Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of COVID-19 Pandemic on the Practice of Community Pharmacy in Kisumu County

Occurrence of pandemics affects every person in one way or another. The recent COVID-19 pandemic demonstrated that even frontline healthcare providers are no exception. Pharmacists and pharmaceutical technologists at the community pharmacy are often among the first healthcare provider’s patients visit during crises. They thus form a crucial team for supply of medicines, counselling, and controlling the spread of infections. There is either scanty or no data on how precisely the dispensation of community pharmacy was affected especially in Africa during the Covid 19 regime. Therefore, this study aimed to assess the effects of COVID-19 pandemic on the practice of community pharmacy in Kisumu County. A descriptive cross-sectional study was conducted involving 100 subjects. Subjects were selected through convenient sampling method. Open ended questionnaires were used to collect data. The questionnaires were self-administered as google forms. Key informant interviews of sampled practitioners were also conducted. A total of 77 subjects responded to the shared questionnaire. A total of 89.6% were pharmaceutical technologists while 10.4% were pharmacists with majority being males (64.9%). Most (57.9%) had between 6 to 10 years of working experience but with no previous experiences (68.8%) on pandemics or national disasters. About 52.6% had received COVID-19 pandemic vaccination and most (70.1%) were trained on COVID-19 management and prevention. A total of 76.6% had been trained on vaccination but only 9.2% were in a position to carry out vaccination in their premises. Further, 58.4% agreed that the pandemic had negatively impacted their practice with most impact felt at socio-economic (97.4%) and psychological levels (75.3%). Moreover, all agreed the pandemic had affected supply chain especially in terms of drug costing (61%) and demand (62.3%). The demand of azithromycin (18.2%), face masks, sanitizers (10.4%) and dexamethasone were largely impacted. A total of 55.8% agreed that technology had impacted their practice during the pandemic in terms of online pharmacy services, especially through online prescription filling (97.7%). To adapt to changing times, practitioners had to increase client engagement (81.8%) and seek aid from financial institutions (55.8%) to stay in business. The pandemic had a slightly more negative than positive impact on the practice of community pharmacy in Kisumu County.
Detection of retinal diseases from ophthalmological images based on convolutional neural network architecture

The retina is an eye layer that incorporates light- and color-sensitive cells as well as nerve fibers. It collects light and distributes it to the brain for image processing through the use of the optic nerve. Diseases that end up causing vision loss and blindness are generated by retinal ailments. As a result, it is imperative to diagnose and treat certain disorders as early as possible. Optical coherence tomography (OCT), an angiography imaging technique, is operated to help diagnose retinal disorders. Deep learning approaches, which are extensively utilized, have now become a convenient way for diagnosing retinal illnesses through OCT images as a result of their effective outcomes in interpreting medical images. To diagnose retinal disorders utilizing OCT scans, this investigation developed a hybrid methodology based on image pre-processing and convolutional neural networks (CNNs) (a deep learning method). Image pre-processing techniques including background filling, resizing, noise reduction, and highlighting are exercised at the pre-processing stage. The segmentation process provides a new CNN architecture with five convolution layers that does have a low computational cost. Compared to other publications using the same data set, the proposed method seems to have a success rate of 99.48 percent in the detection of retinal disorders, closing a significant gap in the literature. The proposed approach has the advantage of maintaining low computing costs in comparison to other studies in the literature. When the conclusions are regarded, it is noticed that the suggested method might be exerted as a decision support system to assist physicians in the clinical context during the diagnosis of retinal disorders.
Pilot-Scale Vinification of Cabernet Sauvignon Using Combined Lactiplantibacillus plantarum and Saccharomyces cerevisiae to Achieve Wine Acidification

Insufficient acidity in grape berries from warm climate regions has been exacerbated due to global warming, thereby becoming a major concern for winemaking. The wine lactic acid bacterium Lactiplantibacillus plantarum has potential to ameliorate wine acidity by producing lactic acid from hexose metabolism, but its impact on wine compositions and sensory outcomes is not well studied. Here, we evaluated acidification and fermentation performance of indigenous L. plantarum in two inoculation regimes (i.e., reverse inoculation and co-inoculation) by conducting pilot-scale vinification using Cabernet Sauvignon with low acidity. Important parameters of the bio-acidified wines, including fermentation kinetics, basic oenological parameters, volatile and sensory profile were compared to those in wines produced by single Saccharomyces cerevisiae with/without chemical acidification. Total titratable acidity in L. plantarum wines were either comparable or significantly higher compared to the chemical acidification control. Chemical profiling reviewed remarkable differences in certain organic acids and major volatile compounds, especially an up to a five-fold, six-fold, and nine-fold increase in lactic acid, ethyl lactate and isoamyl lactate, respectively. Changes in chemical compositions of the bio-acidified wines resulted in differentiated sensory perception compared to the control wines. Except having higher scores for “wine acidity”, the flavour profile of the bio-acidified wines was shifted towards “jammy fruit” and “butter” aromas. Together, these findings highlighted the applicability of using L. plantarum to induce biological acidification along with modulation of wine flavour.
Satisfaction in conventional acrylic complete denture patient with and without denture liners - a systematic review

