Past Usage Research Articles

Microbial diversity and secondary metabolism potential in relation to dark alterations in Paleolithic Lascaux Cave

Tourism in Paleolithic caves can cause an imbalance in cave microbiota and lead to cave wall alterations, such as dark zones. However, the mechanisms driving dark zone formation remain unclear. Using shotgun metagenomics in Lascaux Cave’s Apse and Passage across two years, we tested metabarcoding-derived functional hypotheses regarding microbial diversity and metabolic potential in dark zones vs unmarked surfaces nearby. Taxonomic and functional metagenomic profiles were consistent across years but divergent between cave locations. Aromatic compound degradation genes were prevalent inside and outside dark zones, as expected from past biocide usage. Dark zones exhibited enhanced pigment biosynthesis potential (melanin and carotenoids) and melanin was evidenced chemically, while unmarked surfaces showed genes for antimicrobials production, suggesting that antibiosis might restrict the development of pigmented microorganisms and dark zone extension. Thus, this work revealed key functional microbial traits associated with dark zone formation, which helps understand cave alteration processes under severe anthropization.

Treatment with medication for patients with psychosis within 2 days during waiting in the accident and emergency department and its correlation with length of in-patient stay: retrospective database study

Background One of the ‘critical goals’ for psychiatric liaison services is reducing hospitalisation. Psychotropic medication is a treatment for psychosis, although research determining the efficacy of early medication administration is lacking. Aims To determine whether commencing psychotropic medication within 2 days of psychiatric liaison input in the accident and emergency (A&E) department is correlated with length of in-patient psychiatric admissions for patients with psychosis. Method We gathered data on patients presenting to A&E sites covered by South London and Maudsley (SLaM) National Health Service Trust, who were subsequently admitted to and discharged from SLaM psychiatric in-patient wards with discharge diagnosis of psychosis between 2015 and 2020. The analysis set comprised 228 patients waiting in the A&E department under psychiatric liaison care for ≥2 days, of which 140 were started on medication within those 2 days (group A) and 88 were not (group B). Group A was divided into A1 (patients restarted on previous psychotropic medication taken within 1 week) and A2 (others, including those new to psychotropic medication or with past usage). Results Although Kaplan–Meier survival curves with log-rank tests demonstrated no statistically significant difference of in-patient admission duration between groups A and B or groups B1 and B2, further analysis revealed that subgroup A1 had statistically significant shorter admissions than group B (P = 0.05). Conclusions Restarting patients with psychosis on medication they were taking within the week before A&E department attendance, within 2 days of arrival at the A&E department, is associated with statistically significant shorter admissions. The limitation is a relatively small sample size.

비대면 상담서비스 운영 변화와 과제: 국내 사이트를 중심으로

Objectives This study aims to identify operational changes and challenges in the development of non-face- to-face counseling by comparing the past and present usage of contactless counseling in the context of the rapid increase in the use of contactless counseling due to the spread of COVID-19 and the development of information technology. Methods For comparison with the past, we used the survey methodology from a previous study (Kim, Kye-hyun, & Lim, Eun-mi, 2000) and analyzed 113 counseling sites, including five items (‘service target’, ‘service operator’, ‘counseling method’, ‘counseling content’, and ‘usage fee’) and two additional items (‘counselor’s expertise’ and ‘guidance on using non-face-to-face counseling’) as survey indicators. Results First, we found that counseling methods, which were limited in the early days of non-face-to-face coun-seling, are now being diversified through the development of technology and the expansion of media use. Second, we observed a new way of connecting counselors by establishing platforms for non-face-to-face counseling at large portal sites and private counseling organizations, not limited to the free services provided by public in-stitutions in the past. Third, although guidance for non-face-to-face counseling is currently provided, there is a lack of information on the expertise and confidentiality of counselors. Conclusions This study aims to identify the current status of non-face-to-face counseling in Korea by comparing the past and present operation status of non-face-to-face counseling in Korea, and to identify development tasks. It is expected to provide various implications on the need to strengthen the awareness, professionalism, and eth-ical considerations of non-face-to-face counseling.

