High Cost Research Articles

Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines

Early detection and diagnosis of acute rheumatic fever and rheumatic heart disease are key to preventing progression, and echocardiography has an important diagnostic role. Standard echocardiography might not be feasible in high-prevalence regions due to its high cost, complexity, and time requirement. Handheld echocardiography might be an easy-to-use, low-cost alternative, but its performance in screening for and diagnosing acute rheumatic fever and rheumatic heart disease needs further investigation. In this systematic review and meta-analysis, we searched Embase, MEDLINE, LILACS, and Conference Proceedings Citation Index-Science up to Feb 9, 2024, for studies on the screening and diagnosis of acute rheumatic fever and rheumatic heart disease using handheld echocardiography (index test) or standard echocardiography or auscultation (reference tests) in high-prevalence areas. We included all studies with useable data in which the diagnostic performance of the index test was assessed against a reference test. Data on test accuracy in diagnosing rheumatic heart disease, acute rheumatic fever, or carditis with acute rheumatic fever (primary outcomes) were extracted from published articles or calculated, with authors contacted as necessary. Quality of evidence was appraised using GRADE and QUADAS-2 criteria. We summarised diagnostic accuracy statistics (including sensitivity and specificity) and estimated 95% CIs using a bivariate random-effects model (or univariate random-effects models for analyses including three or fewer studies). Area under the curve (AUC) was calculated from summary receiver operating characteristic curves. Heterogeneity was assessed by visual inspection of plots. This study was registered with PROSPERO (CRD42022344081). Out of 4868 records we identified 11 studies, and two additional reports, comprising 15 578 unique participants. Pooled data showed that handheld echocardiography had high sensitivity (0·87 [95% CI 0·76-0·93]), specificity (0·98 [0·71-1·00]), and overall high accuracy (AUC 0·94 [0·84-1·00]) for diagnosing rheumatic heart disease when compared with standard echocardiography (two studies; moderate certainty of evidence), with better performance for diagnosing definite compared with borderline rheumatic heart disease. High sensitivity (0·79 [0·73-0·84]), specificity (0·85 [0·80-0·89]), and overall accuracy (AUC 0·90 [0·85-0·94]) for screening rheumatic heart disease was observed when pooling data of handheld echocardiography versus standard echocardiography (seven studies; high certainty of evidence). Most studies had a low risk of bias overall. Some heterogeneity was observed for sensitivity and specificity across studies, possibly driven by differences in the prevalence and severity of rheumatic heart disease, and level of training or expertise of non-expert operators. Handheld echocardiography has a high accuracy and diagnostic performance when compared with standard echocardiography for diagnosing and screening of rheumatic heart disease in high-prevalence areas. World Health Organization. For the Chinese, French, Italian, Persian, Portuguese, Spanish and Urdu translations of the abstract see Supplementary Materials section.

Effect of combined additions of Sc, Zr and Ti on hot-cracking resistance and precipitation behaviour in Al-Mg alloy by L-PBF

The request for high-performance aluminum components prompts research into innovative alloys compatible with laser-based additive manufacturing processes, leveraging grain refiners in their composition to mitigate hot-cracking and enhance strength. While the addition of Sc and Zr in Al-Mg alloys has been widely investigated, the high cost and supply risks associated with Sc necessitate reducing its amount and replacing, at least partially, with other inoculants. In this preliminary study, the amount of Sc replaceable with different concentrations of Zr or Ti is evaluated investigating the laser powder bed fusion processability of three powder feedstocks: a pre-alloyed Al-Mg-Zr-Sc powder, a blend of the previous alloy with addition of Zr particles, and a further blend of an alloy depleted of Zr with added Ti particles. The experimental analyses on the laser-processed alloys showed that the standard and the Zr-enriched alloys featured a bimodal microstructure free from cracks with fine equiaxed grains at the edge of the molten pools and coarser grains at their centers. In the alloy variant depleted in Zr with addition of Ti particles a columnar structure was observed and hot-cracks appeared. Thermodynamic simulations of phase formation allowed defining the precipitation kinetics during solidification and direct aging, showing increased precipitation in alloys with higher Zr content, while the presence of Ti resulted in sluggish precipitation. Experimental aging tests demonstrated significant increases in microhardness, with peak values of the modified alloys achieved after 12 h at 375°C.

