Accumulating evidence shows that the posterior cerebellum is involved in mentalizing inferences of social events by detecting sequence information in these events, and building and updating internal models of these sequences. By applying anodal and sham cerebellar transcranial direct current stimulation (tDCS) on the posteromedial cerebellum of healthy participants, and using a serial reaction time (SRT) task paradigm, the current study examined the causal involvement of the cerebellum in implicitly learning sequences of social beliefs of others (Belief SRT) and non-social colored shapes (Cognitive SRT). Apart from the social or cognitive domain differences, both tasks were structurally identical. Results of anodal stimulation (i.e., 2 mA for 20 min) during the social Belief SRT task, did not show significant improvement in reaction times, however it did reveal generally faster responses for the Cognitive SRT task. This improved performance could also be observed after the cessation of stimulation after 30 min, and up to one week later. Our findings suggest a general positive effect of anodal cerebellar tDCS on implicit non-social Cognitive sequence learning, supporting a causal role of the cerebellum in this learning process. We speculate that the lack of tDCS modulation of the social Belief SRT task is due to the familiar and overlearned nature of attributing social beliefs, suggesting that easy and automatized tasks leave little room for improvement through tDCS.
Precision and effectiveness of Artificial Intelligence (AI) models are highly dependent on the availability of genuine, relevant, and representative training data. AI systems tested and validated on poor-quality datasets can produce inaccurate, erroneous, skewed, or harmful outcomes (actions, behaviors, or decisions), with far-reaching effects on individuals' rights and freedoms. Appropriate data governance for AI development poses manifold regulatory challenges, especially regarding personal data protection. An area of concern is compliance with rules for lawful collection and processing of personal data, which implies, inter alia, that using databases for AI design and development should be based on a clear and precise legal ground: the prior consent of the data subject or another specific valid legal basis. Faced with this challenge, the European Union's personal data protection legal framework does not provide a preferred, one-size-fits-all answer, and the best option will depend on the circumstances of each case. Although there is no hierarchy among the different legal bases for data processing, in doubtful cases, consent is generally understood by data controllers as a preferred or default choice for lawful data processing. Notwithstanding this perception, obtaining data subjects' consent is not without drawbacks for AI developers or AI-data controllers, as they must meet (and demonstrate) various requirements for the validity of consent. As a result, data subjects' consent could not be a suitable and realistic option to serve AI development purposes. In view of this, it is necessary to explore the possibility of basing this type of personal data processing on lawful grounds other than the data subject's consent, specifically, the legitimate interest of the data controller or third parties. Given its features, legitimate interests could help to meet the challenge of quality, quantity, and relevance of data curation for AI training. The aim of this article is to provide an initial conceptual approach to support the debate about data governance for AI development in the European Union (EU), as well as in non-EU jurisdictions with European-like data protection laws. Based on the rules set by the EU General Data Protection Regulation (GDPR), this paper starts by referring to the relevance of adequate data curation and processing for designing trustworthy AI systems, followed by a legal analysis and conceptualization of some difficulties data controllers face for lawful processing of personal data. After reflecting on the legal standards for obtaining data subject's valid consent, the paper argues that legitimate interests (if certain criteria are met) may better match the purpose of building AI training datasets.
The air transport industry is a competitive and volatile market, creating a challenging operating environment for both airports and airlines. While airline market structures are rapidly changing, airports are in a continuous need to improve their technical efficiency. Therefore, in this paper, we examine the effect of airline dominance on airport technical efficiency. Previous research is contributed by examining this effect on a balanced panel dataset of medium-sized European airports while considering the effect of the macro-environment, ownership, belonging to an airport group, and the network structure of the dominant carrier (LCC or FSC). Results demonstrate a significant positive relationship between airline dominance and the airport's technical efficiency. The paper has an important policy implication as it highlights the importance of taking into account efficiency effects at airports when evaluating airline consolidation cases.
