Miracle-dz/UniFAR-FTUs huggingface dataset:
Health hazards, risk and safety practices in construction sites – a review study

This review attempts to identify the health hazards, risks and causes of poor safety practices in construction sites. In addition, the differences in safety practices in both developed and developing countries and methods to improve construction site safety are discussed. Effects of some health hazards are chronic while some are acute. Mostly reported acute health hazards are “workers fall from height” and “electric shocks”, while mostly reported chronic health hazard is “exposure to hazardous substances”. Lack of awareness about site safety and dislike to wear Personal Protective Equipment (PPE) were identified as main causes of poor safety practices in construction sites. One of the major needs with regard to the construction industry is to enhance professionals’ interests in active safety management and implementation of awareness programs, which must be developed and implemented among construction workers. Awareness on possible risk factors and knowledge on how to reduce these risk factors among workers and contractors will enhance site safety. ENGINEER, Vol. 48, No.03, pp. 35-44 2015
Analysis of dust on the parameters of PV module and design of an effective solar dust cleaner

Photovoltaic panel output is affected by various factors like solar isolation, temperature, dust, shading, mounting. Among all these factors dust is a prominent factor which greatly reduces panel efficiency. With increase in the quantity of dust both efficiency and power output of the module reduces. As the mass of dust deposition increases, power output and the efficiency of the module decreases. Dust particles are of different shapes and sizes. PV panels installed on ground or roof top are subjected to dust all the time. The amount and type of dust deposition depends on tilt angle, locations, surroundings nearby, rainfall pattern of that area, maintenance method of the installer. PV plant nearby any industry giving out lot of fumes and smokes will have heavy dust deposition over its top surface. Similarly, PV plants located near areas where roads are not concrete can cause dust deposition on its panels. Manual maintenance is labor costing and less efficient. In this work we have performed dust analysis on the performance of PV panel and designed an effective method to clean the panel and make it dust free. Solar dust cleaner and cooler system provides a solution for both dusts cleaning and keeping the panels at less temperature.
Nipple candidiasis and painful lactation: an updated overview

Nipple pain and discomfort during or after breastfeeding remains one of the most common reasons for premature cessation of lactation among the affected women. The belief that yeasts, and especially Candida spp. , are responsible for such symptoms is highly supported by many physicians, midwives, or lactation specialists, but is also viewed with scepticism by other health care providers. The aim of this paper is to provide an updated report of the evidence against, as well as in favour of, the “Candida hypothesis”. Several studies have documented that lactating women with symptoms such as nipple soreness, with or without radiating breast pain, are more likely to test positive for Candida spp. than non-symptomatic women. However, its role as an undisputable aetiopathogenic factor for infection in these cases cannot always be established. Physicians should evaluate thoroughly such patients, because early and correct recognition of the underlying problem can prevent phenomena of early weaning.
AROMA autofocus of SAR moving targets with complicated heading trajectories

Moving targets are often smeared in synthetic aperture radar (SAR) imagery beyond recognition. Recent analysis has yielded an ability to perform Arbitrary Rigid Object Motion Autofocus (AROMA) in order to perform automatic refocus of targets which exhibit arbitrary temporal profiles of target translation and rotation during the SAR collection interval. This investigation examines the efficacy of AROMA for targets with complex heading profiles amidst a background of measured Ku-band SAR imagery. It is found that AROMA is able to generate well focused imagery which corresponds with the underlying true structure of the target scattering shapes.
Radio Frequency Energy Harvesting Technologies: A Comprehensive Review on Designing, Methodologies, and Potential Applications

Radio frequency energy harvesting (RF-EH) is a potential technology via the generation of electromagnetic waves. This advanced technology offers the supply of wireless power that is applicable for battery-free devices, which makes it a prospective alternative energy source for future applications. In addition to the dynamic energy recharging of wireless devices and a wide range of environmentally friendly energy source options, the emergence of the RF-EH technology is advantageous in facilitating various applications that require quality of service. This review highlights the abundant source of RF-EH from the surroundings sources, including nearby mobile phones, Wi-Fi, wireless local area network, broadcast television signal or DTS, and FM/AM radio signals. In contrast, the energy is captured by a receiving antenna and rectified into a working direct current voltage. This review also summarizes the power of RF-EH technology, which would provide a guideline for developing RF-EH units. The energy harvesting circuits depend on cutting-edge electrical technology to achieve significant efficiency, given that they are built to perform with considerably small current and voltage. Hence, the review includes a thorough analysis and discussion of various RF designs and their pros and cons. Finally, the latest applications of RF-EH are presented.
SPECTRAL-SPATIAL MULTISCALE RESIDUAL NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Abstract. In recent years, deep neural networks (DNN) are commonly adopted for hyperspectral image (HSI) classification. As the most representative supervised DNN model, convolutional neural networks (CNNs) have outperformed most algorithms. But the main problem of CNN-based methods lies in the over-smoothing phenomenon. Meanwhile, mainstream methods usually require a large number of samples and a large amount of computation. A multi-task learning spectral-spatial multiscale residual network (SSMRN) is proposed to learn features of objects effectively. In the implementation of the SSMRN, a multiscale residual convolutional neural network (MRCNN) is proposed as spatial feature extractors and a band grouping-based bi-directional gated recurrent unit (Bi-GRU) is utilized as spectral feature extractors. To evaluate the effectiveness of the SSMRN, extensive experiments are conducted on public benchmark data sets. The proposed method can retain the detailed boundary of different objects better and yield a competitive performance compared with two state-of-the-art methods especially when the training samples are inadequate.
Development of Non-Player Character with Believable Behavior: a systematic literature review

Non-player character or non-playable character (NPC) does not always have appropriate and responsive behaviors. When the decision-making process is more artificial than intelligent, it can lead to quality issues in a game and even reduce player interest. Based on this statement, this article presents a Systematic Literature Review on the development of NPC with credible behaviors. The review enabled the mapping and knowledge of the current state of the art extracted from 18 related studies. It was found that certain techniques traditionally used by the gaming industry have become a major obstacle in generating more engaged NPC behavior in the virtual environment.
Beyond skills and knowledge: the role of self-efficacy and peer networks in building capacity for species conservation planning

Abstract Biodiversity loss is one of the greatest global challenges and requires substantial investment in building the capacity of conservation professionals to design and implement robust conservation plans. In this study, we surveyed 155 past participants of training in facilitating species conservation planning processes given by the Conservation Planning Specialist Group of the IUCN Species Survival Commission. Based on a recently developed theory of change for the training, we examined how and to what extent the training contributed to the desired outcome of increasing trainees’ capacity for leading the design and facilitation of species conservation planning processes. Our results indicate that recall of training content, self-efficacy (an individual's belief they can complete a specific task or behaviour successfully) and peer network participation had significant impacts on the outcome of applying training content in the workplace. Furthermore, our results suggest that self-efficacy played a highly influential role in trainees' participation in species conservation planning post-training. The implications of this research point to designing conservation training that considers not only the skills and knowledge to be gained by learners but also the strategies that enhance trainees' self-efficacy in applying new skills and knowledge and in establishing peer networks to support trainees in turning training objectives into realities.
Where psychological science meets moral theory: Linking up motivational primitives with normative ethics

The main normative moral theories in Western thought, from deontology to virtue and consequentialist ethics, revolve around conceptions of the moral right, good, and worth. However, a few elementary psychological motives lie at a deeper level. In the present article, I outline the key tenets of regulatory focus, regulatory mode, and the hedonic principle (approach/avoidance), which I define as “motivational primitives,” and provide a conceptual analysis of their links with specific ethical systems. I unveil how moral judgment in each of them is psychologically construed on the basis of the motivational primitives and their underlying self-regulatory processes. The credibility of the proposed framework will be fully brought to life when researchers, having agreed on satisfactory operationalizations and manipulations of the primitives, will be able to reconcile the speculative and the empirical planes.
Hydrophilic Poly(glutamic acid)-Based Nanodrug Delivery System: Structural Influence and Antitumor Efficacy

Poly(amino acids) have advanced characteristics, including unique secondary structure, enzyme degradability, good biocompatibility, and stimuli responsibility, and are suitable as drug delivery nanocarriers for tumor therapy. The isoform structure of poly(amino acids) plays an important role in their antitumor efficacy and should be researched in detail. In this study, two kinds of pH-sensitive isoforms, including α-poly(glutamic acid) (α-PGA) and γ-PGA, were selected and used as nanocarriers to prepare a nanodrug delivery system. According to the preparation results, α-PGA can be used as an ideal drug carrier. Selecting doxorubicin (DOX) as the model drug, an α-PGA/DOX nanoparticle (α-PGA/DOX NPs) with a particle size of 110.4 nm was prepared, and the drug-loading content was 66.2%. α-PGA/DOX NPs presented obvious sustained and pH-dependent release characteristics. The IC50 value of α-PGA/DOX NPs was 1.06 ± 0.77 μg mL−1, decreasing by approximately 8.5 fold in vitro against 4T1 cells after incubation for 48 h. Moreover, α-PGA/DOX NPs enhanced antitumor efficacy in vivo, the tumor inhibition rate was 67.4%, increasing 1.5 fold over DOX injection. α-PGA/DOX NPs also reduced the systemic toxicity and cardiotoxicity of DOX. In sum, α-PGA is a biosafe nanodrug delivery carrier with potential clinical application prospects.
ACTIVE REINFORCEMENT LEARNING FOR THE SEMANTIC SEGMENTATION OF IMAGES CAPTURED BY MOBILE SENSORS

Abstract. In recent years, various Convolutional Neural Networks (CNN) have been used to achieve acceptable performance on semantic segmentation tasks. However, these supervised learning methods require an extensive amount of annotated training data to perform well. Additionally, the model would need to be trained on the same kind of dataset to generalize well for other tasks. Further, commonly real world datasets are usually highly imbalanced. This problem leads to poor performance in the detection of underrepresented classes, which could be the most critical for some applications. The annotation task is time-consuming human labour that creates an obstacle to utilizing supervised learning methods on vision tasks. In this work, we experiment with implementing a reinforced active learning method with a weighted performance metric to reduce human labour while achieving competitive results. A deep Q-network (DQN) is used to find the optimal policy, which would be choosing the most informative regions of the image to be labelled from the unlabelled set. Then, the neural network would be trained with newly labelled data, and its performance would be evaluated. A weighted Intersection over Union (IoU) is used to calculate the rewards for the DQN network. By using weighted IoU, we target to bring more attention to underrepresented classes.
Formulation of Polymers-Based Methotrexate Patches and Investigation of the Effect of Various Penetration Enhancers: In Vitro, Ex Vivo and In Vivo Characterization

The present study aimed to prepare methotrexate-loaded transdermal patches with different blends of hydrophobic and hydrophilic polymers (Eudragit S-100 and hydroxypropyl methylcellulose) at different concentrations. The polymers employed in transdermal patches formulations served as controlled agent. Transdermal patches were prepared using the solvent casting technique. The suitable physicochemical properties were obtained from the formulation F5 (HPMC and Eudragit S-100 (5:1). Various penetration enhancers were employed in different concentrations to investigate their potential for enhancing the drug permeation profile from optimized formulations. A preformulation study was conducted to investigate drug–excipient compatibilities (ATR-FTIR) and the study showed greater compatibility between drug, polymers and excipients. The prepared patches containing different penetration enhancers at different concentrations were subjected for evaluating different physicochemical parameters and in vitro drug release studies. The obtained data were added to various kinetic models, then formulated patch formulations were investigated for ex vivo permeation studies, in vivo studies and skin drug retention studies. The prepared patches showed elastic, smooth and clear nature with good thickness, drug content, % moisture uptake and weight uniformity. The prepared transdermal patches showed % drug content ranging from 91.43 ± 2.90 to 98.37 ± 0.56, % swelling index from 36.98 ± 0.19 to 75.32 ± 1.21, folding endurance from 61 ± 3.14 to 78 ± 1.54 and tensile strength from 8.54 ± 0.18 to 12.87 ± 0.50. The formulation F5, containing a greater amount of hydrophilic polymers (HPMC), showed increased drug release and permeation and drug retention when compared to other formulated transdermal patch formulations (F1-F9). No significant change was observed during a stability study for a period of 60 days. The rabbit skin samples were subjected to ATR-FTIR studies, which revealed that polymers and penetration enhancers have affected skin proteins (ceramides and keratins). The pharmacokinetic profiling of optimized formulation (F5) as well as formulations with optimized concentrations of penetration enhancers revealed Cmax ranged 167.80 ng/mL to 178.07 ± 2.75 ng/mL, Tmax was 8 h to 10 h, and t1/2 was 15.9 ± 2.11 to 21.49 ± 1.16. From the in vivo studies, it was revealed that the formulation F5-OA-10% exhibited greater skin drug retention as compared to other formulations. These results depicted that prepared methotrexate transdermal patches containing different blends of hydrophobic and hydrophilic polymers along with different penetration enhancers could be safely used for the management of psoriasis. The formulated transdermal patches exhibited sustained release of drug with good permeations and retention profile. Hence, these formulated transdermal patches can effectively be used for the management of psoriasis.
A Review of Control Technologies of Combustion Optimization in Utility Boilers

By using operation data and DCS control system of boilers,through the application of a series of advanced technologies of modelling,optimization and control without any retrofit for boilers,combustion optimization control technologies in utility boilers can improve the operation efficiency and reduce the NO_x emissions of boilers at low cost.So more and more power plants have paid attention to this technology which stands for the developing trends of nextgeneration thermal power automation.This paper elaborates the research and application status of this technology both at home and abroad,briefs its realization principles and applications,and introduce the research and practice work of the anthor’s laboratory.At the end,the difficulties in the application of this technology are summarized.
Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning

The use of Machine Learning (ML) and Sentiment Analysis (SA) on data from microblogging sites has become a popular method for stock market prediction. In this work, we developed a model for predicting stock movement utilizing SA on Twitter and StockTwits data. Stock movement and sentiment data were used to evaluate this approach and validate it on Microsoft stock. We gathered tweets from Twitter and StockTwits, as well as financial data from Finance Yahoo. SA was applied to tweets, and seven ML classification models were implemented: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron (MLP). The main novelty of this work is that it integrates multiple SA and ML methods, emphasizing the retrieval of extra features from social media (i.e., public sentiment), for improving stock prediction accuracy. The best results were obtained when tweets were analyzed using Valence Aware Dictionary and sEntiment Reasoner (VADER) and SVM. The top F-score was 76.3%, while the top Area Under Curve (AUC) value was 67%.
Non-Coding RNAs in the Therapeutic Landscape of Pathological Cardiac Hypertrophy

Cardiovascular diseases are a major health problem, and long-term survival for people diagnosed with heart failure is, still, unrealistic. Pathological cardiac hypertrophy largely contributes to morbidity and mortality, as effective therapeutic approaches are lacking. Non-coding RNAs (ncRNAs) arise as active regulators of the signaling pathways and mechanisms that govern this pathology, and their therapeutic potential has received great attention in the last decades. Preclinical studies in large animal models have been successful in ameliorating cardiac hypertrophy, and an antisense drug for the treatment of heart failure has, already, entered clinical trials. In this review, we provide an overview of the molecular mechanisms underlying cardiac hypertrophy, the involvement of ncRNAs, and the current therapeutic landscape of oligonucleotides targeting these regulators. Strategies to improve the delivery of such therapeutics and overcome the actual challenges are, also, defined and discussed. With the fast advance in the improvement of oligonucleotide drug delivery, the inclusion of ncRNAs-targeting therapies for cardiac hypertrophy seems, increasingly, a closer reality.
Surface Roughness of Dentin and Formation of Early Cariogenic Biofilm after Silver Diamine Fluoride and Potassium Iodide Application

This study aimed to evaluate the effect of silver diamine fluoride (SDF) and potassium iodide (KI) on the formation of cariogenic biofilm and surface roughness in vitro. A total of 48 bovine dentin specimens with artificially induced caries were prepared and divided into 3 groups of 16: untreated control, SDF-treated, and SDF-treated followed by KI (SDFKI). Ten specimens from each group were used to observe microbial adhesion. Multispecies cariogenic biofilms including Streptococcus mutans , Lactobacillus casei , and Candida albicans were cultured on the specimens. Microbes were cultured for 24 hours, and the colony-forming unit was calculated. The remaining specimens were observed by atomic force microscope and scanning electron microscope (SEM). The number of bacteria was significantly lower in the SDF and SDFKI groups. KI did not inhibit the antibacterial activity of SDF significantly. SEM images showed particles generated after SDF and SDFKI application were deposited on the dentin, but there was no significant difference in surface roughness between the 3 groups. This study confirmed that SDF and SDFKI application did not have a significant effect on the surface roughness of dentin, but effectively inhibited the formation of the early cariogenic bacterial film after 24 hours compared to the control.
Specialised minds: extending adaptive explanations of personality to the evolution of psychopathology

Abstract Abstract Traditional evolutionary theory invoked natural and sexual selection to explain species- and sex-typical traits. However, some heritable inter-individual variability in behaviour and psychology – personality – is probably adaptive. Here we extend this insight to common psychopathological traits. Reviewing key findings from three background areas of importance – theoretical models, non-human personality and evolved human social dynamics – we propose that a combination of social niche specialisation, negative frequency-dependency, balancing selection and adaptive developmental plasticity should explain adaptation for individual differences in psychology – ‘specialised minds’ – explaining some variance in personality and psychopathology trait dimensions, which share various characteristics. We suggest that anthropological research of behavioural differences should be extended past broad demographic factors (age and sex) to include individual specialisations. As a first step towards grounding psychopathology in ancestral social structure, we propose a minimum plausible prevalence, given likely ancestral group sizes, for negatively frequency-dependent phenotypes to be maintained as specialised tails of adaptive distributions – below the calculated prevalence, specialisation is highly unlikely. For instance, chronic highly debilitating forms of autism or schizophrenia are too rare for such explanations, whereas attention-deficit-hyperactivity disorder and broad autism phenotypes are common enough to have existed in most hunter-gatherer bands, making adaptive explanations more plausible.