Uptake and outcomes of implementing non-invasive prenatal testing (Prenatal Reflex DNA testing) into first-trimester contingent screening protocols for trisomy 21, 18, 13: A study protocol

Chromosomal anomalies (CAs), such as trisomies 21 (T21), 18 (T18), and 13 (T13), are significant contributors to birth defects and genetic conditions worldwide. Cell-free fetal DNA testing (cff-DNA) is the preferred method for detecting these trisomies from maternal blood samples. However, its adoption is hindered by high costs and complexities. To enhance efficiency and reduce financial strain, we propose a practical protocol. This approach involves a contingent test based on biochemical and ultrasound markers commonly used in clinical practice. We aim to leverage cost-effective contingent prenatal DNA screening for trisomies 21, 18, and 13. This is integrated into standard aneuploidy screening during the first trimester at prenatal centers. Screening participants are categorized into four pathways (1T Quad, 1T Combined, 1T Expanded Combined, and 1T Expanded Combined + Ductus Venosus Pulsatility Index) based on biochemical markers (PAPP-A, Free β-hCG, PlGF, AFP) and ultrasound indicators (NT, DV-PI) as per FMF guidelines. Pregnant subjects (Low/High/Moderate Risk) with a high chance of affected pregnancy (1:1000 ≤ Risk < 1:10) undergo the DNA reflex screening test using stored plasma samples.

Analysis of nickel oxide as a counter electrode for dye-sensitized solar cells using OghmaNano software

Dye-sensitized solar cells (DSSCs), a promising green technology, convert solar energy into electricity more cost-effectively than traditional solar cells. While platinum (Pt) is commonly used in DSSCs, its high cost and toxicity limit practical applications. Recent research aims to develop low-cost counter electrodes with high efficiency. Nickel oxide (NiO), a p-type semiconductor with a wide bandgap, good transmittance, and suitable work function, emerges as a potential alternative for counter electrode of DSSCs. In this work, DSSCs with NiO of thicknesses varying from 100 nm to 1000 nm were simulated to determine its influence on photovoltaic performance using OghmaNano software. The structure of simulated solar cells consists of NiO as counter electrode, zinc oxide (ZnO) as photoanode, N719 as dyes, electrolyte as charge carrier transport, and fluorine-doped tin oxide (FTO) as a contact layer. There are five data of NiO used as an active layer. From the simulation results, NiO-doped gold exhibits the highest power conversion efficiency (PCE) of 15.95% at a thickness of 700 nm, while pure NiO shows the lowest PCE with 4.53% at a thickness of 600 nm. These results have demonstrated that NiO can replace Pt as a counter electrode for DSSCs and doping plays a vital role in increasing efficiency.

Techno-economic study of a hybrid photovoltaic-diesel system for a remote area in southwest Algeria

Exploiting natural sun and wind resources in remote and isolated areas is undoubtedly an excellent decision to generate electrical energy due to their availability and cleanliness. Various systems were used to generate this energy, such as photovoltaics (PV), wind turbine sand other energy systems. Moreover, for optimum energy use, some of these systems are combined either with each other or with other conventional systems, such as diesel generators with PV systems (i.e., hybrid systems). This work aims to present a technical-economic study of PV/diesel autonomous hybrid systems to supply electrical power for an isolated house located in a hot desert climate, Adrar. For optimizing the hybrid systems, hourly input data of solar radiation and load were used according to two configurations, where the annual load is 11.2 kWh/day. The findings showed that the diesel system had a high cost, with a cost for energy of $0.407/kWh and a fuel price of $0.140/l. Among the hybrid power systems but with significant pollution, the proposed hybrid system 2 kw photovoltaic and diesel generator with 2.3 kW has important economic feasibility, where the energy cost amounted to $0.172/kWh. In addition, CO2 emissions are reduced by approximately 5 tons every year compared to an independent diesel generator system.

Planning Innovation for Implementing Modular Prefabricated Construction in Housing Development in Indonesia Using A Riskbased ISO 56002:2019 Approach to Improve Project Performance

The development of housing in Indonesia, growing rapidly due to urbanization and population growth, faces challenges in terms of land, labor, and housing backlog. Despite being innovative, modular prefabrication methods are limited by unattractive designs and high costs, slowing down construction, harming the productivity of construction companies, and hindering the growth of the national construction sector. These challenges are prevalent in various countries and are influenced by low knowledge, conservative mindsets, and inhibiting regulations. In Indonesia, additional issues include a lack of mastery of modular prefabrication methods and awareness of long-term benefits. This research identified innovative risk-based applications of modular prefabrication with an ISO approach to improve project performance. The methodology employed includes a literature review, a questionnaire survey, and phased expert validation. The study found that out of 38 risk factors based on the 4 processes of implementing modular prefabrication methods, the average risk assessment is high. As a result, 5 innovations are proposed as efforts to reduce risk assessments. The research analyzed data from various literature, resulting in low inter-data connectivity. However, the processes and outcomes of this research can be relied upon for further understanding in subsequent research. This study combined proposed new innovations with those from external literature that have the potential to be applied in Indonesia for optimizing the cost efficiency of modular prefabrication methods.