To make a new complete denture, a series of steps is needed, and it is a time-consuming process. Which is not possible in every patient due to financial and medical reasons. The old denture can be relined by denture liners in less clinical and laboratory steps and can successfully increase the efficiency of ill-fitting dentures however, patient satisfaction with denture liners is doubtful. This systematic review was organized from the preferred reporting items for systematic reviews' checklist, and the methods were registered on the international prospective register of systematic reviews (PROSPERO- CRD42020210227). The main systematic review is to assess satisfaction in convectional acrylic complete denture with or without denture liners. Search engines such as PubMed, Science Direct, Cochrane, Ovid, and Google Scholar were used to extract information. The risk of bias was measured with the help of the Cochrane collaboration tool. Initially we found 1711 articles out of which 6 were finalised as per PICO criteria. Patient satisfaction was evaluated by using a visual analogue scale and questionnaire method, which shows denture liners group has more patient satisfaction. Acrylic denture liner and silicon denture liner shows better patient satisfaction compared to the conventional acrylic denture. Among denture liners, there is no significant difference.
Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization

The advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, semantic landmarks, and Wi-Fi landmarks as nodes and the relevance between waypoints and landmarks (i.e., waypoint–waypoint, waypoint–semantic, waypoint–Wi-Fi, semantic–semantic, and Wi-Fi–Wi-Fi) as edges, the optimization graph is constructed. Initializing the venue map is the single-track semantic map with the highest quality, as determined by a proposed map quality evaluation function. The aligned venue and candidate maps are optimized while satisfying the constraints, with the candidate map exhibiting the highest degree of similarity to the venue map. The lightweight venue map is then updated in terms of waypoint and landmark attributes, as well as the relationship between waypoints and landmarks. To determine a pedestrian’s location on a venue map, similarities between a local map and a venue map are evaluated. Experiments conducted in an office building and shopping mall scenes demonstrate that crowdsourcing-based venue maps are superior to single-track semantic maps. Additionally, the landmark matching-based localization method can achieve a mean localization error of less than 0.5 m on the venue map, compared to 0.6 m in a single-track semantic map.
Ball Screw Fault Diagnosis Based on Wavelet Convolution Transfer Learning

The ball screw is the core component of the CNC machine tool feed system, and its health plays an important role in the feed system and even in the entire CNC machine tool. This paper studies the fault diagnosis and health assessment of ball screws. Aiming at the problem that the ball screw signal is weak and susceptible to interference, using a wavelet convolution structure to improve the network can improve the mining ability of signal time domain and frequency domain features; aiming at the challenge of ball screw sensor installation position limitation, a transfer learning method is proposed, which adopts the domain adaptation method as jointly distributed adaptation (JDA), and realizes the transfer diagnosis across measurement positions by extracting the diagnosis knowledge of different positions of the ball screw. In this paper, the adaptive batch normalization algorithm (AdaBN) is introduced to enhance the proposed model so as to improve the accuracy of migration diagnosis. Experiments were carried out using a self-made lead screw fatigue test bench. Through experimental verification, the method proposed in this paper can extract effective fault diagnosis knowledge. By collecting data under different working conditions at the bearing seat of the ball screw, the fault diagnosis knowledge is extracted and used to identify and diagnose the position fault of the nut seat. In this paper, some background noise is added to the collected data to test the robustness of the proposed network model.
Modeling Ordered Choices: A Primer

It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.
Design and optimization of air‐cored double‐sided linear permanent magnet generators for wave energy conversion

Recently, linear generators have been widely employed to convert oceanic wave energy into electricity. Among various linear generators, linear permanent magnet generators (LPMGs) have gained attention due to their special characteristics such as high power density and simple control structure. In this paper, flat double‐sided LPMG is investigated and designed. Considering all effective design variables, a particle swarm optimization algorithm‐based optimization method is employed to maximize efficiency and minimize weight. The optimization results show an increase in efficiency and a reduction in the weight of the generator. Finally, the design results are evaluated and validated using magnetic equivalent circuit and finite element analysis methods.
The Biotics Family: Current Knowledge and Future Perspectives in Metabolic Diseases

