Miracle-dz/UniFAR-FTUs huggingface dataset:
Technological Maturity Classification, Focus: Medical Devices

Investment in science and technology oriented to the health sector have been affected by the appearance of misinformation and the massive use of collaborative tools found on the Internet that provide subjective or dubious information on the development of new medical technologies in Mexico. These new products do not take into account the monitoring of a maturation stage nor do they comply with the necessary regulations for the product to comply with for optimal development in the industry. This article focuses on emphasizing the importance of the classification of technological maturity and steps to be fulfilled for each stage, focusing on essential characteristics of the health sector, the case in Mexico is discussed.
Exploring Clinical Subgroups of Participants with Major Depressive Disorder that may Benefit from Adjunctive Minocycline Treatment

Objective To explore illness-related factors in patients with major depressive disorder (MDD) recipients of adjunctive minocycline (200 mg/day) treatment. The analysis included participants experiencing MDD from a 12-week, double blind, placebo-controlled, randomized clinical trial (RCT). Methods This is a sub-analysis of a RCT of all 71 participants who took part in the trial. The impact of illness chronicity (illness duration and number of depressive episodes), systemic illness (endocrine, cardiovascular and obesity), adverse effects and minocycline were evaluated as change from baseline to endpoint (12-week) using ANCOVA. Results There was a consistent but statistically non-significant trend on all outcomes in favour of the use of adjunctive minocycline for participants without systemic illness, less illness chronicity, and fewer adverse effects. Conclusion Understanding the relationship between MDD and illness chronicity, comorbid systemic illness, and adverse effects, can potentially better characterise those individuals who are more likely to respond to adjunctive anti-inflammatory medications.
Coupled Finite Element Method-Boundary Element Method on Microstrip Transmission Line

Combining the Finite Element Method (FEM) and Boundary Element Method (BEM) opened a wide array of solutions for ordinary and partial differential equations deemed helpful for engineers and scientists. These methods were applied in various branches of sciences and engineering, especially in computational electromagnetics. This study used computational software to illustrate the coupled FEM-BEM solution in a Microstrip Transmission Line. The processes in coupled FEM-BEM solution were observed, i.e., discretize, derive, assemble, and solve for the Electric Field Intensity and Potential solution regions. The triangular and quadrilateral elements are the geometries used in the discretization process. The number of elements using triangular elements garnered the highest number produced compared to quadrilateral elements. Moreover, the number of nodes produced is constant regardless of the elements utilized. Lower capacitance was noted using quadrilateral elements compared to triangular elements.
Trustworthiness-Related Risks in Autonomous Cyber-Physical Production Systems - A Survey

The production industry is looking for new solutions to improve the reliability, safety and efficiency of traditional processes. Current developments in artificial intelligence and machine learning have enabled a high level of autonomy in smart-manufacturing and production systems within Industry 4.0, thus paving the way towards fully Autonomous Cyber-Physical Production Systems (ACPPS). Although ACPPS can have many advantages, there still remains a concern regarding how much we can trust those systems, due to limited predictability, transparency, and explainability, as well as emerging vulnerabilities related to machine learning systems. In this paper, we present the findings of a study conducted on the possible risks related to the trustworthiness of ACPPS, and the consequences they have on the system and its environment.
Evaluating Machine Learning Approaches for Cyber and Physical Anomalies in SCADA Systems

In recent years, machine learning (ML) techniques have been widely adopted as anomaly-based Intrusion Detection System in order to evaluate cyber and physical attacks against Industrial Control Systems. Nevertheless, a performance comparison of such techniques applied to multiple Cyber-Physical Systems datasets is still missing. In light of this, we propose a comparative study about the performance of four supervised ML-algorithms, Random Forest, k-nearest-Neighbors, Support-Vector-Machine and Naïve-Bayes, applied to three different publicly available datasets from water testbeds. Specifically, we consider three different scenarios where we evaluate: (1) the ability to detect cyber and physical anomalies with respect to the nominal samples, (2) the ability to detect specific types of cyber and physical attacks and (3) the ability to recognize unforeseen attacks without providing any previous knowledge about them. Results show the effectiveness of the ML-techniques in identifying cyber and physical anomalies under some assumptions about their effects on the process dynamics.
2010 2nd International Conference on Industrial and Information Systems Information Capacity for Multi-span Nonlinear Optical Fiber Communication Systems

We study the information capacity for an multi-span optical fiber transmission system with losses, dispersion, nonlinearity and amplified spontaneous emission (ASE) noise. A perturbation technique is developed to examine the information capacity, and a semi-analytic expression of the conditional probability distribution function is determined initially. A novel semi-analytical form of the information capacity was obtained, which can be extended to the wavelength division multiplexing (WDM) optical fiber communication systems. Computer simulations are executed for various parameters of the transmission system. The studied results indicate that the capacity does not monotone increase with the input power and is affected by the nonlinear coefficient prominently.
Use of Convolutional Neural Network for Detection of Intracranial Hemorrhage

Intracranial hemorrhage is a medical disorder that occurs when a cranial blood vessel ruptures. Due to the complexity of the pathology, early detection is essential for effective treatment. Computed Axial Tomography (CAT) is essential for the treating physician to understand the location and severity of hemorrhage, the risk of impending cerebral injury, and to guide often emergent patient treatment; however, this paper develops an intelligent system to provide technological tools to support the diagnosis and detection of intracranial hemorrhages by implementing convolutional neural networks. This paper aims to obtain a neuroimaging dataset, perform feature detection through image analysis, and binarily classify the disease. Using this intelligent system as a support tool for the detection of intracranial hemorrhages will contribute significantly to improving diagnosis time and timely and reliable treatment of this disease.
Metodologia para o rateio do bloco hidráulico no planejamento hidrotérmico de médio prazo

A eficiencia na operacao de sistemas predominante hidrotermicos, como o Sistema Interligado Nacional brasileiro (SIN), e estreitamente relacionada a execucao de um planejamento da operacao consistente. Os modelos de decisao estrategica usados em estudos de medio prazo sao capazes de determinar a politica operativa que minimiza os custos de operacao e o risco de deficit usando uma modelagem a sistemas equivalentes de energia. Este trabalho trata de uma proposta para ser acoplada, pela funcao de custo futuro esperado da operacao, a um modelo de decisao estrategica, de modo a permitir a desagregacao dos subsistemas nos seus reservatorios individuais, para cada mes e sequencia hidrologica. Assim, a proposta e desenvolver um modelo que seja capaz de verificar se as metas globais da geracao dos subsistemas, fornecidas pelos os modelos de decisao estrategica, sao alcancaveis. Para tanto, a ferramenta desagrega a solucao encontrada para os sistemas de equivalentes de energia na operacao a usinas individualizadas. A metodologia possui dois modulos: no primeiro e realizado o despacho dos sistemas equivalentes de energia uma rotina iterativa de programacao linear e achadas as metas globais de geracao para cada um deles, considerando as funcoes de custo futuro esperado da operacao e o intercâmbio da energia entre subsistemas. O segundo modulo e um otimizador nao-linear do despacho mensal das usinas hidreletricas de cada subsistema isoladamente, que tenta alcancar as metas de geracao do modulo anterior, maximizando o volume armazenado dos reservatorios. A inovacao deste modelo esta no modulo de simulacao a usinas individualizadas, que nao e baseado em regras heuristicas, mas em uma rotina de programacao nao-linear (PNL). O uso de tecnicas de otimizacao diminui a interferencia do usuario no processo de simulacao, uma vez que seu conhecimento e baseado na experiencia adquirida ao longo do historico da operacao. Alem disso, a proposta dispensa a adaptacoes quando novas politicas operativas forem adotadas. Assim, este modelo, quando estiver completo e validado, podera despontar como uma alternativa ao modelo de simulacao oficial utilizado pelo Setor Eletrico Brasileiro (SEB), cujo modulo de simulacao a usinas individualizadas e baseado em uma politica de operacao paralela dos reservatorios.
Disaster-Responsive Fetal Movement Monitoring System for Flood Affected Rural Areas

Climate-related weather disasters not only cause billions of dollars in damages but also have long-lasting effects on human health. In Year 2022, approximately 600,000 pregnant women were affected by devastating floods in Pakistan. The majority of these pregnant women belonged to rural areas and had limited access to healthcare facilities. Fetal movement serves as a reliable indicator of a healthy fetus. The proposed approach involves using an accelerometer measurement of fetal movement, along with a mobile application that allows for easy usage outside clinical environments, enabling remote fetal movement monitoring. By preprocessing a pre-recorded dataset of the 3D accelerometer measurements, four state-of-the-art machine learning algorithms are implemented to classify fetal movement with a relative degree of accuracy. The Extreme Gradient Boost algorithm demonstrates superior performance in classifying fetal movement, achieving an accuracy of 94.58% and an average accuracy of 87.03% through k-fold cross-validation.
comparison of dexamethasone intravitreal implant and intravitreal triamcinolone acetonide for the treatment of pseudophakic cystoid macular edema in diabetic patients

found in both groups, but both were rated as mild in severity, and no significant difference was found ( P = 0.184, P = 0.766, respectively). Conclusion: Both IVTA and DEX implants could effectively restore visual function and recover morphological change in diabetic patients with PCME for at least 6 months, but repeated intravitreal injection was required in the IVTA group. DEX implant is well tolerated. We suggest that intravitreal injection of DEX implant is a promising new therapeutic option for diabetic patients with PCME.
An NMOS LDO With TM-MOS and Dynamic Clamp Technique Handling Up To Sub-10-μs Short-Period Load Transient

A current-efficient and fast-transient n-type low-dropout regulator (LDO) for high-frequency load transient in mobile phone applications is presented in this article. By using transconductance magnified MOS (TM-MOS), it reduces LDO’s output impedance with fast response speed and keeps high current efficiency. Moreover, based on load-current sensing of TM-MOS, active clamp strategy is implemented to optimize the driving dead zone (DDZ) of this NMOS LDO and achieve good high-frequency load transient performance. Robust loop stability for a wide load-current range is ensured as well with the help of revised Type-II frequency compensation. This circuit has been implemented in a 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> standard CMOS process and occupies an active chip area of <inline-formula> <tex-math notation="LaTeX">$470\times280\,\,\mu \text{m}^{2}$ </tex-math></inline-formula>. With a 1-<inline-formula> <tex-math notation="LaTeX">$\mu \text{F}$ </tex-math></inline-formula> output cap and load steps between 0 A and 300 mA, experimental results show that it features 50/36 mV of undershoot/overshoot at low-frequency load transient and 68/36 mV of undershoot/overshoot at the high-frequency load transient (i.e., 10-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> light-load duration). This regulator consumes 8.2-<inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> quiescent current, achieving 99.68% equivalent current efficiency at 300-mA load current.
Incremental Conductance Based Model Predictive Control Algorithm for Solar PV Module for Tracking of MPP

The great advances in Photovoltaic (PV) efficiency and performance are valuable only if they are coupled with effective mechanisms for tracking and controlling the operating conditions of the PV system to ensure that it operates at or near the Maximum Power Point (MPP). Only then can the system produce the maximum amount of power for a given set of conditions, maximizing the benefits of the advances in PV technology. In this paper, an Incremental Conductance (InC) based Model Predictive Control (MPC) algorithm is used to track the MPP. To assess its effectiveness the performance of an INC-based MPC algorithm in the photovoltaics field has been evaluated against two of the most used algorithms, (Perturb and Observe) P&O and INC. And also compared with PI-based MPP since it is one of the recommended controllers in industrial applications. This comparative analysis has been done for a sudden change in temperature and irradiance. The complete setup has been done in MATLAB/SIMULINK set including DC-DC Boost converter solar PV Module and algorithms. DC-DC Boost converter is used for getting higher value in the output. The overall efficiency is higher in INC-based MPC and less fluctuation is obtained at MPP.
Evaluating Ease of Use and Patient Safety of Dasiglucagon Hypo Pal Autoinjector for the Management of Hypoglycemia

