Fast Pace Research Articles

The Role of Edge Computing in Driving Real-time Personalized Marketing: a Data-driven Business Perspective

The fast pace of marketing innovation in the digital age has sparked a growing need for instant, personalized conversations with customers. This study investigates the vital significance of edge computing in elevating real-time personalized marketing by reducing latency, improving speed of data processing, and allowing companies to quickly, data-driven decisions. Through a rigorous investigation, the research reports on the ways edge computing solves problems linked to cloud solutions, like bandwidth issues and privacy matters, providing a more effective framework for localized data management. Conducting a mixed-methods study using case studies and quantitative performance metrics, this research showcases important enhancements in consumer engagement and conversion rates amongst enterprises that have implemented edge computing for their personalized marketing efforts. Findings indicate a reduction of 30% in data transfer latency and a 25% higher level of interactions with customers in real time as a result of marketing activities made possible by the edge. The paper wraps up with a look at the innovation of this method in the face of technological innovations and presents recommendations for firms attempting to raise consumer engagement via personalized marketing approaches facilitated by edge computing.

Can subsidy policies achieve fuel cell logistics vehicle (FCLV) promotion targets? Evidence from the beijing-tianjin-hebei fuel cell vehicle demonstration city cluster in China

Fuel cell logistics vehicle (FCLV) plays a pillar role in achieving carbon neutrality in the freight sector. Due to high costs and imperfect infrastructure, millions of subsidies are granted to develop FCLV and fulfill promotion targets set by the government. To examine the effectiveness of this action, the study proposes a tripartite evolutionary game theory (EGT) model to simulate the interactions between local governments, private investors, and consumers, and explores the fulfillment of promotion targets in the Beijing-Tianjin-Hebei fuel cell vehicle demonstration city cluster. The obtained results show that: (i) the FCLV promotion targets of demonstration cities except Tangshan could be substantially achieved under the current subsidy; (ii) purchase subsidies for consumers can help achieve promotion targets at a faster pace, while construction subsidies for investors have a limited incentive effect, so more non-financial incentives need to be provided to investors, such as innovative operating models and priority approval of land for hydrogen refueling station construction; (iii) a comparison of subsidies in different cities reveals that Beijing's subsidies are the most efficient and that there is an optimal allocation between consumers and private investors that allows for the faster accomplishment of promotion targets without increasing the fiscal burden.

Causal reasoning in Software Quality Assurance: A systematic review

Context:Software Quality Assurance (SQA) is a fundamental part of software engineering to ensure stakeholders that software products work as expected after release in operation. Machine Learning (ML) has proven to be able to boost SQA activities and contribute to the development of quality software systems. In this context, Causal Reasoning is gaining increasing interest as a methodology to go beyond a purely data-driven approach by exploiting the use of causality for more effective SQA strategies. Objective:Provide a broad and detailed overview of the use of causal reasoning for SQA activities, in order to support researchers to access this research field, identifying room for application, main challenges and research opportunities. Methods:A systematic review of the scientific literature on causal reasoning for SQA. The study has found, classified, and analyzed 86 articles, according to established guidelines for software engineering secondary studies. Results:Results highlight the primary areas within SQA where causal reasoning has been applied, the predominant methodologies used, and the level of maturity of the proposed solutions. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain, but other tasks like testing are rapidly gaining popularity. Both causal inference and causal discovery are exploited, with the Pearl’s graphical formulation of causality being preferred, likely due to its intuitiveness. Tools to favour their application are appearing at a fast pace – most of them after 2021. Conclusions:The findings show that causal reasoning is a valuable means for SQA tasks with respect to multiple quality attributes, especially during V&V, evolution and maintenance to ensure reliability, while it is not yet fully exploited for phases like requirements engineering and design. We give a picture of the current landscape, pointing out exciting possibilities for future research.

Ciprofloxacin hydrochloride-loaded ocular silk fibroin liposomes: Formulation, characterisation, in vitro cytotoxicity, and antimicrobial activity

Ocular drugs have low absorption because of the unique environment in the eye, making ocular drugs one of the most challenging pharmaceutical initiatives. Liposomes have shown to be a promising ocular drug delivery system over the years because of enhanced drug absorption, biocompatibility, and biodegradability. Utilising a mucoadhesive material alongside liposomes could be a promising strategy to increase the therapeutic efficacy of ocular drugs. The present study aimed to develop a silk fibroin (SF)-coated liposomal formulation as an ocular drug delivery system. Regenerated silk fibroin (a novel biopolymer) was coated on ciprofloxacin hydrochloride-loaded liposomes (CPH-SFLs). Studies were carried out on the morphology, drug encapsulation efficiency, and in vitro drug release. Human corneal epithelial cells (HCEC) were used to examine the cellular adherence and cytotoxicity of CPH-SFLs. CPH-SFLs had an average particle size of 183 ± 3 nm as opposed to 169 ± 4 of blank SFLs. CPH-loaded SFLs lacked the endothermic peak of CPH at 150 °C, indicating that the CPH molecules were trapped in the SF polymeric grid. In the case of the formulations, 25% to 45% of the medicine was released at a relatively fast pace over the course of the first 4 hours and then at a slower rate over the course of the next 12-24 hours. CPH-SFLs demonstrated sustained drug release and high in vitro ocular penetration of CPH. The MTT test was conducted to gauge the viability of HCECs, cell viability was higher than 85%, demonstrating that CPH-SFLs had no adverse effects on HCEC. The observed CPH-SFL adhesions to HCECs were swift and persistent, like the cellular uptake of CPH-SFLs by HCECs. When compared to CPH-solution, the produced formulation CPH-SFLs demonstrated significantly (P<0.0001) greater susceptibility. The studies concluded that SF-coated liposomes could be the most viable ocular drug delivery in comparison to conventional eye drops.

NOAAGlobalTemp version 6: An AI-based global surface temperature dataset

Abstract The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.

Understanding The Productivity and Loyalty of Millennial Employees in The Hotel Business: Preliminary Evidence

The company's ability to survive in a highly competitive environment relies heavily on employee productivity and loyalty. Contemporary millennial culture in the hotel industry is a topic of current societal discussion. Therefore, researchers have a keen interest in this area. The population for this study consists of all the employees belonging to the millennial generation working in the Front Office department of a 4-star hotel in Jakarta. The approach employs a combination of two distinct variables using both quantitative and qualitative methodologies. A total of 18 millennial employees from the front office department were surveyed, along with three interviewees consisting of the front office manager, assistant front office manager, and front office supervisor. Based on data collected through interviews, observations, and surveys, researchers have discovered that the millennial generation's front office department exhibits excellent employee productivity, characterised by a strong sense of enthusiasm and ambitious goals. However, the loyalty of the millennial generation towards their work is distinct. These employees frequently switch companies not due to a dislike for corporations, declining performance, or a poor work environment, but rather because they seek to gain extensive experience, build strong professional networks, and aspire to attain higher positions at a faster pace.

An Integrated Depok Smart City Evaluation

Given the complicated pressures brought on by the fast pace of urbanization, innovative and long-lasting solutions to the issues arising from urban expansion are needed. To ensure a greater standard of life for their citizens and make sustainable growth one of their long-term goals, cities will need to make more inventive, persistent, and successful changes to their infrastructure. Nonetheless, smart cities require complex solutions to problems involving ICT, economics, government, social issues, the environment, and transportation. The sustainability of smart cities is now a topic that academics, environmental policymakers, and governmental organizations are more interested in. Depok's smart city must be evaluated to determine its capacity to fulfill the desired vision to help implement the Movement Towards 100 Smart Cities. This study offers an evaluation approach for the Depok smart city. Three indices were used to construct an integrated evaluation approach: the IMD Smart City Index 2023, The Cities of the Future Index, and the Global Power City Index. None of the indexes' results include all six of the Depok Smart City's necessary dimensions. Thus, the advice was to merge the three indices into an integrated evaluation approach for evaluating the six primary dimensions of the Depok Smart City. The results of this study also offer a sample measurement statement according to Depok Smart City. Furthermore, follow-up actions that the government or stakeholders can take to improve Depok's smart city performance include implementing the integrated matrix indicators and evaluating their validity and relevance in the real world.

Digitalisation of gambling harm? Gambling consumption, negative consequences, and clinical characteristics among Swedish help-seekers

Gambling is connected to a range of harms and negative consequences. Gambling is also evolving at a fast pace, particularly in digital environments. Understanding the effects of this on-going change and digitalisation of gambling markets requires systematic measuring of gambling consumption patterns, clinical characteristics, and gambling-related harms.The current study compares two cohorts of help-seekers for gambling in Sweden (2019; 2023), recruited via the Swedish national helpline. The aim is to assess harms and consumption across the two cohorts at a time of increased offer of digital gambling in Sweden. We used eight different screens to measure gambling-related behaviours and consequences, including the Gambling Disorder Identification Test (GDIT). Results were analysed using descriptive statistics, and differences across the two cohorts were tested for statistical significance.The results show that across 2019–2023, gambling consumption has intensified. Online EGMs are by far the most prevalent form of gambling among help-seekers. We also found that the experience of co-morbidities as well as most gambling-related harms were common in both cohorts, with some statistically significant increases in 2023. Harms were more commonly experienced amongst those who engaged in online EGM gambling in comparison to those who did not.The results suggest that the digitalisation of gambling and gambling products appears to be associated with a variety of harmful consequences. The results also show the importance of systematic and comprehensive measurement to capture different severity levels of harms and their wide range amongst those who gamble.

