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Gout is highly prevalent in patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD), owing to impaired uric acid excretion. However, treating gout in this population is challenging due to concerns about medication safety and efficacy with reduced kidney function. This review examines the evidence of various pharmacologic and non-pharmacologic approaches to managing gout in CKD/ESRD. For acute gout flares, there is insufficient evidence to guide optimal dosing of NSAIDs, colchicine, and corticosteroids in advanced CKD. The risks generally outweigh the benefits of NSAIDs and colchicine. Corticosteroids appear safer but require individual risk-benefit assessments. Interleukin-1 inhibitors show promise, but larger studies are needed. For long-term urate lowering, xanthine oxidase inhibitors like allopurinol and febuxostat are preferred over probenecid and other uricosurics. However, studies specifically evaluating urate-lowering therapies in CKD are scarce, resulting in conflicting expert guidelines. Starting with low allopurinol doses and gradual titration can mitigate the risks. Higher allopurinol doses may be needed to reach urate targets in some CKD patients. Febuxostat’s safety in advanced CKD remains debated. Optimal gout management in dialysis patients is also unclear, including when to continue urate-lowering therapy. Overall, gout is often suboptimally treated in CKD/ESRD, highlighting the need for more research to guide therapy in this population. Improving management can significantly reduce the burden of these comorbid diseases.

Thyroid Eye Disease (TED) is a debilitating autoimmune condition characterized by significant inflammation of orbital tissues, including the extraocular muscles and adipose tissues. The pathological mechanisms underlying this inflammation involve a complex interplay of stress responses at the cellular and molecular level. This review aims to critically evaluate and synthesize existing literature on the mechanisms of orbital inflammation in TED. We discuss the role of autoantibodies, cytokines, and reactive oxygen species (ROS) in the initiation and propagation of the inflammatory process. Additionally, we explore how stress responses triggered by these elements affect the integrity of orbital tissues and contribute to its remodeling. Our review underscores the need for continued research in this field, which may pave the way for novel therapeutic strategies for TED.

Coffee is one of the most popular beverages in the world, and at present, specialty coffees are developing for better tastes, flavors, or functions. Fermented coffees also reflect this trend and some brands are commercialized. Unlike general fermented coffees, we tried to ferment coffee extract with several lactic acid bacteria. Finally, we selected three strains that persisted in the coffee extract, and show strong antimicrobial activity, for the fermentation starters. The strains were identified as Pediococcus pentosaceus (KNUT 0384), Lacticaseibacillus paracasei (CJNU 1840), and Lactiplantibacillus plantarum (CJNU 0441) based on 16S rRNA gene sequences. During the fermentation process, the total acidities (p < 0.05 vs. the control, non-fermented coffee extract) and cell masses increased, which indicates that the bacteria metabolized properly in the extract. Furthermore, the fermented coffee extracts showed increased antimicrobial activities against Listeria monocytogenes (p < 0.05 vs. the control) and Streptococcus mutans (p < 0.05 vs. the control), increased anti-oxidative activities (p < 0.05 vs. the control, except for the KNUT 0384 sample), decreased caffeine content (p < 0.05 vs. the control, except for the KNUT 0384 sample), and increased chlorogenic acid content (p < 0.05 vs. the control). Taken together, the fermented coffee extracts with the selected lactic acid bacteria could be specialty coffees where several functions are improved when compared with a control coffee extract.

Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such as net metering (NEM) and Feed-in-Tariff (FiT), operate according to a centralized policy framework, where energy is only traded with the utility, the state-owned grid authority, the service provider, or the power generation/distribution company, who also have the upper hand in deciding on the rates for buying the excess energy. This study presents a comparative analysis of three energy trading mechanisms, P2P energy trading, NEM, and FiT, within a rural microgrid consisting of two prosumers and four consumers. The microgrid serves as a practical testbed for evaluating the economic impacts of these mechanisms, through simulations considering various factors such as energy demand, production variability, and energy rates, and using key metrics such as economic savings, annual energy bill, and wasted excess energy. Results indicate that while net metering and FiT offer stable financial returns for prosumers, P2P trading demonstrates superior flexibility and potentially higher economic benefits for both prosumers and consumers by aligning energy trading with real-time market conditions. The findings offer valuable insights for policymakers and stakeholders seeking to optimize rural energy systems through innovative trading mechanisms.

The modern energy landscape is undergoing a significant transformation towards cleaner, decentralized energy sources. This change is driven by environmental and sustainability needs, causing traditional centralized electric grids, which rely heavily on fossil fuels, to be replaced by a diverse range of decentralized distributed energy resources. Virtual power plants (VPPs) have surfaced as a flexible solution in this transition. A VPP’s primary role is to optimize energy production, storage, and distribution by coordinating output from various connected sources. Relying on advanced communication and control systems, a VPP can balance supply and demand in real time, offer ancillary services, and support grid stability. However, aligning VPPs’ economic and operational practices with broader environmental goals and policies is a challenging yet crucial aspect. This article introduces a new VPP management and optimization algorithm designed for quick and intelligent decision-making, aiming for the lowest levelized cost of energy (LCOE), minimum grid technical losses, and greenhouse gas (GHG) emissions. The algorithm’s effectiveness is confirmed using the IEEE 33-bus grid with 10 different distributed power generators. Simulation results show the algorithm’s responsiveness to complex variables found in practical scenarios, finding the optimal combination of available energy resources. This minimizes the LCOE, technical losses, and GHG emissions in less than 0.08 s, achieving a total LCOE reduction of 16% from the baseline. This work contributes to the development of intelligent energy management systems, aiding the transition towards a more resilient and sustainable energy infrastructure.

Lysosomal acid lipase deficiency (LALD) varies from a severe infantile-onset form (Wolman disease) to a late-onset form known as cholesteryl ester storage disease (CESD), both of which are autosomal recessive disorders caused by biallelic LIPA pathogenic variants. We evaluated seventy-three patients enlisted for liver transplant (LT) at Instituto da Criança (HCFMUSP—Brazil) who were subjected to LAL activity measurement and LIPA Sanger sequencing analysis, resulting in a positive LALD diagnosis for only one of these individuals. This LALD patient presented recurrent diarrhea, failure to thrive, hepatomegaly, and dyslipidemia at the age of 4 months and liver failure by the age of 13 years. The LALD diagnosis confirmation was conducted at 24 years old due to low levels of LAL enzyme activity. The causal homozygous variant LIPA(NM_000235.4):c.266T>C(p.Leu89Pro) was identified, but the patient had already undergone his first LT at 18 years with several rejection episodes. Despite beginning treatment with sebelipase alfa at 26 years old (total of five infusions), this patient died at 28 years from complications after his second liver transplant. LALD is an important differential diagnosis in cases presenting with hepatomegaly, elevated liver enzymes, and dyslipidemia. Detecting low/absent LAL activity and identifying the LIPA causal variant are essential for diagnosis and specific treatment, as well as for appropriate genetic counseling. Early diagnosis, along with sebelipase alfa therapy, may improve the prognosis of affected patients.

The growing popularity of unconventional wells has led to increased interest in assessing and predicting their production performance. These wells, with their extended-reach structures, are able to generate and access larger reservoir volumes. Therefore, understanding the impact of a well’s lateral trajectory on its transient production performance is crucial. This study investigates the effect of lateral-trajectory undulation amplitude on flow behavior based on the experimental results obtained at the University of North Dakota using an undulated two-phase (UTP) flow loop. The experiments involved injecting an air-and-water mixture through a section with variable undulation amplitude followed by a vertical section. The results revealed that the increasing undulation amplitude resulted in lower translational velocity, frequency, and length, with consistent slug acceleration along the system profile. Additionally, the frequency of slugs decreased as they traveled through the vertical section. The measured data indicated that higher undulation amplitudes led to increased horizontal pressure losses and variability, suggesting larger instabilities. The numerical simulations predicted lower translational velocity and frequency, longer slug length, and similar vertical pressure losses when compared to the experimental results.

This study examines the microstructural evolution and mechanical properties of quaternary AlCoCrNi high-entropy alloys after heat treatment at 873 K for 72 and 192 h. The changes in nanostructure and phase transformation based on the heat treatment duration were as follows: B2 dendrite + BCC interdendrite and sigma phases after 72 h; B2 dendrite and interdendritic sigma phases + BCC after 192 h. After annealing, the morphology of the dendritic region shifted from spherical to needle-like, and the interdendritic region transformed from a spinodal-like to a plate-like morphology. Additionally, a phase transformation was observed in the dendritic regions of the annealed alloys at the nano-scale. The presence of the sigma phase in AlCoCrNi high-entropy alloys significantly improved the yield strength to around 1172 MPa; nevertheless, it decreased the compressive strain rapidly to 0.62%.

This work explores a well-established eighth-algebraic-order numerical method belonging to the explicit Numerov-type family. To enhance its efficiency, we integrated a cost-effective algorithm for adjusting the step size. After each step, the algorithm either maintains the current step length, halves it, or doubles it. Any off-step points required by this technique are calculated using a local interpolation function. Numerical tests involving diverse problems demonstrate the significant efficiency improvements achieved through this approach. The method is particularly effective for solving differential equations with oscillatory behavior, showcasing its ability to maintain high accuracy with fewer function evaluations. This advancement is crucial for applications requiring precise solutions over long intervals, such as in physics and engineering. Additionally, the paper provides a comprehensive MATLAB-R2018a implementation, facilitating ease of use and further research in the field. By addressing both computational efficiency and accuracy, this study contributes a valuable tool for the numerical analysis community.

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed “Proof of Need” (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV’s battery, the charger power, the number of connected vehicles per household, the end-user’s preferences, and the local distribution substation’s capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time.

Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever.

Plastics have become indispensable in modern society; however, the proliferation of their waste has become a problem that can no longer be ignored as most plastics are not biodegradable. Depolymerization/degradation through sustainable processes in the context of the circular economy are urgent issues. The presence of multiple types of plastic materials makes it necessary to study the specific characteristics of each material. This mini-review aims to provide an overview of technological approaches and their performance for the depolymerization and/or degradation of one of the most widespread plastic materials, polypropylene (PP). The state of the art is presented, describing the most relevant technologies focusing on advanced oxidation technologies (AOT) and the results obtained so far for some of the approaches, such as ozonation, sonochemistry, or photocatalysis, with the final aim of making more sustainable the PP depolymerization/degradation process.

Pain assessment has become an important component in modern healthcare systems. It aids medical professionals in patient diagnosis and providing the appropriate care and therapy. Conventionally, patients are asked to provide their pain level verbally. However, this subjective method is generally inaccurate, not possible for non-communicative people, can be affected by physiological and environmental factors and is time-consuming, which renders it inefficient in healthcare settings. So, there has been a growing need to build objective, reliable and automatic pain assessment alternatives. In fact, due to the efficiency of facial expressions as pain biomarkers that accurately expand the pain intensity and the power of machine learning methods to effectively learn the subtle nuances of pain expressions and accurately predict pain intensity, automatic pain assessment methods have evolved rapidly. This paper reviews recent spatial facial expressions and machine learning-based pain assessment methods. Moreover, we highlight the pain intensity scales, datasets and method performance evaluation criteria. In addition, these methods’ contributions, strengths and limitations will be reported and discussed. Additionally, the review lays the groundwork for further study and improvement for more accurate automatic pain assessment.

Three years after its emergence, coronavirus disease 2019 (COVID-19) continues to be a leading cause of worldwide morbidity and mortality. This systematic review comprises relevant case reports that discuss non-multisystem inflammatory syndrome in children (non-MIS-C) and postacute sequalae of COVID-19 (PASC) in the paediatric population, also known as long COVID syndrome. The study aims to highlight the prevalent time interval between COVID-19 and the development of non-MIS-C post-infectious sequalae (PIS). Databases were searched for studies that met our inclusion and exclusion criteria. The final screening revealed an equal sex distribution where the commonest age intervals were school-age and adolescence, with 38% of the patients being older than six years. Interestingly, hospital admission during the course of COVID-19 was not a predictor of the subsequent PASC; forty-nine patients (44.9%) were hospitalized while sixty patients (55.1%) were not hospitalized. Moreover, the most predominant time interval between COVID-19 and the developing PASC was within 14 days from the start of COVID-19 infection (61%). These findings suggest a crucial link between COVID-19 and immune PIS in the paediatric population, especially those older than six years. Accordingly, follow-up and management are encouraged in case of unusual symptoms and signs following COVID-19 infection, regardless of the COVID-19 infection severity.

Technical losses in electrical grids are inherent inefficiencies induced by the transmission and distribution of electricity, resulting in energy losses that can reach up to 40% of the generated energy. These losses pose significant challenges to grid operators regarding energy sustainability, reliability, and economic viability. Distributed Energy Resources (DERs) offer promising solutions to lower technical losses by decentralizing energy generation and consumption, reducing the need for long-distance transmission and optimizing grid operation. Hence, estimating the impact of DERs on grid technical losses becomes paramount for grid operators and planners. In response, this article proposes the application of regression modeling and nonlinear curve fitting algorithms to provide a more nuanced understanding and better characterize the intricate interplay between DER deployment and technical losses. Through a comprehensive case study based on more than 1080 computer simulations, we demonstrate the effectiveness of our proposed dynamic polynomial varying coefficient regression model in estimating the impact of DERs on technical losses within electrical grids. The proposed model offers a simple and effective methodology that allows grid operators to gain insights into the nonlinear dynamics of DER integration and make quicker and more informed decisions regarding grid management strategies, infrastructure investments, and policy interventions. Also, this research contributes to advancing the field of grid optimization by offering a simple equation that enhances our ability and haste to assess and mitigate technical losses in the context of an evolving energy landscape characterized by increasing DER adoption.

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices’ hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems’ development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs.

Escalating events such as extreme weather conditions, geopolitical incidents, acts of war, cyberattacks, and the intermittence of renewable energy resources pose substantial challenges to the functionality of global electric grids. Consequently, research on enhancing the resilience of electric grids has become increasingly crucial. Concurrently, the decentralization of electric grids, driven by a heightened integration of distributed energy resources (DERs) and the imperative for decarbonization, has brought about significant transformations in grid topologies. These changes can profoundly impact flexibility, operability, and reliability. However, there is a lack of research on the impact of DERs on the electric grid’s resilience, as well as a simple model to simulate the impact of any disturbance on the grid. Hence, to analyze the electric grid’s resilience, this study employs an extrapolation of Leontief’s input–output (IO) model, originally designed to study ripple effects in economic sectors. Nodes are treated as industries, and power transmission between nodes is considered as the relationship between industries. Our research compares operability changes in centralized, partially decentralized, and fully decentralized grids under identical fault conditions. Using grid inoperability as a key performance indicator (KPI), this study tests the three grid configurations under two fault scenarios. The results confirm the efficacy of decentralization in enhancing the resilience and security of electric grids.

Evidence is conflicting about the diabetes characteristics associated with worse outcome among hospitalized COVID-19 patients. We aimed to assess the role of stress hyperglycemia ratio (SHR) as a prognostic marker among them. In our retrospective cohort study, patients were stratified according to SHR, admission glucose, and glycated hemoglobin tertiles. The primary outcome was a composite endpoint of invasive mechanical ventilation, intensive care unit admission, and in-hospital mortality. The study included 395 patients with a mean age of 59 years, and 50.1% were males. Patients in the third tertile of SHR developed more primary events, and the difference was significant compared to the first tertile (p = 0.038) and close to significance compared to the second tertile (p = 0.054). There was no significant difference in the outcomes across admission glucose and glycated hemoglobin tertiles. A higher SHR tertile was an independent risk factor for the primary outcome (OR, 1.364; 95% CI: 1.014–1.836; p = 0.040) after adjustment for other covariables. In hospitalized COVID-19 diabetic patients, SHR third tertile was significantly associated with worse outcome and death. SHR can be a better prognostic marker compared to admission glucose and glycated hemoglobin. A higher SHR was an independent risk factor for worse outcome and in-hospital mortality.

The stability of nonlinear systems in the control domain has been extensively studied using different versions of the algebraic Riccati equation (ARE). This leads to the focus of this work: the search for the time-varying quaternion ARE (TQARE) Hermitian solution. The zeroing neural network (ZNN) method, which has shown significant success at solving time-varying problems, is used to do this. We present a novel ZNN model called ’ZQ-ARE’ that effectively solves the TQARE by finding only Hermitian solutions. The model works quite effectively, as demonstrated by one application to quadrotor control and three simulation tests. Specifically, in three simulation tests, the ZQ-ARE model finds the TQARE Hermitian solution under various initial conditions, and we also demonstrate that the convergence rate of the solution can be adjusted. Furthermore, we show that adapting the ZQ-ARE solution to the state-dependent Riccati equation (SDRE) technique stabilizes a quadrotor’s flight control system faster than the traditional differential-algebraic Riccati equation solution.

Early and accurate detection of infectious diseases is a key step for surveillance, epidemiology and control, notably timely disease diagnosis, patient management and follow-up. In this study, we aimed to develop handheld ultra-fast duplex PCR assays coupled to amplicon detection by lateral flow (LF) immunoassay to deliver a rapid and simple molecular diagnostic test for concomitant detection and identification of the main Leishmania parasites encountered in Tunisia. We selected two DNA targets to amplify L. major/L. tropica and L. infantum/L. tropica groups of species DNAs, respectively. We optimized the experimental conditions of a duplex ultra-fast PCR. The amplification is performed using a portable Palm convection PCR machine within 18 min, and the products are detected using an LF cassette within 10 min. The test allows the identification of the infecting species according to the position and number of test lines revealed. Tested on a selection of DNAs of representative Leishmania strains of the three studied species (N = 37), the ultra-fast duplex PCR–LF showed consistent, stable and reproducible results. The analytical limit of detection of the test was 0.4 pg for L. major, 4 pg for L. infantum and 40 pg for L. tropica.

Thyroid storm (TS) is a rare and fatal endocrine emergency that occurs due to undiagnosed and inadequately treated hyperthyroidism after stressful conditions in patients with thyroid disorders. The objective of this systematic scoping review was to better understand the pathophysiology of TS and its complications, in terms of myocardial affection, tachyarrhythmia, and cardiogenic shock. In addition, we explored the pharmacological, mechanical, and surgical treatments for TS. We also evaluated the outcomes of TS according to sex and cardiac involvement. Additionally, analytical analysis was performed on the selected data. A literature review of peer-reviewed journals was carried out thoroughly using medical terms, MeSH on PubMed, Google Scholar, and combinations such as thyrotoxicosis-induced cardiomyopathy, thyroid storm, cardiogenic shock, myocardial infarction, endocrine emergency, Burch–Wartofsky score, extracorporeal circulatory support, and thyroidectomy. A total of 231 papers were eligible (2 retrospective studies, 5 case series, and 224 case reports) with a total of 256 TS patients with cardiac involvement between April 2003 and August 2023. All age groups, sexes, patients with TS-induced cardiomyopathy, non-atherosclerotic myocardial infarction, tachyarrhythmia, heart failure, shock, and different forms of treatment were discussed. Non-English language articles, cases without cardiac involvement, and cases in which treatment modalities were not specified were excluded. Female sex was predominant, with 154 female and 102 male patients. Approximately 82% of patients received beta-blockers (BBs), 16.3% were placed on extracorporeal membrane oxygenation (ECMO) support, 16.3% received therapeutic plasma exchange (TPE), and 13.8% underwent continuous renal replacement therapy (CRRT), continuous venovenous hemofiltration (CVVHD), or dialysis. Overall, 18 females and 16 males died. BB-induced circulatory collapse, acute renal failure, CRRT, and ventricular fibrillation were significantly associated with mortality. Awareness of TS and not only thyrotoxicosis is vital for timely and appropriate treatment. The early diagnosis and management of TS in cardiac settings, including pharmacological, mechanical, and surgical modalities, can save high-risk patients. Sex matters in the presentation, treatment, and mortality of this population. However, further large-scale, and well-designed studies are required.

Although the progressive clinical trajectory of motor neurone disease (MND) is widely understood, multiple challenges remain preventing optimal end-of-life care for this population with unique needs from the patient, carer and service provider perspectives. This paper reports on the experiences, gaps in service and unmet needs of MND patients and family carers and explores public health palliative care approaches that would facilitate coordinated and integrated care to respond to their changing needs. This is a qualitative study of responses to questions in an online consumer survey (353 respondents) in Western Australia (2020), focusing on a subset of 29 current and bereaved carers of people with MND who have used health services in the last five years. The analysis identified themes, highlighting the insufficient integration of services across health and social care; poor and unequal access to coordinated palliative care; significant gaps in the knowledge base of the workforce and a failure to meet the consumer expectations of person-centred care. For palliative care to be accessible to those living with MND and other under-served conditions, there needs to be a shift to more comprehensive, inclusive and sustainable options, such as the public health approach to palliative/end-of-life care that engages the assets of local communities in partnership with health services, one example being the “Compassionate Communities Connectors” model of care. Further considerations include advocacy for policy changes, fostering partnerships and developing indicators for evaluating the impact of the proposed models of care. The end result is not only better care but substantial savings for the health system.

A consistently high level of stallion fertility plays an economically important role in modern horse breeding. To better understand the factors affecting variation in stallion fertility, we have performed a statistical analysis study on some breeding factors: year of breeding, stud farm, age of the stallion, number of covered mares per stallion, reproduction methods, and age of the mare. This work was conducted on 94 purebred Arabian stallions in four different regions of Tunisia. The results showed an increase in the number of stallions during the study period, ranging from 11.33% in 2011 to 13.29% in 2018. Sidi Thabet’s stud farm contained the highest number of purebred Arabian stallions. The majority of stallions were between 15 and 21 years old and had covered 1 to 20 mares; 95.19% of stallions were used in natural mating (Nat); 50.36% had low fertility, 17.69% had medium fertility, and 32.3% had excellent fertility according to fertility standards. Depending on the year and stud, there was a variation in fertility per cycle (FERPCE) and end-of-season fertility (FERPSE) of the stallions. The highest average FERPCE and FERPSE values were obtained using artificial insemination with fresh semen (AIF). Analysis of FERPCE and FERPSE showed that the model used in our study explained 40.21% of total variability observations for FERPCE and 42.1% for FERPSE. The used statistical model showed that the breeding year, the stud, the age of the stallion, the number of covered mares by stallions and the method of reproduction significantly affected both FERPCE and FERPSE (with p = 0.001). Low to moderate heritability estimations for FERPCE (hs2 = 0.08) and FERPSE (hes2 = 0.36) were obtained.

Fosfomycin disodium is a potential therapeutic option to manage difficult-to-treat infections, especially when combined with other antimicrobials. In this study, we evaluated the activity of fosfomycin in combination with meropenem or polymyxin B against contemporaneous KPC-2-producing K. pneumoniae clinical isolates (KPC-KPN). Synergistic activity was assessed by checkerboard (CKA) and time–kill (TKA) assays. TKA was performed using serum peak and trough concentrations. The activity of these combinations was also assessed in the Galleria mellonella model. Biofilm disruption was assessed by the microtiter plate technique. CKA resulted in an 8- to 2048-fold decrease in meropenem MIC, restoring meropenem activity for 82.4% of the isolates when combined with fosfomycin. For the fosfomycin + polymyxin B combination, a 2- to 128-fold reduction in polymyxin B MIC was achieved, restoring polymyxin B activity for 47% of the isolates. TKA resulted in the synergism of fosfomycin + meropenem (3.0–6.7 log10 CFU/mL decrease) and fosfomycin + polymyxin B (6.0–6.2 log10 CFU/mL decrease) at peak concentrations. All larvae treated with fosfomycin + meropenem survived. Larvae survival rate was higher with fosfomycin monotherapy (95%) than that observed for fosfomycin + polymyxin B (75%) (p-value < 0.0001). Finally, a higher biofilm disruption was observed under exposure to fosfomycin + polymyxin B (2.4–3.4-fold reduction). In summary, we observed a synergistic effect of fosfomycin + meropenem and fosfomycin + polymyxin B combinations, in vitro and in vivo, against KPC-KPN, as well as biofilm disruption.

The remarkable features of hybrid SMC assisted with fuzzy systems supplying parameters of the controller have led to significant success of these control approaches, especially in the control of multi-input and multi-output nonlinear systems. The development of type-1 fuzzy systems to type-2 fuzzy systems has improved the performance of fuzzy systems due to the ability to model uncertainties in the expression of expert knowledge. Accordingly, in this paper, the basic approach of designing and implementing the interval type-2 fuzzy sliding mode control was proposed. According to the introduced systematic design procedure, complete optimal design of a type-2 fuzzy system structure was presented in providing sliding mode control parameters by minimizing tracking error and control energy. Based on the proposed method, the need for expert knowledge as the main challenge in designing fuzzy systems was eliminated. In addition, the possibility to limit the control outputs to deal with actuators’ saturation was made available. The control method was implemented on a six-degree-of-freedom robot manipulator that was exposed to severe external disturbances, and its performance was compared to a type-1 fuzzy system as well as to the conventional SMC. The achievements revealed improved performance of the combined control system of fuzzy sliding mode type-2 in comparison with its control counterparts.

Premature rain erosion damage development at the leading edges of wind turbine rotor blades impair the efficiency of the turbines and should be detected as early as possible. To investigate the causes of premature erosion damage and the erosion evolution, test specimens similar to the leading edge of a rotor blade were modified with different initial defects, such as voids in the coating system, and impacted with waterdrops in a rain erosion test facility. Using CT and XRM with AI-based evaluation as non-destructive measurement methods showed that premature erosion arises from the initial material defects because they represent a weak point in the material composite. In addition, thermographic investigations were carried out. As it shows results similar to the two lab-based methods, active thermography has a promising potential for future in-situ monitoring of rotor blade leading edges.