Globally, metabolic diseases such as obesity, type 2 diabetes mellitus and non-alcoholic fatty liver disease pose a major public health threat. Many studies have confirmed the causal relationship between risk factors and the etiopathogenesis of these diseases. Despite this, traditional therapeutic management methods such as physical education and diet have proven insufficient. Recently, researchers have focused on other potential pathways for explaining the pathophysiological variability of metabolic diseases, such as the involvement of the intestinal microbiota. An understanding of the relationship between the microbiome and metabolic diseases is a first step towards developing future therapeutic strategies. Currently, much attention is given to the use of biotics family members such as prebiotics (lactolose, soy oligosaccharides, galactooligosaccharides, xylooligosaccharides or inulin) and probiotics (genera Lactobacillus, Bifidobacterium, Lactococcus, Streptococcus or Enterococcus). They can be used both separately and together as synbiotics. Due to their direct influence on the composition of the intestinal microbiota, they have shown favorable results in the evolution of metabolic diseases. The expansion of the research area in the biotics family has led to the discovery of new members, like postbiotics. In the age of personalized medicine, their use as therapeutic options is of great interest to our study.
Hair Growth Regulation by Fibroblast Growth Factor 12 (FGF12)

The fibroblast growth factor (FGF) family has various biological functions, including cell growth, tissue regeneration, embryonic development, metabolism, and angiogenesis. In the case of hair growth, several members of the FGF family, such as FGF1 and FGF2, are involved in hair growth, while FGF5 has the opposite effect. In this study, the regulation of the hair growth cycle by FGF12 was investigated. To observe its effect, the expression of FGF12 was downregulated in mice and outer root sheath (ORS) by siRNA transfection, while FGF12 overexpression was carried out using FGF12 adenovirus. For the results, FGF12 was primarily expressed in ORS cells with a high expression during the anagen phase of hair follicles. Knockdown of FGF12 delayed telogen-to-anagen transition in mice and decreased the hair length in vibrissae hair follicles. It also inhibited the proliferation and migration of ORS cells. On the contrary, FGF12 overexpression increased the migration of ORS cells. FGF12-overexpressed ORS cells induced the telogen-to-anagen transition in the animal model. In addition, FGF12 overexpression regulated the expression of PDGF-CC, MDK, and HB-EGF, and treatment of these factors exhibited hair growth promotion. Altogether, FGF12 promoted hair growth by inducing the anagen phase of hair follicles, suggesting the potential for hair loss therapy.
Gentle Touch Therapy, Pain Relief and Neuroplasticity at Baseline in Fibromyalgia Syndrome: A Randomized, Multicenter Trial with Six-Month Follow-Up

Background: Fibromyalgia (FM) is considered a stress-related disorder characterized mainly by chronic widespread pain. Its pathogenesis is unknown, but cumulative evidence points at dysfunctional transmitter systems and inflammatory biomarkers that may underlie the major symptoms of the condition. This study aimed to evaluate pain scores (primary outcome), quality of life, inflammatory biomarkers and neurotransmitter systems in women with FM (secondary outcomes) subjected to gentle touch therapy (GTT) or placebo. Methods: A total of 64 female patients with FM were randomly assigned to two groups, namely GTT (n = 32) or Placebo (n = 32). Clinical assessments were conducted at baseline and post-intervention with six-month follow-up. We measured serum catecholamines (dopamine), indolamines and intermediary metabolites (serotonin or 5-hydroxyindolacetic acid (5-HIAA)), as well as tetrahydrobiopterin (BH4), which is a cofactor for the synthesis of neurotransmitters and inflammatory biomarkers in women with FM. A group of healthy individuals with no intervention (control group) was used to compare biochemical measurements. Intervention effects were analyzed using repeated measures (RM) two-way ANOVA followed by Bonferroni post hoc test and mixed ANCOVA model with intention to treat. Results: Compared to placebo, the GTT group presented lower pain scores and brain-derived neurotrophic factor (BDNF) levels without altering the quality of life of women with FM. Changes in BDNF had a mediating role in pain. Higher baseline serum BDNF and 5-HIAA or those with a history of anxiety disorder showed a higher reduction in pain scores across time. However, women with higher serum dopamine levels at baseline showed a lower effect of the intervention across the observation period revealed by an ANCOVA mixed model. Conclusions: In conclusion, lower pain scores were observed in the GTT group compared to the placebo group without altering the quality of life in women with FM. Reductions in BDNF levels could be a mechanism of FM pain status improvement. In this sense, the present study encourages the use of these GTT techniques as an integrative and complementary treatment of FM.
Forecasting Fine-Grained Urban Flows Via Spatio-Temporal Contrastive Self-Supervision