The labor market effects of facilitating social security contributions under part-time employment contracts: Evidence from Colombia

We examine the impact of homogenizing the tax base between part-time and full-time labor contracts in a context with high informality. Using employer–employee matched administrative records and household survey data, we estimate the effects of a reform that eliminated a wedge in regulatory costs across different work schedules in Colombia, reducing the relative costs of formal part-time employment. We find that the reform increased the probability of entering the formal sector for previously informal part-time workers (the target population). Using pre-reform variation in firms’ demand for workers eligible for the policy, we find a 5.5 % increase in formal employment at firms more exposed to the policy. Mean wages at these firms rose by 0.88 % after the reform relative to those at firms that tend to hire fewer workers with no formal sector experience. Firms with more exposure to the reform also experienced higher churn, consistent with the policy creating incentives for firms to rotate across workers at a faster pace.

A survey of video-based human action recognition in team sports

Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.

Osteoporosis classification using VGG-16 and logistic regression: A radiograph and BMD data approach

Osteoporosis is an ailment associated with the bones, in which the bone resorption takes place at a much faster pace as compared to the formation of bones, eventually leading to the deterioration of bone mineral density (BMD). Ultimately, it adversely affects the strength of bones. To determine different diseases, deep learning is used in almost every sector of healthcare. In the context of Osteoporosis, there are numerous machine learning technologies that have been utilized for early detection of the disease. Certainly, these techniques provided great accuracy, but their scope of study was limited exclusively to individual factors. This paper proposes a model which studies multiple aspects leading to the early prognosis of disease, thus increasing the reliability. The aspects are Bone Density Measure, the X-rays of affected bone, lifestyle of the patient which may include medical history if any, fracture status and the specific bone. The dataset used for the research contains 2000 X-rays in total and 500 BMD reports of 500 distinct patients. in Logistic regression is used for the BMD based classification, where the accuracy achieved is 98.66%, with a recall of 97%, precision of 100% and f1-score of 98% for osteoporotic category. The VGG16 model used for the classification based on image dataset achieves the accuracy of 97.19% which is acceptable comparative to existing methods.

Incorporating C3N5 and NiCo2S4 to Form a Novel Z-Scheme Heterojunction for Superior Photocatalytic Degradation of Norfloxacin

Due to a combination of increased urbanization, industrialization, and population growth, many pharmaceutical pollutants are currently being discharged into the environment. A possible strategy is critical for eliminating antibiotic pollutants from the environment, and photocatalysis has been generally recognized as an excellent method for successfully degrading antibiotics at a faster pace. In this work, we employed a hydrothermal synthesis approach to create a novel C3N5/NiCo2S4 Z-scheme-based heterojunction with better interfacial charge transfer and used it as a catalyst for the degradation of norfloxacin antibiotic. The optimized 1:1 C3N5/NiCo2S4 (50CN/NCS) shows the highest photocatalytic efficiency of 86.5% in 120 min towards the degradation of norfloxacin (NOR). Such an effective performance can be attributed to the high responsive nature of photocatalyst in the visible region and superior transfer of interfacial charges via Z-scheme transfer in heterojunction. The high charge transfer efficiency and reduced recombination of charge carriers in heterojunction was confirmed by EIS and PL results. The influence of some key factors such as pollutant concentration, catalyst dosage, pH, and coexisting ions on the photocatalytic activity is also investigated in this work. The optimized heterojunction 50CN/NCS also degraded 89.1%, 78.3%, and 93.2% removal of the other pollutants CIP, SDZ, and BPA, respectively.

