Spatial distribution and characterization of consumers of E-grocery services in sicily: Insights for sustainable urban logistics

The present research aims to provide new insights into e-commerce patterns. The study investigates the correlations between variables related to demographics, residence and online food and beverage purchasing habits. Data was acquired through the administration of an online questionnaire in 2022. The results helped identify the location of the city most affected by the analyzed phenomenon. The novelty of the research focused on the analyzed area of island type characterized by the strong food and wine traditions and the need to want to analyze what possible effects the growth in demand for e-grocery may have on certain parts of the city. The results laid the foundations for more profound studies on demand for e-grocery relative to an island context. Moreover, the findings make it possible to investigate in subsequent steps different types of correlations between socio-demographic and spatial variables connected to the dislocation of homes and the main poles of attraction for the purchase of physical and virtual food and drink markets

A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques

An Integrated Approach to Analysing the Urban Growth Patterns: The Case of Sialkot, Punjab, Pakistan

Urban growth is a worldwide phenomenon, and urbanisation is increasing rapidly, particularly in developing countries. The high pace of unmanaged urbanisation and consequent low-density urban sprawl poses severe challenges to most big cities globally. Such growth features are primarily contributing to haphazard changes in land uses, leading to agricultural loss. This research adopts an integrated approach to analysing the urban growth patterns in Sialkot, Pakistan. It utilises Landsat satellite data and examines the change of land use and land cover (LULC) over 28 years (1990 - 2018). It estimates the agricultural area converted into built-up area during this time frame. Moreover, a spatiotemporal saturation analysis is also performed to analyse the nature of urban growth further. This change analysis is then compared to urban growth strategies introduced under previous urban master plans. The results indicate that the built-up area of Sialkot city has increased from 2,786.49 ha (28.89%) to 7,191.63 ha (74.56%) during the years 1990 - 2018. In comparison, the agriculture area has reduced from 69.5% to 24.84%. Similarly, the saturation value has decreased from 0.85 to 0.75, depicting the city is moving towards urban sprawl. The policy review and interview results indicate a lack of focus toward implementation of urban master plans, which has contributed to ribbon development in Sialkot. The study provides recommendations for concerned urban planning authorities to control urban sprawl in Sialkot

Visual Twin for Pipeline Leak Detection

We describe a visual digital twin system to allow for both operation and training of a data-driven pipeline leak detection system. We show system design in terms of its data inputs and the software system which incorporates this data in real time. This system allows visualization of pipeline data and machine learning-driven leak detection in a pipeline sitting in a subsea context. The intended purpose of the system is to both train operators of the leak detection system in its use and also provide high situational awareness to those tasked with monitoring pipeline deployments. The visual digital twin system uses gaming engine technology to achieve high visual quality. We also construct a novel software system enhancement to incorporate live data streams into the gaming engine environment. This allows real-time driving of gaming engine visualization elements with which we may augment the gaming engine environment. In terms of visualization, we focus on addressing problems of large ranges of multiple scales and providing high situational awareness which minimize operator fatigue and cognitive load. We show how multiple camera views in combination with a convenient user interface can help to address these issues. We demonstrate a digital twin system for leak detection. We show its realtime operation in a gaming engine environment with the ability to instantaneously incorporate outside data sources into the visualizations. We demonstrate using simulated pipeline flow data from sensors such as pressure, temperature, etc. This is visualized in the context of a subsea pipeline on a sea floor. Given the large range of scales, we demonstrate how we can view both the full kilometer scale pipeline and smaller subsections in the context of specific sensor data streams. The overall system demonstrates a novel combination of advanced software systems which incorporates real-time data stream with visualization using a high-fidelity gaming engine. The data used represents a leak detection scenario where both operator training and situational awareness are key desired outcomes

