Additional Factors Research Articles (Page 1)

Let G be a finite group. By a sequence over G, we mean a finite unordered string of terms from G with repetition allowed, and we say that it is a product-one sequence if its terms can be ordered so that their product is the identity element of G. Then, the Davenport constant D ( G ) is the maximal length of a minimal product-one sequence, that is a product-one sequence which cannot be factored into two non-trivial product-one subsequences. The Davenport constant is a combinatorial group invariant that has been studied fruitfully over several decades in additive combinatorics, invariant theory, and factorization theory, etc. Apart from a few cases of finite groups, the precise value of the Davenport constant is unknown. Even in the abelian case, little is known beyond groups of rank at most two. On the other hand, for a fixed positive integer r, structural results characterizing which groups G satisfy D ( G ) = r are rare. We only know that there are finitely many such groups. In this paper, we study the classification of finite groups based on the Davenport constant.

Abstract. Nitro-aromatic compounds (NACs) are important atmospheric pollutants that impact air quality, atmospheric chemistry, and human health. Understanding the relationship between NAC formation and key environmental driving factors is crucial for mitigating their environmental and health impacts. In this work, we combined an ensemble machine learning (EML) model with the SHapley Additive exPlanation (SHAP) and positive matrix factorization (PMF) model to identify the key driving factors for ambient particulate NACs, covering primary emissions, secondary formation, and meteorological conditions based on field observations at urban, rural, and mountain sites in eastern China. The EML model effectively reproduced ambient NACs and recognized that anthropogenic emissions (i.e., coal combustion, traffic emission, and biomass burning) were the most important driving factors, with a total contribution of 49.3 %, while significant influences from meteorology (27.4 %) and secondary formation (23.3 %) were also confirmed. Seasonal variation analysis showed that direct emissions presented positive responses to NAC concentrations in spring, summer, and autumn, while lower temperatures had the largest positive impact in winter. By evaluating NAC formation and loss under various locations in winter, we found that anthropogenic sources played a dominant role in increasing NAC levels in urban and rural sites, while reduced ambient temperature, along with secondary formation from gas-phase oxidation, was the main reason for relatively high particulate NAC levels at the mountain site. This work provides a reliable modeling method for understanding the dominant sources and influencing factors for atmospheric NACs and highlights the necessity of strengthening emission source controls to mitigate organic aerosol pollution.

The geometry of intersecting codes and applications to additive combinatorics and factorization theory

This study applies an extended Technology Acceptance Model (TAM) to examine how Household Income, Farm Size, and Digital Literacy influence rural farmers’ intentions to adopt E-commerce in Pakistan. Based on a quantitative survey of 304 farmers, the study explores the effects of these socioeconomic and techno-logical factors on Perceived Ease of Use, Perceived Usefulness, Attitude, and ul-timately, Intention to Adopt E-commerce. Results reveal that higher-income households and those with larger farms are 25–30% more likely to adopt E-commerce, underlining the importance of financial capacity and operational scale. Digital Literacy signif-icantly enhances perceptions of ease and useful-ness, which in turn positively shape attitudes and intentions. The model ex-plains a moderate portion of variance (R² = 0.23 to 0.31), indicating that addi-tional factors—such as trust in online sys-tems, accessibility, and community support—warrant further study. The findings suggest that targeted digital liter-acy initiatives and financial support could reduce barriers to adoption, while E-commerce platforms must be simplified and tailored to rural users. This re-search deepens the understanding of rural digital inclusion and offers guidance for future interventions.

Purpose of the study: The purpose of this study was to determine the effect of adding sorbitol and glycerol on the quality of bioplastics, as well as to determine the right formulation for making bioplastic starch from snake fruit and cassava seeds. Methodology: The method used was a Completely Randomized Design (CRD) with two factors, namely the addition of the first factor of sorbitol (1, 2, and 3 mL) and the second factor of glycerol addition (1, 2, and 3 mL), each experiment was repeated three times. The data obtained were analyzed using the analysis of variance test at a significant level of 0.05. Main Findings: The variation in sorbitol and glycerol addition significantly affects the characteristics of bioplastics, as confirmed by a One-Way ANOVA test (sig. < 0.05), indicating distinct differences based on the type and amount of plasticizer used. Optimal formulations for bioplastics made from salak seeds and cassava starch include: highest water resistance (96.19%) with 2 mL sorbitol, optimal thickness (0.33 mm) with 1 mL sorbitol, greatest tensile strength (68.93 kg/cm²) with 2 mL glycerol, and highest elongation (5.88%) with 3 mL glycerol. Novelty/Originality of this study: This study contributes to the advancement of bioplastic development by utilizing salak seed and cassava starch as novel base materials. The resulting bioplastics offer the potential to serve as environmentally friendly alternatives to conventional plastics, with the key advantage of being biodegradable. This innovation supports efforts to reduce synthetic plastic waste, which is notoriously difficult to decompose.