Background and Objectives: An elevated procalcitonin level has classically been linked to bacterial infections. Data on the association between elevated procalcitonin and the outcome of coronavirus disease 2019 (COVID-19) are conflicting. Some linked it to associated bacterial co-infections, while others correlated the elevation with disease severity without coexisting bacterial infections. We aimed to investigate the association between high procalcitonin and the severity of COVID-19. Materials and Methods: Hospitalized patients with confirmed COVID-19 pneumonia were divided into two groups: the normal-procalcitonin group and the high-procalcitonin group (>0.05 ng/mL). Patients with concomitant bacterial infections on admission were excluded. The primary outcomes were the need for intensive care unit (ICU) admission, progression to invasive mechanical ventilation (IMV), and in-hospital 28-day mortality. Results: We included 260 patients in the normal procalcitonin group and 397 patients in the high procalcitonin group. The mean age was 55 years and 49% were females. A higher number of patients in the elevated procalcitonin group required ICU admission (32.7% vs. 16.2%, p < 0.001) and IMV (27.2% vs. 13.5%, p < 0.001). In-hospital mortality was significantly higher in the elevated procalcitonin group (18.9% vs. 8.5%, p < 0.001). After adjusting for other covariates, procalcitonin > 0.05 ng/mL was an independent predictor of progression to IMV (OR, 1.71; 95% CI, 1.08–2.71; p = 0.022), ICU admission (OR, 1.73; 95% CI, 1.13–2.66; p = 0.011), and in-hospital mortality (OR, 1.99; 95% CI, 1.14–3.47; p = 0.015). An elevated procalcitonin level was the strongest predictor of in-hospital mortality. Conclusions: Measurement of procalcitonin can have a prognostic role among COVID-19 patients. The admission procalcitonin level can identify patients at risk of ICU admission, progression to IMV, and in-hospital mortality.

Pneumococcal disease affects people across all ages but is more prevalent in young children and the elderly. Despite the availability of the pneumococcal vaccine for adults, the disease burden and mortality associated with it remains a challenge. A few studies conducted in Lebanon have reported epidemiology of pneumococcal disease, concurring the high burden among adults and older adults in the region. The pneumococcal vaccine is a part of the routine immunization schedule for children, but there are no recommendations for adult vaccination. A medical advisory board was hence conducted in September 2020 to discuss the burden of pneumococcal disease (PD) among adults in Lebanon. The participants were experts from the fields of internal medicine, family medicine, hematology, cardiology, oncology, endocrinology, pulmonology, and infectious diseases. The experts reached a consensus that there is a need to take steps to increase the rate of adult vaccination uptake and create awareness among physicians, pharmacists, caregivers, and patients. The physicians should be trained on adult immunization and should actively discuss the importance of the pneumococcal vaccine, especially with high-risk adult patients. Implementing adult vaccination as a routine practice and involving various stakeholders to address the gaps can help in reducing the burden of pneumococcal disease in adults.

A cytochrome c552 mutant from Thermus thermophilus HB8 (rC552 C14A) was reported, where the polypeptide with replaced Cys14 by alanine, overexpressed in the cytosol of E. coli. The apo-form of the C14A mutant (apo-C14A) without the original prosthetic group was obtained by simple chemical treatments that retained compact conformation amenable to reconstitution with heme b and zinc(II)-protoporphyrin(IX), gradually followed by spontaneous formation of a covalent bond between the polypeptide and porphyrin ring in the reconstituted apo-C14A. Further analysis suggested that the residual Cys11 and vinyl group of the porphyrin ring linked through the thiol-ene reaction promoted by light under ambient conditions. In this study, we describe the kinetic improvement of the covalent bond formation in accordance with the mechanism of the photoinduced thiol-ene reaction, which involves a thiyl radical as a reaction intermediate. Adding a radical generator to the reconstituted C14A mutant with either heme-b or zinc(II) porphyrin accelerated the bond-forming reaction, which supported the involvement of a radical species in the reaction. Partial observation of the reconstituted C14A in a dimer form and detection of sulfuryl radical by EPR spectroscopy indicated a thiyl radical on Cys11, a unique cysteinyl residue in rC552 C14A. The covalent bond forming mediated by the radical generator was also adaptable to the reconstituted apo-C14A with manganese(II)-protoporphyrin(IX), which also exhibits light-mediated covalent linkage formation. Therefore, the radical generator extends the versatility of producing c-type-like cytochrome starting from a metallo-protoporphyrin(IX) and the apo-C14A instantaneously.

Fuzzy theory is widely studied and applied. This article introduces an adaptive control scheme for a class of non-linear systems with Markov jump switching. The introduced scheme supposes that the system is submitted to external disturbances under imperfect premise matching. By using discrete-time Takagi–Sugeno fuzzy models, a sliding mode observer-based control scheme is utilized to estimate unmeasured states of the system. We build two fuzzy switching manifolds for the disturbance and sliding mode observer systems. Then, a linear matrix inequality-based criterion is developed using slack matrices. This criterion proves that the sliding mode dynamics are robustly admissible under an H-infinity performance often used in control theory. Hence, new adaptive sliding mode controllers are synthesized for the disturbance and sliding mode observer systems. This allows the reachability of pre-designed sliding surfaces to be guaranteed. Finally, experimental numerical illustrations on a bio-economic system and a tunnel diode circuit are presented to show potential applications, as well as validating the effectiveness of the scheme proposed in the present investigation.

Leishmania major cutaneous leishmaniasis (CL) lesions are characterized by an intense process of parasite destruction and antigen processing that could limit microscopic amastigote detection. The aim of our study was to develop a direct immunofluorescence (DIF) assay for in situ visualization of L. major antigens and access its reliability in the routine diagnosis of CL. The developed DIF assay used IgG polyclonal antibodies produced in rabbits by intravenous injections of live L. major metacyclic promastigotes chemically coupled to fluorescein isothiocyanate. Applied to L. major infected RAW macrophages, corresponding macrophage-derived amastigotes and dermal scrapings from CL lesions, the immunofluorescence assay stained specifically Leishmania amastigotes and showed a diffuse Leishmania antigen deposit into cytoplasm of phagocytic cells. Reliability of DIF in CL diagnosis was assessed on 101 methanol-fixed dermal smears from 59 positive and 42 negative CL lesions diagnosed by direct microscopy and/or kDNA real-time PCR. Sensitivity and specificity of DIF was 98.3% and 100%, respectively, being more sensitive than microscopy (p < 0.001) and as sensitive as ITS1-PCR. ITS1-PCR-RFLP allowed Leishmania species identification in 56 out of the 58 DIF-positive smears, identifying 52 L. major, two L. infantum and two L. tropica cases, which indicates antigenic cross-reactivity between Leishmania species.

Well planning for every drilling project includes cost estimation. Maximizing the rate of penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses required for the drilling budget. The empirical formulas developed to predict ROP have limited field applications. Since real-time drilling data acquisition and computing technologies have improved over the years, we implemented the data-driven approach for this purpose. We investigated the potential of machine learning and deep learning algorithms to predict the nonlinear behavior of the ROP. The well was drilled to confirm the geothermal reservoir characteristics for the FORGE site. After cleaning and preprocessing the data, we selected two models and optimized their hyperparameters. According to our findings, the random forest regressor and the artificial neural network predicted the behavior of our field ROP with a maximum absolute mean error of 3.98, corresponding to 19% of the ROP’s standard deviation. A tool was created to assist engineers in selecting the best drilling parameters that increase the ROP for future drilling tasks. The tool can be validated with an existing well from the same field to demonstrate its capability as an ROP predictive model.

We report the study of sandfly Leishmania infection in an area of low incidence of visceral leishmaniasis in Tunisia. Sandflies were collected monthly using CDC light-traps set in houses and animal shelters during May–November 2016 and 2017. All males were identified at the species level. A sample of 878 females including all gravid specimens was subjected to kDNA qPCR for Leishmania detection and parasite load estimation. Leishmania species were determined by ITS1 PCR sequencing, and species identification of infected sandflies was performed by DNA barcoding. Phlebotomus perfiliewi and P. perniciosus were the dominant species during the two-year period. However, comparison of their relative abundances showed that P. perniciosus was more abundant during peaks of 2017 with longer activity duration. Real-time kDNA PCR did not detect Leishmania infection in 2016, although it identified four positive specimens (0.7%) in 2017. All four infected specimens were identified as P. perniciosus. ITS1 PCR sequencing allowed L. infantum identification in one kDNA qPCR-positive specimen. This was a P. perniciosus gravid female with a high parasite load caught during the long-lasting peak of 2017. This work highlights the usefulness of multi-seasonal studies of sandfly dynamics and kDNA qPCR in screening Leishmania infection and determining L. infantum vectors in hypo-endemic foci of human leishmaniasis.

We analyze a set of explicit Runge–Kutta pairs of orders six and five that share no extra properties, e.g., long intervals of periodicity or vanishing phase-lag etc. This family of pairs provides five parameters from which one can freely pick. Here, we use a Neural Network-like approach where these coefficients are trained on a couple of model periodic problems. The aim of this training is to produce a pair that furnishes best results after using certain intervals and tolerance. Then we see that this pair performs very well on a wide range of problems with periodic solutions.

Aims: This feasibility study aimed to systematically identify and address the support needs of parents of children with life-limiting illnesses and to assess whether the systematic approach was acceptable and relevant to parents. Methods: The CSNAT (Paediatric) intervention consisted of two assessment visits with the paediatric palliative care team, 2–8 weeks apart, comprising conversations about sources for support in a tertiary children hospital in Western Australia (2018–2019). Audio-recorded telephone interviews were conducted with parents, and inductive thematic analysis was undertaken. Results: All 28 parents who were involved in the intervention agreed to be interviewed. Five themes summarised their experience: caregiving challenges, perceived gaps and feelings of isolation; the usefulness and practicality of the systematic assessment; emotional responses to self-reflection; feelings of validation and empowerment; and received supports responsive to their needs. Conclusions: Parents appreciated the value of this systematic approach in engaging them in conversations about their needs and solutions to address them. While clinical service support was affirmed by parents, they were left wanting in other areas of practical, psychosocial, and emotional support. Palliative care services need to build stronger partnerships with supportive community networks through compassionate communities volunteer models of care to address the non-clinical needs of these families.

Few studies have described the goals and wishes of parents caring for their children with rare diseases, specifically when children are unable to communicate their preferences directly. The purpose of this study was to describe the parent’s understanding of their child’s illness, goals of care, and what mattered most to their child from the parent’s perspective. Six families completed a feasibility study of the FAmily CEntered (FACE)-Rare pACP intervention. Qualitative content analysis was performed on transcripts of videotaped responses to the Respecting Choices Next Steps pACP Conversation facilitated conversation guide about the goals of care. Codes were grouped into themes, with direct participant quotations representing the themes. Five themes emerged: getting out and moving freely; feeling included and engaged; managing symptoms and disease burden; coordinating care among many care team members; and managing today and planning for the future. In the context of pACP, families reported that what mattered most to their children included the freedom of movement and human connection and engagement, while parents strived to be effective caregivers and advocates for their child with a rare and severely disabling disease.

In this study, a multiscale monitoring method for nonlinear processes was developed. We introduced a machine learning tool for fault detection and isolation based on the kernel principal component analysis (PCA) and discrete wavelet transform. The principle of our proposal involved decomposing multivariate data into wavelet coefficients by employing the discrete wavelet transform. Then, the kernel PCA was applied on every matrix of coefficients to detect defects. Only those scales that manifest overruns of the squared prediction errors in control limits were considered in the data reconstruction phase. Thus, the kernel PCA was approached on the reconstructed matrix for detecting defects and isolation. This approach exploits the kernel PCA performance for nonlinear process monitoring in combination with multiscale analysis when processing time-frequency scales. The proposed method was validated on a photovoltaic system related to a complex industrial process. A data matrix was determined from the variables that characterize this process corresponding to motor current, angular speed, convertor output voltage, and power voltage system output. We tested the developed methodology on 1000 observations of photovoltaic variables. A comparison with monitoring methods based on neural PCA was established, proving the efficiency of the developed methodology.