The factory of the future is steering away from conventional assembly line production with sequential conveyor technology, towards flexible assembly lines, where products dynamically move between work-cells. Flexible assembly lines are significantly more complex to plan compared to sequential lines. Therefore there is an increased need for autonomously generating flexible robot-centered assembly plans. The novel Autonomous Constraint Generation (ACG) method presented here will generate a dynamic assembly plan starting from an initial assembly sequence, which is easier to program. Using a physics simulator, variations of the work-cell configurations from the initial sequence are evaluated and assembly constraints are autonomously deduced. Based on that the method can generate a complete assembly graph that is specific to the robot and work-cell in which it was initially programmed, taking into account both part and robot collisions. A major advantage is that it scales only linearly with the number of parts in the assembly. The method is compared to previous research by applying it to the Cranfield Benchmark problem. Results show a 93% reduction in planning time compared to using Reinforcement Learning Search. Furthermore, it is more accurate compared to generating the assembly graph from human interaction. Finally, applying the method to a real life industrial use case proves that a valid assembly graph is generated within reasonable time for industry.
The development of a reliable and automated condition monitoring methodology for the detection of mechanical failures in rotating machinery has garnered much interest in recent years. Thanks to the rise in popularity of machine learning techniques, the number of purely data-driven approaches that try to tackle the issue of vibration-based condition monitoring has also drastically improved. Instead of directly using the vibration measurement data as input to a machine learning model, this work first exploits the cyclostationary characteristics inherent to vibration waveforms originating from rotating machinery. The proposed methodology first estimates the two-dimensional cyclic spectral coherence map of a vibration signal in order to decompose the cyclic modulations on the cyclic and carrier frequency plane. While this provides an effective tool to visualize any potential modulation signatures of faulty gears or bearings, it does not allow for easy inspection over time due to its dimensions. To tackle this issue, this paper proposes an unsupervised deep learning approach to wield this vast amount of data as a tool for detecting persistent changes in the modulation characteristics of the vibration signals. In the first phase, a deep autoencoder learns to reconstruct predictions of the cyclic coherence maps based on unlabeled healthy vibration data and the machine operating conditions. Two post-processing steps improve the predictions by mitigating frequency shifts and outlier or noisy measurements. Lastly, the residual error between the predicted and the actual coherence map is then aggregated and employed for further alarming based on thresholds. The autoencoder model is trained using five years of gearbox vibration data from five different wind turbines. The methodology is then validated on two faulty and eight healthy turbines. The results confirm that the proposed approach can deliver clear indications of failure for the faulty turbine while being completely devoid of any significant alarm trends for the healthy turbines. Thanks to the combination of highly effective cyclostationary signal processing with deep learning while using the operating conditions, the proposed methodology can detect and track incipient mechanical faults from non-stationary vibration data of rotating machinery. Lastly, it is important to emphasize that the proposed method is capable of learning the healthy behavior on one turbine and predicting the expected behavior on another turbine.
Ag-based semiconductors have attracted significant attention as promising visible-light photocatalysts for environmental purification. In this study, electronic and photocatalytic properties of AgTi2(PO4)3 NASICON-type phosphate, have been addressed in detail, combining experimental results and theoretical calculations. The as-prepared sample was characterized for its morphological, structural and optical properties by various techniques. The generalized gradient approximation by Perdew-Burke-Ernzerhof (GGA-PPE) within density functional theory (DFT) was used to investigate the electronic structure. We have applied corrective Hubbard U terms to Ti 3d orbitals in order to better reproduce the experimental band gap of 2.6 eV. The photocatalytic activity has then been performed for rhodamine B dye degradation under visible light illumination. Efficient dye degradation up to 97.2 % was achieved in 120 min. In addition, the catalyst exhibited good stability over four consecutive cycles. Finally, combining experimental and theoretical findings, the origin of the photocatalytic activity was identified and a photodegradation mechanism was proposed.
In the present study, we tested the common assumption that teachers with more experience consider themselves better prepared for online teaching and learning (OTL). Utilizing the data from a survey of 366 higher-education teachers from Portugal at the beginning of the COVID-19 pandemic in 2020, we performed structural equation modeling to quantify the experience-readiness relationship. The survey contained an assessment of teachers' OTL readiness which was measured by their perceptions of the institutional support, online teaching presence, and TPACK self-efficacy. In contrast to the linearity assumption "the more experienced, the better prepared", we found robust evidence for a curvilinear relationship. Teachers' readiness for OTL increased first and then decreased with more experience-this applied especially to the self-efficacy dimension of readiness. Further analyses suggested that the experience-readiness relationship does not only exist at the level of aggregated constructs but also at the level of indicators, that is, specific areas of knowledge, teaching, and support. We argue that both novice and experienced teachers in higher education could benefit from experience-appropriate, pedagogical, and content-related support programs for OTL.