As a critical task of the urban traffic services, fine-grained urban flow inference (FUFI) benefits in many fields including intelligent transportation management, urban planning, public safety. FUFI is a technique that focuses on inferring fine-grained urban flows depending solely on observed coarse-grained data. However, existing methods always require massive learnable parameters and the complex network structures. To reduce these defects, we formulate a contrastive self-supervision method to predict fine-grained urban flows taking into account all correlated spatial and temporal contrastive patterns. Through several well-designed self-supervised tasks, uncomplicated networks have a strong ability to capture high-level representations from flow data. Then, a fine-tuning network combining with three pre-training encoder networks is proposed. We conduct experiments to evaluate our model and compare with other state-of-the-art methods by using two real-world datasets. All the empirical results not only show the superiority of our model against other comparative models, but also demonstrate its effectiveness in the resource-limited environment.
Rare Decays at LHCb

Rare loop-induced decays are sensitive to New Physics in many Standard Model extensions. In this paper we discuss the reconstruction of the radiative penguin decays ${mathrm{B^0}} rightarrow {mathrm{K}^{*0}}gamma$, ${mathrm{B^0_{mathrm{s}}}} rightarrow phi gamma$, ${mathrm{B^0}} rightarrow omega gamma$, the electroweak penguin decay ${mathrm{B^0}}r ightarrow {mathrm{K}^{*0}}mu^+mu^-$, the gluonic penguin decays ${mathrm{B^0}} rightarrow {mathrm{K^0_S}}phi$, ${mathrm{B^0_{mathrm{s}}}}r ightarrow phi phi$ and the decay ${mathrm{B^0_{mathrm{s}}}} rightarrow mu^+ mu^-$ at LHCb. The selection criteria, evaluated efficiencies, expected annual yields and $mathcal{B}/mathcal{S}$ estimates are presented.
Contribution of acoustic harmonics to sound quality of pure electric powertrains

A novel method based on contribution estimation for sound quality evaluation and improvement of electric powertrain is presented in this study. Firstly, the noise samples of an electric powertrain for electric vehicles are collected in a semi-anechoic chamber. Then the subjective evaluation of an electric powertrain is conducted. Through the correlation analysis between the acoustic energy of powertrain noise in each frequency band and subjective perception, the sensitive frequency ranges of electric powertrain noise which human hearing is susceptible to are determined. Subsequently, the acoustic harmonics of electric powertrain noise in sensitive frequency are distinguished by order tracking. After that, the contribution of sensitive harmonics to both psycho-acoustical parameters and subjective perception is estimated. Finally, the gear train model for optimisation of gear tooth micro-geometry is built to improve the sound quality of electric powertrain. The results of psycho-acoustical parameters and subjective rating of acoustic noise radiated by electric powertain after optimisation show that it is possible for the electric powertrain to improve the sound quality.
Analysis of Electronic Channel Service Quality on Customer Satisfaction of Bank Syariah Indonesia (BSI) During the Covid-19 Pandemic

AbstractPurpose - This study aims to examine and analyze how the influence of Electronic Channel (E-Channel) service quality on customer satisfaction of Bank Syariah Indonesia.Method - The research uses a quantitative approach. The population used in this study is the customers of Bank Syariah Indonesia using E-Channel. With the number of samples in this study amounted to 100 respondents.Result - The variables of reliability, privacy and easy to use have a positive and significant effect on customer satisfaction. Meanwhile, the efficiency variable has a negative and significant effect on customer satisfaction. And for the design variable has no effect on customer satisfaction.Implication - The implication is that the quality of e-channel services (electronic channel) provided by the bank will affect customer satisfaction.Originality - This study analyzes the quality of service in the E-Channel includes five components, namely Reliability, Efficiency, Privacy/Security, Design, and Easy to Use on customer satisfaction.
Data from the PASSI d’Argento Surveillance System on Difficulties Met by Older Adults in Accessing Health Services in Italy as Major Risk Factor to Health Outcomes

(1) Age-friendly health systems ensure access to quality healthcare services to all people, especially older adults. (2) We used data on elderly population collected from 2016 to 2019 by the Italian ongoing surveillance system PASSI d’Argento to analyze the prevalence and associations between accessing health services and modifiable risk factors included in the 25 × 25 strategy for the burden of noncommunicable diseases with health outcomes. (3) Chronic diseases and hospitalization as descriptors of health status showed that the elderly perceived as having poor access to care and prevention incurred a higher risk of hospitalization. The association between difficulties in accessing health services and hospitalization was always the highest in terms of the adjusted prevalence ratio (aPR), regardless of the other behavioral risk factors considered, controlling each model with sociodemographic conditions. Elderly hospitalized at least once for two days or more in the last 12 months had greater risk to have problems in accessing health services, whereas the model included health conditions such as obesity (aPR = 1.95 95% CI 1.75–2.17), smoking (aPR = 1.95 95% CI 1.76–2.16), alcohol use (aPR = 1.93 95% CI 1.73–2.14), hypertension (aPR = 1.92 95% CI 1.73–2.13) and diabetes (aPR = 1.91 95% CI 1.73–2.12). (4) Health policies should encompass socio-economic and living environment barriers which prevent access to care among older adults.