Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of a Novel Amino Acid Formula on Nutritional and Metabolic Status, Anemia and Myocardial Function in Thrice-Weekly Hemodialysis Patients: Results of a Six-Month Randomized Double-Blind Placebo-Controlled Pilot Study

(1) Background: Chronic Kidney Disease (CKD) induces metabolic derangement of amino acid (AA) kinetics, eliciting severe damage to the protein anabolism. This damage is further intensified by a significant loss of AAs through hemodialysis (HD), affecting all tissues with a high metabolic turnover, such as the myocardium and body muscle mass. (2) Aim: to illustrate the effects of a novel AA mixture in boosting mitochondrial energy production. (3) Methods: A strict selection of 164 dialysis patients was carried out, allowing us to finally identify 22 compliant patients who had not used any form of supplements over the previous year. The study design envisaged a 6-month randomized, double-blind trial for the comparison of two groups of hemodialysis patients: eleven patients (67.2 ± 9.5 years) received the novel AA mix (TRG), whilst the other eleven (68.2 ± 10.5 years) were given a placebo mix that was indistinguishable from the treatment mix (PLG). (4) Results: Despite the 6-month observation period, the following were observed: maintenance of target hemoglobin values with a reduced need for erythropoiesis-stimulating agents in TRG > 36% compared to PLG (p < 0.02), improved phase angle (PhA) accompanied by an increase in muscle mass solely in the TRG group (p < 0.05), improved Left Ventricular Ejection Fraction (LVEF > 67%) in the TRG versus PLG group (p < 0.05) with early but marked signs of improved diastolic function. Increased sensitivity to insulin with greater control of glycemic levels in TRG versus PLG (p = 0.016). (5) Conclusions: the new AA mix seemed to be effective, showing a positive result on nutritional metabolism and cardiac performance, stable hemoglobin levels with the need for lower doses of erythropoietin (EPO), insulin increased cell sensitivity, better muscle metabolism with less loss of mass.
Insights into Concrete Forming, Reinforcing, and Pouring in Building Construction

The goal of this paper is to yield insights into the forming, reinforcing, and pouring of concrete buildings, focusing on design, functionality, failure, and safety aspects. The concrete construction process has different moving parts along the form–pour–strip cycle, and the authors aimed to analyze the information pertaining to formwork and rebar design, reasons for failures of formwork and hardening concrete, various hazards on jobsites, and safe work practices. The intent of this study was to seek and analyze the experiences of industry professionals on the above-stated aspects. In total, five well-experienced industry professionals working in concrete forming, reinforcing, and pouring were interviewed. Through comparative analysis of the interview results, the authors were able to gather valuable data related to designs of formwork system and rebars, field practices, jobsite hazards, and safety requirements. Interview results revealed that functionality and applied loads are the determining factors in any design considerations when it comes to formwork system and rebars for concrete buildings. Another finding was that a construction project does not wait for a concrete element to reach its full compressive strength at 28 days before proceeding with the work since the element can hold most construction loads after 3 or 4 days. In addition, the failures which occur in concrete buildings are a result of formwork breakdown, defective formwork layout, and concrete damage due to corroded rebars and inadequate curing procedure. Lastly, interview results revealed that the single leading hazard during concrete works is working at heights which can be minimized using fall arrest systems or engineered guardrails for improved safety.
Review: Home energy management system in a Smart Grid scheme to improve reliability of power systems

Home energy management system (HEMS) concept rises from the development of smart homes that build interaction between users with their home appliances in order to operate automatically, multi-functionally, adaptably and efficiently. In line with technological developments and published regulations related to environmental issues, smart home applications evolve into HEMS applications which are not only to provide ease and convenience, but also to monitor and to make efficient energy use at home, thereby reducing peak power quantity and electricity bill. Smart grid is an intelligent power grid starting from its generation, transmission and distribution. It combines computing technology, artificial intelligence and communications technology which creates a smarter power system and is able to produce better power quality and lower generation cost. In the smart grid scheme, by means of HEMS applications, consumers can participate in improving the quality of power systems. This study will discuss about the development of HEMS in associated with smart grid technology particularly the role of HEMS application with its DSM (Demand Side Managment) and PEV (Plug-in Electric Vehicles) programs in the smart grid scheme to improve quality of power systems. Several studies have shown that the contribution of HEMS to the smart grid system can improve the power losses and voltage profile.
A VINS Combined With Dynamic Object Detection for Autonomous Driving Vehicles

