CoV-2 vaccination campaigns have reduced infection and death rates worldwide. However, a recent increase in infection rates has been observed associated with the appearance of SARS-CoV-2 variants, including the more recently described lineage B.1. 617.2 (Delta variant) and lineage B.1. 1.529/BA.1 (Omicron variant). These new variants put the effectiveness of international vaccination at risk, with the appearance of new outbreaks of COVID-19 throughout the world. This emergence of new variants has been due to multiple predisposing factors, including molecular characteristics of the virus, geographic and environmental conditions, and the impact of social determinants of health that favor the genetic diversification of SARS-CoV-2. We present a literature review on the most recent information available on the emergence of new variants of SARS-CoV-2 in the world. We analyzed the biological, geographical, and sociocultural factors that favor the development of these variants. Finally, we evaluate the surveillance strategies for the early detection of new variants and prevent their distribution outside these regions.
GRMHD Simulations and Modeling for Jet Formation and Acceleration Region in AGNs

Relativistic jets are collimated plasma outflows with relativistic speeds. Astrophysical objects involving relativistic jets are a system comprising a compact object such as a black hole, surrounded by rotating accretion flows, with the relativistic jets produced near the central compact object. The most accepted models explaining the origin of relativistic jets involve magnetohydrodynamic (MHD) processes. Over the past few decades, many general relativistic MHD (GRMHD) codes have been developed and applied to model relativistic jet formation in various conditions. This short review provides an overview of the recent progress of GRMHD simulations in generating relativistic jets and their modeling for observations.
New Insights into the Role of the Complement System in Human Viral Diseases

The complement system (CS) is part of the human immune system, consisting of more than 30 proteins that play a vital role in the protection against various pathogens and diseases, including viral diseases. Activated via three pathways, the classical pathway (CP), the lectin pathway (LP), and the alternative pathway (AP), the complement system leads to the formation of a membrane attack complex (MAC) that disrupts the membrane of target cells, leading to cell lysis and death. Due to the increasing number of reports on its role in viral diseases, which may have implications for research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this review aims to highlight significant progress in understanding and defining the role of the complement system in four groups of diseases of viral etiology: (1) respiratory diseases; (2) acute liver failure (ALF); (3) disseminated intravascular coagulation (DIC); and (4) vector-borne diseases (VBDs). Some of these diseases already present a serious global health problem, while others are a matter of concern and require the collaboration of relevant national services and scientists with the World Health Organization (WHO) to avoid their spread.
Novel Small-Molecule Inhibitors of the SARS-CoV-2 Spike Protein Binding to Neuropilin 1

Furin cleavage of the SARS-CoV-2 spike protein results in a polybasic terminal sequence termed the C-end rule (CendR), which is responsible for the binding to neuropilin 1 (NRP1), enhancing viral infectivity and entry into the cell. Here we report the identification of 20 small-molecule inhibitors that emerged from a virtual screening of nearly 950,000 drug-like compounds that bind with high probability to the CendR-binding pocket of NRP1. In a spike NRP1 binding assay, two of these compounds displayed a stronger inhibition of spike protein binding to NRP1 than the known NRP1 antagonist EG00229, for which the inhibition of the CendR peptide binding to NRP1 was also experimentally confirmed. These compounds present a good starting point for the design of small-molecule antagonists against the SARS-CoV-2 viral entry.