Miracle-dz/UniFAR-FTUs huggingface dataset:
An analysis of the causes and contributing factors to human trafficking: A South African perspective

Abstract Trafficking in human beings, especially women and girls, is not new. Historically, it has taken many forms, but in the context of globalization, has acquired shocking new dimensions. It is a complex, multi-faceted phenomenon involving multiple stakeholders at the institutional and commercial level. This article attempts to explore and analysis the causes and contributing factors to human trafficking. This research was carried out utilising a qualitative approach. Forty interviews were carried out among officials deployed in the Directorate for Priority Crime Investigation (DPCI), the South African Police Service (SAPS), the Department of Home Affairs (DHA), the Department of Social Development (DSD), the Gauteng Provincial Office, as well as with the victims regarding their views and experiences on the stakeholder’s involved in combating and investigating human trafficking. The interviews were analysed according to the phenomenographic approach to identify the participants’ responses. The reason for this choice was to identify key or knowledgeable participants about human trafficking in three selected areas of Gauteng Province. The key findings indicated that the growing demand for foreign workers for domestic and care-giving roles, and lack of adequate corresponding regulations contribute immensely on human trafficking. The findings further indicated that lack of legitimate and fulfilling employment opportunities, particularly in rural communities contribute to human trafficking. The findings further indicated that demand for women’s sexual services and the growth of the billion-rand sex entertainment industry contribute to human trafficking. Based on the findings, the authors provided with the recommendations such as: improved awareness in social media platforms, multi-disciplinary unit dealing with human trafficking within the DPCI, and tougher laws and legislations on prostitution, women’s sexual services and sex entertainment industry.
Personal identity and community resilience: Sadiq Khan’s restorative rhetoric in response to terrorism

ABSTRACT Multiple terror attacks rocked London in 2017. These attacks brought to the forefront of public consciousness London Mayor Sadiq Khan’s unique identity as a Muslim leader confronting Islamic terrorism in a Western city. Through theoretical lenses of restorative rhetoric and the Disaster Communication Intervention Framework, this rhetorical criticism analyzes how Khan situated himself as a rhetorical symbol against terrorism while crafting a vision of unity and resilience rooted in diversity. By analyzing Khan’s anti-terrorism rhetoric in 2017 and the years prior, we explore how Khan used personal identity in his calls for collective response to disaster. The study contributes to scholarly understanding of how political leaders respond to disasters and offers practical guidelines for leaders working to foster post-disaster resilience in diverse communities.
Machine Learning Approaches for Predicting Willingness to Pay for Shrimp Insurance in Vietnam

Insurance premium prediction is a problem for limited-resource farmers. Econometric methods have generated inaccurate premium forecasts. This article investigates the efficacy of machine learning in predicting insurance premium. Machine learning techniques and survey data on willingness to pay were collected from 534 farmers in Ben Tre, Khanh Hoa, Quang Ninh, and Tra Vinh Provinces, Vietnam. The top-performing models were cubist, random forest, and support vector machines. The cubist model, with the highest R2 and lowest root mean square error, was the most appropriate to forecast premiums. Quantity harvested, total cost, stocking density, and willingness to participate in an insurance program were the top-ranked predictors of premium. Predicted premium payments varied by province. Partial dependence plots showed the economic relationship between predicted premium levels and selected variables. The model results demonstrate that machine learning is useful in forecasting insurance premium and exhibits promise for improving econometric techniques in premium determination.
Integration of Transcriptomic and Proteomic Analyses Reveals New Insights into the Regulation of Immune Pathways in Midgut of Samia ricini upon SariNPV Infection

