Dieth­yl[N-(3-meth­oxy-2-oxidobenzyl­idene)-N′-(oxidomethyl­ene)hydrazine-κ3 O,N,O′]tin(IV)

In the mol­ecule of the title compound, [Sn(C2H5)2(C9H8N2O3)], the Sn atom is five-coordinated in a distorted trigonal-bipyramidal configuration by two O and one N atoms of the tridentate Schiff base ligand in the equatorial plane, and by two C atoms of ethyl groups in the axial positions. In the crystal structure, inter­molecular C—H⋯O hydrogen bonds link the mol­ecules into centrosymmetric dimers

(2,2′-Bipyridine-κ2 N,N′){[(3-meth­oxy-2-oxidobenzyl­idene-κO 2)hydrazono]methano­lato-κ2 N 2,O}dimethyl­tin(IV)

In the crystal structure of the title compound, [Sn(CH3)2(C9H8N2O3)(C10H8N2)], the Sn atom exhibits a penta­gonal bipyramidal coordination geometry defined by two C, three N and two O atoms. The bond distances for Sn—C, Sn—N and Sn—O are in the ranges 2.097 (3)–2.098 (3), 2.298 (2)–2.623 (2) and 2.157 (2)–2.266 (2) Å, respectively. The mol­ecular structure of the monomeric compound is stabilized by three intra­molecular C—H⋯O hydrogen bonds, all involving bipyridine C—H groups

IMU sensing–based Hopfield neuromorphic computing for human activity recognition

Aiming at the self-association feature of the Hopfield neural network, we can reduce the need for extensive sensor training samples during human behavior recognition. For a training algorithm to obtain a general activity feature template with only one time data preprocessing, this work proposes a data preprocessing framework that is suitable for neuromorphic computing. Based on the preprocessing method of the construction matrix and feature extraction, we achieved simplification and improvement in the classification of output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons by constructing a feature matrix, which changed the weights of different categories to classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion process, which helps improve the classification accuracy and avoids falling into the local optimal value caused by single sensor data. Experimental results show that the framework has high classification accuracy with necessary robustness. Using the proposed method, the classification and recognition accuracy of the Hopfield neuromorphic algorithm on the three classes of human activities is 96.3%. Compared with traditional machine learning algorithms, the proposed framework only requires learning samples once to get the feature matrix for human activities, complementing the limited sample databases while improving the classification accuracy

Crowd control, planning, and prediction using sentiment analysis: an alert system for city authorities

Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%; with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words

A framework for implementing best laboratory practices for non-integrated point of care tests in low resource settings

The method we respond to pandemics is still inadequate for dealing with the point of care testing (POCT) requirements of the next large epidemic. The proposed framework highlights the importance of having defined policies and procedures in place for non-integrated POCT to protect patient safety. In the absence of a pathology laboratory, this paradigm may help in the supply of diagnostic services to low-resource centers. A review of the literature was used to construct this POCT framework for non-integrated and/or unconnected devices. It also sought professional advice from the Chemical Pathology faculty, quality assurance laboratory experts and international POCT experts from the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). Our concept presents a comprehensive integrated and networked approach to POCT with direct and indirect clinical laboratory supervision, particularly for outpatient and inpatient care in low-resource health care settings.https://ifcc.org/ifcc-communications-publications-division-cpd/ifcc-publications/ejifcc-journal/am2024Chemical PathologySDG-03:Good heatlh and well-bein

Edge intelligence in private mobile networks for next generation railway systems

The integration of Private Mobile Networks (PMN) with edge intelligence is expected to play an instrumental role in realizing the next generation of industry applications. This combination collectively termed as Intelligent Private Networks (IPN) deployed within the scope of specific industries such as transport systems can unlock several use-cases and critical applications that in turn can address rising business demands. This article presents a conceptual IPN that hosts intelligence at the network edge employing emerging technologies that satisfy a number of Next Generation Railway System (NGRS) applications. NGRS use-cases along with their applications and respective beyond 5G (B5G) enabling technologies have been discussed along with possible future research and development directions that will allow these promising technologies to be used and implemented widely

Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached$8. 8 trillion (95% uncertainty interval UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this,$13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and$1.4 billion was repurposed from existing health projects. $3.1 billion (22.4$2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7$1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd