Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacists as Strategic Leaders of Manufacturing Pharmaceutical Companies with Operations in South Africa

The manufacturing sector of the pharmaceutical industry has faced criticism for disparities in access to pharmaceuticals, especially within the context of past incidents and the COVID-19 pandemic. Balancing profitability with the public responsibility to produce affordable, safe and effective medicines is challenging. The World Health Organisation (WHO) recognises the significant role pharmacists play in discovering, manufacturing and dispensing medicines. Pharmacists are responsible for safeguarding pharmaceuticals at all levels of care and where medicines are used. The research aimed to assess the involvement of pharmacists in the strategic leadership of Multinational Pharmaceutical Companies (MPCs) operating in South Africa. The study assessed the presence of pharmacists, recognised as custodians of medicines, in the strategic leadership of pharmaceutical companies operating in South Africa but headquartered globally. A desktop review was done to assess the company profiles, including revenue, size, number of employees and professional backgrounds of the persons in strategic leadership, including board and executive levels. The pharmaceutical companies were headquartered in eleven countries across Asia (3), Africa (1), North America (1), and Europe (6). On average, these companies operated in 86.6 countries (SD ±46.2). The strategic leadership roles within MPCs were comprised of individuals with backgrounds in commerce, sciences, and engineering. Predominantly, professionals with backgrounds in commerce held significant representation in both board membership and executive leadership within these companies. Notably, only 3.2% (33 out of 1023) of leaders possessed a pharmacy qualification, with a mere 27% (9 out of 33) being female. This was the least represented professional background among the strategic leaders, and the likelihood was affected by gender. The pharmacists more likely to hold strategic positions were predominantly male, had additional qualifications, and were situated in specific countries like India and South Africa. Pharmaceutical companies are essential in producing medicines to address global healthcare needs, functioning as healthcare service providers. Strategic leaders in these companies guide the manufacturing sites’ strategic goals of the companies. The study’s outcomes revealed a restricted presence of pharmacists in leadership roles despite their typical responsibility for manufacturing sites. These pharmacists were often found to have limited authority and were excluded from pivotal decision-making processes, resulting in significant implications for patient welfare.
An Overview of Rice QTLs Associated with Disease Resistance to Three Major Rice Diseases: Blast, Sheath Blight, and Bacterial Panicle Blight

Rice (Oryza sativa L.) is one of the most important crops that are produced as human food, directly feeding people more than any other crop. Hence, it is important to increase the yield potential of rice through improving the disease resistance to prevailing rice diseases. Blast caused by the fungus Magnaporthe oryzae, sheath blight caused by the fungus Rhizoctonia solani Kühn, and bacterial panicle blight caused by the bacteria Burkholderia glumae and B. gladioli are serious rice diseases in many rice-producing regions. In spite of the chronic damages from these major diseases, the quantitative resistance to each of them is not known very well and any available disease-resistant varieties are rare or not stable. Although gene-for-gene resistance that is mediated by an R-Avr interaction has been intensively studied for blast, quantitative (or horizontal) resistance to a broad spectrum of races in M. oryzae is still poorly understood. Identification of the quantitative trait loci (QTLs) related to these diseases and using marker technology can facilitate marker-assisted selection to screen resistant traits in individual resources, which could ultimately lead to the development of novel disease-resistant rice varieties. This article is a summary of identified QTLs that are associated with rice diseases, including blast, sheath blight, and bacterial panicle blight that can be used in breeding programs.
Improved Masking Multiplication with PRGs and Its Application to Arithmetic Addition

At Eurocrypt 2020, Coron et al. proposed a masking technique allowing the use of random numbers from pseudo-random generators (PRGs) to largely reduce the use of expansive true-random generators (TRNGs). For security against d probes, they describe a construction using 2d PRGs, each of which is fed with at most 2d random variables in a finite field, resulting in a randomness requirement of O∼d2. In this paper, we improve the technique on multiple frontiers. On the theoretical level, we push the limits of the randomness requirement by providing an improved masking multiplication using only d PRGs, each of which is fed with d random variables, saving more than half random bits. On the practical level, considering that the masking of arithmetic addition usually requires more randomness (than multiplication), we apply the technique to the algorithm proposed at FSE 2015 that is a very efficient scheme performing arithmetic addition modulo 2w. It significantly reduces the randomness cost of masked arithmetic addition, and further advocates the advantage of masking with PRGs. Furthermore, we apply our masking scheme to the Speck, XTEA, and Sparkle, and provide the first (to the best of our knowledge) higher order masked implementations for the ciphers using ARX structure.
Cohesive Attention-Based Explanations for Sequences and Explainability in Presence of Event Types