Simple Summary SariNPV is one of the main pathogens of Samia ricini and its infection of Samia ricini sericulture has caused significant economic losses to society. In this study, we aim to reveal the molecular mechanism of pathogen–host interactions in SariNPV-infected S. ricini through transcriptomic and proteomic analyses. Using RNA-sequencing and iTRAQ, we mapped the differentially expressed genes (DEGs) and proteins (DEPs) that are involved in the immune responses of S. ricini upon virus invasion. Based on our analyses, we identified specific DEGs and DEPs that are involved in various essential biological signaling pathways and immune-related pathways upon SariNPV invasion. These DEGs and DEPs play an important role in triggering host immune responses to pathogens. Our study provides molecular insights into host immune responses regarding pathogen invasion, in particular, the immune response mechanism and network of S. ricini in response to SariNPV infection. Abstract Samia ricini nucleopolyhedrovirus (SariNPV) is one of the main pathogens of S. ricini sericulture and its infection causes severe impacts on economic sericulture development. To explore and reveal the molecular mechanisms of S. ricini in response to SariNPV infection, we employed RNA sequencing (RNA-seq), adopting isobaric tags for relative and absolute quantitation (iTRAQ), and carried out combination analysis of the obtained differentially expressed genes (DEGs) and proteins (DEPs). Through transcriptome sequencing, a total of 2535 DEGs were detected, and with iTRAQ, 434 DEPs with significant expression difference were identified. Through correlation analysis, we found that the expression trends of 116 differentially expressed proteins were the same as those of differentially expressed genes (including 106 up-regulated and 10 down-regulated). Twenty-five key differentially expressed genes (proteins) involved in several signaling and immune related pathways (mainly involving Toll, Imd, Jak-STAT and Wnt signaling pathways, as well as other immune related pathways) were screened through real-time quantitative PCR. Our results, not only provide insights into the pathogenic mechanism of SariNPV infection in ricin silkworm and the immune response mechanism within the host, but also provide a significant contribution for identifying and preventing diseases caused by SariNPV.
The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase

Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. Particularly, our study focuses on columnar NoSQL databases. We propose and evaluate two solutions to address the data recovery problem in columnar NoSQL and implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted across industries. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments.
Evaluation of Kerr Nonlinearity Mitigation based on Digital Backpropagation and Digital Coherent Superposition

We experimentally investigate the performance of Kerr nonlinearity mitigation by employing two different nonlinearity mitigation approaches in a 4x28-GBd single-polarization 16-QAM signal transmission system over a 400-km dispersion-compensated link. One scheme employs digital coherent superposition of phase-conjugated twin waves at the receiver whereas the other scheme is based on a digital backpropagation employing the split-step Fourier method. The results for both nonlinearity mitigation schemes are compared in the single-channel and the WDM scenario. Both schemes show significant signal quality-factor improvements compared to the signal transmission without any nonlinearity mitigation technique.
An Industry-4.0-Complaint Sustainable Bitcoin Model Through Optimized Transaction Selection and Sustainable Block Integration

Cryptocurrencies are the new form of trade that has revolutionized how we look into our financial institutions. Bitcoin dominates the industry with the highest market share among the hundreds of other cryptocurrencies. However, high energy consumption leading to increasing carbon emission, prioritizing high-value transactions, and long waiting times are some of the flaws preventing it from reaching its full potential. Owing to the block rewards getting halved every four years, miners and researchers are fearful that this would be the breaking point of Bitcoin’s success. This article proposes an Industry-4.0-compliant next-generation Bitcoin architecture by introducing a dynamic and sustainable block concept. Along with our modified knapsack algorithms, i.e., priority-based 0/1 knapsack and advanced-priority-based 0/1 knapsack, we can ensure a balanced transaction selection, quicker verification, higher transaction throughput, reduced carbon emission, and increased earnings for the miners. Moreover, with the addition of only one of our proposed sustainable blocks, we can cut down verification times by 50% and increase throughput by 39%. We can also reduce carbon emissions per transaction by 61.3%, which would help reduce Bitcoins’ large carbon footprint, enabling us to approach greener digital transactions.
Financial Sector Development and the Preference for Informal Remittance Channels: Evidence from Bangladesh

Abstract The potential socioeconomic benefits of remittances remain untapped as a large portion of remittances is transferred through informal channels worldwide. In this paper, we explored the effect of the financial sector development in the home country on the preference for informal remittance channels using a nationally representative household dataset from Bangladesh along with subdistrict level data on bank branches. However, the identification of the effect of the banking network on the choice of remittance channel is challenging due to the unobserved heterogeneity problem. We circumvent this problem by using an instrumental variable (IV) approach. We found that greater accessibility to the banking network at the subdistrict level significantly reduces the use of the informal remittance transfer channels: a 10% increase in the availability of the total number of bank branches reduces the probability of using the informal channels, at least once, by 1.79% and hundi by 2.3% on average. The results are consistent and robust across different specifications and estimation methods. The main policy implication of this result is that the expansion of bank branches can enhance the inflows of remittances through formal channels and thereby can magnify the macroeconomic benefits of remittances globally.
Recent Advances in MXene/Epoxy Composites: Trends and Prospects

