Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Drug Prescription Patterns in the Neurology Department of a Tertiary Care Hospital: A Comprehensive Analysis

Objective: Neurological disorders are a major cause of morbidity, mortality and disability. These adversely affect the quality of life among all age group patients. Majority of neurological disorders include Epilepsy, Headache, Cerebrovascular disorders with hemiparesis, Neuropathic pain, Extrapyramidal syndrome, Cervical myelopathy and Parkinson’s disease. This study was done with aim to evaluate the prescription pattern of various drugs in different types of neurological disorders and to use the WHO / INRUD prescribing indicators to assess the rational drug prescribing. Materials and Methods: A Prescription-based, Observational prospective study was carried out in a tertiary care hospital at South India. The prescription data of patients attending the neurology outpatient department was analysed over a period of 2 months. Rational use of drugs was analysed by the INRUD Scale. Results: Out of these, 166 patients were male, whereas 134 patients were female. Most prevalent neurological disorders were Trigeminal neuralgia (25.66%), 9.97% of the patient population received injections, with 4% receiving Inj. Dexamethasone 8mg. Overall drug prescribing performance scoring was 3.55. Conclusion: Our study revealed good Prescribing pattern as shown by WHO scale- IRUD/INRUD. Further such studies with large sample size are needed so as to guide clinicians toward rationale drug. Keywords: Neurological; International Network of Rational Use of Drugs; Drug Utilisation, Rational Use.
Application of Paper-Based Microfluidic Analytical Devices (µPAD) in Forensic and Clinical Toxicology: A Review

The need for providing rapid and, possibly, on-the-spot analytical results in the case of intoxication has prompted researchers to develop rapid, sensitive, and cost-effective methods and analytical devices suitable for use in nonspecialized laboratories and at the point of need (PON). In recent years, the technology of paper-based microfluidic analytical devices (μPADs) has undergone rapid development and now provides a feasible, low-cost alternative to traditional rapid tests for detecting harmful compounds. In fact, µPADs have been developed to detect toxic molecules (arsenic, cyanide, ethanol, and nitrite), drugs, and drugs of abuse (benzodiazepines, cathinones, cocaine, fentanyl, ketamine, MDMA, morphine, synthetic cannabinoids, tetrahydrocannabinol, and xylazine), and also psychoactive substances used for drug-facilitated crimes (flunitrazepam, gamma-hydroxybutyric acid (GHB), ketamine, metamizole, midazolam, and scopolamine). The present report critically evaluates the recent developments in paper-based devices, particularly in detection methods, and how these new analytical tools have been tested in forensic and clinical toxicology, also including future perspectives on their application, such as multisensing paper-based devices, microfluidic paper-based separation, and wearable paper-based sensors.
Lagrangian Decomposition for Dynamic Real-Time Optimization: Applied to Production Optimization Network

The scope of this thesis was to investigate the use of lagrangian decomposition for a dynamic realtime optimization(RTO) problem. The decomposition was performed on a gas lifted two-well network, controlled by a nonlinear model predictive controller(NMPC). Even though decomposition is a well known and utilized method, it has not been investigated much for the upstream production of oil and gas. The dynamic lagrangian dual decomposition in this thesis is an extension of previous work with steady state decomposition for gas lifted well networks. The lagrangian dual decomposed NMPC was modeled and simulated in Matlab using CasADi v3.1. 0, the NLP problem was solved with the IPOPT solver. The base case refers to the lagrangian decomposition simulations, which were performed for two different constraints becoming active. The simulation results were compared to a centralized NMPC and a steady state optimization for the same system. A successful decomposed NMPC should yield the same converged simulation values as the centralized NMPC and steady state optimization. Numerical methods were applied to improve the base case decomposed NMPC. The line search method, adjusting the step length Æ for each update of the lagrangian multiplier ∏, was included to reach a better convergence of ∏ and lower the number of iterations needed. The augmented lagrangian, penalizing constraint violations, was included to improve the start-up dynamics of the base case decomposed NMPC. The base case decomposition simulation results show that the decomposed NMPC converges to the same results as the centralized NMPC and a steady state optimization performed for the same system. However, in certain cases, the dynamics in the initial part of the simulation could be problematic. For the case where the gas rate capacity is the active constraint, the decomposed simulation oscillates around the trajectory of the centralized NMPC, even violating the constraint. When the available gas lift is the active constraint, the start-up dynamics is not a problem. The line search method successfully brings down the number of iterations, but at the cost of a larger constraint violation than in the base case decomposition. The augmented lagrangian decomposition decreases the constraint violations and converges faster than the base case decomposition. Combined with the fact that ∏ converges and that the simulation needs few iterations, it makes this the best method for dynamic decomposition of this system. Since the system used in this problem is very simple, the results are only of academic interest. No conclusion can be made on the realistic uses of dynamic decomposition for control within the oil and gas industry. However, the results show a possibility for this type of control system to be suitable for use in more complex systems.
Using Three-Tier Test to Identify the Quantity of Student that Having Misconception on Newton's Laws of Motion Concept