While many methods such as Locally Interpretable Model-agnostic Explanation (LIME), Integrated Gradients and Layerwise Relevance Propagation (LRP) have been developed to explain how recurrent neural networks make predictions, the explanations generated by each method often times vary dramatically. There is no consensus about which explainability method most accurately and robustly determine features important for model prediction. We consider a classification task on a sequence of events with different types and apply both gradient-based and attention-based explanation models to compute explanations on the event type level. We show that attention-based models return a higher similarity score between explanations for models initialized with different random seeds. However, there are still significant differences in explanations between model runs. We develop an optimization-based model to find a low-loss, high-accuracy path between two sets of trained weights to understand how model explanations morph between different local minima. We use this low-loss path to provide insight as to why explanations vary on two sentiment datasets.
A Network Architecture Based on Feature Fusion for Acoustic Scene Classification

A network architecture based on feature fusion is proposed to enhance representation learning for improving the accuracy of acoustic scene classification (ASC). In the proposed network model, by utilizing feature fusion blocks, global information is successively transferred from the Swin Transformer branch into the convolutional neural network (CNN) branch, while local features from the CNN branch are progressively sent into the patch embedding for enriching local details of the Swin Transformer branch. This bidirectional interaction way makes the CNN and Swin Transformer complement each other, and their local features and global representations are fully fused, which helps improve the accuracy of ASC. The experimental results prove that the proposed method reaches the superior performances over the reference deep learning methods in ASC.
The Implementation and Effectiveness of Geographic Information Systems Technology and Methods in High School Education

Geographic information systems (GIS) technology and methods have transformed decision-making in universities, government, and industry by bringing digital spatial data sets and geographic analysis to the desktop computer. Some educators consider GIS to be one of the most promising means for implementing educational reform. However, GIS technology has been adopted by only 1% of India high schools. The reasons behind the interest in GIS technology and methods, their slow implementation, their extent in the curriculum, and their effectiveness in teaching and learning are unclear. To address these concerns, this research: (1) describes the geographic and curricular extent to which GIS technology and methods are being implemented in secondary education in the United States, (2) explains why and how GIS is being implemented, and (3) assesses the effects of inquiry-based lesson modules that use GIS technology on teaching and on the acquisition of standardsbased geographic content and skills.
Development of Punjabi Automatic Speech Recognition Application for Mobile Devices

Speech recognition based mobile device applications are gaining popularity due to their ease of use, flexibility as well as ability to provide hands-free access to device features and functions for persons without hands or with disabilities like blindness. The increased accuracy of Automatic Speech Recognition (ASR) systems and support to multiple languages is further motivating the researchers to develop such applications. The development of an application for mobile device helps to validate the applicability of an ASR system in real world environment. An android based speech recognition application for calling and messaging features has been designed and developed in this paper. User can send message or call to any phone number or contact stored in the mobile phone using Punjabi speech. Punjabi based ASR system having pronunciation dictionary, acoustic model and language model is used to implement the underlying speech recognition for Punjabi speakers. Speech user interface has been designed using Android, while AndroidPocketSphinx has been used as speech decoder. The successfully running application has been tested for multiple male and female native Punjabi speakers, followed by thorough performance analysis. The results shown are quite optimistic with a very low combined error of 1.6% for all female speakers and 2.1% for all male speakers. The application gives 98.4% success rate or recognition accuracy for female speakers and 97.9% for male speakers for the underlying Punjabi based ASR system.
Small-Angle Neutron Scattering Insights into 2-Ethylhexyl Laurate: A Remarkable Bioester

Commercial (protiated) samples of the “green” and biodegradable bioester 2-ethylhexyl laurate (2-EHL) were mixed with D-2-EHL synthesized by hydrothermal deuteration, with the mixtures demonstrating bulk structuring in small-angle neutron scattering measurements. Analysis in a polymer scattering framework yielded a radius of gyration (Rg) of 6.5 Å and a Kuhn length (alternatively described as the persistence length or average segment length) of 11.2 Å. Samples of 2-EHL dispersed in acetonitrile formed self-assembled structures exceeding the molecular dimensions of the 2-EHL, with a mean aggregation number (Nagg) of 3.5 ± 0.2 molecules across the tested concentrations. We therefore present structural evidence that this ester can function as a nonionic (co)surfactant. The available surfactant-like conformations appear to enable performance beyond the low calculated hydrophilic–lipophilic balance value of 2.9. Overall, our data offer an explanation for 2-EHL’s interfacial adsorption properties via self-assembly, resulting in strong emolliency and lubricity for this sustainable ester-based bio-oil.
Effectiveness of Percutaneous Electrical Nerve Stimulation Versus Conventional Therapy in Adults with Plantar Fasciitis

Background: Plantar Fasciitis is a degenerative condition of the heel. Percutaneous electrical nerve stimulationand conventional therapy was used as intervention treatment for heel pain.Purpose: The Purpose of this was to compare the efficacy of Pens and Conventional therapy in management ofplantar fasciitis.Materials and Methods: The experimental and control groups included 30 young males and females who wererandomly assigned to each group. The experimental group patients underwent Pens and conventional groupunderwent ultrasound treatment, plantar stretch, and exercise. Before and after four -week sessions of therapyeach participant was asked to score their morning pace using a numerical rating scale and functional foot indexas outcome measures.Result: The experimental group showed a substantial difference compared to the conventional group (p <0.0001),indicating a statistically significant outcome. When comparing both groups there was a statistically significantdifference after intervention.Conclusion: Patients treated for heel pain reacted well to the conventional strategy used in this study, but Pensfollowed by active stretching dramatically reduced pain severity and improved the quality of foot function.
Classification of diseases in snake plants using convolutional neural network

snake plant has an important role in human life, as well as in increasing the aesthetic value of the environment. Limited knowledge about diseases in snake plants has a crucial result in improper handling and control when the plant is attacked by disease. Advances in deep learning technology and Convolutional Neural Network (CNN) have presented high opportunities with their advantages in recognizing patterns and features from image data. This research will use a CNN model with VGG-19 architecture to classify diseases in the leaves of the snake plant. It is expected that by using the pre-trained VGG-19 model, the model can recognize complex visual patterns in snake plants. Diseases to be classified include several types of diseases that often attack snake plants such as anthracnose, rust, water soaked lesion, and healthy plants for comparison. The highest value of training accuracy reached a value of 98.08%, validation accuracy of 94.02%, and testing accuracy reached 94%.
Meiotic cellular rejuvenation is coupled to nuclear remodeling in budding yeast

Production of healthy gametes in meiosis relies on the quality control and proper distribution of both nuclear and cytoplasmic contents. Meiotic differentiation naturally eliminates age-induced cellular damage by an unknown mechanism. Using time-lapse fluorescence microscopy in budding yeast, we found that nuclear senescence factors – including protein aggregates, extrachromosomal ribosomal DNA circles, and abnormal nucleolar material – are sequestered away from chromosomes during meiosis II and subsequently eliminated. A similar sequestration and elimination process occurs for the core subunits of the nuclear pore complex in both young and old cells. Nuclear envelope remodeling drives the formation of a membranous compartment containing the sequestered material. Importantly, de novo generation of plasma membrane is required for the sequestration event, preventing the inheritance of long-lived nucleoporins and senescence factors into the newly formed gametes. Our study uncovers a new mechanism of nuclear quality control and provides insight into its function in meiotic cellular rejuvenation.
Wind and Solar Pattern Analysis for Enhanced Grid Planning: A Literature Review

The increasing integration of renewable energy sources into the electrical grid, such as wind and solar power, has created a pressing need for advanced resource planning strategies. Accurate categorization and analysis of wind and solar patterns are essential for effective grid management, resource allocation, and comprehensive grid planning. This review investigates and evaluates various models employed for categorizing and analysing wind and solar patterns, with a specific focus on grid integration and planning. A summary of the difficulties of integrating intermittent and variable energy sources into the grid is reviewed, along with the significance of initial categorization and clustering of the renewable energy resources for overcoming these difficulties. Through an in-depth analysis of clustering models, utilizing statistical and temporal datasets, this research offers valuable insights to enhance grid planning and resource management. Additionally, the study explores current practices and emerging trends in capacity expansion models, transmission planning, and infrastructure upgrades. Key findings from this research include a comprehensive understanding of wind and solar patterns, which are foundational for effective grid planning and resource management. The analysis aids in creating models that equip decision-makers and grid planners with essential insights to ensure grid stability and reliability amidst the growing adoption of renewable energy sources.
Gender Classification Accuracy via Two-Dimensional Body Joints using Convolutional Neural Networks