Inversion of in situ borehole gamma spectrometry data is a faster and relatively less laborious method for calculating the vertical distribution of radioactivity in soil than conventional soil sampling method. However, the efficiency calculation of a detector for such measurements is a challenging task due to spatial and temporal variation of the soil properties and other measurement parameters. In this study, the sensitivity of different soil characteristics and measurement parameters on simulated efficiencies for a 662 keV photon peak were investigated. In addition, a Bayesian data inversion with a Gaussian process model was used to calculate the activity concentration of 137 Cs and its uncertainty considering the sources of uncertainty identified during the sensitivity analysis, including soil density, borehole radius, and the uncertainty in detector position in the borehole. Several soil samples were also collected from the borehole and surrounding area, and 137 Cs activity concentration was measured to compare with the inversion results. The calculated 137 Cs activity concentrations agree well with those obtained from soil samples. Therefore, it can be concluded that the vertical radioactivity distribution can be calculated using the probabilistic method using in situ gamma spectrometric measurements.
Carbon reduction requirements while securing energy demand create a huge development opportunity for district energy systems (DESs) supported by renewable energy. In this study, a DES was fueled with kitchen waste (KW) feedstock for bioenergy production optimization and sector decarbonization. Inspired by the cascading principle, gasification and anaerobic digestion have been used to treat mixed KW for maximizing bioenergy recovery. Furthermore, a multi-objective mixed-integer model with time series was built to express complex processes such as technology deployment and mass and renewable electricity flows using mathematical language, achieving a trade-off between economic and environmental benefits. The KW treatment industry in Chongqing city was chosen as the location for a case study, in which two electricity trade scenarios were simulated by adjusting the associated parameters. The results show that a DES with surplus electricity feed-in mode helps avoid inevitable adverse events on the main grid, as it ensures that at least 9,457 kWh per day, and up to a maximum of 16,793 kWh, will not be lost by long-distance transmission. Therefore, the mode also contributes more to carbon reduction benefits, but the levelized carbon reduction cost is between 353 and 470 CNY/tone CO2, which is higher than the average carbon tax level published by the trading market.
A Neumaier graph is a non-complete edge-regular graph containing a regular clique. A Neumaier graph that is not strongly regular is called a strictly Neumaier graph. In this work we present a new construction of strictly Neumaier graphs, and using Jacobi sums, we show that our construction produces infinitely many instances. Moreover, we prove some necessary conditions for the existence of (strictly) Neumaier graphs that allow us to show that several parameter sets are not admissible.
Non-invasive brain stimulation (NIBS) techniques have been increasingly used over the dorsolateral prefrontal cortex (DLPFC) to enhance working memory (WM) performance. Notwithstanding, NIBS protocols have shown either small or inconclusive cognitive effects on healthy and neuropsychiatric samples. Therefore, we assessed working memory performance and safety of transcranial direct current stimulation (tDCS), intermittent theta-burst stimulation (iTBS), and both therapies combined vs placebo over the neuronavigated left DLPFC of healthy participants. Twenty-four subjects were included to randomly undergo four sessions of NIBS, once a week: tDCS alone, iTBS alone, combined protocol and placebo. The 2-back task and an adverse effect scale were applied after each NIBS session. Results revealed a significantly faster response for iTBS (b= -21.49, p= 0.04), but not for tDCS and for the interaction tDCS vs. iTBS (b= 13.67, p= 0.26 and b= 40.5, p= 0.20, respectively). No changes were observed for accuracy and no serious adverse effects were found among protocols. Although tolerable, an absence of synergistic effects for the combined protocol was seen. Nonetheless, future trials accessing different outcomes for the combined protocols, as well as studies investigating iTBS over the left DLPFC for cognition and exploring sources of variability for tDCS are encouraged.