Several epidemiological studies have appreciated the impact of "duration" and "level" of hyperglycemia on the initiation and development of chronic complications of diabetes. However, glycemic profiles could not fully explain the presence/absence and severity of diabetic complications. Genetic issues and concepts of "hyperglycemic memory" have been introduced as additional influential factors involved in the pathobiology of late complications of diabetes. In the extended phase of significant diabetes randomized, controlled clinical trials, including DCCT/EDIC and UKPDS, studies have concluded that the quality of glycemic or metabolic control at the early time around the diabetes onset could maintain its protective or detrimental impact throughout the following diabetes course. There is no reliable indication of the mechanism by which the transient exposure to a given glucose concentration level could evoke a consistent cellular response at target tissues at the molecular levels. Some biological phenomena, such as the production and the concentration of advanced glycation end products (AGEs), reactive oxygen species (ROS) and protein kinase C (PKC) pathway activations, epigenetic changes, and finally, the miRNAs-mediated pathways, may be accountable for the development of hyperglycemic memory. This work summarizes evidence from previous experiments that may substantiate the hyperglycemic memory soundness by its justification in molecular terms.

BackgroundThe COVID-19 pandemic has had a major impact on the organisation of health services worldwide. In the first wave, many therapeutic options were explored, exposing patients to significant iatrogenic risk....

Microgreens represent a valuable source of health-promoting compounds and also a research avenue, since such organisms have a very high plasticity related to environmental cues, allowing biotechnological development with low costs. Ocimum basilicum L. species naturally synthesize valuable, phenolic compounds, among which rosmarinic acid is most prominent. Within the current research, basil plantlets were grown for 10 days under either full spectrum light (white light) or modulated blue/red/far-red/UV spectrum elicitation with an additional factorization, by applying fertilization. Biomass accumulation reached up to 0.8 g/20 plantlets, while chlorophyll fluorescence was in the 0.75–0.78 range and remained uniform across treatments, indicating that no significant stress was exerted under modified light treatment. However, total phenolic contents and, in particular, rosmarinic acid contents, were markedly enhanced (up to 7.5 mg/g in the red cultivar) under modulated light treatment and fertilization, compared to full spectrum light. Moreover, in the red cultivar, gene expression was enhanced, 1.3–6.3 fold for genes coding for enzymes involved in phenylpropanoid synthesis pathways, such as phenylalanine ammonia lyase (PAL), tyrosine aminotransferase (TAT), Catechol-O-methyltransferase (COMT) and rosmarinic acid synthetase (RAS). Overall, light modulation coupled with fertilization led to the production of basil microgreens with up to 10% more total phenolics and up to 25% more rosmarinic acid. The results show that, using relatively simple growth equipment and setup, synthesis of health related, valuable compounds can be modulated in microgreens and, hence, serves as an avenue for businesses to develop cost effective biotechnological processes.

Abstract The Ayamurasaki variety of purple sweet potato is one of the sweet potato cultivars that are widely grown in Indonesia. Purple sweet potato is known to contain non digestible oligosaccharides (NDOs) that can act as prebiotics for the development of probiotic in yoghurt making. This study was aimed to evaluate the effect of adding purple sweet potato paste on the physical, chemical, sensory quality, and growth of lactic acid bacteria in yoghurt. The model used in this study was a non-factorial Completely Randomized Design (CRD) with single factor of purple sweet potato paste addition. Parameters observed were viscosity, pH, total titratable acid as lactic acid, anthocyanin content, total sugar, color index (°Hue), total lactic acid bacteria, antioxidant activity (% Inhibition), and organoleptic by hedonic value of color, aroma, taste, viscosity, and general acceptance. The results showed that the paste addition had a highly significant effect (P < 0,01) on the viscosity, pH, total acid, anthocyanin content, color (°Hue), total lactic acid bacteria, organoleptic by hedonic value of color, aroma, viscosity, and general acceptance of yoghurt.

Following disasters such as hurricanes, self-compassion (e.g., being understanding and showing care toward oneself) can be a valuable personal resource that facilitates social support and reduces posttraumatic symptoms. As a result of their increased connection to other people and interpersonal competence, self-compassionate people may perceive more social support following a traumatic event, which in turn reduces posttraumatic stress symptoms (PTSS). The present study is the first to utilize a longitudinal design and latent variable modeling to test this mediation hypothesis. A three-wave longitudinal design was utilized to assess hurricane exposure, self-compassion, perceived social support, and PTSS in hurricane survivors at baseline (T1), 3-month (T2), and 6-month (T3) follow-up. Participants at T1 included 261 hurricane survivors (88.5% women) who were racially diverse and particularly vulnerable to loss of resources (53.2% with an income of less than $30,000). Participants were recruited using online, print, and face-to-face methods, and all survey responses were completed online. Participants reported high hurricane stressor exposure (M = 9.14 serious stressors out of a possible 24). Controlling for hurricane exposure, self-compassion at T1 predicted PTSS at T3, and this was mediated by perceived social support at T2. Following hurricane exposure, self-compassionate people experience less PTSS over time because they perceive their social support resources to be more robust. Implementation of self-compassion education and training following a disaster could improve perceived social support networks that provide an additional protective factor against PTSS. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract: Autism severity has been found to be associated with social anhedonia. However, the mechanisms linking the two have not been clarified. Objective: The study was designed to examine the link between autistic traits and social anhedonia. The present study tested a serial mediation model, in which it was hypothesized that emotional intelligence and self-efficacy were serial mediators of the relationship between autistic traits and social anhedonia. Methods: Data from 245 participants (57.4% females, n = 134) ranging in age from 18 to 65 years were collected through self-report. Participants completed an online composite questionnaire consisting of The Comprehensive Autistic Trait Inventory, the Wong and Law Emotional Intelligence Scale, The General Self-Efficacy Scale and the Revised Social Anhedonia Scale. Results: The relationship between autistic traits and social anhedonia was mediated by emotional intelligence and serially mediated by emotional intelligence and self-efficacy. Self-efficacy alone failed to link autistic traits to social anhedonia. Conclusion: The study provides evidence for the significant role of emotional intelligence and self-efficacy as mechanisms underlying the relationship between autistic traits and social anhedonia. The findings are discussed in terms of elucidating the processes through which autistic traits may confer vulnerability to compromised emotional intelligence and self-efficacy, which then serve as additional risk factors for social anhedonia.

Around 10% of adults infected with SARS-CoV-2 that survive a first episode of COVID-19 appear to experience long-term clinical manifestations. The signs and symptoms of this post-acute COVID-19 syndrome (PACS) include fatigue, dyspnea, joint pain, myalgia, chest pain, cough, anosmia, dysgeusia, headache, depression, anxiety, memory loss, concentration difficulties, and insomnia. These sequelae remind the constellation of clinical manifestations previously recognized as myalgic encephalomyelitis (ME) or chronic fatigue syndrome (CFS). This condition has been described following distinct infectious events, mostly acute viral illnesses. In this way, the pathophysiology of PACS might overlap with mechanisms involved in other post-infectious fatigue syndromes. The risk of PACS is more frequent in women than men. Additional host genetic factors could be involved. There is a dysregulation of multiple body organs and systems, involving the immune system, the coagulation cascade, endocrine organs, autonomic nervous system, microbiota-gut-brain axis, hypothalamic-pituitary-adrenal axis, hypothalamic-pituitary-thyroid axis, etc. Hypothetically, an abnormal response to certain infectious agents could trigger the development of postinfectious fatigue syndromes.

