Value Stream Mapping and Simulation Modelling for Healthcare Transactional Process Improvement

Lean management philosophy was originated in Japan from the Toyota production system. The main idea is to determine and eliminate waste. The concept of end-to-end value allows organizations to achieve competitive advantage through best quality product and services through minimum operational cost. These days there is more to be achieved by applying lean to services and transactional processes floors. Lean facilitators are facing challenges when trying to transform an organization to be a lean enterprise because it is possible in production systems, but that is not easier in the services and transactional sectors, which means there are challenges that should be considered. Some of the challenges for the service sector are; complex and mixed value streams, information and people are processed instead of parts and human interaction is a major part of the service sector

The Impact of TQM on Performance Measurement: Empirical Study of Bahraini Private Universities

An investigation on the impact of TQM dimensions on the performance of universities is the core of this paper based on Lecturers perception in Bahrain Private Universities. Also, to investigate the effect of Performance Measurement due to the demographic data of Lecturers in Bahrain Private Universities (Gender, Age, Experience, and Education Level). Correlational and quantitative methods were used in the study. The instrument for this research is the questionnaire that was developed to address all studied variables. The target population was 517 lecturers that work in the Bahrain private universities from 13 universities and with a sample size of 100 lecturers. The researchers used SPSS version 26 for all statistical analyses, and Descriptive analysis included mean and standard deviation computation. Meanwhile, Pearson correlation, regression analysis, T-test, and One Way ANOVA test were incorporated into the inferential analysis. The results show that Continuous Improvement, Education and Training, and Quality of Work Life significantly affect performance measurement, while Resources and Teamwork significantly not contributed to explaining performance measurement. Furthermore, there is no significant effect on Performance Measurement due to Gender and Education Level. Moreover, the results disclosed a significant effect on Performance Measurement due to Age and Experience

Finding hidden semantics of text tables

Combining data from different sources for further automatic processing is often hindered by differences in the underlying semantics and representation. Therefore when linking information presented in documents in tabular form with data held in databases, it is important to determine as much information about the table and its content. Important information about the table data is often given in the text surrounding the table in that document. The table's creators cannot clarify all the semantics in the table itself therefore they use the table context or the text around it to give further information. These semantics are very useful when integrating and using this data, but are often difficult to detect automatically. We propose a solution to part of this problem based on a domain ontology. The input to our system is a document that contains tabular data and the system aims to find semantics in the document that are related to the tabular data. The output of our system is a set of detected semantics linked to the corresponding table. The system uses elements of semantic detection, semantic representation, and data integration. Semantic detection uses a domain ontology, in which we store concepts of that domain. This allows us to analyse the content of the document (text) and detect context information about the tables present in a document containing tabular data. Our approach consists of two components: (1) extract, from the domain ontology, concepts, synonyms, and relations that correspond to the table data. (2) Build a tree for the paragraphs and use this tree to detect the hidden semantics by searching for words matching the extracted concepts. Semantic representation techniques then allow representation of the detected semantics of the table data. Our system represents the detected semantics, as either 'semantic units' or 'enhanced metadata'. Semantic units are a flexible set of meta-attributes that describe the meaning of the data item along with the detected semantics. In addition, each semantic unit has a concept label associated with it that specifies the relationship between the unit and the real world aspects it describes. In the enhanced metadata, table metadata is enhanced with the semantics and representation context found in the text. Integrating data in our proposed system takes place in two steps. First, the semantic units are converted to a common context, reflecting the application. This is achieved by using appropriate conversion functions. Secondly, the semantically identical semantic units, will be identified and integrated into a common representation. This latter is the subject of future work. Thus the research has shown that semantics about a table are in the text and how it is possible to locate and use these semantics by transforming them into an appropriate form to enhance the basic table metadata

Trends in Using IoT with Machine Learning in Health Prediction System

Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.</jats:p

Date Palm Leaflet-Derived Carbon Microspheres Activated Using Phosphoric Acid for Efficient Lead (II) Adsorption