Fear of COVID-19 Diagnosis and Cannabis Use Patterns Among Adults with Chronic Health Conditions: Results from COVID-19 Cannabis Health Study

ABSTRACT Given the scarcity of literature regarding COVID-19 impacts on cannabis consumers with chronic illnesses who widely use cannabis to manage their health conditions, we aim to assess the effect of COVID-19 on cannabis use patterns among adults living with chronic diseases. Cannabis consumers with a chronic disease (N = 1,466) from COVID-19 Cannabis Health Study were included in this analysis. The primary outcomes were change in cannabis dosage, past 30-day cannabis usage, and change in route of cannabis administration, with the main exposure being self-reported fear of COVID-19 diagnosis. Significant differences were found between changes in cannabis dose by fear of COVID-19 diagnosis (p = .03) where 41.5% of adults fearing diagnosis reported an increase in their cannabis dosage since the pandemic. Multinomial logistic regression showed that individuals who feared COVID-19 diagnosis had 50% greater odds [aOR = 1.50, 95% CI: 1.02–2.24 of decreasing their cannabis in reference to their dose staying the same, than those without fear of COVID-19, adjusting for socio-demographics. Our findings highlight the significant association between fear of COVID-19 diagnosis and changes in cannabis usage patterns among adults with chronic diseases. This underscores the pandemic’s impact on health-related behaviors and the role of cannabis as a coping mechanism during uncertain times.

How Climate Change Will Shape Pesticide Application in Quebec’s Golf Courses: Insights with Deep Learning Based on Assessing CMIP5 and CMIP6

The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn the attention of professionals, including engineers, decision makers, and golf course managers. This study evaluates how climate projections from CMIP6, using Canadian Earth System Models (CanESM2 and CanESM5), impact pesticide application trends on Quebec’s golf courses. Through the comparison of temperature and precipitation projections, it was found that a more substantial decline in precipitation is exhibited by CanESM2 compared to CanESM5, while the latter projects higher temperature increases. A comparison between historical and projected pesticide use revealed that, in most scenarios and projected periods, the projected pesticide use was substantially higher, surpassing past usage levels. Additionally, in comparing the two climate change models, CanESM2 consistently projected higher pesticide use across various scenarios and projected periods, except for RCP2.6, which was 27% lower than SSP1-2.6 in the second projected period (PP2). For all commonly used pesticides, the projected usage levels in every projected period, according to climate change models, surpass historical levels. When comparing the two climate models, CanESM5 consistently forecasted greater pesticide use for fungicides, with a difference ranging from 65% to 222%, and for herbicides, with a difference ranging from 114% to 247%, across all projected periods. In contrast, insecticides, growth regulators, and rodenticides displayed higher AAIR values in CanESM2 during PP1 and PP3, showing a difference of 28% to 35.6%. However, CanESM5 again projected higher values in PP2, with a difference of 1.5% to 14%.

Pentachlorophenol impairs the antimicrobic capability of blood clam via undermining humoral immunity and disrupting humoral-cellular crosstalk

Due to past massive usage and persistent nature, pentachlorophenol (PCP) residues are prevalent in environments, posing a potential threat to various organisms such as sessile filter-feeding bivalves. Although humoral immunity and its crosstalk with cellular one are crucial for the maintaining of robust antimicrobic capability, little is known about the impacts of PCP on these critical processes in bivalve mollusks. In this study, pathogenic bacterial challenge and plasma antimicrobic capability assays were carried out to assess the toxic effects of PCP on the immunity of a common bivalve species, blood clam (Tegillarca granosa). Moreover, the impacts of PCP-exposure on the capabilities of pathogen recognition, hemocyte recruitment, and pathogen degradation were analyzed as well. Furthermore, the activation status of downstream immune-related signalling pathways upon PCP exposure was also assessed. Data obtained illustrated that 28-day treatment with environmentally realistic levels of PCP resulted in evident declines in the survival rates of blood clam upon Vibrio challenge along with markedly weakened plasma antimicrobic capability. Additionally, the levels of lectin and peptidoglycan-recognition proteins (PGRPs) in plasma as well as the expression of pattern recognition receptors (PRRs) in hemocytes were found to be significantly inhibited by PCP-exposure. Moreover, along with the downregulation of immune-related signalling pathway, markedly fewer chemokines (interleukin 8 (IL-8), IL-17, and tumor necrosis factor α (TNF-α)) in plasma and significantly suppressed chemotactic activity of hemocytes were also observed in PCP-exposed blood clams. Furthermore, compared to that of the control, blood clams treated with PCP had markedly lower levels of antimicrobic active substances, lysozyme (LZM) and antimicrobial peptides (AMP), in their plasma. In general, the results of this study suggest that PCP exposure could significantly impair the antimicrobic capability of blood clam via undermining humoral immunity and disrupting humoral-cellular crosstalk.

Is AI my co-author? The ethics of using artificial intelligence in scientific publishing

ABSTRACT The recent emergence of Large Language Models (LLMs) and other forms of Artificial Intelligence (AI) has led people to wonder whether they could act as an author on a scientific paper. This paper argues that AI systems should not be included on the author by-line. We agree with current commentators that LLMs are incapable of taking responsibility for their work and thus do not meet current authorship guidelines. We identify other problems with responsibility and authorship. In addition, the problems go deeper as AI tools also do not write in a meaningful sense nor do they have persistent identities. From a broader publication ethics perspective, adopting AI authorship would have detrimental effects on an already overly competitive and stressed publishing ecosystem. Deterrence is possible as backward-looking tools will likely be able to identify past AI usage. Finally, we question the value of using AI to produce more research simply for publication’s sake.

Modeling e-commerce retailer demand using Stacking Ensemble and K-means clustering patterns.

Demand forecasting plays a crucial role in e-commerce. By accurately forecasting product demand, e-commerce companies can better manage inventory levels and decrease excess or out-of-stock situations, thereby reducing inventory costs and increasing customer satisfaction. To address the issue of the traditionally low accuracy in market demand forecasting by practitioners who relied on manual experience, this paper proposed a demand forecasting model for e-commerce retailers based on a Stacking ensemble model. The paper successfully modeled and analyzed past user behavior data from JD.com platform, and the model achieved automated forecasting of future demand based on historical data. Upon testing, the proposed Stacking ensemble model demonstrated RMSE (Root Mean Square Error) is 256.34 and 1-WMAPE is 0.916 close to 1, indicating that the model performed well and was capable of accurately predicting the demand for these products. Additionally, this paper also employed a K-means clustering model based on a cost function to classify products into five distinct categories according to their demand characteristic indicators and product attribute indicators.

Predictive modeling for identification of older adults with high utilization of health and social services

Aim Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aims to utilize machine learning models to detect high-risk groups of excessive health and social services utilization at early stage, facilitating the implementation of preventive interventions. Methods We used pseudonymized data covering a four-year period and including information on a total of 33,374 senior citizens from Southern Finland. The endpoint was defined based on the occurrence of unplanned healthcare visits and the total number of different services used. Input features included individual’s basic demographics, health status and past usage of healthcare resources. Logistic regression and eXtreme Gradient Boosting (XGBoost) methods were used for binary classification, with the dataset split into 70% training and 30% testing sets. Results Subgroup-based results mirrored trends observed in the full cohort, with age and certain health issues, e.g. mental health, emerging as positive predictors for high service utilization. Conversely, hospital stay and urban residence were associated with decreased risk. The models achieved a classification performance (AUC) of 0.61 for the full cohort and varying in the range of 0.55–0.62 for the subgroups. Conclusions Predictive models offer potential for predicting future high service utilization in the older adult population. Achieving high classification performance remains challenging due to diverse contributing factors. We anticipate that classification performance could be increased by including features based on additional data categories such as socio-economic data.

Capturing the voice of the customer: focus groups versus netnography?

The rapid growth, large volume, and easy access to publicly available netnographic data have increased the need for understanding its potential in leveraging innovation. At the heart of quality management is a focus on customers and their needs, driving the innovation of products and services. In this study, we systematically compare netnography and focus groups as research methods regarding their ability to capture various types of customer needs. This study makes four valuable contributions to quality management theory: (1) netnography and focus groups captures similar customer needs, (2) netnography provides clear need statements, which are rich and overrepresented compared to focus groups, (3) both netnography and focus groups are vulnerable to groupthink, where customers prioritize consensus; however, this effect is more evident in netnography, (4) netnography captures more need statements about current product and service usage, while focus groups focus more on past product and service usage.

A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

AbstractOver the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.

The (Mal)Functionality of Vernacular

Abstract Continuing a dialogue with Ülo Valk on the value of the etic term vernacular in folk-loristic scholarship, this essay responds to his claim that despite the stigma of the word’s past usage and its rejection by other fields it holds promise for folkloristics because of its conceptual flexibility, which he finds is especially conducive to the study of belief and religious practices. Pointing out that flexibility – or “fuzziness” to quote other critics – suggests imprecision, residualism, hierarchy, and lack of analytical instrumentality, this essay contends that use of vernacular reveals more about its users than the groups and practices it purports to describe. Recounting the intellectual history of the term and its adoption in folkloristic circles as well as the author’s own scholarship, this essay maintains that the term has limited, if any, use in folkloristics and ethnology because of its negative assumptions and “fuzzy” logic. It can be reflexively analyzed, however, to understand scholars’ perceptions of cultural phenomena and their conflicts with cognitive categories of practice and belief enacted by cultural participants.

Vaping Behavior and Intention to Quit among Undergraduate Students in a Hispanic-Serving University.

The surge in electronic nicotine delivery systems (ENDS) usage, particularly among young adults, poses significant public health concerns. This study aimed to identify predictors of e-cigarette use, quit attempts, and frequency among undergraduate students in a Hispanic-serving university in Texas. A cross-sectional study was conducted between August 1 and October 26, 2023, recruiting undergraduate students through the Sona system, an online experiment management platform. Participants completed an online survey that covered demographics, educational status, vaping status, initiation age, reasons for first and current e-cigarette use, frequency of past usage, intentions to quit, and quit attempt frequency. Statistical analysis included descriptive statistics, multinomial logistic regression, and multivariable linear regression. Among 316 participants, 33.9% reported current e-cigarette use. Junior and senior students, as well as prior tobacco users, were more likely to be current vapers. Prior vaping experience was more prevalent among Hispanic individuals and those with a history of tobacco use. Notably, 74.3% of current users attempted to quit in the past year, with a higher frequency of quit attempts among females, Hispanic students, and those with vaping acquaintances. However, the vaping behavior and quit attempt patterns were similar across other categories. This study highlights how various factors influence e-cigarette use among college students. It suggests that prevention and quitting programs should consider the specific needs of different groups. Future studies will continue to look at different student groups to find the most effective ways to help them quit vaping.

Enhancing fraud detection in banking by integration of graph databases with machine learning

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security.•This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach.•The relational graph of features in the dataset used is modelled using Neo4J as a graph database.•Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

Recommendation System for Academic Queries Using Machine Learning

One kind of recommendation system that uses human expertise to provide individualized recommendations is the academic query recommendation system. Because the recommendations made by these systems are based on the knowledge and experience of actual people, they are frequently more reliable and accurate than those made by fully automated systems. The following elements are required to create a recommendation system for academic queries: feature extraction and selection, user interface design, data collecting and preprocessing, and recommendation algorithm development. In order to promote student participation and interaction, the system can also make use of social networks and collaboration tools. An expert recommendation system can help college students become more motivated and engaged, perform better academically, and have better career outcomes. The system can benefit universities as well as. There are numerous courses accessible on e-learning platforms; users must choose the one that best suits their needs. Recommendation systems are crucial in enabling users to make better automated course selections. It suggests that consumers choose the option of their choice according to their preferences. This technology can determine the users' preferences and past usage to determine what they enjoy most. The suggested work might assist students in choosing online courses based on their preferences. Keywords: Recommendation System, Human Expertise, Machine learning

Effects of Çemen pastes prepared in different formulations on physicochemical, microbiological, and textural properties of beef hamburger patties during refrigerated storage.

This study aimed to investigate the effects of çemen pastes prepared in different formulations on physicochemical, microbiological, and textural properties of hamburgers during refrigerated storage (4°C; 60 d). Çemen pastes were produced by using different combination doses of fenugreek seed flour, sweet red pepper, and garlic powder. As a result of çemen paste usage in hamburgers, cooking losses and dimensional shrinkage decreased, whereas moisture and fat retention ratios increased (p < .05). The hardness, gumminess, and chewiness values of hamburgers containing çemen paste were generally lower than those of control (p < .05). Çemen paste addition to hamburgers generally did not cause a difference in terms of microbial growth and moisture, fat and ash contents. Protein contents of hamburgers containing çemen paste or breadcrumbs were generally higher than that of control (p < .05). Çemen paste usage in hamburgers generally decreased the L* values and increased the b* values (p < .05). In general, addition of 3.5% or higher doses of both sweet red pepper and garlic powder caused higher a* values in hamburger patties (p < .05). Lower oxidation levels were generally observed in hamburgers containing 3% or lower doses of fenugreek seed flour and 4.5% of garlic powder in çemen paste (p < .05). It was concluded that çemen paste usage in hamburger patty processing has the potential to improve the quality characteristics and delay oxidative changes.

Prediction of Intermittent Demand Occurrence using Machine Learning

Demand forecasting plays a pivotal role in modern Supply Chain Management (SCM). It is an essential part of inventory planning and management and can be challenging at times. One of the major issues being faced in demand forecasting is insufficient forecast accuracy to predict the expected demand and fluctuation in actual vs. the predicted demand results in fore-casting errors. This problem is further exaggerated with slow-moving and intermittent demand items. Every organization encounters large proportions of items that have small ir-regular demand with long periods of zero demand, which are known as intermittent demand Items. Demand for such items occur sporadically and with considerable fluctuation in the size of the demand. Forecasting of the intermittent demand entails the prediction of demand series that is characterized by the time interval between demand being significantly greater than the unit forecast period. Because of this there are multiple periods of no demand in the intermittent demand time series. The challenge with these products with low irregular demand is that these items need to be stocked and replenished at regular interval irrespective of the demand cycle, thus adding to the cost of holding the inventory. Since the demand is not continuous, Traditional Forecasting models are unable to provide reliable estimate of required inventory level and replenishment point. Forecast errors would resulting in obsolescent stock or unfulfilled demand. The current paper presents a simple yet powerful approach for generating a demand forecasting and replenishment process for such low volume intermittent demand items to come up with a recommendation for dynamic re-order point, thus, improving the inventory performance of these items. Currently, the demand forecast is generally based on past usage patterns. The rise of Artificial Intelligence/Machine Learning (AI/ML) has provided a strong alternative to solve the problem of forecasting Intermittent Demand. The intention is to highlight that machine learning algorithm is more efficient and accurate than traditional forecasting method. As we move forward to industry 4.0, the digital supply chain is considered as the most essential com-ponent of the value chain wherein the inventory size is controlled, and the demand predicted.

Association between statin use and tuberculosis risk in patients with bronchiectasis: a retrospective population-based cohort study in Taiwan

BackgroundChronic airway diseases have been associated with an increased risk of tuberculosis (TB); however, data in patients with bronchiectasis is limited. Statins have been shown to exhibit anti-inflammatory effects by...

The chemical composition, classification, and geographical distributions of soda-saline lakes in Eastern Tanzania's rift valley

Study regionSoda-Saline Lakes in eastern Tanzania's rift valley. Study focusThis study examined the chemical composition, classification, and geographical distribution of soda-saline lakes in the eastern Tanzania rift valley. New hydrological insight of the regionThe results revealed that lake water pH ranged from 9.0 to 10.2, EC ranged from 2843 to 109,800 µS/cm, and Na+ dominated over other cations with mixed dominance of HCO3- + CO32-, Cl-, and SO42-. The study also revealed that lakes Balangida and Balangida Lelu had higher sulphate levels than the other lakes, presumably due to sulfate-rich bedrock and local agricultural input. The study suggests that trace elements and heavy metals in lake water depend on their geology, past usage, and specific environmental conditions. The saturation index (SI) showed that the lakes were oversaturated with dolomite, calcite, and aragonite but undersaturated with anhydrite, gypsum, and halite. Lakes Natron and Manyara are classified as soda types, lakes Balangida and Eyasi are classified as soda-saline types; and lakes Singidani, Kindai, Mikuyu, Balangida Lalu, and Sulunga are classified as saline types. The geographical distribution patterns showed that soda-type and soda-saline lakes were most common in northern Tanzania (Arusha and Manyara). In contrast, saline-type lakes were common in the central regions between Dodoma and Singida. The dominant volcanic nature in the northern part possibly influences soda and soda-saline types.

Changing ecological environment before and after coastline filling designs

Coastlines are tectonically active places with an impact on physical as well as social and economic restructuring of cities. They play an essential role in the development of urban areas. Furthermore, coastal environments may offer important health benefits. They may also present numerous threats to human health and well-being time to time. With the rapid urbanization in coastal cities, the lack of urban green space and transportation problems have emerged recently, and the cities have grown towards the sea by coastal filling areas as a solution. Especially the construction of highway on the coastline in the Black Sea region of Turkey has adversely impacted relationship between the people and the sea in the city of Trabzon. In this study, initially a literature review was conducted and parameters were specified for coastline usages. A survey study was conducted with 140 individuals who actively use the coastline in the city of Trabzon, in order to specify the past and current coastline usages. The evaluation of the past and current usage shows that people want to meet the sea but feel uncomfortable with the landscape with the fillings. According to the Anova test, the unmodified form of area was chosen to ‘have a more natural landscape (5.77)’ while the modified form of area was preferred ‘to be renovated (4.12)’. This highlights the users’ longing for the past. Therefore, authorities need to address the impact of differences between parameters in depth on the usage of the area on the coastal landscape. Coastal ecology solutions should also be produced with the ecological design approaches.