This study aims to identify students quantity who are having the misconception on Newton's laws of motion concept using a Three-tiered Test. The sampling technique used in this study is purposive sampling technique and has been conducted on 56 students at Senior High School. A three-tier "Newton’s Law Of Motion Test" with 10 items is using as instrument to collected date in this study. The results showed that the quantity of students who experienced misconception with the highest category is on the concept of determining the relationship between the mass of objects and the time required for free fall that is equal to 89.3%. While the lowest category is in the concept of explaining the relationship between acceleration, mass and force with the time required for the object to fall freely that is equal to 26.8%.
Effect of airway masks on physiological parameters of healthcare workers: a clinical trial

Background: Airway masks helps protect the wearer’s respiratory environment. There are many types of airway masks which differ in materials and effectiveness. This study aims to evaluate the effect of a surgical mask, the N95 mask, and an elastomeric respirator mask on cardiopulmonary, metabolic, and subjective parameters on healthcare workers. Methods: We conducted a controlled clinical trial on healthcare workers aged between 17-35 years old. Each subject performed a treadmill test (speed 5.6 km/hour) for 30 minutes while their physiological variables were monitored (pulse rate, respiratory rate, oxygen saturation, end-tidal CO2, body temperature, Borg scale, talk test, blood lactate, intermittent blood sugar, and subjective indicators). Each healthcare workers will be tested for four treatments, namely without using a mask, surgical mask, N95 mask, and elastomeric respirator. Results: All healthcare workers (age 25.10 ± 2.2 years old; 5 males and 5 females) completed the protocol with no adverse event. Pair-wise comparison using two-way ANOVA reported no significant difference within the mask condition for pulse rate (p=0.6497), respiratory rate (p=0.6772), oxygen saturation, (p=0.2587), end-tidal CO2 (p=0.0191), body temperature (p=0.7425), Borg scale (p=0.0930), blood lactate (p=0.6537) and glucose (p=0.8755). A statistically significant difference was reported in talk test (p=0.0129) with elastomeric respirator group showing highest result compared to control. Similarly, statistical significance was reported in subjective indicator of tightness (p=0.0017) with highest mean rank seen in N95 mask condition. However, these differences were clinically insignificant. Conclusions: The effect of surgical mask, N95 mask, and elastomeric respirator on the cardiopulmonary parameters, metabolic parameters, and subjective indicators during 30 minutes of low-moderate intensity exercise is negligible and generally well tolerated by healthcare workers. Registration: TCTR20230630001
Pricing and inventory decision in a two-layer supply chain under the Weibull distribution product deterioration: an application of NSGA-II

Academicians and practitioners have focused a lot of attention on the separate issues of pricing and inventory control in a competitive setting. However, integrating these choices in a competitive environment has received scant attention for deteriorating inventory systems from academics despite being crucial to practitioners. From this perspective, our research focuses on designing a supply chain model with inventory coordination to reflect time systems with improved accuracy and optimal control systems. In this research, we develop a two-layer supply chain model consisting of one manufacturer and one retailer incorporating the inventory classification of the retailer. Price-sensitive market demand and two-parameter time-varying Weibull distribution deterioration have been assumed to develop the mathematical model. First, a collective decision on price and inventory control of a deteriorating product has been evaluated in a duopoly environment. Secondly, to explore the decentralized scenario, we have proposed the NSGA-II algorithm to solve the bi-objective programming problem of the two-layer supply chain. The paper aims to explore product collaborative pricing policies and the inventory decision of the deteriorating item in two-layer supply chain coordination. Finally, numerical research is conducted to execute the centralized supply chain and NSGA-II application in a decentralized supply chain. The research findings can provide valuable insights for members of the two-layer supply chain to make optimal product pricing and inventory scheduling decisions.
An Efficient Hybrid Classifier Model for Customer Churn Prediction

—Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall.
Essays in Nonparametric Econometrics

This thesis is concerned with various non- and semiparametric estimation problems in a locally stationary time series setting. It consists of three essays that are self-contained and can be read separately. The first essay studies a nonparametric regression model which allows for locally stationary regressors and a regression function that changes smoothly over time. The second essay provides testing theory in this framework. The third essay deals with a semiparametric multiplicative volatility model in a locally stationary setting.
The Interconnections Between ICT, Industry 4.0 and Agile Manufacturing

Technological progress is the driving force behind industrial development. It is a multi-dimensional and multi-level phenomenon. In this article we focus on its three manifestations: information and communication technologies (ICT), Industry 4.0 and agile manufacturing. The aim of this article is to analyse the relationship between these constructs as they are undoubtedly interrelated. ICT plays a key role, but it is not a goal itself. They are a prerequisite for the implementation of Industry 4.0, but together with it they serve to achieve agility by the manufacturing system and, as a result, achieve a competitive advantage by companies operating in turbulent and unpredictable environment. The literature ﬁndings in this paper are part of a broader study conducted on the impact of ICT on agility of SMEs operating in India. Therefore, we include also subsections showing the level of this relationship in Indian SMEs.
Influence of selling situational leadership style on employee commitment within non-governmental organizations in Kenya

This paper addresses the influence of selling situational leadership style on employee commitment within Non-Government Organizations (NGOs) in Kenya. Methodically, the study was guided by the positivism research philosophy. The study applied a descriptive correlational research design which was used to analyze data and provide responses to the research question and confirm or reject the correlation between two or more variables using hard data from the use of various data collectionmethods. The major emphasis is on determining cause and effect relationships. The population of the study was 80,299 employees from 2,816 NGOs operating within Kenya. Stratified random sampling was used to select 383 respondents for the study, having a response rate of 94.5%. Primary data was collected using semi-structured questionnaires that were designed based on the research objectives. Both descriptive and inferential statistics were employed in the analysis. The regression analysis revealed that selling situational leadership style influenced the employee commitment (β = 0.432, p=0.00) showing that a change of selling situational leadership style by a unit influenced employee commitment by 22.6%. From the empirical evidence and conclusion, this study recommends that the NGOs’ managers need to apply selling situational leadership style so that employees that are provided with equal chances at the workplace, feel acknowledged for their contribution. In turn, this leads to a sense of belonging and shared purpose among them. Thus, this encourages them and motivates them to give their best efforts to the companies.
The Energetic Particle Environment of the Lunar Nearside: SEP Influence

The energetic particle environment of the lunar nearside is quite different from that of the lunar farside. Due to the shielding of the Earth’s magnetosphere and the Moon, the lunar nearside may receive much fewer energetic particles from the Sun. It is currently impossible to directly measure the received energetic particle flux of the whole lunar surface. By using the ARTEMIS and Wind observations, we qualitatively studied the shielding effect of the Earth’s magnetosphere and the lunar body from solar energetic particles (SEPs). We found that the Earth’s magnetosphere can effectively shield SEPs with energies up to 4 Mev during the SEP event. However, in the solar wind, the Moon can provide partial shielding from SEPs with energies ≤100 KeV. SEPs with energies above 150 KeV in the lunar shadow show no difference in flux from in the solar wind, which suggests that the nearside and farside are the same in receiving SEPs with energies above 150 KeV during the SEP event.
Phylogenomic analysis of the Porphyromonas gingivalis - Porphyromonas gulae duo: approaches to the origin of periodontitis

Porphyromonas gingivalis is an oral human pathogen associated with the onset and progression of periodontitis, a chronic immune-inflammatory disease characterized by the destruction of the teeth-supporting tissue. P. gingivalis belongs to the genus Porphyromonas, which is characterized by being composed of Gram-negative, asaccharolytic, non-spore-forming, non-motile, obligatory anaerobic species, inhabiting niches such as the oral cavity, urogenital tract, gastrointestinal tract and infected wound from different mammals including humans. Among the Porphyromonas genus, P. gingivalis stands out for its specificity in colonizing the human oral cavity and its keystone pathogen role in periodontitis pathogenesis. To understand the evolutionary process behind P. gingivalis in the context of the Pophyoromonas genus, in this study, we performed a comparative genomics study with publicly available Porphyromonas genomes, focused on four main objectives: (A) to confirm the phylogenetic position of P. gingivalis in the Porphyromonas genus by phylogenomic analysis; (B) the definition and comparison of the pangenomes of P. gingivalis and its relative P. gulae; and (C) the evaluation of the gene family gain/loss events during the divergence of P. gingivalis and P. gulae; (D) the evaluation of the evolutionary pressure (represented by the calculation of Tajima-D values and dN/dS ratios) comparing gene families of P. gingivalis and P. gulae. Our analysis found 84 high-quality assemblies representing P. gingivalis and 14 P. gulae strains (from a total of 233 Porphyromonas genomes). Phylogenomic analysis confirmed that P. gingivalis and P. gulae are highly related lineages, close to P. loveana. Both organisms harbored open pangenomes, with a strong core-to-accessory ratio for housekeeping genes and a negative ratio for unknown function genes. Our analyses also characterized the gene set differentiating P. gulae from P. gingivalis, mainly associated with unknown functions. Relevant virulence factors, such as the FimA, Mfa1, and the hemagglutinins, are conserved in P. gulae, P. gingivalis, and P. loveana, suggesting that the origin of those factors occurred previous to the P. gulae - P. gingivalis divergence. These results suggest an unexpected evolutionary relationship between the P. gulae - P. gingivalis duo and P. loveana, showing more clues about the origin of the role of those organisms in periodontitis.
Quantum chip design optimization and automation in superconducting coupler architecture