With the increasing demand for gender-related data in various applications such as border security systems and targeted marketing, gender of female and male classification has gained significant importance in the field of computer vision and deep learning. Existing research has focused on gender classification through facial image, external appearance, or gait movement. However, there is a lack of studies specifically targeting gender classification using two-dimensional body joints. This paper introduces a novel prediction pipeline to enhance the accuracy of gender classification based solely on two-dimensional joint images. Our proposed approach utilizes deep learning (Convolutional Neural Network) technique. We conducted experiments on the BBC Pose and Short BBC Pose datasets, preprocessing the images by filtering out frames with missing human figures, removing background noise, and labeling the joints through transfer learning with a C5 pre-trained model based on ResNet-152. Our results demonstrate that the deep learning method outperforms other approaches, successfully classifying gender (female and male) using two-dimensional joint images and achieving an accuracy of 66.5%.
Late Pleistocene and Holocene shorelines of Andréeland, Spitsbergen (Svalbard)-geomorphological evidence and palaeo-oceanographic significance

The littoral areas of northern Andreeland (Spitsbergen, Svalbard) are characterized by numerous beach ridges. The prerequisites of their formation were the combined effects of wave action in the open fjord due to the influence of the West Spitsbergen Current on the wave regime up to 80° N and uplift due to the glacio-isostatic rebound in the course of deglaciation. Based on geomorphological mapping and the radiocarbon dating of mollusc shells, a scenario for late Pleistocene and Holocene shoreline and landscape evolution is proposed. Since the formation of the beach ridges reflects pulses of the Gulf Stream, they provide an excellent terrestrial geo-archive for palaeo-oceanography.
A Novel Dynamic Community Detection Model in Social Networks

Social networks are becoming more and more popular and important. A key feature is that they are dynamic and develop with time. As a result, their community evolves along with the social networks. Detecting community structure dynamically is an important research topic. Specifically, how social networks along with communities evolve is still not revealed. In this paper, we propose the importance of individual changing topics that is a key factor in community evolution. this paper designs a novel dynamic community detection model of social networks by considering the natural evolutionary features DCDIT (Dynamic Community Detection Motivated by Individual Changing Topics). The key mechanism is that individual changing topics drive network and community structure evolve. Our approach begins by dividing social networks into distinct snapshots. Following this, it identifies community structures through the analysis of varying individual topics and information from the preceding snapshot. Subsequently, it determines the topic distributions that interest users and those prevalent in communities. This model is benchmarked against four advanced baseline models using two datasets. Experimental outcomes reveal that DCDIT surpasses all baseline models in performance.
Design and Research of Full Reference Image Quality Assessment Algorithm

Visual signals play an important medium role in human communication with the outside world. However, the various links of the process of image acquisition, storage, transmission all may produce changes to the image information and degradation of the image structure. Therefore, it becomes very important to assess the image quality. However, subjective assessment wastes time and energy, which is not feasible in practical applications, and subjective experiments are affected by many factors such as viewing distance, display equipment, lighting conditions, visual ability of the observer, and emotion. Therefore, it is necessary to design mathematical model that can automatically and accurately predict the subjective quality. This research adopted LOG-PSNR image quality prediction model based on the LOG operator. The model is compared with SSIM and NLOG-MSE two models on the LIVE database. The experimental results suggest that the LOG-PSNR algorithm has good consistency with the subjective scores of the database, it shows that the perceived quality of the image can be well reflected by calculating the difference between the reference image and the distorted image after removing the first-order and second-order statistical redundancy of the image by using the LOG operator.
Relationship between Athletes’ Big Five Model of Personality and Athletic Performance: Meta-Analysis

Academic interest in athletic performance is ongoing. To examine the correlation between athletic performance and athletes’ personality types, data extraction in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was completed in October 2021, and a meta-analysis was performed using 180 data from 18 selected papers using the “meta” package version 4.8-4 of R Studio 3.3. 3. As a result, these selected studies proved to have reliable quality in proceeding with this study via quality assessment. The overall effect of personality on athletic performance (AP) was ESr = 0.124, p < 0.01. Furthermore, only conscientiousness (ESr = 0.178, p < 0.001) and extroversion (ESr = 0.145, p < 0.01), among the five personality types, showed statistically significant results, and these two personality types had a positive correlation with performance. In the publication bias test, this study found that (a) agreeableness had a publication bias; but, with an additional test using trim-and-fill, (b) the effect was not significant enough to be considered. In addition, the analysis of the moderating effects was conducted in four aspects, and all moderating effect analyses showed statistically significant differences between the groups, demonstrating the heterogeneity of this study. Therefore, this study found a significant relationship between personality and athletic performance and showed the importance of conscientiousness and extroversion.