Background Despite the potential of occupational passive shoulder exoskeletons (PSEs) to relieve overhead work, limited insights in overhead work precision performance impedes large-scale adoption in industry. Objective To investigate the effect of PSE support on the reduction in task performance caused by physical fatigue. Methods This experiment consisted of a randomized, counterbalanced cross-over design comparing Exo4Work PSE support and no support, in a physically fatigued state and a control condition. Precision performance was determined using execution speed and drilling errors. Muscle activity and shoulder joint kinematics were recorded. Results Fatigue altered task performance, shoulder joint kinematics, muscle activity and subjective experience during overhead work. The PSE support mitigated the fatigue-induced changes in shoulder kinematics. Additionally, a part of the fatigue-induced co-activation of shoulder stabilizing muscles was avoided when working with the PSE. The PSE support also reduced the activity of the anterior and medial deltoid. Conclusion Physical fatigue provokes compensatory movements and increased co-contraction of muscles when executing overhead work. These fatigue-induced alterations are generally believed to increase the overall musculoskeletal load. The support provided by the PSE reduced muscle activity of muscles working to elevate the arm, but also partially mitigated those fatigue-induced effects. Significance This study shows that the effect of PSE support on precision performance is limited, and suggested that, apart from the known effects of PSE support during overhead work, wearing the exoskeleton in a physically fatigued state may provide additional advantages.
Environmental contamination by radioactive materials can be characterized by in situ gamma surface measurements. During such measurements, the field of view of a gamma detector can be tens of meters wide, resulting in a count rate that integrates the signal over a large measurement support volume/area. The contribution of a specific point to the signal depends on various parameters, such as the height of the detector above the ground surface, the gamma energy and the detector properties, etc. To improve the spatial resolution of the activity concentration, contributions of a radionuclide from nearby areas to the count rate of a single measurement should be disentangled. The experiments described in this paper, deployed 2D inversion of in situ gamma spectrometric measurements using a non-negative least squares-based Tikhonov regularization method. Data were acquired using a portable LaBr3 gamma detector. The detector response as a function of the distance of the radioactive source, required for the inversion process, was simulated using the Monte Carlo N-Particle (MCNP) transport code. The uncertainty on activity concentration was calculated using the Monte Carlo error propagation method. The 2D inversion methodology was first satisfactorily assessed for 133Ba and 137Cs source activity distributions using reference pads. Secondly, this method was applied on a 137Cs contaminated site, making use of above-ground in-situ gamma spectrometry measurements, conducted on a regular grid. The inversion process results were compared with the results from in-situ borehole measurements and laboratory analyses of soil samples. The calculated 137Cs activity concentration levels were compared against the activity concentration value for exemption or clearance of materials which can be applied by default to any amount and any type of solid material. Using the 2D inversion and the Monte Carlo error propagation method, a high spatial resolution classification of the site, in terms of exceeding the exemption limit, could be made. The 137Cs activity concentrations obtained using the inversion process agreed well with the results from the in-situ borehole measurements and those from the soil samples, showing that the 2D inversion is a convenient approach to deconvolute the contribution of radioactive sources from nearby areas within a detector's field of view, and increases the resolution of spatial contamination mapping.
This study investigates shopper behavior when interacting with an employee-robot team (vs. both actors in isolation), along the metrics of the POS conversion funnel. An unobtrusive field study was conducted using video observations, evenly spread over four conditions: (1) a control condition (i.e., no stimulus), (2) a frontline employee, (3) a humanoid service robot, and (4) an employee-robot team. The results indicate that the service robot was the better option to generate attention and stop passers-by, but in this condition the least amount of passers-by were lured into the store. While the frontline employee initiated the lowest amount of interactions, he could convert the highest number of passersby into actual buyers. The robot-employee team managed to encourage the highest number of passers-by to look at the store, but did not convert more of them into actual buyers than the robot on its own.
