Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Trajectory Optimization for UAV-Based Hybrid FSO/RF Communications with Buffer Constraints

This paper focuses on an unmanned aerial vehicle (UAV) assisted hybrid free-space optical (FSO)/radio frequency (RF) communication system. Considering the rate imbalance between the FSO and RF links, a buffer is employed at the UAV. Initially, theoretical models of energy consumption and throughput are obtained for the hybrid system. Based on these models, the theoretical expression of the energy efficiency is derived. Then, a nonconvex trajectory optimization problem is formulated by maximizing the energy efficiency of the hybrid system under the buffer constraint, velocity constraint, acceleration constraint, start–end position constraint, and start–end velocity constraint. By using the sequential convex optimization and first-order Taylor approximation, the nonconvex problem is transformed into a convex one. An iterative algorithm is proposed to solve the problem. Numerical results verify the efficiency of the proposed algorithm and also show the effects of buffer size on a UAV’s trajectory.
Compact and robust supercontinuum generation and post-compression using multiple thin plates

Abstract We report on compact and robust supercontinuum generation and post-compression using transmission of light through multiple thin solid plates at the SwissFEL X-ray free-electron laser facility. A single stage consisting of three thin plates followed by a chirped mirror compressor achieves compression of initially 30-fs pulses with 800-nm center wavelength to sub-10-fs duration. We also demonstrate a two-stage implementation to compress the pulses further to sub-5-fs duration. With the two-stage setup, the generated supercontinuum includes wavelengths ranging from 500 to 1100 nm. The multi-plate setup is compact, robust, and stable, which makes it ideal for applications at free-electron laser facilities such as pump-probe experiments and laser-arrival timing tools.
Open loop control of hysteresis band amplitudes in direct torque control of induction machines

In direct torque control (DTC) of induction machines there are flux and torque ripples, since none of the selected inverter's vectors are able to generate the stator voltage required to produce the desired changes in the electromagnetic torque and stator flux linkage in most of the switching instances. Also, when direct torque control is implemented in a fully digital form these ripples will increase due to some characteristics of discrete signal processing. In this paper, the influence of the amplitudes of flux and torque hysteresis bands and the sampling time of the control program on the torque and flux ripples are investigated. New formulas are then derived for hysteresis band amplitudes to reduce flux and torque ripples and prevent the inverter switching frequency exceeding a desired limit. Extensive simulation results confirm the superiority of the DTC under the proposed method over the conventional DTC.
Governance approach to the prioritization of sustainable development goals in the city of Medellin (Colombia)

Abstract This article proposes a governance approach for the prioritization of sustainable development goals (SDGs) in the city of Medellín (Colombia, South America). The governance approach proposed in this study stems from the articulation of technical and participatory approaches. The technical approach took as reference the document CONPES 3918, which defines the national sustainable development agenda in Colombia. The participatory approach was supported by the Medellín Citizen Perception Survey, which reflects the most important aspects of the quality of life for citizens. The results indicate that priorities for sustainable development in Medellín should be oriented to SDG 17, SDG 3, SDG 8, SDG 5, and SDG 4. The consistency test (CR) indicates the instrument applied is consistent.
DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups

Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients’ age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance.
Carboplatin Dosing in Children Using Estimated Glomerular Filtration Rate: Equation Matters

Simple Summary Carboplatin is a chemotherapeutic agent that is usually dosed based on body surface area or weight. However, carboplatin is cleared from the body by the kidneys. Therefore, taking the patient’s kidney function into account to calculate the adequate dose of carboplatin might result in a better exposure of carboplatin within a patient. In this study we sought to validate a carboplatin dosing method based on kidney function and compare several methods for kidney function-based carboplatin dosing in children suffering from retinoblastoma. We were able to show that carboplatin dosing based on a marker of kidney function (cystatin C) resulted in more adequate dosing than dosing on body surface area or weight. Abstract Renal function-based carboplatin dosing using measured glomerular filtration rate (GFR) results in more consistent drug exposure than anthropometric dosing. We aimed to validate the Newell dosing equation using estimated GFR (eGFR) and study which equation most accurately predicts carboplatin clearance in children with retinoblastoma. In 13 children with retinoblastoma 38 carboplatin clearance values were obtained from individual fits using MWPharm++. Carboplatin exposure (AUC) was calculated from administered dose and observed carboplatin clearance and compared to predicted AUC calculated with a carboplatin dosing equation (Newell) using different GFR estimates. Different dosing regimens were compared in terms of accuracy, bias and precision. All patients had normal eGFR. Carboplatin exposure using cystatin C-based eGFR equations tended to be more accurate compared to creatinine-based eGFR (30% accuracy 76.3–89.5% versus 76.3–78.9%, respectively), which led to significant overexposure, especially in younger (aged ≤ 2 years) children. Of all equations, the Schwartz cystatin C-based equation had the highest accuracy and lowest bias. Although anthropometric dosing performed comparably to many of the eGFR equations overall, we observed a weight-dependent change in bias leading to underdosing in the smallest patients. Using cystatin C-based eGFR equations for carboplatin dosing in children leads to more accurate carboplatin-exposure in patients with normal renal function compared to anthropometric dosing. In children with impaired kidney function, this trend might be more pronounced. Anthropometric dosing is hampered by a weight-dependent bias.
Recognition of 48 Human Behaviors from Video