Introduction: Scaling up vaccination against COVID-19 is central to controlling the COVID-19 epidemic in the United States. Several vaccines are now approved for the prevention of COVID-19, but public concerns over safety and efficacy have heightened distrust and vaccine hesitancy. This is particularly concerning among people with HIV (PWH) who may be vulnerable to more severe COVID-19 disease. Here, we aimed to identify and understand COVID-19 vaccine hesitancy in a sample of PWH in the U.S. Methods: We conducted a cross-sectional online survey among PWH in the U.S. between 6 December 2020 and 8 January 2021. Measures included demographics, participants’ HIV and health-related attributes, COVID-19 history and experiences, COVID-19 vaccine-related concerns, and standardized measures of attitudes towards COVID-19 vaccines. Multivariate linear regression was used to identify factors associated with vaccine hesitancy in this sample. Results: Among the 1030 respondents, most were male (89.7%), White (66.0%), and identified as gay or lesbian (84.5%). Participants’ mean time living with HIV was 17.0 years (standard deviation (SD) = 11.1). The mean score for vaccine hesitancy was 1.5 (SD = 0.5; range: 1–5); 935 participants (90.8%) had a score greater than 1.0, indicating most participants had some degree of vaccine hesitancy. The final multivariate linear regression showed that greater vaccine hesitancy was associated with being Black (b = 0.149, p = 0.005), single (b = 0.070, p = 0.018), politically conservative (b = 0.157, p = 0.010), “anti-vaxxer” (b = 1.791, p < 0.001), concern about side effects (b = 0.226, p < 0.001), concern about safety (b = 0.260, p < 0.001), and being worried that the vaccine will not be effective (b = 0.169, p = 0.008) and they were being experimented on (b = 0.287, p < 0.001). Participants who were male White (b = −0.093, p = 0.008) and university graduates (b = −0.093, p < 0.001) and had a CD4 count of 200 cells/mm3 (b = −0.082, p = 0.048) and a liberal political orientation (b = −0.131, p < 0.001) were associated with lower vaccine hesitancy. Conclusions: Our findings provide important insights regarding COVID-19 vaccine hesitancy among PWH. Further efforts are required to understand how various social, political, and psychological factors contribute to COVID-19 vaccine hesitancy among key populations.

Metabolic syndrome (MetS) forms a cluster of metabolic dysregulations including insulin resistance, atherogenic dyslipidemia, central obesity, and hypertension. The pathogenesis of MetS encompasses multiple genetic and acquired entities that fall under the umbrella of insulin resistance and chronic low-grade inflammation. If left untreated, MetS is significantly associated with an increased risk of developing diabetes and cardiovascular diseases (CVDs). Given that CVDs constitute by far the leading cause of morbidity and mortality worldwide, it has become essential to investigate the role played by MetS in this context to reduce the heavy burden of the disease. As such, and while MetS relatively constitutes a novel clinical entity, the extent of research about the disease has been exponentially growing in the past few decades. However, many aspects of this clinical entity are still not completely understood, and many questions remain unanswered to date. In this review, we provide a historical background and highlight the epidemiology of MetS. We also discuss the current and latest knowledge about the histopathology and pathophysiology of the disease. Finally, we summarize the most recent updates about the management and the prevention of this clinical syndrome.

Animal models for complex diseases are needed to position and analyze the function of interacting genes. Previous positional cloning identified Ncf1 and Clec4b to be major regulators of arthritis models in rats. Here, we investigate epistasis between Ncf1 and Clec4b, two major regulators of arthritis in rats. We find that Clec4b and Ncf1 exert an additive effect on arthritis given by their joint ability to regulate neutrophils. Both genes are highly expressed in neutrophils, together regulating neutrophil availability and their capacity to generate reactive oxygen species. Using a glycan array, we identify key ligands of Clec4b and demonstrate that Clec4b-specific stimulation triggers neutrophils into oxidative burst. Our observations highlight Clec4b as an important regulator of neutrophils and demonstrate how epistatic interactions affect the susceptibility to, and severity of, autoimmune arthritis.

Global economic growth, demographic explosion, digitization, increased mobility, and greater demand for heating and cooling due to climate change in different world areas are the main drivers for the surge in energy demand. The increase in energy demand is the basis of economic challenges for power companies alongside several socio-economic problems in communities, such as energy poverty, defined as the insufficient coverage of energy needs, especially in the residential sector. Two main strategies are considered to meet this increased demand. The first strategy focuses on new sustainable and eco-friendly modes of power generation, such as renewable energy resources and distributed energy resources. The second strategy is demand-side oriented rather than the supply side. Demand-side management, demand response (DR), and energy efficiency (EE) programs fall under this category. On the other hand, the decentralization and digitization of the energy sector convoyed by the emersion of new technologies such as blockchain, Internet of Things (IoT), and Artificial Intelligence (AI), opened the door to new solutions for the energy demand dilemma. Among these technologies, blockchain has proved itself as a decentralized trading platform between untrusted peers without the involvement of a trusted third party. This newly introduced Peer-to-Peer (P2P) trading model can be used to create a new demand load control model. In this article, the concept of an energy cap and trade demand-side management (DSM) model is introduced and simulated. The introduced DSM model is based on the concept of capping consumers’ monthly energy consumption and rewarding consumers who do not exceed this cap with energy tradeable credits that can be traded using blockchain-based Peer-to-Peer (P2P) energy trading. A model based on 200 households is used to simulate the proposed DSM model and prove that this model can be beneficial to both energy companies and consumers.

The aim of this study is to develop a new observer-based stabilization strategy for a class of Lipschitz uncertain systems. This new strategy improves the performances of existing methods and ensures better convergence conditions. Sliding window approach involves previous estimated states and measurements in the observer and the control law structures which increase the number of decision variables in the constraint to be solved and offers less restrictive Linear Matrix Inequality (LMI) conditions. The established sufficient stability conditions are in the form of Bilinear Matrix Inequality (BMI) which is solved in two steps. First, by using a slack variable technique and an appropriate reformulation of the Young’s inequality. Second, by introducing a useful approach to transform the obtained constraint to a more suitable one easily tractable by standard software algorithms. A comparison with the standard case is provided to show the superiority of the proposed H∞ observer-based controller which offers greater degree of freedom. The accuracy and the potential of the proposed process are shown through real time implementation of the one-link flexible joint robot to ARDUINO UNO R3 device and numerical comparison with some existing results.