Introduction Severe hypoglycemia is a medical emergency that must be treated promptly in order to prevent complications or death in a patient. Patients who have been diagnosed with Type 1 diabetes and treated with insulin have the potential to experience a severe hypoglycemia episode, or low blood sugar. When a patient is experiencing severe hypoglycemia, it is imperative for this patient to receive glucose quickly. However, patients who are experiencing low blood sugar may not be able to manage for themselves, take food or liquid orally or may be unconscious. Glucagon is a medication that reverses low blood sugar. Up until 2018, glucagon had to be reconstituted immediately before injection because of the medication’s lack of stability after reconstitution. A medication recently approved for patients 6 years through adult is Dasiglucagon. This form of glucagon comes in a ready-to-administer, pre-filled syringe, making it easier for caretakers and bystanders to administer in an emergency situation due to the readiness of the medication. Purpose The purpose of the paper is to evaluate literature that pertains to the ease of use and patient safety of dasiglucagon HypoPal autoinjector for the management of severe hypoglycemia.
Time-Dependent Analysis of Human Neurophysiological Activities during an Ecological Olfactory Experience

It has been demonstrated that odors could affect humans at the psychophysiological level. Significant research has been done on odor perception and physiological mechanisms; however, this research was mainly performed in highly controlled conditions in order to highlight the perceptive phenomena and the correlated physiological responses in the time frame of milliseconds. The present study explored how human physiological activity evolves in response to different odor conditions during an ecological olfactory experience on a broader time scale (from 1 to 90 s). Two odors, vanilla and menthol, together with a control condition (blank) were employed as stimuli. Electroencephalographic (EEG) activity in four frequency bands of interest, theta, alpha, low beta, and high beta, and the electrodermal activity (EDA) of the skin conductance level and response (SCL and SCR) were investigated at five time points taken during: (i) the first ten seconds of exposure (short-term analysis) and (ii) throughout the entire exposure to each odor (90 s, long-term analysis). The results revealed significant interactions between the odor conditions and the time periods in the short-term analysis for the overall frontal activity in the theta (p = 0.03), alpha (p = 0.005), and low beta (p = 0.0067) bands, the frontal midline activity in the alpha (p = 0.015) and low beta (p = 0.02) bands, and the SCR component (p = 0.024). For the long-term effects, instead, only one EEG parameter, frontal alpha asymmetry, was significantly sensitive to the considered dimensions (p = 0.037). In conclusion, the present research determined the physiological response to different odor conditions, also demonstrating the sensitivity of the employed parameters in characterizing the dynamic of such response during the time. As an exploratory study, this work points out the relevance of considering the effects of continuous exposure instead of short stimulation when evaluating the human olfactory experience, providing insights for future studies in the field.
ZOOARCHAEOLOGICAL MEASURES OF RESOURCE INTENSIFICATION AND DEPLETION: EXAMPLES FROM EASTERN NORTH AMERICA