UNDERSTANDING FACTORS OF USING PUBLIC TRANSPORTATION AMONG WOMEN IN KUALA LUMPUR

In recent decades, the percentage of women working outside their homes has ascended in many developing countries depicts the needs on the understanding of how women travel daily. Many claims, public transportation facilities, and infrastructure related to it do not consider the needs of women travelers but fit men's standards. As a result, many face difficulties became dependent on men for traveling or facing safety-related issues that deter them to use public transportation independently. Thus, this study is to identify factors of public transportation usage among women users in Kuala Lumpur. The study has able to identify three main attributes in public transportation usage factors that influence women users. Using the Structural Equation Modelling (SEM) researchers have found that situational attributes have a larger influence on public transportation frequency, in the use of public transportation and surrounding condition of the public transportation in the mode choice decisions of their travel preferences.

Exploring Minecraft in the primary school syllabus for enhancing Arabic learning: a systematic literature review

This study provides a systematic literature review of the research done in exploring Minecraft in the primary school syllabus for enhancing learning through Arabic published since 2019 until 2023. Starting from 12,247 sources, 30 articles were selected using predefined selection criteria. The documents were analysed and coded using the categories: Minecraft, Arabic language and its challenges, primary school syllabus, context, role of technology, pedagogical practice, and learning impact. The searching based on three majors’ authentic sources which are 1) Mendeley, 2) JSTOR and 3) Taylor & Francis. That information allowed an identification of major educational outcomes related to the integration of Minecraft will enhance the learning for Arabic language in an effective way and will improve the pedagogy ways and teaching method among educators and students. In addition, the study contributes with a set of identified research gaps and recommendations for future research. As a results, 30 articles showed that by using Minecraft as a gamification can enhance students' understanding on Arabic Learning

Visual Twin for Pipeline Leak Detection

We describe a visual digital twin system to allow for both operation and training of a data-driven pipeline leak detection system. We show system design in terms of its data inputs and the software system which incorporates this data in real time. This system allows visualization of pipeline data and machine learning-driven leak detection in a pipeline sitting in a subsea context. The intended purpose of the system is to both train operators of the leak detection system in its use and also provide high situational awareness to those tasked with monitoring pipeline deployments. The visual digital twin system uses gaming engine technology to achieve high visual quality. We also construct a novel software system enhancement to incorporate live data streams into the gaming engine environment. This allows real-time driving of gaming engine visualization elements with which we may augment the gaming engine environment. In terms of visualization, we focus on addressing problems of large ranges of multiple scales and providing high situational awareness which minimize operator fatigue and cognitive load. We show how multiple camera views in combination with a convenient user interface can help to address these issues. We demonstrate a digital twin system for leak detection. We show its realtime operation in a gaming engine environment with the ability to instantaneously incorporate outside data sources into the visualizations. We demonstrate using simulated pipeline flow data from sensors such as pressure, temperature, etc. This is visualized in the context of a subsea pipeline on a sea floor. Given the large range of scales, we demonstrate how we can view both the full kilometer scale pipeline and smaller subsections in the context of specific sensor data streams. The overall system demonstrates a novel combination of advanced software systems which incorporates real-time data stream with visualization using a high-fidelity gaming engine. The data used represents a leak detection scenario where both operator training and situational awareness are key desired outcomes

Synthesis, characterization, and optoelectronic properties of phenothiazine-based organic co-poly-ynes

We present the synthesis and characterization of seven new organic co-poly-ynes P1-P7 incorporating the phenothiazine (PTZ) motif and evaluate their optoelectronic properties and performance in polymer light-emitting diodes and polymer solar cells (PLEDs/PSCs). The co-poly-ynes were obtained in moderate to high yields via Sonogashira coupling reactions and characterized using analytical, spectroscopic and electrochemical techniques and complementary quantum-chemical modelling. The materials show strong optical absorption in the visible region of the spectrum and most also show strong emission with quantum yields in the range of 13-41% relative to rhodamine 6G (R6G). PLED devices based on the co-poly-ynes were prepared and the most promising was measured to have a brightness of up to 1.10 × 104 cd m-2. PSCs based on donor materials incorporating some of the polymers were prepared and demonstrated power conversion efficiencies of up to 0.24%. This journal is </p