We have developed a system that recognizes 48 human behaviors from video. The essential elements are (i) inference of the actors in the scene, (ii) assessment of event-related properties of actors and between actors, (iii) exploiting the event properties to recognize the behaviors. The performance of our recognizer approaches human performance, yet the performance for unseen variations of the behaviors needs to be improved.
Data analytics and machine learning: root-cause problem-solving approach to prevent yield loss and quality issues in semiconductor industry for automotive applications

Quality requirements in the semiconductor industry for automotive products are increasing rapidly with the movement to autonomous vehicles and higher levels of safety. It is no longer possible to express maximum failure requirements in parts per million (ppm). Individual failing parts observed in the field and reported by customers can trigger a significant quality response. ‘Zero-defect’ (ZD) is no longer considered a utopian ideal, but a required potentially reachable goal for semiconductor manufacturers. Projects and studies that include artificial intelligence and big data, are seen as key drivers to reach a ZD level of quality. Competing objectives targeted in any industrial project, such as quality improvement and gross margin, must also be considered. Initial projects in machine learning (ML), focusing on yield-loss issues, are being deployed within the manufacturing sites. These projects interconnect typical internal data collected from the manufacturing and assembly lines with engineering, qualification and reliability data. For a specific case study of unexpected abnormally high variability on some parameters, this paper presents a problem-solving approach in a big-data environment. Models implemented and results obtained towards root-cause problem solving for this issue, are discussed. This overall approach may be replicated in other ML projects.
Computed Tomography Texture Analysis of Carotid Plaque as Predictor of Unfavorable Outcome after Carotid Artery Stenting: A Preliminary Study

Novel biomarkers are advocated to manage carotid plaques. Therefore, we aimed to test the association between textural features of carotid plaque at computed tomography angiography (CTA) and unfavorable outcome after carotid artery stenting (CAS). Between January 2010 and January 2021, were selected 172 patients (median age, 77 years; 112/172, 65% men) who underwent CAS with CTA of the supra-aortic vessels performed within prior 6 months. Standard descriptors of the density histogram were derived by open-source software automated analysis obtained by CTA plaque segmentation. Multiple logistic regression analysis, receiver operating characteristic (ROC) curve analysis and the area under the ROC (AUC) were used to identify potential prognostic variables and to assess the model performance for predicting unfavorable outcome (periprocedural death or myocardial infarction and any ipsilateral acute neurological event). Unfavorable outcome occurred in 17/172 (10%) patients (median age, 79 years; 12/17, 70% men). Kurtosis was an independent predictor of unfavorable outcome (odds ratio, 0.79; confidence interval, 0.65–0.97; p = 0.029). The predictive model for unfavorable outcome including CTA textural features outperformed the model without textural features (AUC 0.789 vs. 0.695, p = 0.004). In patients with stenotic carotid plaque, kurtosis derived by CTA density histogram analysis is an independent predictor of unfavorable outcome after CAS.
Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Simple Summary Radiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the role of radiomics in providing more accurate diagnoses, prognostication, and surveillance of patients with high-grade glioma, and on the potential application of radiomics in clinical practice, with the overarching goal of advancing precision medicine for optimal patient care. Abstract Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.
Therapeutic Approaches in Lysosomal Storage Diseases

Lysosomal Storage Diseases are multisystemic disorders determined by genetic variants, which affect the proteins involved in lysosomal function and cellular metabolism. Different therapeutic approaches, which are based on the physiologic mechanisms that regulate lysosomal function, have been proposed for these diseases. Currently, enzyme replacement therapy, gene therapy, or small molecules have been approved or are under clinical development to treat lysosomal storage disorders. The present article reviews the main therapeutic strategies that have been proposed so far, highlighting possible limitations and future perspectives.
Study on Electrochemical Capacitance using Templated Carbons

This research was partially supported by the Strategic International Cooperative Program, Japan Science and Technology Agency (T.K.) and MINECO (Spanish-Japanese project PRI-PIBJP-2011-0766); and a Grant-in-Aid for Scientific Research (B), 26286020 (H.N.). This research was partially supported also by Nano-Macro Materials, Devices and System Research Alliance and by Network Joint Research Center for Materials and Devices.
Benefits of Intergenerational Contact: Ageism, Subjective Well-Being, and Psychosocial Developmental Strengths of Wisdom and Identity