The impact that increased human population size and agricultural intensification had on the prehistoric animal ecology of eastern North America is poorly understood. New methods for examining archaeologically recovered faunal remains permit a more detailed understanding of the interaction between humans and their environment. Techniques for the accurate estimation and comparison of age, size and sex parameters of prehistoric deer and raccoon populations are presented. Measurements include ageing by tooth eruption and wear, estimating deer weight based on osteological measurements of the astragalus, and determining raccoon sex based on canine tooth measurements. Combined examination of the age and size parameters of deer over time indicates that the population density and distribution of deer in the Late Prehistoric period was substantially impacted by human activity. This resource depletion is indicated by a decline in the presence of old individuals and an increase in body size in areas surrounding major Mississippian sites. Deer populations appear stable at distance from these sites and maintain the parameters of age and size seen in the Archaic period. An understanding of this variation over space and time is used to interpret the efficiency of various deer hunting techniques. Examination of the distribution of raccoon remains by sex at archaeological sites demonstrates targeted procurement by Late Prehistoric farmers. Intensified procurement of raccoon is revealed by an increased presence of males over time in archaeological samples, a good indication of deliberate trapping. Intensified procurement may relate to a crop protection effort; stable carbon isotope data from the Angel site suggest that raccoon and squirrel may have consumed sufficient quantities of maize to modify their dC values
Interventions to improve equational reasoning: replication and extension of the Cuisenaire-Gattegno curriculum effect

Introduction The ability to reason about equations in a robust and fluent way requires both instrumental knowledge of symbolic forms, syntax, and operations, as well as relational knowledge of how such formalisms map to meaningful relationships captured within mental models. A recent systematic review of studies contrasting the Cuisenaire-Gattegno (Cui) curriculum approach vs. traditional rote schooling on equational reasoning has demonstrated the positive efficacy of pedagogies that focus on integrating these two forms of knowledge. Methods Here we seek to replicate and extend the most efficacious of these studies (Brownell) by implementing the curriculum to a high degree of fidelity, as well as capturing longitudinal changes within learners via a novel tablet-based assessment of accuracy and fluency with equational reasoning. We examined arithmetic fluency as a function of relational reasoning to equate initial performance across diverse groups and to track changes over four growth assessment points. Results Results showed that the intervention condition that stressed relational reasoning leads to advances in fluency for addition and subtraction with small numbers. We also showed that this intervention leads to changes in problem solving dispositions toward complex challenges, wherein students in the CUI intervention were more inclined to solve challenging problems relative to those in the control who gave up significantly earlier on multi-step problems. This shift in disposition was associated with higher accuracy on complex equational reasoning problems. A treatment by aptitude interaction emerged for both arithmetic equation reasoning and complex multi-step equational reasoning problems, both of which showed that the intervention had greatest impact for children with lower initial mathematical aptitude. Two years of intervention contrast revealed a large effect (d = 1) for improvements in equational reasoning for the experimental (CUI) group relative to control. Discussion The strong replication and extension findings substantiate the importance of embedding these teaching aides within the theory grounded curricula that gave rise to them.
Evolution of coordinated punishment to enforce cooperation from an unbiased strategy space

The emergence and maintenance of punishment to protect the commons remains an open puzzle in social and biological sciences. Even in societies where pro-social punishing is common, some individuals seek to cheat the system if they see a chance to do so—and public goods are often maintained in spite of cheaters who do not contribute. We present a model accounting for all possible strategies in a public goods game with punishment. While most models of punishment restrict the set of possible behaviours, excluding seemingly paradoxical anti-social strategies from the start, we show that these strategies can play an important role in explaining large-scale cooperation as observed in human societies. We find that coordinated punishment can emerge from individual interactions, but the stability of the associated institutions is limited owing to anti-social and opportunistic behaviour. In particular, coordinated anti-social punishment can undermine cooperation if individuals cannot condition their behaviour on the existence of institutions that punish. Only when we allow for observability and conditional behaviours do anti-social strategies no longer threaten cooperation. This is due to a stable coexistence of a minority supporting pro-social institutions and those who only cooperate if such institutions are in place. This minority of supporters is enough to guarantee substantial cooperation under a wide range of conditions. Our findings resonate with the empirical observation that public goods are resilient to opportunistic cheaters in large groups of unrelated individuals. They also highlight the importance of letting evolution, and not modellers, decide which strategies matter.