Efficiently handling constraints in mixed-integer nonlinear programming problems using gradient-based repair differential evolution.

Mixed integer nonlinear programming (MINLP) addresses optimization problems that involve continuous and discrete/integer decision variables, as well as nonlinear functions. These problems often exhibit multiple discontinuous feasible parts due to the presence of integer variables. Discontinuous feasible parts can be analyzed as subproblems, some of which may be highly constrained. This significantly impacts the performance of evolutionary algorithms (EAs), whose operators are generally insensitive to constraints, leading to the generation of numerous infeasible solutions. In this article, a variant of the differential evolution algorithm (DE) with a gradient-based repair method for MINLP problems (G-DEmi) is proposed. The aim of the repair method is to fix promising infeasible solutions in different subproblems using the gradient information of the constraint set. Extensive experiments were conducted to evaluate the performance of G-DEmi on a set of MINLP benchmark problems and a real-world case. The results demonstrated that G-DEmi outperformed several state-of-the-art algorithms. Notably, G-DEmi did not require novel improvement strategies in the variation operators to promote diversity; instead, an effective exploration within each subproblem is under consideration. Furthermore, the gradient-based repair method was successfully extended to other DE variants, emphasizing its capacity in a more general context.

Efficient deep learning-based approach for malaria detection using red blood cell smears.

Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.

Giant Aortic Root Thrombus in a Chronic, Asymptomatic Stanford Type A Aortic Dissection.

Type A aortic dissection rarely becomes chronic because of high early mortality. Thrombus in the false lumen and an immobile flap are indicative of this condition. A 61-year-old man with an initial diagnosis of gastroenteritis later presented with a diastolic murmur. Echocardiography revealed chronic Stanford A aortic dissection with a thrombus causing severe aortic regurgitation.

A deep learning approach for Named Entity Recognition in Urdu language.

Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania donovani: An Update and Literature Review.

Leishmaniasis, a disease caused by Leishmania parasites and transmitted via sandflies, presents in two main forms: cutaneous and visceral, the latter being more severe. With 0.7 to 1 million new cases each year, primarily in Brazil, diagnosing remains challenging due to diverse disease manifestations. Traditionally, the identification of Leishmania species is inferred from clinical and epidemiological data. Advances in disease management depend on technological progress and the improvement of parasite identification programs. Current treatments, despite the high incidence, show limited efficacy due to factors like cost, toxicity, and lengthy regimens causing poor adherence and resistance development. Diagnostic techniques have improved but a significant gap remains between scientific progress and application in endemic areas. Complete genomic sequence knowledge of Leishmania allows for the identification of therapeutic targets. With the aid of computational tools, testing, searching, and detecting affinity in molecular docking are optimized, and strategies that assess advantages among different options are developed. The review focuses on the use of molecular docking and molecular dynamics (MD) simulation for drug development. It also discusses the limitations and advancements of current treatments, emphasizing the importance of new techniques in improving disease management.

Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.

A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.

Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.

In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were designed, or chosen, to deal with the stationary noise suppression of cellular handsets, to provoke different rates of exhaled air, and to stimulate different levels of fluency. Time-independent and time-dependent engineered features were proposed and selected, and a k-fold scheme with double validation was adopted to select the models with the greatest potential for generalization. Moreover, score fusion methods were also investigated to optimize the complementarity of the controlled phonetizations and features that were engineered and selected. The results reported here were obtained from 104 participants, where 34 corresponded to healthy individuals and 70 were patients with respiratory conditions. The subjects' vocalizations were recorded with a telephone call (i.e., with an IVR server). The system provided an accuracy of 59% (i.e., estimating the correct mMRC), a root mean square error equal to 0.98, false positive rate of 6%, false negative rate of 11%, and an area under the ROC curve equal to 0.97. Finally, a prototype was developed and implemented, with an ASR-based automatic segmentation scheme, to estimate dyspnea on line.

Diagnostic agreement of quantitative flow ratio with fractional flow reserve in a Latin-American population.