The Organisation for Economic Co-operation and Development (OECD) 216 test guideline investigates the impact of agrochemicals on soil nitrogen transformation. After an evaluation of 465 OECD 216 studies, we describe two distinct yet contrasting outcomes in control nontreated samples that are possible in this testing framework, which we term the "rise" (consistent increases in nitrate concentrations throughout the test period) and "dip" (initial decline in nitrate concentration between Days 0-7, followed by a net-generation of nitrate across Days 7-28) responses. We raise significant concerns that control data from standardized, internationally recognized test guidelines can demonstrate such dissimilar patterns. We propose that, when present, the dip response undermines the intended functioning of the test system and removes the ability to draw appropriate ecotoxicological inferences from the data. In this work, we hypothesize the dip response is a product of conducting the study in low nitrogen content soils. Our results indicate that the dip response can be alleviated by using ammonium sulfate as an immediately available inorganic nitrogen source in place of the guideline-mandated complex, organic lucerne meal, demonstrating the influence of nitrogen availability and accessibility. However, not all low nitrogen soils exhibited the dip response, indicating the involvement of additional unidentified factors. Using our data and real-world regulatory examples, we advocate that datasets displaying the dip response should not be considered valid OECD 216 studies due to the influence of soil properties precluding an assessment of whether any impacts observed are driven solely by the test compound in question or are instead a product of the soil used. We propose methods to account for these soil-specific responses that could be integrated into the conduct and interpretation of OECD 216 studies. Such amendments will improve the reliability and robustness of the study system and enhance confidence in ecotoxicological conclusions derived from OECD 216 datasets. Integr Environ Assess Manag 2024;20:1611-1624. © 2024 SETAC.

The fear-avoidance model is a well-established framework for understanding the transition from acute to chronic pain. However, its applicability to concussions is not yet well understood. Here, we conduct the first mixed methods analysis of the fear-avoidance model in young adults with a recent concussion and co-occurring anxiety and assess the model's alignment with their lived experience. We conducted a mixed methods analysis using a cross-sectional parallel design. Seventeen participants completed questionnaires corresponding with the elements in the fear-avoidance model (e.g., pain catastrophizing, avoidance, disability, anxiety, depression, etc.) and participated in semistructured interviews probing their experiences following their concussion between March 2021 and February 2022. We calculated bivariate correlations for quantitative data and analyzed the qualitative data using hybrid inductive-deductive thematic analysis. Quantitative results demonstrated strong and medium-sized correlations among theorized relationships within the fear-avoidance model (rs = .40-.85) with the majority being statistically significant. Qualitative results provided substantial convergent and complementary support (e.g., bi-directionality of some relationships, associations between nonadjacent model components, centrality of anxiety in symptom persistence) for the application of the fear-avoidance model to concussions. Findings highlighted additional factors (social factors and post-injury endurance patterns) relevant to this population. The fear-avoidance model is a useful lens for understanding the lived experience of young adults with a recent concussion and co-occurring anxiety. Psychosocial treatment for this population would benefit from focusing on the interplay of concussion symptoms, anxiety, depression, disability, and pain-related fear, offering adaptive confrontation strategies, and addressing the interpersonal impact of concussion. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The study of coronary artery calcification (CAC) may assist in identifying additional coronary artery problem protective factors. On the contrary side, due to the wide variety of CAC as individuals, CAC research is difficult. Due to this, evaluating data for investigation is becoming complicated. To use a multi-layer perceptron, we investigated the accuracy and reliability of synthetic CAC coursework or hazard classification in pre or alors chest computerized tomography (CT) of arrangements resolutions in this analysis. Photographs of the chest from similar individuals as well as calcium-just and non-gated pictures were incorporated. This cut thickness ordered CT pictures (bunch A: 1 mm; bunch B: 3 mm). The CAC rating was determined utilizing calcification score picture information, and became standard for tests. While the control treatment's machine learning program was created using 170 computed tomography pictures and evaluated using 144 scans, group A's machine learning algorithm was created using 150 chest CT diagnostic tests. 334 external related pictures (100 μm: 117; 0.5 mm x: 117) of 117 individuals and 612 inside design organizing (1 mm: 294; mm3: 314) of 406 patients were surveyed. Pack B had 0.94, however, tests An and b had 0.90 (95% CI: 0.85-0.93) ICCs between significant learning and gold expenses (0.92-0.96). Dull Altman plots agreed well. A machine teaching approach successfully identified 71% of cases in category A is 81% of patients in section B again for cardiac risk class. Regression risk evaluation algorithms could assist in categorizing cardiorespiratory individuals into distinct risk groups and conveniently personalize the treatments to the patient's circumstances. The models would be based on information gathered through CAC. On both 1 and 3-mm scanners, the automatic determination of a CAC value and cardiovascular events categorization that used a depth teaching approach was reliable and precise. The layer thickness of 0.5 mm on chest CT was slightly less accurate in CAC detection and risk evaluation.

Fetal movements are often used as a surrogate for fetal wellbeing. Previous research suggests a link between maternal perception of decreased fetal movements (DFM) and small-for-gestational-age (SGA) infants. The aim of this study was to investigate the association between maternal presentation with DFM and birth-weight centile categories at a large Australian perinatal center. This was a retrospective study of non-anomalous singleton infants born at ≥ 28 + 0 weeks' gestation between January 2016 and October 2020 at the Mater Mothers' Hospital in Brisbane, Australia. The primary outcome was the rate of DFM according to birth-weight centile category. Maternal demographic characteristics included age, body mass index, ethnicity, parity, medical conditions and previous stillbirth. The association between DFM and birth-weight centile was evaluated using adjusted multinomial regression models. Robust standard errors were used to account for clustering at the patient level. Wald tests and Akaike's and Bayesian information criteria were used to evaluate models. Over the 5-year study period, 45 042 women met the inclusion criteria. Of these, 6690 (14.9%) women presented with DFM. Of the DFM cohort, 80.9% (5411/6690) had only one presentation with DFM, and 19.1% (1279/6690) had two or more presentations. The overall stillbirth rate was similar in women with DFM (0.1% (8/6690)) and those without DFM (0.1% (50/38 352)). There was no association between DFM (either single or multiple) and infant birth-weight centile. This study suggests that presentation with DFM is not associated with infant size. Clinicians should consider additional risk factors and the overall clinical context when deciding appropriate management. DFM is not necessarily an indication for an immediate or urgent ultrasound scan to assess fetal size. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Excessive workload of medical workers resulting from insufficient staffing and prolonged stress lead, among others, to burnout, which is a serious problem in the medical community. Research shows that the incidence of anxiety and stress disorders is increasing. For years, the social climate around medical staff in Poland has also been deteriorating. The media write more often about errors and omissions, and less about the daily work of medics. This leads to a decline in public confidence, as well as hate and acts of aggression. The occurrence of the phenomenon of violence against medical staff may be a factor in the development of many diseases associated with high levels of stress and should be of interest to occupational medicine services. The aim of the work is to describe the phenomenon of violence against health care workers and its impact on the working conditions and health of workers. The literature from 2010-2022 was reviewed in PubMed and Web of Science databases by entering the following entries: "violence", "aggression", "healthcare", "stress", "nurses", "doctors", "workplace". Eighty-three works on the occurrence of the phenomenon of violence against health care workers were qualified for the study. The results of the analysis indicate a shortage of studies on Polish medical entities. The phenomenon of violence against health care workers is a serious problem of public health in the world. The most common forms of workplace violence were verbal violence, physical assault, bullying, sexual harassment and racial harassment. Most often, the violence was committed by patients and their relatives, colleagues and superiors. Aggression towards medical staff is not a new phenomenon, and Poland as a country is not alone in dealing with this problem. Aggression and violence are most often observed in hospital departments, especially psychiatric departments, hospital emergency departments and emergency rooms. Patients and their families are most often regarded as the source of rude behavior. Crisis situations, such as the COVID-19 pandemic, have intensified the scale of the phenomenon. Managing a pandemic also requires establishing preventive procedures for aggression and violence. Additional factors hindering the work of medical personnel may lead to The shortage of studies on Polish medical entities indicates the need to conduct work aimed at determining the scale of the phenomenon and its causes, taking into account the division into organizational units as well as groups of patients and their relatives. Accurate determination of the scale of the phenomenon and predisposing factors will allow to take appropriate innovative preventive actions, which will contribute to limiting the negative consequences. Managers ofmedical entities should take steps to increase the number of reports. Violence has a negative impact on the mental health of medical staff and may cause irreversible physical and mental harm to those who experience it; therefore, it is very important to involve occupational health services in actions to solve the problem.

Over several months, 14 people were admitted in 6 hospitals with severe symptoms of intoxication with psychoactive substances as a result of mass poisoning. All symptoms occurred after taking a drink that contained crushed phenazepam tablets. Samples of blood (n=10) and urine (n=6) taken from 14 sufferers for forensic, chemical and toxicological examination were analyzed using the HPLC-MS/MS method. Phenazepam was detected in the biomaterial of all 14 patients. Other psychoactive substances (baclofen, pregabalin, chlorprothixene, chlorpromazine, phenibut, tramadol, diazepam), narcotic substances and ethanol were also found in the sufferers. The phenazepam concentration in the blood was in the range of 109.75-786.50 ng/ml, in the urine - 8.97-101.28 ng/ml. The pharmacokinetic and toxicokinetic characteristics of toxicants as well as additional factors characterizing the phenotype of the sufferer in addition to drug's content in the biological material must be taken into account to determine the toxicity level of phenazepam against the background of combined action with other psychoactive substances.

Existing methods for software requirements elicitation, five-point Likert scales and voting methods for requirements prioritization, and usability and user experience evaluation methods do not enable prioritizing the learning analytics dashboard requirements. Inspired by management and product design field, this research applies Kano’s two-factor theory to prioritize the features of learning analytics dashboards (LADs) of adaptive learning platform (ALP) called Rhapsode<sup>TM</sup> learner, based on students’ perceived usefulness to support designers’ decision-making. Comparing usability and user experience methods for evaluating LAD features, this paper contributes with the protocol and a case applying Kano method for evaluating the perceived importance of the dashboards in ALP. The paper applies Kano’s two-factor questionnaire on the 13 LADs features of Rhapsode<sup>TM</sup> learner. Responses from 17 students are collected using a questionnaire, which is used to showcase the strength of the two-factor theory through five tabular and graphical techniques. Through these five tabular and graphical techniques, we demonstrate the application and usefulness of the method as designers and management are often carried away by the possibilities of insights instead of actual usefulness. The results revealed a variation in the categorization of LADs depending on the technique employed. As the complexity of the techniques increases, additional factors that indicate data uncertainty are gradually incorporated, clearly highlighting the growing requirement for data. In the case of RhapsodeTM learner platform, results based on the students responses show that 11 of 13 LADs being excluded due to low significance level in categorization (technique 1) and low response rate.

Inferring resting-state functional connectivity (FC) from anatomical brain wiring, known as structural connectivity (SC), is of enormous significance in neuroscience for understanding biological neuronal networks and treating mental diseases. Both SC and FC are networks where the nodes are brain regions, and in SC, the edges are the physical fiber nerves among the nodes, while in FC, the edges are the nodes' coactivation relations. Despite the importance of SC and FC, until very recently, the rapidly growing research body on this topic has generally focused on either linear models or computational models that rely heavily on heuristics and simple assumptions regarding the mapping between FC and SC. However, the relationship between FC and SC is actually highly nonlinear and complex and contains considerable randomness; additional factors, such as the subject's age and health, can also significantly impact the SC-FC relationship and hence cannot be ignored. To address these challenges, here, we develop a novel SC-to-FC generative adversarial network (SF-GAN) framework for mapping SC to FC, along with additional metafeatures based on a newly proposed graph neural network-based generative model that is capable of learning the stochasticity. Specifically, a new graph-based conditional generative adversarial nets model is proposed, where edge convolution layers are leveraged to encode the graph patterns in the SC in the form of a graph representation. New edge deconvolution layers are then utilized to decode the representation back to FC. Additional metafeatures of subjects' profile information are integrated into the graph representation with newly designed sparse-regularized layers that can automatically select features that impact FC. Finally, we have also proposed new post hoc explainer of our SF-GAN, which can identify which subgraphs in SC strongly influence which subgraphs in FC by a new multilevel edge-correlation-guided graph clustering problem. The results of experiments conducted to test the new model confirm that it significantly outperforms existing state-of-the-art methods, with additional interpretability for identifying important metafeatures and subgraphs.