Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrasting dynamics of hydrological processes in the Volta River basin under global warming

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of twelve general circulation models (GCM) from CMIP5 that are dynamically downscaled by five regional climate models (RCM) from CORDEX-Africa is used. In total, 43 RCM-GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The corrected simulations are then used as input to the fully distributed mesoscale Hydrologic Model (mHM) for hydrological projections over the twenty-first century (1991–2100). Results reveal contrasting changes in the seasonality of rainfall depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture and terrestrial water storage) is only projected under RCP8.5. High and low flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water-energy-food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socio-economic development.
The Impact of Executive Compensation on Banking Performance: Evidence from the Ghanaian Banking Crises

During the period 2012-2018, nine Ghanaian banks went bankrupt, causing depositors and shareholders to lose billions of Ghana Cedis. This development stunned the whole Ghanaian community, as many had not anticipated the occurrence of a banking crisis of that magnitude, since most of these banks were renowned indigenous banks. One of the fundamental causes of these banks’ failure was a lack of sound corporate governance practices. In this regard, the current study attempts to focus on one of the corporate governance practices—executive compensation—by giving an in-depth analysis of how poor executive compensation contributed to the failure of these banks. The study found that there was weak adherence to common execution compensation principles and practices of corporate governance. Banks did not have the appropriate compensation systems designed for the banks’ executives and non-executives. In addition, the compensation committee of some banks was not properly constituted whereas others had no compensation committee.
Somaliland women’s perception of stillbirth - a descriptive survey study

Background: Somali women, not only those living in Somaliland but also those living abroad as asylum seekers and refugees, are highly vulnerable in terms of perinatal health outcomes. Respectful and supportive care is critical for all women when stillbirth occurs and improving bereavement care and reducing the stigma that surrounds stillbirth are global priorities. Culturally- and context-specific approaches that build on an understanding of the needs of women giving birth to a stillborn baby, no matter where or why, are required. Objective: This study aims to investigate and analyze Somali women’s experiences of stillbirth, including their perceived reasons for losing their unborn baby, the premonitions they had before giving birth and their experiences of psychosocial support from healthcare professionals and relatives. Methods: A descriptive retrospective study was conducted at the Borama regional hospital in Somaliland. A study-specific questionnaire was developed that gathered personal information and data on topics related to women’s experiences of stillbirth. Women who had either experienced a stillbirth at the hospital or had been referred there after a stillbirth 2015 were approached and 75 women agreed to participate in the study. Results: Most of the women were multiparas and had experienced a previous stillbirth. Before having it confirmed that their baby was no longer alive most of the woman reported that they had felt no fetal movements and had a premonition that something was wrong. The most common perceived cause of stillbirth that the women reported was prolonged labour followed by a ‘big baby’. Thirty-three women (44%) felt it was important to know the cause of the stillbirth and eight reported feeling angry or disappointed (11 %) with the health care providers who assisted them during labour, birth, or post-partum, although 41 women (55%) were satisfied with their treatment. A third of the women blamed themselves for their stillbirth and a majority spoke to others about it. Conclusions: Our results show that women in Somaliland share similar perceptions of stillbirth as women in high income countries. This raises important implications for antenatal care and preventive interventions and stressed the need to respond to women’s concerns regardless of background, context or setting. A maternal healthcare approach that is equal in its global application must be established to enable health care providers to give relevant information and care both in the cultural setting of Somaliland and elsewhere in the world where Somali-born women live and give birth.
Congestion Control

Network congestion control remains a critical issue and a high priority, especially given the growing size, demand, and speed (bandwidth) of the increasingly integrated services networks. Designing effective congestion control strategies for these networks is known to be difficult because of the complexity of the structure of the networks, nature of the services supported, and the variety of the dynamic parameters involved. In addition to these, the uncertainties involved in identification of the network parameters lead to the difficulty of obtaining realistic, cost effective, analytical models of these networks. This renders the application of classical control system design methods (which rely on availability of these models) very hard, and possibly not cost effective. Consequently, a number of researchers are looking at alternative nonanalytical control system design and modeling schemes that have the ability to cope with these difficulties in order to devise effective, robust congestion control techniques as an alternative (or supplement) to traditional control approaches. These schemes employ artificial neural networks, fuzzy systems, and design methods based on evolutionary computation (collectively known as Computational Intelligence). In this chapter we firstly discuss the difficulty of the congestion control problem and review control approaches currently in use, before we motivate the utility of Computational Intelligence based control. Then, through a number of examples, we illustrate congestion control methods based on fuzzy control, artificial neural networks and evolutionary computation. Finally, some concluding remarks and suggestions for further work are given.
Liver Transplantation for Biliary Atresia in Adulthood: Single-Centre Surgical Experience