Prioritizing Crip Futures: Applying Crip Theory to Create Accessible Academic Experiences in Higher Education

The purpose of this paper is to explore how crip theory can be applied in higher education to resist ableism and create accessible academic experiences for disabled college students. Using personal narratives about psychological and learning disabilities as our anchors, we describe how ableism shapes college students' academic experiences, such as the fast pace of academic rigor and faculty perceptions of accommodations providing an unfair advantage for disabled students. We then review crip theory tenets, including fluidity, interdependence, and crip time, and offer possibilities for applying those tenets in practice. Some of these possibilities include collaborative conversations between students and disability services providers, embracing imperfection, co-constructing course access options, and de-stigmatizing non-normative academic timelines. We frame our thoughts about practice through the notion of crip manifestos (Kafer, 2017), which suggest both practical considerations grounded in current ableist realities and radical practices that envision crip futures.

Effective Utilization of Pre Engineering building (PEB) construction for Development of Rapid Healthcare facilitates in Medical Emergency

World is developing at a faster pace in all spheres of technology. The parameters whichdetermine the development have changed a lot in the recent past and will keep changing in future too.Rapid rise in levels of education, high rates of technological innovations and applications, ever faster andcheaper communication that dissolves physical and social barriers both within countries andinternationally, greater availability and easier access to information, and the further opening up of globalmarkets are the set of catalytic forces that is accelerating the speed of social change throughout theworld.Pre Engiering building is one of the best application of fast track construction Pre-Engineered Buildingconcept involves the steel building systems which are predesigned and prefabricated .The presentconstruction methodology calls for the best aesthetic look, high quality fast construction, costeffective innovative touch. One has to think for alternative construction system like pre-engineeredsteel buildings. In recent years, the introduction of Pre Engineered Building (PEB) concept in the designof structures has helped in optimizing design. In this report the detail explore of pre engineering as afast tack construction’s application discuss . As the world battles the ongoing COVID-19 pandemic, ithas become very clear that societies depend on functioning healthcare facilities. The sufficientavailability of hospital beds, healthcare staff, protective gear or ventilators can make all the differencebetween a situation that is still manageable and a severe crisis. for this infrastructure facilities should beprepare in short time ,For this EPS Pannel and UPVC panels will be use for rapid construction ofhealthcare facilities

Insight of recent artificial intelligence-based strategy to effectively screen COVID-19

&lt;p&gt;&lt;span lang="EN-US"&gt;The recent era of pandemic by corona virus disease (COVID-19) has witnessed a faster evolution of various technological solution to thwart the life-threating situation. The most important step was to select a faster mode of screening COVID-19 using chest x-ray (CXR) which could be actually ten folds faster than conventional invasive screening methods. However, the method of determining the presence of COVID-19 from CXR is critically challenging owing to the dynamic and complex nature of disease. Such problem is attempted to be solved by harnessing the potential of artificial intelligence (AI). Hence, this paper contributes towards discussion of most recent and current implementation strategies formulated by AI models towards diagnosing COVID-19. The study outcome of this paper yields an interesting learning outcome to show that AI models’ adoption is increasing in faster pace and yet challenges do exist till date. The outcome of study will assist in better adoption of AI models towards screening COVID-19.&lt;/span&gt;&lt;/p&gt;

Milking the label: The impact of category labels on consumer liking and category fit of non-dairy milks

The influence of labels on expectation is well documented, but the effect of category labels on consumer perception is not well understood. The fast pace of emerging product categories highlights the importance of ensuring that category labels meet consumer expectations. To examine the impact of category expectations on liking, 384 consumers were assigned one of four conditions where they were told they were drinking either milk (broad condition), plant-based milk (intermediate condition), almond milk (specific condition), or given no information (control group). All groups were given six almond milks and asked to rate their liking on a 9-point hedonic scale. Consumers in the broad, intermediate, and specific conditions were also asked to rate category fit on a 9-point Likert scale. Category label had a significant impact on overall liking, with the specific condition having significantly higher liking than the control, as well as a significantly higher liking than the intermediate and control conditions. External preference mapping showed that the sensory drivers of liking were constant across the four conditions, but the sensory drivers of category fit changed depending on which category condition participants were part of. In the broad condition, sweetness and vanilla flavour contributed to category fit. Creaminess was more important for the intermediate condition, while nutty and roasted flavours determined category fit for the specific condition. Our results suggest that increased specificity in category labels can increase product liking, underscoring the importance of sensory evaluation for understanding the link between category labels and consumer perception.

A Comprehensive Review on Redundancy usage of Animal models in Novel Drug testing

Millions of animals are used for laboratory research and development purposes each year; these living creatures endure suffering before being killed. Animal testing has other drawbacks in addition to bioethical concerns, such as high costs, the need for specialized labor, permission, and time commitment. As a result, Researchers have worked to make several substitute techniques that may replace using animals in tests. These techniques potentially save millions of animals' lives every year, in addition to providing precise findings. In- silico pharmacology methods are used in conjunction with computer and robotics research methodologies to develop alternative techniques for animal experimentation. In this context, several approaches are listed. Certain techniques are just as trustworthy as in-vivo animal models when it comes to precisely predicting the actions of drugs. These substitute techniques also have a number of benefits over using animals in experiments. Given that up to 90% of clinical trials fail, there is rising ethical concern over the use of excessive animals in drug research and development. The non-animal models described in this article have the potential to accelerate the medication delivery process at a faster pace. This review provides researchers and readers who are not aware of about predictive in- silico techniques a basic knowledge of the underlying theory. New advancements, software, acceptability hurdles, integrated techniques, and current applications are all covered, with links to more resources provided for each area. Furthermore, these alternative methods offer a variety of advantages over experimental animals.

Drunk in China? The Imperative for Effective Interventions Against Alcohol Abuse

Background: Alcohol use and abuse remain prevalent in China, though mounting evidence shows that even drinking in moderation is detrimental to health. While many countries’ intake is on the decline, China’s alcohol consumption is rising fast and is on the path to overtaking countries like the United States, even on a per capita level. Objectives: This paper aims to analyze the danger of lax enforcement of laws and regulations against alcohol use and abuse and underscores the imperative for effective health interventions to curb problematic alcohol consumption in China. Results: Different from their Western counterparts, Chinese drinkers often consume more spirit with a high percentage of alcohol in social settings that encourage the “ganbei culture”—making toasts with alcohol filled to the brim and downed in one go to show respect—which perpetuates excessive drinking at a fast pace. Though the country has various laws and regulations in place to curb problematic alcohol consumption, like workplace drinking, their impacts are dismal. Considering that China has 1.4 billion people, the country’s uncurbed alcohol consumption trend could have a detrimental effect on national strategic objectives like “Healthy China 2030” and international ones like the Sustainable Development Goals. To further compound the situation, prevalent campaigns promoted by liquor companies—like alcohol-infused coffee, chocolate, and ice cream—may groom young people to develop alcohol consumption habits, if not addictions, for generations to come. Conclusions: We developed the Framework of 5Vs of China’s “Ganbei Culture to shed light on the issue, with the hope that it, along with the overarching insights of this paper, can assist health professionals and policymakers in better guarding and improving public health against the harms of alcohol use and abuse in China and beyond.

Association between fast eating speed and metabolic dysfunction-associated steatotic liver disease: a multicenter cross-sectional study and meta-analysis

BackgroundWith the fast pace of modern life, people have less time for meals, but few studies have examined the association between the habit of fast eating and metabolic diseases.ObjectiveCombining the results of the current study and the prior ones, we aimed to investigate the possible relationship between fast eating and the risk of metabolic dysfunction-associated steatotic liver disease (MASLD).MethodsThis is a sub-analysis of a multicenter cross-sectional study of 1965 participants investigated the association between fast eating and MASLD in Chinese. Fast eating was defined as meal time less than five minutes and participants were divided into three categories based on their self-reported frequency of fast eating: ≤1 time/month, ≤1 time/week and ≥2 times/week. We further conducted a literature search for available studies published before November, 2023 as well as a meta-analysis to investigate the association between fast eating and MASLD.ResultsThe proportion of MASLD was 59.3%, 50.5%, and 46.2% in participants with fast eating ≥2 times/week, ≤1 time/week and ≤1 time/month, respectively (P for trend <0.001). The frequency of fast eating was independently associated with risk of MASLD after multiple adjustment for sex, age, demographics, smoking and drinking status, BMI and clinical metabolic parameters (OR, 1.29; 95%CI, 1.09–1.53). Participants who ate fast frequently (≥2 times/week) had 81% higher risk of MASLD (P = 0.011). A meta-analysis of five eligible studies confirmed that frequent fast eating was associated with increased risk of MASLD (pooled OR, 1.22; 95%CI, 1.07–1.39).ConclusionsFrequent fast eating was associated with an increased risk of MASLD.

Research on the Influencing Factors of Digital Economy Development in Chinese Cities

In today's context, digital economy has become a key engine of economic growth worldwide, and China, as a developing country with a high population density and a fast pace of technological development, has attracted global attention to the development process of digital economy. In the urban digital economy, the quality of its development is often affected by many factors, such as policy environment, market size, technological innovation and network security, etc. In this paper, we will take this as the background to elaborate, and hope to be able to provide reference for the relevant people.