This paper proposes a visual-inertial navigation system (VINS) combined with a dynamic object detection (DOD) algorithm, to improve the localization and state estimate accuracy of autonomous driving vehicles (ADV) in dynamic environments. Firstly, based on the YOLOv5 network, we train the proposed DOD model to detect dynamic objects in the road environment. Secondly, by removing the feature points in the dynamic object regions, we track the remaining feature points to eliminate the influence of the dynamic object. Furthermore, we model the global positioning system (GPS) measurement as a general factor and introduce its residual factor into the cost function to eliminate the cumulative error. Finally, we validate the performance of the proposed method on public datasets and real-world experiments. The results show that the proposed method can effectively eliminate the influence of the dynamic object and eliminate the cumulative error. It provides theoretical guidance for ADV navigation in dynamic or large-scale outdoor environments.
Reinforcement Learning for Primary care e Appointment Scheduling

Patient scheduling is a complex task that plays a crucial role in the quality of care. Effective scheduling avoids patient and physician dissatisfaction, and as such, is an important determinant of care. However, the task of patient scheduling is traditionally done ad hoc, with disregard of potential aspects that may determine appointment duration and overall waiting time. Since scheduling in this setting is frequently done one patient at a time, the problem cannot be readily solved by optimization methods. Furthermore, the constant changing nature of clinical settings implies that one-fits-all heuristic scheduling approaches would generally be poor, rendering the task of automated patient scheduling resource intensive, costly to maintain, and thus, generally unfeasible. The present work aims to develop a framework to create fully configurable patient scheduling environments, study the performance of the Advantage Actor-Critic reinforcement learning algorithm to schedule appointments in a set of increasingly challenging environments, and benchmark it against a set of simple deterministic heuristics. A micro-service application was developed to create and test any scheduling environments over which to develop and test agents. The platform enabled online monitoring of the agent performance and data collection for posterior analysis. It also enabled humans to work as agents. The platform was developed in such a way that it can be run in local machines or cloud infrastructure. We devised and developed a set of seven specific appointment scheduling environments that implement the basic requirements of scheduling in Primary Care, and studied the performance of RL agents implemented using the A3C algorithm. Finally, we have shown that RL agents are able to learn the underlying rules that govern such environments, that needed to be considered to arrive at optimal or near optimal scheduling solutions, and thus are promising algorithms for production environments.
OPTIMAL A

We consider a price-maker company which generates electricity and sells it in the spot market. The company can increase its level of installed power by irreversible installations of solar panels. The electricity price evolves as an Ornstein–Uhlenbeck process, whose drift is negatively impacted by the current level of the company’s installed power. The company aims at maximizing the total expected profits from selling electricity in the market, net of the total expected proportional costs of installation. This problem is modeled as a two-dimensional degenerate singular stochastic control problem . We find that the optimal installation strategy is triggered by a curve which separates the waiting region , where it is not optimal to install additional panels, and the installation region , where it is. Such a curve is the unique strictly increasing solution of a first-order ordinary differential equation. Finally, we show numerically the dependence of the optimal installation strategy on the model’s parameters.
A Gm-Boosting Technique for Millimeter-Wave Low-Noise Amplifiers in 28-nm Triple-Well Bulk CMOS Using Floating Resistor in Body Biasing

This paper presents a simple yet effective $G_{m}$ -boosting technique for improving gain and noise performance of millimeter-wave (mm-wave) low-noise amplifiers (LNAs) comprising triple-well transistors typically found in the modern bulk CMOS processes. The proposed technique uses a resistor that connects the p-well and deep n-well terminals of the triple-well transistor, leaving the terminals floating instead of conventionally connecting them to the ground and supply voltage. This arrangement exploits a leakage current through a diode formed between the drain/source and p-well of each transistor, thus autonomously setting its bulk potential for increased transconductance, while ensuring its robustness to the process variation. The improved isolation between the p-well and the substrate further improves the gain and noise performance. We provide a theoretical analysis of this floating resistor-based body biasing method and support it with simulation results. For experimental validation, a two-stage cascode LNA was designed and fabricated in 28-nm bulk CMOS. The measurement results show that 3.3–4dB noise figure (NF) and 19.1–16.1dB gain are achieved at 24.7–29.5GHz. To ensure a fair comparison, another identical LNA with the normally expected triple-well biasing was also fabricated. The proposed method reveals a 0.6dB improvement in minimum NF and an additional ~3.5dB gain without any significant linearity degradation.
Analytical modeling of dielectric modulated negative capacitance MoS2 field effect transistor for next‐generation label‐free biosensor

In this paper, a dielectric modulated negative capacitance (NC) MoS2 FET‐based biosensor is proposed for label‐free detection of biomolecules such as enzymes, proteins, DNA, and so forth. Various reports present on experimental demonstration and modeling of NC MoS2 FET but it is never utilized as a dielectric modulated biosensor. So, in this work, the modeling, characterization and sensitivity analysis of dielectric modulated NC MoS2 FET is focused. For immobilization of biomolecules, a nanocavity is formed below the gate by etching some portion of the gate oxide material. The immobilization of biomolecules in the cavity leads to a variation of different electrostatic properties such as surface potential, threshold voltage, drain current, and subthreshold swing which can be utilized as sensing parameters. An analytical model for the proposed biosensor is also developed in the subthreshold region by considering the properties of two‐dimensional ferroelectric materials and benchmarked with TCAD device simulations. The effect of change of gate length and doping concentration on different electrical properties is also analyzed to estimate the optimum value of channel doping. The results prove that the proposed device can be used for next‐generation low power label‐free biosensor which shows enhanced sensitivity as compared to traditional FET‐based biosensors.
Automatic keyword assignment system for medical research articles using nearest-neighbor searches

: Assigning accurate keywords to research articles is increasingly important concern. Keywords should be selected meticulously to describe the article well since keywords play an important role in matching readers with research articles in order to reach a bigger audience. So, improper selection of keywords may result in less attraction to readers which results in degradation in its audience. Hence, we designed and developed an automatic keyword assignment system (AKAS) for research articles based on k-nearest neighbor ( k -NN) and threshold-nearest neighbor ( t -NN) accompanied with information retrieval systems (IRS), which is a corpus-based method by utilizing IRS using the Medline dataset in PubMed. First, AKAS accepts an abstract of the research article or a particular text as a query to the IRS. Next, the IRS returns a ranked list of articles to the given query. Then, we selected a set of documents from this list using two different methods, which are k -NN and t -NN representing the first k documents and documents whose similarity is greater than the threshold value of t , respectively. To evaluate our proposed system, we conducted a set of experiments on a selected subset of 458,594 PubMed articles. Then, we performed an experiment to observe the performance of AKAS results by comparing with the original keywords assigned by authors. The results we obtained showed that our system suggests keywords more than 55% match in terms of F-score. We presented both methods we used and results of experiments, in detail.
A Blockchain Architecture for Trusted Sub-Ledger Operations and Financial Audit Using Decentralized Microservices

Blockchain has become an unavoidable future in enterprise finance, particularly enabling and securing cross-company transactions. By introducing a comparable notion of smart contract, the trusted sub-ledger operation (TSLO), this article will propose a complete architecture based on the Blockchain to solve the traceability and validity of accounting data by assets groupement. TSLO is a more flexible and adaptable method for asset management in the corporate accounting system and the enterprise resource planner. This method is built on a decentralized microservices tree (DMST) and is an extendable E-Bidding form of TEA (Triple Entry Accounting). Instead of using a multi-ledger architecture, the Hyperledger Fabric skeleton, limited to participant channels inside one entity or organization, our approach uses decentralized sub-ledgers with an implementation tree (DMST) for an assets-driven transactions. Furthermore, the government’s audit and taxation procedures for financial groups are more accessible by combining Proof of Authority and Proof of Stake to assure the logic of More stake more reputation to preserve.
An Improved Lightweight Two-Factor Authentication Protocol for IoT Applications

The Internet of things (IoT) is defined as a network of connected objects or devices that gather information and share or exchange them through communication systems. In many applications, maintaining the privacy and security of the exchanged data is of great importance. Therefore, protecting sensitive data against security threats is a major concern in IoT applications. Using multifactor authentication protocols is an efficient solution to provide these features. Recently, a two-factor authentication protocol using physical unclonable functions and wireless fingerprints has been proposed by Aman et al. In this article, we first analyze Aman et al.’s protocol and discuss its weaknesses and vulnerabilities. Then, we propose an improved two-factor authentication protocol to provide strong mutual authentication and overcome the mentioned issues. The informal security analysis of the proposed scheme demonstrates that it is resistant to several well-known attacks. We also perform a security analysis of the proposed scheme using Burrows–Abadi–Needham logic. We further evaluate the proposed protocol in terms of computational complexity and security features. The results demonstrate that our scheme has significantly better computational efficiency and provides better security features compared to Aman et al.’s protocol.
Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework

Background Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation. Objective The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care. Methods We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study. Results The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting considerations—user-centered design and privacy and security—that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes. Conclusions Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns.
A trace partitioned Gray code for q-ary generalized Fibonacci strings

Abstract We provide a trace partitioned Gray code for the set of q-ary strings avoiding a pattern constituted by k consecutive equal symbols. The definition of this Gray code is based on two different constructions, according to the parity of q. This result generalizes, and is based on, a Gray code for binary strings avoiding k consecutive 0's.
Communication in Oncology Outpatient Clinic Settings: Congruence of Quality of Life Assessment between Patient-Physician and Patient-Caregiver Dyads

Objectives The aims of this study were to investigate the congruence of HRQOL reports between patient-physician and patient-caregiver dyads and to determine the association of variables, if any, with the congruence between dyads. Methods This correlational study with a cross-sectional design first approached physicians who provided care for patients with advanced cancer at the participating institution. Then, participating physicians' patients and their caregivers were recruited. All participants were required to independently fill out an HRQOL questionnaire during their outpatient visits. Descriptive statistics, weighted kappa, Wilcoxon signed-rank test, and linear regression were employed for data analysis. Results A total of 52 patient-physician and 27 patient-caregiver dyads were examined. Patients suffered from considerable problems in all three HRQOL domains: symptom, functioning, and overall HRQOL. The patients' level of agreement was moderate with the caregivers and fair with the physicians. A significant relationship was found between several patient-related variables and disagreement. Conclusion These patients with advanced cancer experienced a compromised HRQOL, warranting immediate attention. When there are barriers to obtaining a patient's self-report, clinicians may consider caregivers as a reasonable source. Patients with special characteristics need additional attention because their problems may be at a greater risk of being overlooked.
Lactoferrin Decreases Enterotoxigenic Escherichia coli-Induced Fluid Secretion and Bacterial Adhesion in the Porcine Small Intestine

Enterotoxigenic Escherichia coli (ETEC) infections are one of the most prevalent causes of post-weaning diarrhea in piglets, resulting in morbidity, mortality and elevated use of antibiotics. The emergence and further spread of antimicrobial resistance together with the growing demand for high quality animal protein requires the identification of novel alternatives for antimicrobials. A promising alternative is lactoferrin, as we previously showed that it can both inhibit the growth and degrade bacterial virulence factors of porcine ETEC strains in vitro. Aiming to confirm these findings in vivo, we performed a small intestinal segment perfusion experiment in piglets. Here, we showed that lactoferrin could not only decrease ETEC-induced fluid secretion, but also their ability to colonize the small intestinal epithelium. Furthermore, while ETEC infection induced pro-inflammatory cytokine mRNA expression in this experiment, lactoferrin was not able to counteract these responses. In addition, a bacterial motility assay showed that lactoferrin can reduce the motility of ETEC. Our findings further support the use of lactoferrin as an alternative for antimicrobials and also show its potential for the prevention of ETEC infections in pigs.
A Review of Spiking Neural Networks

Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages in Spatio-temporal dynamics, diverse coding mechanisms, and event-driven properties. This paper is a review of SNN in order to help researchers from other areas to know and became familiar with the field of SNN or even became interested in SNN. Neuron models, coding methods, training algorithms, and neuromorphic computing platforms will be introduced in this paper. This paper analyzes the disadvantages and advantages of several kinds of neural models, coding methods, learning algorithms, and neuromorphic computing platforms, and according to these to propose some expected development, such as improving the balance between bio-mimicry and cost of computing for neuron models, compounding coding methods, unsupervised learning algorithms in SNN, and digital-analog computing platform.