Epoxy resins are thermosets with interesting physicochemical properties for numerous engineering applications, and considerable efforts have been made to improve their performance by adding nanofillers to their formulations. MXenes are one of the most promising functional materials to use as nanofillers. They have attracted great interest due to their high electrical and thermal conductivity, hydrophilicity, high specific surface area and aspect ratio, and chemically active surface, compatible with a wide range of polymers. The use of MXenes as nanofillers in epoxy resins is incipient; nevertheless, the literature indicates a growing interest due to their good chemical compatibility and outstanding properties as composites, which widen the potential applications of epoxy resins. In this review, we report an overview of the recent progress in the development of MXene/epoxy nanocomposites and the contribution of nanofillers to the enhancement of properties. Particularly, their application for protective coatings (i.e., anticorrosive and friction and wear), electromagnetic-interference shielding, and composites is discussed. Finally, a discussion of the challenges in this topic is presented.
A Fuzzy DEMATEL-ANP-Based Approach to Prioritize Activities in Enterprise Architecture

Prioritization of activities is a multicriteria problem that includes both quantitative and qualitative factors. Moreover, due to the nature of organizations and activities, the impact of activities on each other is fuzzy. Prioritizing activities in real and fuzzy situations will help an organization’s decision makers to make the right decisions. In this paper, a new fuzzy hybrid methodology is proposed to describe and prioritize the activities of the organization in fuzzy conditions. First, the activities of the organization are described in a new fuzzy format based on activity model (OV-5) of the C4ISR framework. Furthermore, fuzzy DEMATEL is used to calculate the interdependencies between activities, and then fuzzy ANP is used to calculate the weight of each activity. Finally, by combining the results of the two methods, the priority of activities was calculated. Additionally, an empirical study is presented to illustrate the application of the proposed method.
Plasmonic Superstructure Arrays Fabricated by Laser Near-Field Reduction for Wide-Range SERS Analysis of Fluorescent Materials

Surface-enhanced Raman scattering (SERS) enables trace-detection for biosensing and environmental monitoring. Optimized enhancement of SERS can be achieved when the energy of the localized surface plasmon resonance (LSPR) is close to the energy of the Raman excitation wavelength. The LSPR can be tuned using a plasmonic superstructure array with controlled periods. In this paper, we develop a new technique based on laser near-field reduction to fabricate a superstructure array, which provides distinct features in the formation of periodic structures with hollow nanoclusters and flexible control of the LSPR in fewer steps than current techniques. Fabrication involves irradiation of a continuous wave laser or femtosecond laser onto a monolayer of self-assembled silica microspheres to grow silver nanoparticles along the silica microsphere surfaces by laser near-field reduction. The LSPR of superstructure array can be flexibly tuned to match the Raman excitation wavelengths from the visible to the infrared regions using different diameters of silica microspheres. The unique nanostructure formed can contribute to an increase in the sensitivity of SERS sensing. The fabricated superstructure array thus offers superior characteristics for the quantitative analysis of fluorescent perfluorooctanoic acid with a wide detection range from 11 ppb to 400 ppm.
Signal Sequence-Dependent Orientation of Signal Peptide Fragments to Exosomes

Signal peptides (SPs) not only mediate targeting to the endoplasmic reticulum (ER) but also play important roles as biomarkers and substances with physiological activity in extracellular fluids including blood. SPs are thought to be degraded intracellularly, making it unclear how they are transported from the ER to the extracellular fluid. In a recent study, we showed that a C-terminal fragment of the SP of a type I membrane protein, amyloid precursor protein (APP), was secreted into the extracellular fluid via exosomes using transformed HEK293 cells expressing APP SP flanking a reporter protein. In the present study, we demonstrate that a N-terminal fragment of the SP from a type II membrane protein, human placental secreted alkaline phosphatase (SEAP), is contained in exosomes and secreted into the extracellular fluid using HEK-Blue hTLR3 cells, which express both a human toll-like receptor 3 gene and an inducible SEAP reporter gene. When HEK-Blue hTLR3 cells were stimulated with a TLR3 ligand, a N-terminal fragment of SEAP SP in exosomes was increased in parallel with SEAP secretion in a concentration-dependent manner. These results indicated that SP fragments are exosomal components. In addition, migrating SP fragments were determined by characteristics of the signal–anchor sequence of membrane proteins. Furthermore, we found that SP fragments could bind to calmodulin (CALM), which is a cytosolic protein and also a component of exosomes, suggesting its involvement in the transportation of SP fragments from the endoplasmic reticulum to exosomes.
Deep Reinforcement Learning Based Dynamic Proportional-Integral (PI) Gain Auto-Tuning Method for a Robot Driver System