Background: Biliary atresia (BA) is the most common indicator for liver transplant (LT) in children, however, approximately 22% will reach adulthood with their native liver, and of these, half will require transplantation later in life. The aim of this study was to analyse the surgical challenges and outcomes of patients with BA undergoing LT in adulthood. Methods: Patients with BA requiring LT at the age of 16 or older in our unit between 1989 and 2020 were included. Pretransplant, perioperative variables and outcomes were analysed. Pretransplant imaging was reviewed to assess liver appearance, spontaneous visceral portosystemic shunting (SPSS), splenomegaly, splenic artery (SA) size, and aneurysms. Results: Thirty-four patients who underwent LT for BA fulfilled the inclusion criteria, at a median age of 24 years. The main indicators for LT were synthetic failure and recurrent cholangitis. In total, 57.6% had significant enlargement of the SA, 21% had multiple SA aneurysm, and SPSS was present in 72.7% of the patients. Graft and patient survival at 1, 5, and 10 years was 97.1%, 91.2%, 91.2% and 100%, 94%, 94%, respectively Conclusions: Good outcomes after LT for BA in young patients can be achieved with careful donor selection and surgery to minimise the risk of complications. Identification of anatomical variants and shunting are helpful in guiding attitude at the time of transplant.
Vertical root fractures: A time-dependent clinical condition. A case-control study in two colombian populations

Background This nested case-control study can be viewed as an efficient way to sample subjects from a large cohort study case-control study aimed to analyze the effect of different clinical factors on the appearance of vertical root fractures in endodontically-treated teeth (ETT) over time. Material and Methods By matching 90 cases and 270 controls nested in a cohort of 450 patients. Incident “cases” included those ETT in which a confirmed VRF. The “controls” were ETT with clinical and radiographic evidence of normality. When an “incident case” was detected, three random “controls” according to the evaluation time registered in years were selected. Time interval corresponded to the exposure time from the end of the endodontic treatment until the tooth was included in the study. Demographic and clinical parameters included: age, gender, type, and location of the tooth, type of endodontic treatment, number of appointments necessary to complete the endodontic treatment, use of intra-canal medication, the apical extension of the filling, type of coronal restoration, the role of the tooth in the rehabilitation treatment, presence of intra-radicular posts, and presence of an adjacent implant, were analyzed over time. Statistical analysis: univariate descriptive analysis, Pearson’s χ2 test, and a logistic regression model adjusted for the most significant variables with a 95% confidence interval. Results The prevalence of vertical root fractures was 16.42%. The multivariate analysis confirmed that re-treatment (OR:12.19; OR:4.28;P<0.05) lasting five to ten years and intra-canal medication (OR:6.16;P=0.004) for more than eleven years significantly more associated with the risk of vertical root fracture. For teeth with intra-canal post or direct coronal restorations, the risk of vertical root fracture was three times lower. Conclusions Endodontic re-treatment and the use of intracanal medication such as calcium hydroxide should be considered primary and secondary risk factors, respectively, according to the appearance of VRF over time. Key words:Apical surgery, endodontic re-treatment, endodontically-treated teeth, risk factors, vertical root fracture.
Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.
The High Impact of Staphylococcus aureus Biofilm Culture Medium on In Vitro Outcomes of Antimicrobial Activity of Wound Antiseptics and Antibiotic

The staphylococcal biofilm-based infections of wounds still pose a significant therapeutical challenge. Treated improperly, they increase the risk of limb amputation or even death of the patient. The present algorithms of infected wound treatment include, among others, the application of antiseptic substances. In vitro wound biofilm models are applied in order to scrutinize their activity. In the present work, using a spectrum of techniques, we showed how the change of a single variable (medium composition) in the standard in vitro model translates not only to shift in staphylococcal biofilm features but also to the change of efficacy of clinically applied wound antimicrobials such as octenidine, polyhexamethylene biguanide, chlorhexidine, hypochlorite solutions, and locally applied gentamycin. The data presented in this study may be of a pivotal nature, taking into consideration the fact that results of in vitro analyses are frequently used to propagate application of specific antimicrobials in hospitals and ambulatory care units.
New Methods for Confusion Detection in Course Forums: Student, Teacher, and Machine

This article provides computational and rule-based approaches for detecting confusion that is expressed in students' comments in couse forums. To obtain reliable, ground truth data about which posts exhibit student confusion, we designed a decision tree that facilitates the manual labeling of forum posts by experts. However, manual labeling is costly in time and resources, which limits the amount of data that can be generated using this process. Our strategy for overcoming these limitations was to generate rules for detecting confusion based on student input via hashtags, which reflect the student's affective states. We show that the resulting rules closely align with the ground truth judgement of experts. We next applied these rules to datasets of students' forum posts in a large-scale biology course, thereby automatically generating thousands of labeled instances of “confused posts. ” Finally, the resulting dataset was used to train a machine learning model for detecting whether students' posts exhibit confusion in the absence of hashtags. In this task, the pretrained language model based on bidirectional encoder representation from transformers (BERT) was able to outperform traditional machine learning models for classifying confusion in posts. This model was also able to generalize and detect student confusion across different offerings of the same course. Ultimately, the use of pretrained language models of this type will provide teachers with better technologies for detecting and alleviating confusion in online discussion forums by leveraging the combined input of teachers and students.
miR-29b Regulates TGF-β1-Induced Epithelial–Mesenchymal Transition by Inhibiting Heat Shock Protein 47 Expression in Airway Epithelial Cells