Background Cycling for transportation and recreation is gaining in popularity, especially in older age groups. The rise in electric assisted cycles (EAC) may also have a role to play in this. With an increase in the number of cyclists comes an increase in the prevalence of cycle crashes. However, there is a lack of knowledge on EAC crashes and crash studies including cycle use data. An important question is also whether the high number of serious road injuries among older cyclists, is due to increased risk or more serious consequences in the event of a crash. Study aim To compare the odds of reporting a cycle crash on a conventional (CC) against electrically assisted cycle (EAC), while controlling for age, gender, BMI, impairments while cycling, cycling frequency and region of residence. Methods A 12-month retrospective cross-sectional survey-based study, including male and female cyclists aged 40+ years, was conducted in Belgium and the Netherlands. Socio-demographics, physical and mental impairments while cycling (such as lower reaction time), crash details and cycling frequency data were collected. Cyclists were grouped into CC, EAC or both (CC + EAC) based on the type of cycle they used during the study period. Logistic regression models were used to calculate the odds of reporting a cycle crash. Main and interaction effects were studied. Results 1,919 cyclists were included in the data analysis (63.2 ± 11.1 years; 50% women). 319 (17% of the total sample) cyclists reported a crash in the previous 12 months, of which 36% were EAC crashes. Those reporting a crash were significantly younger compared to those not reporting a crash. The following significant main effects were observed: those cycling on an EAC had a higher odds of reporting a cycle crash compared to those cycling on a CC (OR = 1.41, 95% CI = 1.01–1.97); cyclists in the category average and high on mental impairments while cycling had a higher odds of reporting a cycle crash compared to those in the category low (OR = 1.72, 95% CI = 1.23–2.40 and OR = 3.49, 95% CI = 2.51–4.90, respectively); higher cycling frequency is related to higher odds of reporting a cycle crash (OR = 3.25, 95% CI = 2.25–4.90). A significant interaction effect was observed between age category and gender (OR = 1.93, 95% CI = 1.15–3.26). Post-hoc tests revealed that men in the younger age category (40–64 years) had the highest probability (18.95%) of reporting a cycle crash, whereas men in the oldest age category (65+ years) had the lowest probability (9.99%) of reporting a cycle crash. No significant difference between age categories in women was observed. Conclusion This study indicates that within a cohort of middle aged and older adults living in regions with high to low cycling modal shares, cycle type, mental impairments while cycling, cycling frequency and region of residence play a significant role in the odds of reporting a (minor) cycle crash. Men in the age category 40–64 years have a significantly higher probability of reporting a cycle crash compared to men of 65+ years. Safety campaigns and instructions should pay particular attention to men in the age category 40–64 years and those with a mental impairment while cycling.
Ethnobiological knowledge is an important part of people's capacity to manage, conserve, and improve the governance of mangrove ecosystems. This paper assesses the ethnobiological importance of mangroves to coastal communities adjacent to seven mangrove forests in Southern and North-western Sri Lanka. 197 households were interviewed, and respondents identified various mangrove ecosystem goods and services. Fruit juice produced from Sonneratia spp. and salads made with Acrostichum aureum L. young leaves constitutes valuable edible products in both regions. Rhizophora mucronata Lamk. and Lumnitzera racemosa Willd., were employed as alternative sources of fuel. Other uses of mangroves include wood for construction, chemical, and medicinal products. However, the usage extent was significantly higher in the Southern province (87.6%) compared to the North-western province (51%). Five indices were developed to understand the ethnobiological knowledge of respondents (Mangrove Use Index, Perception Index, Regulation Awareness Index, and Knowledge Index, Mangrove Dynamics Index). Except for the Mangrove Use Index, the rest of the indices were significantly different between the provinces. Communities with higher mangrove knowledge showed lesser usage. Respondents had negative attitudes towards the regulations that limit/did not allow the community to enter mangrove forests. Community participation, ethnobiological importance, and perspectives regarding how the community wants to manage mangrove forests should be taken into account to avoid conflicts in the future. Considering local perceptions and translating them into mangrove management regulations can be effective in guiding sustainable mangrove management in Sri Lanka as well as in other countries in the world.