Background: Consumer experience of palliative care has been inconsistently and selectively investigated. Methods: People in Western Australia who had experienced a life limiting illness in the past five years were recruited via social media and care organisations (2020) and invited to complete a cross sectional consumer survey on their experiences of the care they received. Results: 353 bereaved carers, current carers and patients responded. The winners, those who received the best quality end-of-life care, were those who were aware of palliative care as an end-of-life care (EOLC) option, qualified for admission to and were able to access a specialist palliative care program, and with mainly a cancer diagnosis. The losers, those who received end-of-life care that was adequate rather than best practice, were those who were unaware of palliative care as an EOLC option or did not qualify for or were unable to access specialist palliative care and had mainly a non-cancer diagnosis. Both groups were well supported throughout their illness by family and a wider social network. However, their family carers were not adequately supported by health services during caregiving and bereavement. Conclusions: A public health approach to palliative and end of life care is proposed to integrate tertiary, primary, and community services through active consumer engagement in the design and delivery of care. Therefore, suggested strategies may also have relevance in many other international settings.

In this paper, the problem of reliable control design with mixed H∞ /passive performance is discussed for a class of Takagi–Sugeno TS fuzzy descriptor systems with time-varying delay, sensor failure, and randomly occurred non-linearity. Based on the Lyapunov theory, firstly, a less conservative admissible criterion is established by combining the delay decomposition and reciprocally convex approaches. Then, the attention is focused on the design of a reliable static output feedback (SOF) controller with mixed H∞ /passive performance requirements. The key merit of the paper is to propose a simple method to design such a controller since the system output is subject to probabilistic missing data and noise. Using the output vector as a state component, an augmented model is introduced, and sufficient conditions are derived to achieve the desired performance of the closed-loop system. In addition, the cone complementarity linearization (CCL) algorithm is provided to calculate the controller gains. At last, three numerical examples, including computer-simulated truck-trailer and ball and beam systems are given to show the efficacy of our proposed approach, compared with existing ones in the literature.

This article presents evidence found in a search of national and international literature for patient preferences concerning settings in which to receive palliative care and the appropriateness of different models of palliative care. The purpose was to inform end-of-life care policy and service development of the Western Australian Department of Health through a rapid review of the literature. It was found that consumer experience of palliative care is investigated poorly, and consumer contribution to service and policy design is limited and selective. Most patients experience a mix of settings during their illness, and evidence found by the review has more to do with qualities and values that will contribute to good end-of-life care in any location. Models of care do not make systematic use of the consumer data that are available to them, although an increasingly common theme is the need for integration of the various sources of care supporting dying people. It is equally clear that most integration models limit their attention to end-of-life care provided by health services. Transitions between settings merit further attention. We argue that models of care should take account of consumer experience not by incorporating generalised evidence but by co-creating services with local communities using a public health approach.

Inherited retinal diseases (IRDs) are a heterogeneous group of conditions that include retinitis pigmentosa (RP) and Leber congenital amaurosis (LCA) and early-onset severe retinal dystrophy (EO[S]RD), which differ in severity and age of onset. IRDs are caused by mutations in >250 genes. Variants in the RPE65 gene account for 0.6–6% of RP and 3–16% of LCA/EORD cases. Voretigene neparvovec is a gene therapy approved for the treatment of patients with an autosomal recessive retinal dystrophy due to confirmed biallelic RPE65 variants (RPE65-IRDs). Therefore, the accurate molecular diagnosis of RPE65-IRDs is crucial to identify ‘actionable’ genotypes—i.e., genotypes that may benefit from the treatment—and is an integral part of patient management. To date, hundreds of RPE65 variants have been identified, some of which are classified as pathogenic or likely pathogenic, while the significance of others is yet to be established. In this review, we provide an overview of the genetic diagnostic workup needed to select patients that could be eligible for voretigene neparvovec treatment. Careful clinical characterization of patients by multidisciplinary teams of experts, combined with the availability of next-generation sequencing approaches, can accelerate patients’ access to available therapeutic options.

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

Algeria ranks second after Afghanistan for the incidence of cutaneous leishmaniasis (CL) worldwide. Here, we report a 34-years retrospective analysis of CL in Algeria and focused on the most affected region, the M’Sila province. All 66 cutaneous isolates corresponded to Leishmania (L.) major. Our study of the sandfly and rodent fauna further highlighted the high density of Phlebotomus papatasi and additional phlebotomine species of medical importance, not previously identified in M’Sila. Wild rodents belonging to nine species were trapped in M’Sila, and Psammomys obesus and Meriones shawi were found infected by L. major. In addition, Leishmania infantum was isolated from two visceral leishmaniasis cases, one dog and its proven vectors (P. perniciosus, P. longicuspis, and P. perfiliewi) inventoried during the survey. The high incidence of CL in the M’Sila province is likely a consequence of the increase in minimum temperatures recorded that constitutes suitable conditions for establishing a high endemicity and leads to an explosive rise in leishmaniases cases in this region. A thorough investigation of the underlying risk factors is urgently needed to detect new cases earlier. All these would improve the preparedness to fight the disease.

This paper tackles the control problem of nonlinear disturbed polynomial systems using the formalism of output feedback linearization and a subsequent sliding mode control design. This aims to ensure the asymptotic stability of an unstable equilibrium point. The class of systems under investigation has an equivalent Byrnes–Isidori normal form, which reveals stable zero dynamics. For the case of modeling uncertainties and/or process dynamic disturbances, conventional feedback linearizing control strategies may fail to be efficient. To design a robust control strategy, meta-heuristic techniques are synthesized with feedback linearization and sliding mode control. The resulting control design guarantees the decoupling of the system output from disturbances and achieves the desired output trajectory tracking with asymptotically stable dynamic behavior. The effectiveness and efficiency of the designed technique were assessed based on a benchmark model of a continuous stirred tank reactor (CSTR) through numerical simulation analysis.

The present study investigated iron molybdate (Fe2(MoO4)3), synthesized via a simple method, as a nanosorbent for methylene blue (MB) dye removal from aqueous solutions. Investigations of the effects of several parameters like contact time, adsorbent dose, initial dye concentration, temperature and pH were carried out. The results showed that MB removal was affected, significantly, by adsorbent dose and pH. Interestingly, lower values of adsorbent dose resulted in the removal of higher amounts of MB. At the optimum pH, the removal efficiency of 99% was gained with an initial MB concentration of ≤60 ppm. The kinetic study specified an excellent correlation of the experimental results with the pseudo-second-order kinetics model. Thermodynamic studies proved a spontaneous, favorable and endothermic removal. The maximum amount of removal capacity of MB dye was 6173 mg/g, which was determined from the Langmuir model. The removal efficiency was shown to be retained after three cycles of reuse, as proven by thermal regeneration tests. The presence and adsorption of the dye onto the Fe2(MoO4)3 nanoparticle surface, as well as the regeneration of the latter, was ascertained by scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR). These findings are indicative that the investigated nanosorbent is an excellent candidate for the removal of MB in wastewater.