The present study examines the benefits of an in-person intergenerational contact program called SAGE (Successful Aging and Inter-Generational Experiences). The SAGE Program pairs older adults (M age 85 years) and younger adults (M age 23 years) for 2 to 3-hour weekly meetings over a 7-week period, where participants can share memories, skills, and values, and foster new perspectives and friendships. We expected the SAGE Program to benefit both older and younger participants with respect to identity processes, subjective well-being, positive mood, and wisdom while reducing ageist beliefs compared to old and young participants serving as their matched controls. Overall, participants in the SAGE Program reported greater identity synthesis,subjective well-being, and positive mood. Exploratory analyses suggested that identity synthesis is a likely mediator of that effect. The SAGE Program did not reduce ageist beliefs, but age differences in ageism were found. We address additional results,limitations, and future research directions.
Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

Classification of brain signal features is a crucial process for any brain–computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changse with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects’ data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients’ data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller.
Research on Alarm Reduction of Intrusion Detection System Based on Clustering and Whale Optimization Algorithm

With the frequent occurrence of network security events, the intrusion detection system will generate alarm and log records when monitoring the network environment in which a large number of log and alarm records are redundant, which brings great burden to the server storage and security personnel. How to reduce the redundant alarm records in network intrusion detection has always been the focus of researchers. In this paper, we propose a method using the whale optimization algorithm to deal with massive redundant alarms. Based on the alarm hierarchical clustering, we integrate the whale optimization algorithm into the process of generating alarm hierarchical clustering and optimizing the cluster center and put forward two versions of local hierarchical clustering and global hierarchical clustering, respectively. To verify the feasibility of the algorithm, we conducted experiments on the UNSW-NB15 data set; compared with the previous alarm clustering algorithms, the alarm clustering algorithm based on the whale optimization algorithm can generate higher quality clustering in a shorter time. The results show that the proposed algorithm can effectively reduce redundant alarms and reduce the load of IDS and staff.
Classification of Capsule Endoscopy Images based on Feature Concatenation of Deep Neural Networks

Wireless capsule endoscopy (WCE) is a noninvasive way of detecting abnormalities in digestive tract. These abnormalities need to be detected at the early stages before they turn malignant. The classification of these abnormalities has put many challenges due to the variations in lesion shape and color, lighting conditions, and other factors. Existing methods based on handcrafted features give less accuracy due to the limited capability of feature representation. This study proposes a new approach for classifying wireless capsule endoscopy images using feature concatenation of deep convolutional neural network models. The features of two pre-trained models are concatenated and tested using a newly created dataset. The dataset is created using images taken from the Kvasir capsule endoscopy and Red lesion endoscopy dataset which is publicly available. This system improves diagnostic efficiency and brings great assistance to the doctor.
Study of acute intestinal obstruction management and its outcome

Background: Acute intestinal blockage (AIO) is a common abdominal emergency with a high morbidity and mortality rate. Early blockage diagnosis, skilled operative management, correct technique during surgery and intense postoperative therapy yield superior results. The aim was to study the aetiology, clinical presentation, therapy and outcomes of patients with IO who presented in the emergency department.Methods: This prospective study was conducted at the general surgery department from June 2019 to May 2021. The study comprised 60 patients with IO who presented to the OPD or emergency room and the data was analysed.Results: Among 60 patients with IO, peak incidence was observed in patients above 50 years of age, predominantly among males. Abdominal pain turned out to be the most common symptom of IO in our patients. Patients obstructed in the small intestine were found to be predominant. Post-operative adhesions were the most common cause of IO.Conclusions: Early diagnosis, proper preoperative hydration, fast investigations, and early operational intervention were found to increase survival in patients with intestinal obstruction in the current study.
Caring and Grieving in the Context of Social and Structural Inequity: Experiences of Australian Carers With Social Welfare Needs

Caring for and bereavement following the death of someone with a life-limiting illness may precipitate social welfare needs related to income support and housing. Nevertheless, carer experiences of welfare policy and institutions have not received significant attention. This qualitative study explored experiences of carers who navigated social welfare policy while caring for someone with a life-limiting illness, and in bereavement. In-depth interviews were conducted with 12 bereaved carers in an area associated with socioeconomic disadvantage. Carers differentially encountered precariousness, with some experiencing structural vulnerability. These positionalities appeared to be shaped by policy and process-related burdens, perceptions of the welfare state, and degrees of legitimisation or disenfranchisement of forms of capital and coping orientations. Recommendations that may improve carer experience were identified. Implications relate to the need for an expanded conceptualisation of vulnerability in health and welfare practice, policy that authentically validates caring and grieving, and upstream strategies that address inequity.
Process embedded design of integrated management systems

Purpose – The purpose of this paper is to provide a process‐based design of integrated management systems (IMS) implementation.Design/methodology/approach – An extensive survey of peer‐reviewed literature was conducted. Based on the literature review, a comprehensive methodology for the design and implementation of an IMS was developed.Findings – A critical review of the strategies employed and of difficulties encountered in IMS implementation reveals the need for a context‐ and process‐based design of IMS. At the operational level core activities are first designed from the perspective of stakeholders' requirements and then treated with operational excellence tools to strip away waste. The transformed core processes are then integrated with mainstream individual management systems to form one composite and holistic management system. The institutionalisation of IMS needs to be addressed in its design (through process embedded design) as well as at the users' level (through education and training of emplo.