Quantitative flow ratio (QFR) is a recently proposed angiographic index that allows to assess the pressure loss in coronary arteries in a similar fashion as the fractional flow reserve (FFR). The purpose of this study was to evaluate the diagnostic performance of QFR as compared to FFR, in a Latin-American population of patients with suspected ischaemic heart disease. QFR was retrospectively derived from coronary angiograms. The association, diagnostic performance, and continuous agreement of fixed-flow QFR (fQFR) and contrast-flow QFR (cQFR) with FFR was assessed by continuous and dichotomous methods. 90 vessels form 66 patients were finally included. The study comprised coronary stenoses of intermediate severity, both angiographically (diameter stenosis: 46.6 ± 12.8%) and physiologically [median FFR = 0.83 (quartile 1-3, 0.76-0.89)]. The correlation of FFR with both fQFR [ρ = 0.841, (95% CI 0.767 to 0.893), p < 0.001] and cQFR [ρ = 0.833, (95% CI 0.755 to 0.887), p < 0.001] was strong. The diagnostic performance of cQFR was good [area under the ROC curve of 0.92 (95% CI 0.86 to 0.97, p < 0.001)], with 0.80 as the optimal cQFR cut-off against FFR ≤ 0.80. This 0.80 cQFR cut-off classified correctly 83.3% of total stenoses, with a sensitivity of 85.2% and specificity of 80.6%. QFR was strongly associated with FFR and exhibited a high diagnostic performance in this Latin-American population.

A Custom EOG-Based HMI Using Neural Network Modeling to Real-Time for the Trajectory Tracking of a Manipulator Robot.

Although different physiological signals, such as electrooculography (EOG) have been widely used in the control of assistance systems for people with disabilities, customizing the signal classification system remains a challenge. In most interfaces, the user must adapt to the classification parameters, although ideally the systems must adapt to the user parameters. Therefore, in this work the use of a multilayer neural network (MNN) to model the EOG signal as a mathematical function is presented, which is optimized using genetic algorithms, in order to obtain the maximum and minimum amplitude threshold of the EOG signal of each person to calibrate the designed interface. The problem of the variation of the voltage threshold of the physiological signals is addressed by means of an intelligent calibration performed every 3 min; if an assistance system is not calibrated, it loses functionality. Artificial intelligence techniques, such as machine learning and fuzzy logic are used for classification of the EOG signal, but they need calibration parameters that are obtained through databases generated through prior user training, depending on the effectiveness of the algorithm, the learning curve, and the response time of the system. In this work, by optimizing the parameters of the EOG signal, the classification is customized and the domain time of the system is reduced without the need for a database and the training time of the user is minimized, significantly reducing the time of the learning curve. The results are implemented in an HMI for the generation of points in a Cartesian space (X, Y, Z) in order to control a manipulator robot that follows a desired trajectory by means of the movement of the user's eyeball.

Atherosclerotic Peripheral Artery Disease in the United States: Gender and Ethnic Variation in a Multiple Cause-of-Death Analysis.

BACKGROUND: Atherosclerotic peripheral artery disease (PAD) is an important cause of morbidity in the United States. In this article, we conducted a multiple cause-of-death analysis of PAD to determine patterns and trends in its contribution to mortality. METHODS: The Centers for Disease Control and Prevention statistics data were used to determine the number of deaths with the following 10th revision of the International Statistical Classification of Diseases and Related Health Problems codes selected as an underlying cause of death (UCOD) or a contributing cause considering multiple causes of death (MCOD): 170.2, 170.9, 173.9, 174.3, and 174.4. The age-adjusted death rates per 100 000 population by age, gender, race, ethnicity, and region were computed for the United States between the years 1999 and 2017. In these years, there were 47 728 569 deaths from all causes. RESULTS: In 1999 to 2017 combined, there were a total of 311 175 deaths associated with PAD as an UCOD. However, there were 1 361 253 deaths with PAD listed as an UCOD or a contributing cause in MCOD, which is 4.3 times higher than UCOD. Age-adjusted MCOD rates were higher in males (25.6) than in females (19.4). Among non-Hispanics, the rate in African American males and females was 1.2 times higher than in Caucasians. Age-adjusted MCOD rates have declined in African Americans and Caucasians irrespective of gender from 2000 to 2017. CONCLUSION: Peripheral artery disease is mentioned 4 times as often on death certificates as a contributing cause of death as it is chosen as the UCOD. Overall, age-adjusted MCOD rates were higher in African Americans than Caucasians, males than females, and declined between 2000 and 2017.