\ua9 2024 by the authors.The removal of lead metals from wastewater was carried out with carbon microspheres (CMs) prepared from date palm leaflets using a hydrothermal carbonization process (HTC). The prepared CMs were subsequently activated with phosphoric acid using the incipient wetness impregnation method. The prepared sample had a low Brunauer–Emmet–Teller (BET) surface area of 2.21 m2\ub7g−1, which increased substantially to 808 m2\ub7g−1 after the activation process. Various characterization techniques, such as scanning electron microscopy, BET analysis, Fourier transform infrared, and elemental analysis (CHNS), were used to evaluate the morphological structure and physico-chemical properties of the CMs before and after activation. The increase in surface area is an indicator of the activation process, which enhances the absorption properties of the material. The results demonstrated that the activated CMs had a notable adsorption capacity, with a maximum adsorption capacity of 136 mg\ub7g−1 for lead (II) ions. This finding suggests that the activated CMs are highly effective in removing lead pollutants from water. This research underscores the promise of utilizing activated carbon materials extracted from palm leaflets as an eco-friendly method with high potential for water purification, specifically in eliminating heavy metal pollutants, particularly lead (II), contributing to sustainability through biomass reuse

Celecoxib exerts protective effects in the vascular endothelium via COX-2-independent activation of AMPK-CREB-Nrf2 signalling

Although concern remains about the athero-thrombotic risk posed by cyclo-oxygenase (COX)-2-selective inhibitors, recent data implicates rofecoxib, while celecoxib appears equivalent to NSAIDs naproxen and ibuprofen. We investigated the hypothesis that celecoxib activates AMP kinase (AMPK) signalling to enhance vascular endothelial protection. In human arterial and venous endothelial cells (EC), and in contrast to ibuprofen and naproxen, celecoxib induced the protective protein heme oxygenase-1 (HO-1). Celecoxib derivative 2,5-dimethyl-celecoxib (DMC) which lacks COX-2 inhibition also upregulated HO-1, implicating a COX-2-independent mechanism. Celecoxib activated AMPKα(Thr172) and CREB-1(Ser133) phosphorylation leading to Nrf2 nuclear translocation. Importantly, these responses were not reproduced by ibuprofen or naproxen, while AMPKα silencing abrogated celecoxib-mediated CREB and Nrf2 activation. Moreover, celecoxib induced H-ferritin via the same pathway, and increased HO-1 and H-ferritin in the aortic endothelium of mice fed celecoxib (1000 ppm) or control chow. Functionally, celecoxib inhibited TNF-α-induced NF-κB p65(Ser536) phosphorylation by activating AMPK. This attenuated VCAM-1 upregulation via induction of HO-1, a response reproduced by DMC but not ibuprofen or naproxen. Similarly, celecoxib prevented IL-1β-mediated induction of IL-6. Celecoxib enhances vascular protection via AMPK-CREB-Nrf2 signalling, a mechanism which may mitigate cardiovascular risk in patients prescribed celecoxib. Understanding NSAID heterogeneity and COX-2-independent signalling will ultimately lead to safer anti-inflammatory drugs

Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP\u27s successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components

Assessment and Management of Atopic Dermatitis in Primary Care Settings

An increasingly common chronic inflammatory skin condition is atopic dermatitis (AD). It exhibits severe itching as well as recurring eczematous lesions. New difficulties for treatment selection and approach occur with the expansion of available therapy alternatives for healthcare professionals and patients.&nbsp; The article highlights recent developments in scientific research on atopic dermatitis diagnosis and assessment that have led to the identification of novel therapeutic targets and the development of targeted therapies, both of which have the potential to completely change the way AD is treated, particularly in a primary care setting

Searching for solar KDAR with DUNE

The observation of 236 MeV muon neutrinos from kaon-decay-at-rest (KDAR) originating in the core of the Sun would provide a unique signature of dark matter annihilation. Since excellent angle and energy reconstruction are necessary to detect this monoenergetic, directional neutrino flux, DUNE with its vast volume and reconstruction capabilities, is a promising candidate for a KDAR neutrino search. In this work, we evaluate the proposed KDAR neutrino search strategies by realistically modeling both neutrino-nucleus interactions and the response of DUNE. We find that, although reconstruction of the neutrino energy and direction is difficult with current techniques in the relevant energy range, the superb energy resolution, angular resolution, and particle identification offered by DUNE can still permit great signal/background discrimination. Moreover, there are non-standard scenarios in which searches at DUNE for KDAR in the Sun can probe dark matter interactions

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics