Massive Problem Research Articles

Excess dietary sugar and its impact on periodontal inflammation: a narrative review.

Sugar is omnipresent in the current food environment and sugar consumption has drastically risen over the past century. Extensive evidence highlights the negative health consequences of consuming excess dietary sugars, leading the World Health Organization (WHO) and the American Heart Association (AHA) to devise guidelines to restrict sugar intake. According to the WHO's Global Oral Health Status Report of 2022, oral diseases and severe periodontitis are a massive public health problem, and dietary sugars are a modifiable risk factor. We conducted a literature review using key databases to summarise the health effects of excessive sugar consumption and their potential role in periodontal inflammation. Available evidence suggests that excess dietary fructose and sucrose can cause low-grade systemic inflammation; and induce dysbiosis in both gut and the oral microbiota. Also, dietary sugar is potentially addictive and hypercaloric and its overconsumption can lead to obesity, metabolic syndrome, and other risk factors for periodontal inflammation. Hence, an unbalanced diet with excess dietary sugars holds the potential to initiate and aggravate periodontal inflammation. In the modern food environment that enables and facilitates a high-sugar diet, adopting a diverse diet and restricting sugar intake according to WHO and AHA guidelines seem beneficial to systemic and periodontal health. Since clinical evidence is limited, future research should study the effectiveness of dietary interventions that control sugar consumption in preventing and managing the global public health problem of periodontal inflammation.

Study on use of recycled waste materials on the performance of asphalt binder and mixes: A comprehensive review

All aspects of daily living frequently involve the use of plastics. Due to the lack of effective waste treatment methods, massive global environmental problems will be caused by the accumulation of waste plastics. The use of waste materials in road building is of tremendous interest since it has the potential to reduce hazards and pollution while conserving natural resources. High-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyethylene terephthalate (PET), ethyl vinyl acetate (EVA), and polyvinyl chloride are the most appropriate and relevant of waste plastics. There are other types of waste materials used in the asphalt such as reclaimed asphalt pavement (RAP), crumb rubber (CR), steel slag, glass powder etc. These all-waste materials have been briefly discussed. In this review article several concerns have been raised about the physical and chemical qualities, of asphalt pavements that have been mixed with waste materials. Using waste materials in asphalt pavements can raise several concerns regarding the physical and chemical qualities of the resulting mixtures. These concerns typically revolve around factors such as durability, performance, environmental impact, and overall sustainability. This research article provides a thorough analysis of the wet and dry procedures used to mix different types of waste plastics. Overall, including waste plastics into asphalt mix improved rutting, fatigue, and moisture resistance. Additionally, recycling waste plastics in this manner helps in reducing environmental pollution and conserving natural resources. Overall, the inclusion of waste plastics in asphalt mixtures can contribute to the development of more sustainable and durable pavements with improved performance in terms of rutting, fatigue, and moisture resistance. There are still certain issues like compatibility and low temperature performances that need to be addressed. To overcome the aforementioned issues, many strategies are used, including the use of nanomaterials, chemical additives, and polymer additives. Additional research should be conducted to investigate the ageing effect, pavement performance monitoring, polymer stability, and the economic and environmental considerations associated with the usage of plastics in asphalt pavements.

Model-Based Co-Clustering in Customer Targeting Utilizing Large-Scale Online Product Rating Networks

Given the widely available online customer ratings on products, the individual-level rating prediction and clustering of customers and products are increasingly important for sellers to create targeting strategies for expanding the customer base and improving product ratings. However, the massive missing data problem is a significant challenge for modeling online product ratings. To address this issue, we propose a new co-clustering methodology based on a bipartite network modeling of large-scale ordinal product ratings. Our method extends existing co-clustering methods by incorporating covariates and ordinal ratings in the model-based co-clustering of a weighted bipartite network. We devise an efficient variational EM algorithm for model estimation. A simulation study demonstrates that our methodology is scalable for modeling large datasets and provides accurate estimation and clustering results. We further show that our model can successfully identify different groups of customers and products with meaningful interpretations and achieve promising predictive performance in a real application for customer targeting.

Dual Domain Decomposition Method for High-Resolution 3D Simulation of Groundwater Flow and Transport

The high-resolution 3D groundwater flow and transport simulation problem requires massive discrete linear systems to be solved, leading to significant computational time and memory requirements. The domain decomposition method is a promising technique that facilitates the parallelization of problems with minimal communication overhead by dividing the computation domain into multiple subdomains. However, directly utilizing a domain decomposition scheme to solve massive linear systems becomes impractical due to the bottleneck in algebraic operations required to coordinate the results of subdomains. In this paper, we propose a two-level domain decomposition method, named dual-domain decomposition, to efficiently solve the massive discrete linear systems in high-resolution 3D groundwater simulations. The first level of domain decomposition partitions the linear system problem into independent linear sub-problems across multiple subdomains, enabling parallel solutions with significantly reduced complexity. The second level introduces a domain decomposition preconditioner to solve the linear system, known as the Schur system, used to coordinate results from subdomains across their boundaries. This additional level of decomposition parallelizes the preconditioning of the Schur system, addressing the bottleneck of the Schur system solution while improving its convergence rates. The dual-domain decomposition method facilitates the partition and distribution of the computation to be solved into independent finely grained computational subdomains, substantially reducing both computational and memory complexity. We demonstrate the scalability of our proposed method through its application to a high-resolution 3D simulation of chromium contaminant transport in groundwater. Our results indicate that our method outperforms both the vanilla domain decomposition method and the algebraic multigrid preconditioned method in terms of runtime, achieving up to 8.617× and 5.515× speedups, respectively, in solving massive problems with approximately 108 million degrees of freedom. Therefore, we recommend its effectiveness and reliability for high-resolution 3D simulations of groundwater flow and transport.

Textile Recycling and Recovery: An Eco-friendly Perspective on Textile and Garment Industries Challenges

The world has been facing a growing crisis in textile waste due to global population growth and improved living conditions, combined with a decrease in the life cycles of textile products. Textile recycling is one of the key aspects for reducing the massive waste problem generated by the fashion and textile industries. Despite the need to develop textile waste recycling industries, acceptable practical interim measures still need to be taken. Textile waste can affect people’s lives economically and environmentally. Producing apparel from natural or synthetic origin can produce pollutants and waste at each stage. In this review article, the effects of the fashion and textile industry have been studied from economic and environmental perspectives. The available technologies and methods for waste recovery and recycling at each stage have been studied, and the uses of products after recycling have been systematically investigated. Despite all the progress made in the waste recycling processes of the fashion industry, many deficiencies and challenges still need to be addressed. Among the most critical challenges are the large scale of industries, the problems of collecting and classifying waste materials, and the presence of contaminants including blends and chemicals. There is also need for more awareness among consumers about the importance of fiber-to-fiber recycling processes, because to date the progress in this field is out of proportion to its necessity. Overall, this article is a valuable resource for anyone interested in understanding the current state of recycling and recovery in the textiles, garment, and fashion industries.

Optimisation of Agriculture Production

The Mechanism for A lack of understanding of ideal agricultural practises, which, if applied, can increase yields at low costs while optimising resource utilisation, is a key cause of agriculture's suffering. Many farmers are abandoning or moving away from farming, particularly agriculture, because they are not receiving a good return on their investment. In other words, they take out enormous loans to buy the tools, seeds, fertilizer, pesticides, and other resources required to grow crops, but the crop output is insufficient to meet the loan total, resulting in severe losses and debt. As a result, a substantial portion of our country's rich land, which was formerly the primary source of GDP, is being wasted, dealing a serious damage to the Indian economy. Through this research, we hope to provide a small but effective answer to this massive problem by assisting farmers in farming optimally—that is, by assisting them in selecting the crop that will generate the best yield given the farm's current soil and climate

Domain-specific Embeddings for Question-Answering Systems: FAQs for Health Coaching

FAQs are widely used to respond to users’ knowledge needs within knowledge domains. While LLM might be a promising way to address user questions, they are still prone to hallucinations i.e., inaccurate or wrong responses, which, can, inter alia, lead to massive problems, including, but not limited to, ethical issues. As a part of the healthcare coach chatbot for young Nigerian HIV clients, the need to meet their information needs through FAQs is one of the main coaching requirements. In this paper, we explore if domain knowledge in HIV FAQs can be represented as text embeddings to retrieve similar questions matching user queries, thus improving the understanding of the chatbot and the satisfaction of the users. Specifically, we describe our approach to developing an FAQ chatbot for the domain of HIV. We used a predefined FAQ question-answer knowledge base in English and Pidgin co-created by HIV clients and experts from Nigeria and Switzerland. The results of the post-engagement survey show that the chatbot mostly understood the user’s questions and could identify relevant matching questions and retrieve an appropriate response.

Stigma, Support, and Ideation About Suicide in Indonesian Twitter: A Topic Modelling Study.

Stigma surrounding suicide is a massive problem in Indonesia. Thus, it is important to study how conversations about suicide take place. We take a machine learning approach and study tweets with suicide keywords to understand how people converse about suicide or express suicide ideation. Tweets with suicide-related keywords were extracted from May to June 2023. 20,057 tweets were subject to topic modelling with an 11-topic solution. While most topics contain negative messages, no purely stigmatizing topics emerge, despite prior research suggesting overwhelming stigma. Various kinds of existential, emotional, and social tweets about suicide take place among Indonesian users, indicating that Indonesian Twitter users utilize the platform to express their thoughts and emotions. Notably, religious-spiritual keywords are highly prevalent, suggesting that in a highly religious society, there is a need for policy makers and awareness campaigns to frame their positive messaging within the society's religious context.

Information and Communication Technologies in Inclusive Education: A Bibliometric Approach in Scopus

The rise of information and communication technologies has changed lifestyles and academic sectors, facilitating the inclusion of diverse students in the classroom. The present study aimed to analyze the bibliometrics in Scopus on ICT in inclusive education between 2002 and 2023. It was a bibliometric analysis that defined the metrics of scientific production. The indicators were obtained from 94 documents chosen from the Scopus database using keywords in English (information and communication technologies, inclusive education). The results show that the year with the highest scientific production was 2022 (n=22; 23.4%). In addition, Spain (21.6%; n=27) is the country with the most publications, as is the University of Seville (n=5). The journal Sustainability Switzerland published 6 (96 citations) papers, with authors Asongu, S.A. and Fernández-Batanero, J.M. being the most cited (88 and 42 citations respectively). From the studies analyzed, it is concluded that the field of ICT in inclusive education has grown in resources, authorship and thematic variety, due to the potential of ICT to solve massive problems, which improves access, equity and participation in education.

Research on Wireless Multimedia Big Data Transmission Model Based on Non-linear Algebraic Equation with the Adaptive Scheduling Algorithm

With the rapid development of computer technology, image processing and automatic book recognition has become a very important and practical field, and this discipline has a broad prospect and great potential. The digital vision system is a multi-plane information analysis method based on the human brain, which is applied to library identification problems by studying several regular changes in the distribution of objects themselves in two-dimensional space and three-dimensional position, in order to improve book quality and reduce costs. In addition, digital reading devices have become an essential part of people's daily learning life, and multimedia retrieval methods have also undergone radical changes, image processing is becoming more and more complex, and algorithms must become more efficient and reliable as the amount of computing increases to a certain level, so as to adapt to this fast and powerful data storage and query needs. Therefore, the trend is to improve the speed and reliability of computer systems. In this research, we provide a nonlinear model of optimal allocation employing an ant colony management strategy, a genetic operator, and an adaptive scheduling algorithm based on topology (dB) to discover the best scheduling scheme and test its feasibility via simulation. It proposes a viable idea for tackling the practical wireless multimedia massive data transmission model problem by applying the method to the real-time processing process in other sectors with high importance and resilience.

Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning

Multi-depot vehicle routing problem with time windows (MDVRPTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive problems instantly. Thus, this article presents a novel reinforcement learning algorithm integrated with a multi-head attention mechanism and a local search strategy to solve the problem efficiently. The routing optimization was regarded as a vehicle tour generation process and an encoder-decoder was used to generate routes for vehicles departing from different depots iteratively. A multi-head attention strategy was employed for mining complex spatiotemporal correlations within time windows in the encoder. Then, a decoder with multi-agent was designed to generate solutions by optimizing reward and observing transition state. Meanwhile, a local search strategy was employed to improve the quality of solutions. The experiments results demonstrate that the proposed method can significantly outperform traditional methods in effectiveness and robustness.

Why data about people are so hard to govern

AbstractHow data on individuals are gathered, analyzed, and stored remains largely ungoverned at both domestic and global levels. We address the unique governance problem posed by digital data to provide a framework for understanding why data governance remains elusive. Data are easily transferable and replicable, making them a useful tool. But this characteristic creates massive governance problems for all of us who want to have some agency and choice over how (or if) our data are collected and used. Moreover, data are co‐created: individuals are the object from which data are culled by an interested party. Yet, any data point has a marginal value of close to zero and thus individuals have little bargaining power when it comes to negotiating with data collectors. Relatedly, data follow the rule of winner take all—the parties that have the most can leverage that data for greater accuracy and utility, leading to natural oligopolies. Finally, data's value lies in combination with proprietary algorithms that analyze and predict the patterns. Given these characteristics, private governance solutions are ineffective. Public solutions will also likely be insufficient. The imbalance in market power between platforms that collect data and individuals will be reproduced in the political sphere. We conclude that some form of collective data governance is required. We examine the challenges to the data governance by looking a public effort, the EU's General Data Protection Regulation, a private effort, Apple's “privacy nutrition labels” in their App Store, and a collective effort, the First Nations Information Governance Centre in Canada.

THE MANAGEMENT OF WAQF IN PERLIS: A STUDY AT STATE ISLAMIC RELIGIOUS COUNCIL OF PERLIS, MALAYSIA

Waqf properties contribute to a huge percentage of societal wealth in several Muslim countries. However, numerous Muslim countries are facing massive socio-economic problems such as poverty, education, and lack of basic healthcare services. This encourages contemporary Muslim scholars to formulate new financing methods that can be used to develop waqf properties and ensure that these modes confirmed with the Shariah. However, these methods are not maximumly been practiced in several states in Malaysia due to problems such as management, lack of funds, customer awareness, and others. Indeed, the applications of some of these financing methods have been proven successful in the development of Waqf properties. This paper attempts to review deeply on the main resources of waqf properties in Perlis, the current development of waqf properties as well as the issue in the management of waqf asset to aid the development of waqf properties in the Perlis Islamic Religious Council. By implementing qualitative research methods, this research design to ensure the finding of this study will give a massive contribution, especially to the Perlis Islamic Religious Council to practice an instrumental approach to developing the waqf properties to meet urgent social needs of society. The study discusses the findings of the current waqf management practices in Perlis specifically on waqf development such as al-mudharabah, al-musharakah, al-muzara’ah and implementation of cash waqf. Based on the findings, there are several methods been used to manage waqf properties in Perlis such as al-Musyakarah, al-Mudharabah, al-Muzaraah and also cash waqf been implemented.

Profile of adverse drug reactions reported via the Continuum+ platform: Results from three-year regional follow-up

In 2017, the Continuum+ platform was launched to provide a monitoring solution to home-based cancer care patients: AKO@dom monitoring. This platform also offers the follow-up of adverse drug reactions (ADRs) via direct notification to regional centers of pharmacovigilance (RCPVs). According to previous studies, the AKO@dom monitoring has successfully maintained treatment at the maximum effective dosage, managing ADRs and patient satisfaction. However, on the pharmacovigilance side, opinions are more divided. Due to the launch of the AKO@dom-PICTO experimentation in December 2021, in which our RCPV takes part, and to provide more data on pharmacovigilance, we decided to conduct a descriptive analysis of cases reported to our RCPV via the Continuum+ platform between 2019 and 2022. During these three years, we analyzed 1070 events, corresponding to 37patients. Patients were primarily women (74.8%) aged around seventy with breast cancer. The most used drugs were tyrosine kinase inhibitors: palbociclib (29.7%), axitinib (16.2%), and cabozantinib (13.2%). Patients had an average of 8 ADRs, including one serious and/or unexpected ADR. Although the Continuum+ platform makes it possible to considerably limit under-reporting in pharmacovigilance, it has shortcomings. The lack of medical elements and context in notifications is a massive problem for analyzing pharmacovigilance reports. Improved access to the platform's medical information for RCPVs and pharmacovigilance training for healthcare professionals would make Continuum+ a helpful tool in pharmacovigilance.

Signal detection algorithms for massive MIMO system

As 5G communication networks are maturing, we have higher and higher requirements for the detection of communication signals. In this paper, for the Massive MIMO system signal detection problem, we mainly summarize the detection algorithms that can be used to replace the traditional ZF and MMSE, so as to avoid large-scale matrix inverse and reduce the computational complexity. It mainly includes the general iterative method, typically represented by SSOR, which makes the transmit signal matrix constantly close to the ideal value by iterating; the other is the level expansion class solution method, which takes the order expansion of the level as the initial value of the iteration to accelerate the convergence rate of the algorithm, typically represented by the MLI algorithm. However, today where the demand for communication is gradually increasing and the number of users is constantly getting larger, the performance of the above algorithms may degrade seriously, so the AI signal detection algorithm is a good alternative, which learns autonomously through deep neural networks, including model-driven and data-driven schemes.

Unraveling the gut-brain connection: The association of microbiota-linked structural brain biomarkers with behavior and mental health.

The gut microbiota can influence human behavior. However, due to the massive multiple-testing problem, research into the relationship between microbiome ecosystems and the human brain faces drawbacks. This problem arises when attempting to correlate thousands of gut bacteria with thousands of brain voxels. We performed brain magnetic resonance imaging (MRI) scans on 133 participants and applied machine-learning algorithms (Ridge regressions) combined with permutation tests. Using this approach, we were able to correlate specific gut bacterial families with brain MRI signals, circumventing the difficulties of massive multiple testing while considering sex, age, and body mass index as confounding factors. The relative abundance (RA) of the Selenomonadaceae, Clostridiaceae, and Veillonellaceae families in the gut was associated with altered cerebellar, visual, and frontal T2-mapping and diffusion tensor imaging measures. Conversely, decreased relative abundance of the Eubacteriaceae family was also linked to T2-mapping values in the cerebellum. Significantly, the brain regions associated with the gut microbiome were also correlated with depressive symptoms and attentional deficits. Our analytical strategy offers a promising approach for identifying potential brain biomarkers influenced by gut microbiota. By gathering a deeper understanding of the microbiota-brain connection, we can gain insights into the underlying mechanisms and potentially develop targeted interventions to mitigate the detrimental effects of dysbiosis on brain function and mental health.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Die bayerische Landtagswahl vom 8. Oktober 2023: Votum für Kontinuität der schwarz-orangenen Koalition

The Bavarian election on October 8, 2023, has led to a shift to the right . The Free Voters and the Alternative for Germany (AfD) are the election winners and are once again making significant gains compared to 2018 . The parties of the Berlin coalition are the election los- ers . The Greens, in particular, received fewer votes than five years earlier, but together with the Free Voters and the AfD, they remain relatively equal behind the still dominant Chris- tian Social Union (CSU) . The CSU remains on its lowest result since 1950 and the SPD achieved its worst result in a German western state . The Liberals are punished the hardest, failing to reach the 5%-threshold for the seventh time in ten parliamentary terms . Massive problems such as rising inflation and energy prices, as well as increasingly serious challenges posed by climate change, migration and increasing right-wing populism, are also putting the wealthy state of Bavaria to the test and allowing state-specific issues to take a back seat in the election . The decisive factors for the election result were the general satisfaction with the work of the Prime Minister, the desire for stability and continuity in difficult times, but also a growing concern that the CSU can no longer cope with the growing problems on its own . That is why the citizens voted for the continuation of the black-orange coalition, which was formed only 19 days after the election .

Imaginação de políticas alternativas para o desenvolvimento indiano antitóxico

ABSTRACT Worsening joblessness in India is put forward here from the multiple and inter-connected argumentative lens of formal-informal dichotomy by labour productivity differential, market size constraint, labour market flexibility, capital inflows, political constraint, corporate industrialization and welfare economic implications. In the process, the limitations of the classic developmental ideas of Kuznets, Lewis and Schumpeter are critiqued. Thereafter, an alternative policy framework is proposed to achieve economic development focused on employment, equity and environmental uplift. The paper shows how the massive unemployment problem can be managed in India’s democratic set up by departing from conventional wisdom about industrialization.

Hauntology of Trash in Environmental Education

Industrialisation in third-world countries has brought about developmentalism that persists in their societal dynamics. Third-world countries have been producers of raw natural resources, yet there are massive consumers of processed products from first-world countries. This exploitation of production and consumption affected how third-world countries, especially Indonesia, perceived environmental education (EE). Massive consumption has led to massive trash and waste; currently, environmental policy in Indonesia focuses solely on the massive trash problem, which is reflected in the country’s education policies. The problem of trash and its management haunts every effort to commence EE. Trash management has become one of the variables used to measure a school’s capability to manage the environment. The variable then becomes a ghost, a spectre of terror, that haunts school policies, including planning, managing, and resolving the trash problem to achieve the “Adiwiyata” title or the like. Admittedly, trash induces terror when it experiences a change in a domain from school to other institutions to the public. It is indubitable that trash management is not only a matter of education but also of public policy and facilities. However, as it stands, trash is implicitly only seen as a means to a prestigious identity: schools flocking to orient themselves towards SDGs, birthing environmental/ecological heroes, and equipping themselves with jargon like “green”, “clean”, and “eco”. Ambiguity persists in commencing EE, either as an obligation or as a mere ceremonial event to celebrate the narratives of SDGs. Therefore, the implementation of SDGs is always haunted by trash, starting from when it is present at school until when it is absent. In the end, is the ideal trash management EE to teach possible amidst the hegemony of the SDGs narrative? In this paper, we argue that using hauntology to analyse the trash problem would reveal gaps in practising EE between the global North and the global South, where a history of developmentalism and consumerism places vast environmental issues, such as the planetary approach and conservation, seemingly too far to reach.