The Rash, the Weakness, and the Nephritis: Nerve and Kidney Biopsy Findings in Eosinophilic Granulomatosis With Polyangiitis.

We present a case of eosinophilic granulomatosis with polyangiitis (EGPA) or Churg-Strauss syndrome in a 66-year Caucasian female who presented with a severe pruritic itch and a progressive upper and lower extremity weakness of unknown duration. The diagnosis of EGPA in this patient remained elusive for an extended period of time due to the absence of respiratory symptoms. In this article, we also discuss the histologic features of EGPA seen in biopsies of the kidney and the nerves and highlight the value they play in diagnosis.

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms.

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

Intraoperative radiotherapy for gastrointestinal malignancies: contemporary outcomes with multimodality therapy.

The integration of intraoperative radiotherapy (IORT) into the multimodal treatment of gastrointestinal cancer is feasible and leads to high rates of local control. In-field tumoral control using IORT-containing strategies can be achieved in over 90 % of most cases, regardless of the site or status of the tumor (primary or recurrent). Electron beam IORT, or intraoperative electron radiation therapy, is the dominant technology used in institutions reporting data in publications the 21st century. Neither surgery nor systemic therapy is compromised by the integration of IORT-containing radiotherapy.

La Cruz, símbolo de vida: Una reflexión teológica de la Fiesta de la Cruz en Obrajes

Esta tesina comprende tres capítulos: 1. La Fiesta de la Cruz en Obrajes 2. La importancia de la Cruz para el pueblo; reflexiones teológicas 3. La Fiesta de la Cruz y su valor simbólico para la Iglesia evangélica metodista

Intoxicación sistemática por Mercurio elemental depositado en el organismo / Sistematic intoxication for elementary Mercury in the body

Se presentan dos casos de intoxicación sistemática por mercurio elemental depositado en el organismo. En el primer caso, un hombre de 25 años se inyectó el metal en las venas de manos y pies, falleció un mes después con necrosis hepática y renal que se atribuyeron al efecto sistemático. En el segundo caso, un hombre de 30 años, sufrió la explosión de un compresor que le incrustó mercurio elemental en la piel de los antebrazos; dos meses después presentó manifestaciones neurológicas correspondientes a hidrargirismo crónico que desaparecieron tras la resección quirúrgica de las áreas de piel afectadas. En ambos casos los análisis toxicológicos fueron positivos por mercurio. Estos casos constituyen excepciones al concepto de que el mercurio elemental persistente en el organismo no tiene manifestaciones tóxicas sistemáticas de importancia

Segunda huelga médica en Costa Rica (1965) / Second medical strike in Costa Rica (1965)

La segunda huelga médica en Costa Rica ocurrió en julio de 1965. Fue promovida por el Sindicato de Profesionales en Ciencias Médicas del Seguro Social (SIPROCIMECA) con el apoyo de la Unión Médica Nacional. Consistió en un día de paro de actividades hospitalarias. Se originó en la solicitud de mejores salarios por parte de los médicos que laboraban en el Seguro Social y culminó con la promulgación de la Ley del estatuto de Servicios Médicos de 1966. Esta ley vino a regular las jerarquías hospitalarias y los nombramientos de médicos. En este movimiento sindical se destacaron tres aspectos: 1) la realidad del médico costarricense como asalariado; 2) la politización del Seguro Social que más tarde se oficializaría con la presidencia ejecutiva designada por el Poder Ejecutivo; 3) el efecto de la amenaza de huelga médica, que entonces funcionaba porque no sobraban médicos. Palabras claves: Huelga médica, Seguro Social, médico como asalariado en Costa Rica.