An ensemble docking-based virtual screening and molecular dynamics simulation of phytochemical compounds from Malaysian Kelulut Honey (KH) against SARS-CoV-2 target enzyme, human angiotensin-converting enzyme 2 (ACE-2)

The human angiotensin-converting enzyme 2 (ACE-2) receptor is a metalloenzyme that plays an important role in regulating blood pressure by modulating angiotensin II. This receptor facilitates SARS-CoV-2 entry into human cells via receptor-mediated endocytosis, causing the global COVID-19 pandemic and a major health crisis. Kelulut honey (KH), one of Malaysian honey recently gained attention for its distinct flavour and taste while having many nutritional and medicinal properties. Recent study demonstrates the antiviral potential of KH against SARS-CoV-2 by inhibiting ACE-2 in vitro, but the bioactive compound pertaining to the ACE-2 inhibition is yet unknown. An ensemble docking-based virtual screening was employed to screen the phytochemical compounds from KH with high binding affinity against the 10 best representative structures of ACE-2 that mostly formed from MD simulation. From 110 phytochemicals previously identified in KH, 27 compounds passed the ADMET analysis and proceeded to docking. Among the docked compound, SDC and FMN consistently exhibited strong binding to ACE-2's active site (-9.719 and −9.473 kcal/mol) and allosteric site (-7.305 and −7.464 kcal/mol) as compared to potent ACE-2 inhibitor, MLN 4760. Detailed trajectory analysis of MD simulation showed stable binding interaction towards active and allosteric sites of ACE-2. KH's compounds show promise in inhibiting SARS-CoV-2 binding to ACE-2 receptors, indicating potential for preventive use or as a supplement to other COVID-19 treatments. Additional research is needed to confirm KH's antiviral effects and its role in SARS-CoV-2 therapy, including prophylaxis and adjuvant treatment with vaccination

Age-sex differences in the global burden of lower respiratory infections and risk factors, 1990-2019 : results from the Global Burden of Disease Study 2019

BACKGROUND: The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across all age groups by sex, for 204 countries and territories. METHODS: In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466-469, 470.0, 480-482.8, 483.0-483.9, 484.1-484.2, 484.6-484.7, and 487-489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4-B97.6, J09-J15.8, J16-J16.9, J20-J21.9, J91.0, P23.0-P23.4, and U04-U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23 109 site-years of vital registration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian meta-regression tool, to analyse age-sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and claims and inpatient data. Additionally, we estimated age-sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors. FINDINGS: Globally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240-275) LRI incident episodes in males and 232 million (217-248) in females. In the same year, LRIs accounted for 1·30 million (95% UI 1·18-1·42) male deaths and 1·20 million (1·07-1·33) female deaths. Age-standardised incidence and mortality rates were 1·17 times (95% UI 1·16-1·18) and 1·31 times (95% UI 1·23-1·41) greater in males than in females in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126·0% [95% UI 121·4-131·1]) and deaths (100·0% [83·4-115·9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest decline was observed for LRI deaths in males younger than 5 years (-70·7% [-77·2 to -61·8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53·0% [95% UI 37·7-61·8] in males and 56·4% [40·7-65·1] in females), and more than a quarter of LRI deaths among those aged 5-14 years were attributable to household air pollution (PAF 26·0% [95% UI 16·6-35·5] for males and PAF 25·8% [16·3-35·4] for females). PAFs of male LRI deaths attributed to smoking were 20·4% (95% UI 15·4-25·2) in those aged 15-49 years, 30·5% (24·1-36·9) in those aged 50-69 years, and 21·9% (16·8-27·3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21·1% (95% UI 14·5-27·9) in those aged 15-49 years and 18·2% (12·5-24·5) in those aged 50-69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11·7% (95% UI 8·2-15·8) of LRI deaths. INTERPRETATION: The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children younger than 5 years was clearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, including promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities. FUNDING: Bill & Melinda Gates Foundation