Factors driving business intelligence adoption: an extended technology-organization-environment framework

Business intelligence (BI) is a vital component for businesses of all scales, offering actionable insights crucial for timely decision-making. This technology has become integral across diverse enterprises. Recognizing the factors influencing BI adoption is imperative, and this article employs the organization, complexity, knowledge, technology, user perception and experience, economic, environmental, and social (OCKTUEES) framework to identify key aspects. Building upon the TOE framework, it pinpoints significant variables, emphasizing the importance of factors like user perception and experience, technology, social, economical, and environmental. Employing structural equation modelling on primary data yields actionable insights to address BI adoption challenges. Analysis reveals the user perception and experience, technology, social, economic, and environmental as the top factors. However, the organization appears vulnerable, necessitating a mitigation strategy for successful BI adoption. The study predicts insignificant variables requiring mitigation, such as high costs, inadequate resources, organizational size, security and privacy concerns, risk of open-source adoption, and perception of analytics impacting jobs. This research aids those navigating the BI implementation journey.

Performance evaluation of novel 9-level RSMLI topology for grid-tied solar-PV system

The shortage of traditional fossil fuels like coal, petrol and natural-gas are increased day-by-day, fulfills the most of energy demand. Most of engineers are trying to maximize the energy demand by employing renewable energy with existing micro-grid system. Owing to merits, the solar-PV system plays a significant alternative among all other renewable energy sources due to abundant and virtuous nature. For grid-tied solar-PV system, the cascaded H-bridge multilevel inverter is the most significant over the classical 2-level inverter due to provision of isolated input DC sources. But the cascaded H-bridge topology is designed for limited voltage levels due to its larger number of switches for higher voltage levels, high cost, large-size, and more weight. To alleviate these demerits, a reduced-switch multilevel inverter has been generally preferable for higher voltage levels. In this work, a novel 9-level reduced-switch multilevel inverter (RSMLI) topology has been proposed by utilizing low number of switching devices. The performance of proposed novel 9-level RSMLI topology has been verified in grid-tied solar-PV system by using MATLAB/Simulink tool, simulation results are presented with attractive comparisons.

Development of alginate beads loaded with bioactive ingredients from Chlorella vulgaris cultivated in food industry wastewaters

Microalgae are increasingly recognized for their potential in the food sector due to their rapid growth and rich content in proteins, carbohydrates, and pigments. The extraction of pigments from microalgal biomass holds promise for their utilization as natural colorants and antioxidants. To address the economic challenges associated with microalgae cultivation, particularly high nutrient costs, this study focused on cultivating Chlorella vulgaris in a mixture of food industry wastewaters (brewery wastewater, expired orange juice and cheese whey). This resulted in a microalgae-bacteria consortium with a concentration of 2.2 g·L−1 dry weight after 5 days of cultivation. The wastewater bioremediation ranged between 23 and 77 %, and the produced microalgae biomass contained 21.68 ppm lutein, 36.47 ppm a-chlorophyll and 11.19 ppm b-chlorophyll. The extraction process employed a solvent screening strategy with food industry-accepted solvents used to extract high-value compounds efficiently. Ethanol was the most effective solvent, outperforming acetone, ethyl acetate, and hexane. An upscaled extraction process was conducted using 10 g lyophilized microalgae. After ethanol evaporation, the microalgal extract was re-diluted in sunflower oil and encapsulated in alginate beads, achieving an encapsulation yield of 93.3 %. Lutein, identified as one of the main compounds in the extract, exhibited a satisfactory encapsulation efficiency of 55 %. The efficient encapsulation of the microalgae components in alginate beads was verified by the alteration of the colorimetric parameters of these alginate beads compared to those loaded with pure sunflower oil as well as empty beads. These findings highlight the potential of microalgae as a valuable tool for transforming food industry wastewater into high-value products, especially when microalgal extract is encapsulated in alginate beads. This strategy presents substantial prospects for the food industry, merging effective wastewater valorization with biomass production and bioactive compound extraction. It exemplifies a circular bioeconomy, showcasing sustainable resource management and value creation.

Planned expansion of transportation infrastructure in Brazil has implications for the pattern of agricultural production and carbon emissions

High transportation costs have been a barrier to the expansion of agriculture in the interior of Brazil. To reduce transportation costs, Brazil launched the National Logistics Plan, aiming to expand its railway network by up to 91 % by 2035. Such a large-scale infrastructure investment raises concerns about its economic and environmental consequences. By combining geospatial estimation of transportation cost with a grid-resolving, multi-scale economic model that bridges fine-scale crop production with its trade and demand from national and global perspectives, we explore impacts of transportation infrastructure expansion on agricultural production, land use changes, and carbon emissions both locally and nationally in Brazil. We find that globally, the impacts on output and land use changes are small. However, within Brazil, the plan's primary impacts are impressive. PNL2035 results in the reduction of transportation costs by 8–23 % across states (depending on expansion's extent) in the interior Cerrado biome. This results in cropland expansion and increases in terrestrial carbon emissions in the Cerrado region. However, the increase in terrestrial carbon emissions in the Cerrado is offset by spillover effects elsewhere in Brazil, as crop production shifts away from the Southeast-South regions and accompanying change in the mix of transportation mode for farm products from roadway to more emission-efficient railway. Furthermore, we argue that the transportation infrastructure's impact on the enhanced mobility of labor and other agricultural inputs would further accentuate the regional shift in agricultural production and contribute to carbon emission mitigation. Upon its completion, PNL2035 is expected to result in the reduction of net national emissions by 1.8–30.7 million metric ton of CO2-equivalent, depending on the impacts on labor and purchased input mobility. We conclude that the omission of spillover effects due to infrastructure expansion can lead to misleading assessments of transport policies.

User authentication using gait and enhanced attribute-based encryption: a case of smart home

With the increasing popularity of the internet of things (IoT) application such as smart home, more data is being collected, and subsequently, concerns about preserving the privacy and confidentiality of these data are growing. When intruders attack and get control of smart home devices, privacy is compromised. Attribute-based encryption (ABE) is a new technique proposed to solve the data privacy issue in smart homes. However, ABE involves high computational cost, and the length of its ciphertext/private key increases linearly with the number of attributes, thus limiting the usage of ABE. This study proposes an enhanced ABE that utilises gait profile. By combining lesser number of attributes and generating a profiling attribute that utilises gait, the proposed technique solves two issues: computational cost and one-to-one encryption. Based on experiment conducted, computational time has been reduced by 55.27% with nine static attributes and one profile attribute. Thus, enhanced ABE is better in terms of computational time.

Economic optimization of hybrid renewable energy resources for rural electrification

In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.

HPETC: History Priority Enhanced Tensor Completion for Network Distance Measurement

In network distance measurement, how to estimate the whole network distance data from partially observed samples has attracted lots of attention because of its significance for network performance evaluation. Matrix completion becomes the most effective approach. However, the two-dimension matrix can only capture the spatial features in the network distance data while ignoring the temporal features. To conquer the problem, few recent studies begin to model the network distance data as a three-dimension tensor and propose tensor completion approaches for distance estimation. Although promising, existing tensor completion approaches still suffer the problem of low recovery accuracy and high measurement cost because they ignore the history priority information. To fully utilize both spatial and temporal features hidden in the distance data, this paper formulates a novel History Priority Enhanced Tensor Completion (HPETC) for distance estimation as a weighted tensor nuclear norm minimization problem where the weight is defined based on the history subspaces information. To solve the weighted tensor nuclear norm minimization problem, we firstly transform it into a factorization-based Frobenius norm minimization problem to avoid costly T-SVD computations, and then propose an iterative algorithm to solve the transformed problem. We further derive a theoretical sampling bound that is lower than the existing sampling bound, thus leads a lower measurement cost. We demonstrate the effectiveness of the proposed algorithm by conducting extensive experiments using two real network distance datasets. The result shows that the proposed algorithm can not only improve the estimation accuracy but also reduce the sampling complexity compared to the state-of-the-art approaches.

Review on Biomass Derived Activated Carbons as Electrochemical Electrode Material for Supercapacitor Device

Abstract: To face the challenge of the finite nature of fossil fuels and large energy crises across the globe, there is an urgent requirement for sustainable and renewable energy sources. Moreover, it is essential to focus on energy storage in order to meet the demand of future generations. Among various energy storage devices such as fuel cells, batteries, capacitors, supercapacitors, flywheels, etc., it is the supercapacitor device that has elicited extensive research interest recently because of prominent features like high power density, fast recharge capability, and long cycle life. The main objective of this article is to review the enhancement of the electrochemical performance of supercapacitor devices. The electrochemical properties of the supercapacitor device majorly depend on the electrode materials used, which include carbonaceous materials, metal oxides, and conducting polymers. In order to reduce energy shortages and environmental pressure, carbon materials derived from biomass/waste materials have been considered remarkable candidates for electrode materials with the advantages of high abundance, low cost, and environmental friendliness. This review shows the complied study of various methodologies for the preparation of activated carbons derived from different biomass residues such as plants, animals, and microorganisms, which have been investigated in the past few years as electrochemical electrode materials for supercapacitors. Further, ongoing challenges and potential improvements in this area for creating efficient energy storage devices are also discussed. The goal of this review article is to aid in the creation of new insights for energy storage applications of biomass-generated carbons that will lead to sustainable energy development.

Optimizing Network Reconfiguration to Reduce Power Loss and Improve the Voltage Profile in the Distribution System: A practical Case Study

The electricity provider incurs a high costs and investments to transfer the electrical power to customers in the distribution network. Hence, the reliability of the distribution network (DN) is particularly important in the lives of customers. A distribution system faces many issues due to its direct impact of the voltage profile (VP) and power losses (PL) problems. The aim of this study is to provide an effective scheme to address the of VP and PL problems. In this research, a Selective Particle Swarm Optimization (SPSO) algorithm was used to find combination of switches to enhance VP and reduce PL. The proposed method has been tested in the distribution network system, 125-buses Sigeri-Makassar Indonesia. There are three kinds of considered load scenarios, namely: nominal load; active power load increase; active and reactive power load increase. The research results indicated that the VP of the system can increased from 0.918 pu to be 0.922 pu and decreased PL from 0.6482 MW to be 0.6052 MW; the VP can increased from 0.895 pu to be 0.901 pu and reduced PL from 1.0469 MW to be 0.97532 MW, the VP can increased from 0.895 pu to be 0.900 pu and reduced PL from 1.1929 MW to be 1.0758 MW, for scenarios 1, 2 and 3, respectively. The simulation results showed that the proposed method provides optimal solution, which can enhance the VP and reduce PL. This is indicated that the proposed method is an effective strategy for enhancing voltage stability and solve PL problem.

BrepMFR: Enhancing machining feature recognition in B-rep models through deep learning and domain adaptation

Feature Recognition (FR) plays a crucial role in modern digital manufacturing, serving as a key technology for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP) and Computer-Aided Manufacturing (CAM) systems. The emergence of deep learning methods in recent years offers a new approach to address challenges in recognizing highly intersecting features with complex geometric shapes. However, due to the high cost of labeling real CAD models, neural networks are usually trained on computer-synthesized datasets, resulting in noticeable performance degradation when applied to real-world CAD models. Therefore, we propose a novel deep learning network, BrepMFR, designed for Machining Feature Recognition (MFR) from Boundary Representation (B-rep) models. We transform the original B-rep model into a graph representation as network-friendly input, incorporating local geometric shape and global topological relationships. Leveraging a graph neural network based on Transformer architecture and graph attention mechanism, we extract the feature representation of high-level semantic information to achieve machining feature recognition. Additionally, employing a two-step training strategy under a transfer learning framework, we enhance BrepMFR's generalization ability by adapting synthetic training data to real CAD data. Furthermore, we establish a large-scale synthetic CAD model dataset inclusive of 24 typical machining features, showcasing diversity in geometry that closely mirrors real-world mechanical engineering scenarios. Extensive experiments across various datasets demonstrate that BrepMFR achieves state-of-the-art machining feature recognition accuracy and performs effectively on CAD models of real-world mechanical parts.

Techno-Economic Analysis of Solar PV Electricity Generation at the University of Environment and Sustainable Development in Ghana

The electricity indebtedness of Ghanaian schools is increasing due to the high cost of electricity in the country. As a result, the utility companies frequently disconnect the schools from the grid to force them to settle their debts. Meanwhile, the school can reduce its bills from rooftop solar PV systems, yet little attention is given. The study uses the University of Environment and Sustainable Development (UESD) as a case to assess the technical feasibility, economic viability, and potential benefits of implementing rooftop solar PV electricity in Ghanaian schools. Google Earth software was used to estimate the structure's rooftop surface areas to determine the potential energy harvest from a PV plant.In contrast, the economic analysis uses the net present value (NPV), internal rate of returns (IRR), discounted payback period (DPP) and profitability index (IP) to appraise the intended project. The results show that using rooftops of school structures can provide the needed electricity for local consumption and export excess to the grid for sales. The NPV, IRR, IP, and DPP recorded from the study are million GHS 15.15, 21%, 1.6 and 8 years, respectively. The findings highlight the potential of rooftop solar PV systems to meet educational institutions' electricity demand, exploring the possibility of exporting excess to the national grid for revenue (income generator), reducing operational costs, and contributing to a sustainable energy transition.

Délais d'attente d'implantation de valve aortique transcutanée (TAVI) et mortalité durant la période d'attente en Algérie

BackgroundTrans Aortic Valve Implantation (TAVI) has become the primary treatment for aortic stenosis in patients over 75 years old. Despite its clinical efficacy, it's adoption in emerging countries remains low due to the high cost of prostheses and limited healthcare funding resources. This leads to prolonged waiting times for the TAVI procedure, which may lead to complications; these data are missing particularly in emerging countries. AimsTo describe waiting time for TAVI and mortality rate in this waiting period. Materials and methodsThis was prospective registry, patients referred for TAVI were prospectively followed; waiting time was calculated from the first visit after referral to TAVI implantation, clinical and, call fellow up was performed every 3 months. We divided patients into two groups: Group 1 (G1) patients still awaiting TAVI (105 patients), and those who underwent TAVI (36 patients). Group 2 (G2) patients who died while awaiting TAVI (16 patients, 10,2 %). ResultsDemographic characteristics were similar, with a tendency for older age in G2 (79.5 ± 5.7 years vs. 82.5 ± 7.4 years, p=0,06). G2 exhibited more left ventricular ejection fraction (LVEF) impairment (8.5% vs. 25%, p=0,03) and a higher rate of severe heart failure with dyspnea stages III or IV (2.8% vs. 12.5%, p<0,001). The mean follow-up in G1 was 242.9 ± 137.4 days; the waiting time for TAVI was 231.7 ± 134.1 days, and the average time between the first consultation and death while awaiting TAVI (G2) was 335.1 ± 167.4 days. Conclusionin our series, waiting time is high due to limited Trans aortic heart valve availability, mortality during this wait exceeds 10%. Adverse prognostic factors include impaired LVEF and severe dyspnea stages III or IV.

Smart prison technology and challenges: a systematic literature reviews

&lt;span lang="EN-US"&gt;The rapid rise of intelligent technology, particularly in government, is igniting a new phase of the industrial revolution around the world. As governmental entities, prisons oversee upholding social order and lowering current crime. The concept of the smart prison has not received much attention but is gaining traction. The goal of this research is to conduct a literature review to identify current prison technologies and to analyse the challenges associated with implementing smart prisons using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. Nine credible publishers were looked up between October 2022 and December 2022. The initial search yielded 362 articles, of which 25 were included in the final phase. This research provides the current state of prison according to technology-organization-environment (TOE). Some challenges arise in the context of TOE, such as the high cost of smart technology, inadequate technology design, poor management, ineffective service, overcrowding, ageing facilities, increasing violence, disease spread, and ethical problems. This study also classifies smart prison technology based on the internet of things (IoT) architecture layer. By providing the first comprehensive review on smart prison technology, this study makes an essential contribution to the subject of prisons.&lt;/span&gt;

Alzheimer Disease

Alzheimer's disease (AD) is typified by memory deficiencies that worsen with time, along with cognitive and behavioral abnormalities that eventually result in dementia. AD is one of the most difficult diseases to treat because of its prevalence, high cost of care, effects on patients and caregivers, and lack of mechanism-based therapies. The malfunction and loss of neurons in particular circuits and locations, especially in the populations of nerve cells supporting memory and cognition, causes the AD condition.The neuropathology of AD is characterized by intracellular and extracellular protein aggregation accumulations. In the neuronal perikarya and dystrophic neurites, abnormally phosphorylated tau forms paired helical filaments (PHFs) that combine to form neurofibrillary tangles (NFTs). The extracellular deposition of β-pleated assemblies of Aβ peptide, which result in widespread and neuritic senile plaques, is the second pathogenic hallmark.