To meet the growing trend of stringent fuel economy regulations, automakers around the world are designing modules such as engines, motors, transmissions and batteries to be as efficient as possible. In order to verify the effect of these designs on the overall fuel efficiency of the vehicle, the vehicle equipped with each module is placed on the chassis dynamometer, driven to follow the target vehicle speed, and actual fuel efficiency is measured. These tests are traditionally performed by human operators, but are now being replaced by robots (physical or software) to ensure the accuracy and reliability of test results. Although the conventionally proposed proportional integral (PI)-based controller has a simple structure and is easy to implement, it requires the process of finding the optimal gain whenever the test conditions such as vehicle or drive cycle change, which is difficult and time consuming. In this study, we propose a proportional integral controller gain adjustment algorithm using deep reinforcement learning. The reinforcement learning agent learns to dynamically modify the PI gain value of the acceleration/deceleration pedal to better follow the target vehicle in a simulation environment. The perturbation is used in each training episode to reduce the difference between the simulation and real testing environment. Upon completion of the training process, the trained agent performs an adjustment process that generates a reference gain table. We then use this reference gain table to perform a real test. The performance of the proposed system was evaluated using Hyundai Tucson HEV (NX4) on an AVL chassis dynamometer. We also compared the performance of our proposed algorithm to traditional fuzzy logic-based PI controllers. The obtained experimental results show that the proposed control system achieved a performance improvement of aounrd 46.8% compared to the conventional PI control system in terms of root mean square error.
Diagnostic performance of chest computed tomography for COVID-19 in children: a systematic review and meta-analysis of clinical and computed tomography features in 987 patients

Purpose The outbreak of a new coronavirus is still spreading worldwide, affecting children and adults. However, COVID-19 in children shows distinctive characteristics in clinical and radiological presentation. We aimed to assess the diagnostic performance of chest CT and clarify the clinicoradiological CT features of COVID-19 among children with COVID-19. Material and methods Adhering to PRISMA-DTA guidelines, we searched databases (PubMed, Google Scholar, and Web of Science) to identify relevant articles. The search keywords were: “Chest CT” AND “COVID-19” OR “coronavirus” OR “SARS-COV-2” AND “Children” OR “Pediatric”. Published reports providing clinical and imaging findings of paediatric COVID-19 were included. Results Twenty-eight studies were included, with 987 patients. Most of the patients were symptomatic (76.9%; 95% CI: 69.2-84.7%), with fever being the most frequent manifestation (64%; 95% CI: 58.0-71.2%). Only 2.3% of the cases were critical, and mortality was reported in one case. The proportion of COVID-19 detected by chest CT among children is relatively high (658/987), with ground-glass opacity (GGO) being the most prevalent feature (52.5%; 95% CI: 40.5-64.7%). The pooled sensitivity of chest CT in all patients was 67%; however, it was different between symptomatic and asymptomatic patients (71% and 33%, respectively). The pooled specificity was (67%), which was calculated after considering the symptomatic PCR-positive patients as the gold standard. Conclusions Chest CT showed moderate pooled sensitivity and specificity among symptomatic children with COVID-19 and low sensitivity among asymptomatic children. This means that CT is not to be used as a screening tool or for confirmation of the diagnosis in children and should be reserved for specific clinical situations.
The Tensor Multi-linear Channel and its Shannon Capacity

Tensors are multi-way arrays which can be used to model systems spanning many domains. This work proposes to use tensors for characterizing, analyzing, and designing multi-domain communication systems. Most modern day communication systems make use of coding and modulation across different domains such as space, frequency, time. Hence a unified mathematical framework characterizing such a multiple-domain system in an intuitive manner is well needed. In this paper, we present such a unified framework that characterizes a communication system with N input domains, M output domains and an M + N domains multi-linear tensor channel. The proposed framework is generic where the physical interpretation of the domains is system specific. We illustrate a few examples from multi-antenna multicarrier and multi-user systems that fit the proposed framework. Assuming a fixed tensor channel, we provide an information theoretic analysis by deriving its Shannon capacity and power allocation under a variety of power constraints. In this paper we show how the tensor framework’s suitability to mathematically describe a family of power constraints can be used to design and analyze various multiple domain communication systems. The tensor based approach extends water filling from a matrix setting to tensors, encapsulating the effects of multiple domains thereby allowing joint multi-domain pre-coding.We show that the capacity prelog for a tensor channel increases exponentially in the number of domains, indicating the potential of tensor based multi-domain communication systems to provide the large information transmission rates envisaged for 5G and beyond systems. We also show the application of the tensor framework in characterizing the capacity and rate regions of multi-user MIMO channels. Both Multiple Access and Interference channel are considered where the tensor based approach leads to a coordinated users transmission scheme. Such a scheme ensures higher achievable sum rates as compared to independent user transmissions.