Superconducting coupler architecture demonstrates great potential for scalable and high-performance quantum processors, yet how to design efficiently and automatically ‘Qubit–Coupler–Qubit (QCQ)’ of high performance from the layout perspective remains obscure. In this work, this issue is studied for the first time resulting in three key findings. Firstly, we acquire the crucial zero-coupling condition that is only dependent on the geometric design of the layout. Secondly, the upper bound of the qubit–qubit effective coupling is found as 0.0822 ωl/βs2 which surprisingly depends only on the artificially pre-decided quantities ωl,βs instead of specific layouts. Thirdly, we propose an optimal layout design procedure to reach the very upper bound, leading to efficient and high-performance layout design. The effectiveness of the procedure has been demonstrated scrupulously using electromagnetic simulation experiments. As a stirring application, we report a state-of-the-art 3202 um long-range and scalable QCQ layout that is especially crucial to quantum error correction. Our work provides practical guides to optimize the performance of the existing coupler architecture, find out novel layouts, and further advance the progress of quantum chip design automation.
Dual Antiplatelet Therapy: A Concise Review for Clinicians

Dual antiplatelet therapy (DAPT) combines two antiplatelet agents to decrease the risk of thrombotic complications associated with atherosclerotic cardiovascular diseases. Emerging data about the duration of DAPT is being published continuously. New approaches are trying to balance the time, benefits, and risks for patients taking DAPT for established cardiovascular diseases. Short-term dual DAPT of 3–6 months, or even 1 month in high-bleeding risk patients, is equivalent in terms of efficacy and effectiveness compared to long-term DAPT for patients who experienced percutaneous coronary intervention in an acute coronary syndrome setting. Prolonged DAPT beyond 12 months reduces stent thrombosis, major adverse cardiovascular events, and myocardial infarction rates but increases bleeding risk. Extended DAPT does not significantly benefit stable coronary artery disease patients in reducing stroke, myocardial infarction, or cardiovascular death. Ticagrelor and aspirin reduce cardiovascular events in stable coronary artery disease with diabetes but carry a higher bleeding risk. Antiplatelet therapy duration in atrial fibrillation patients after percutaneous coronary intervention depends on individual characteristics and bleeding risk. Antiplatelet therapy is crucial for post-coronary artery bypass graft and transcatheter aortic valve implantation; Aspirin (ASA) monotherapy is preferred. Antiplatelet therapy duration in peripheral artery disease depends on the scenario. Adding vorapaxar and cilostazol may benefit secondary prevention and claudication, respectively. Carotid artery disease patients with transient ischemic attack or stroke benefit from antiplatelet therapy and combining ASA and clopidogrel is more effective than ASA alone. The optimal duration of DAPT after carotid artery stenting is uncertain. Resistance to ASA and clopidogrel poses an incremental risk of deleterious cardiovascular events and stroke. The selection and duration of antiplatelet therapy in patients with cardiovascular disease requires careful consideration of both efficacy and safety outcomes. The use of combination therapies may provide added benefits but should be weighed against the risk of bleeding. Further research and clinical trials are needed to optimize antiplatelet treatment in different patient populations and clinical scenarios.
Aplicando algoritmos de clusterização para encontrar inconsistências em bases de dados fiscais

Ainda que os dados sobre a propriedade estejam cada vez mais digitalizados, os registros ainda incluem informações desatualizadas advindas de bases de dados historicamente inconsistentes. Os registros de propriedade antigos são automaticamente inseridos em novos formatos digitais, ficando em conflito com campos e bases atuais mais padronizadas. Tal é o caso do banco de imóveis de Fortaleza, onde este estudo se baseia. Esse artigo apresenta como algoritmos de agrupamento podem ajudar a encontrar inconsistências nas bases prediais. Os resultados estimam que 2.048 registros prediais estejam inconsistentes, necessitando limpeza e correção.