Tissue remodeling contributes to ongoing inflammation and refractoriness of chronic rhinosinusitis (CRS). During this process, epithelial-mesenchymal transition (EMT) plays an important role in dysregulated remodeling and both microRNA (miR)-29b and heat shock protein 47 (HSP47) may be engaged in the pathophysiology of CRS. This study aimed to determine the role of miR-29b and HSP47 in modulating transforming growth factor (TGF)-β1-induced EMT and migration in airway epithelial cells. Expression levels of miR-29b, HSP47, E-cadherin, α-smooth muscle actin (α-SMA), vimentin and fibronectin were assessed through real-time PCR, Western blotting, and immunofluorescence staining. Small interfering RNA (siRNA) targeted against miR-29b and HSP47 were transfected to regulate the expression of EMT-related markers. Cell migration was evaluated with wound scratch and transwell migration assay. miR-29b mimic significantly inhibited the expression of HSP47 and TGF-β1-induced EMT-related markers in A549 cells. However, the miR-29b inhibitor more greatly induced the expression of them. HSP47 knockout suppressed TGF-β1-induced EMT marker levels. Functional studies indicated that TGF-β1-induced EMT was regulated by miR-29b and HSP47 in A549 cells. These findings were further verified in primary nasal epithelial cells. miR-29b modulated TGF-β1-induced EMT-related markers and migration via HSP47 expression modulation in A549 and primary nasal epithelial cells. These results suggested the importance of miR-29b and HSP47 in pathologic tissue remodeling progression in CRS.
Consumers’ Attitude towards Sustainable Food Consumption during the COVID-19 Pandemic in Romania

The COVID-19 pandemic affected consumers’ behavior worldwide. This paper aims to analyze consumers’ sustainable food behavior during the COVID-19 pandemic. The research was based on an online survey during May–October 2020 among 859 Romanian consumers. Principal component analysis and cluster analysis were employed to group the consumers based on their behavior. Furthermore, the binary-logistic regression was used to identify the socio-demographic profile of the identified groups. Based on the cluster analysis, the consumers were grouped into three main groups: indifferent, pro-environment protection, and health concerned. The results indicate a positive attitude towards sustainable food behavior. Consumers’ attitude towards sustainable food choice is mainly influenced by age and education level. The study offers valuable information for future public policy and marketing campaign regarding sustainable food behavior.
Exploring the factors in aligning information systems and organizational strategies in tall organizational structures in an uncertain environment

Factors affecting the alignment of information systems (ISs) and organizational strategies (i.e., strategic alignment) vary depending on the organizational contexts, comprising both the structural and environmental contexts. Previous studies of strategic alignment were largely focused on organizations with flatter organizational structures and low environmental uncertainty. Therefore, findings from previous studies might not be applicable to organizations with tall organizational structures and in uncertain environments, like Iranian organizations. To this end, we aimed to discover less‐explored factors in achieving strategic alignment based on the contexts of Iranian organizations, using grounded theory. We conducted semi‐structured interviews with 29 experts in different organizations to illustrate and substantiate the development of an eightfold framework that specifies the factors for achieving strategic alignment. Specifically, the developed eightfold framework resulted in identification of 8 factors and 51 elements in achieving strategic alignment. Here, we identified five factors in achieving strategic alignment in addition to the factors reported by previous research (i.e., senior management, communication, and partnership), namely IS leadership, IS capabilities, organizational development, human resources, and IT infrastructure. The results indicate that these factors play a prominent role in determining the challenges and discerning the momentous requirements for achieving strategic alignment in organizations with tall structures in uncertain environments.
Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning

Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.
Combining Distributed and Kernel Tracing for Performance Analysis of Cloud Applications

Distributed tracing allows tracking user requests that span across multiple services and machines in a distributed application. However, typical cloud applications rely on abstraction layers that can hide the root cause of latency happening between processes or in the kernel. Because of its focus on high-level events, existing methodologies in applying distributed tracing can be limited when trying to detect complex contentions and relate them back to the originating requests. Cross-level analyses that include kernel-level events are necessary to debug problems as prevalent as mutex or disk contention, however cross-level analysis and associating events in the kernel and distributed tracing data is complex and can add a lot of overhead. This paper describes a new solution for combining distributed tracing with low-level software tracing in order to find the latency root cause better. We explain how we achieve a hybrid trace collection to capture and synchronize both kernel and distributed request events. Then, we present our design and implementation for a critical path analysis. We show that our analysis describes precisely how each request spends its time and what stands in its critical path while limiting overhead.