A growing number of studies demonstrate that belief in free will (FWB) is dynamic, and can be reduced experimentally. Most of these studies assume that doing so has beneficial effects on behavior, as FWBs are thought to subdue unwanted automatic processes (e.g. racial stereotypes). However, relying on automatic processes can sometimes be advantageous, for instance during implicit learning (e.g. detecting and exploiting statistical regularities in the environment). In this registered report, we tested whether experimentally reducing FWBs positively affected implicit motor learning. We hypothesized that reducing FWBs would lead to both faster and stronger implicit learning, as measured using the alternating serial reaction time (ASRT) task. While we did show a manipulation effect on free will beliefs, there was no detectable effect on implicit learning processes. This finding adds to the growing body of evidence that free will belief manipulations do not meaningfully affect downstream behavior.
Several studies show that political violence justification (PVJ) is associated with unfavourable social and economic characteristics such as poor education, low income, and poverty at the national level. However, the mechanisms by which these factors interact remain unclear. This study aims to find out whether poverty at the country level (contextual poverty) moderates the relationship between individuals’ educational level and household income, and PVJ in the European Union (EU). We perform an analysis using a dataset of 15.347 individuals from twelve EU countries who participated in the European Values Survey, 2017. Logistic regressions models with interaction terms were used to analyze factors related to PVJ. Our findings are twofold. First, we find no evidence of educational level and PVJ's relationship in countries with medium levels of contextual poverty. In contrast, in countries with low levels of contextual poverty, individuals with medium educational level were associated with higher PVJ. Second, individuals living in countries with lower levels of contextual poverty and higher household income were associated with higher PVJ. We conclude that more individuals tend to justify political violence in countries with lower contextual poverty levels—individuals with medium educational level and higher household income. To our knowledge, this is the first study to find evidence that education and socioeconomic status may amplify PVJ in the EU.
We aimed to identify targets for neuropalliative care interventions in sporadic Creutzfeldt-Jakob disease by examining characteristics of patients and sources of distress and support among former caregivers. We identified caregivers of decedents with sporadic Creutzfeldt-Jakob disease from the University of California San Francisco Rapidly Progressive Dementia research database. We purposively recruited 12 caregivers for in-depth interviews and extracted associated patient data. We analysed interviews using the constant comparison method and chart data using descriptive statistics. Patients had a median age of 70 (range: 60–86) years and disease duration of 14.5 months (range 4–41 months). Caregivers were interviewed a median of 22 (range 11–39) months after patient death and had a median age of 59 (range 45–73) years. Three major sources of distress included (1) the unique nature of sporadic Creutzfeldt-Jakob disease; (2) clinical care issues such as difficult diagnostic process, lack of expertise in sporadic Creutzfeldt-Jakob disease, gaps in clinical systems, and difficulties with end-of-life care; and (3) caregiving issues, including escalating responsibilities, intensifying stress, declining caregiver well-being, and care needs surpassing resources. Two sources of support were (1) clinical care, including guidance from providers about what to expect and supportive relationships; and (2) caregiving supports, including connection to persons with experience managing Creutzfeldt-Jakob disease, instrumental support, and social/emotional support. The challenges and supports described by caregivers align with neuropalliative approaches and can be used to develop interventions to address needs of persons with sporadic Creutzfeldt-Jakob disease and their caregivers. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Background Health education and self-management are among key strategies for managing diabetes and hypertension to reduce morbidity and mortality. Inappropriate self-management support can potentially worsen chronic diseases outcomes if relevant barriers are not identified and self-management solutions are not contextualised. Few studies deliberately solicit suggestions for enhancing self-management from patients and their providers. Objective This qualitative study aimed to unravel experiences, identify self-management barriers, and solicit solutions for enhancing self-management from patients and their healthcare providers. Methods Eight in-depth interviews were conducted with healthcare providers. These were followed by four focus group discussions among patients with type-2- diabetes and or hypertension receiving chronic disease care from two health facilities in a peri-urban township in Cape Town, South Africa. The Self-Management framework described by Lorig and Holman, based on work done by Corbin and Strauss was used to analyse the data. Results Patients experienced challenges across all three self-management tasks of behavioural/medical management, role management, and emotional management. Main challenges included poor patient self-control towards lifestyle modification, sub-optimal patient-provider and family partnerships, and post-diagnosis grief-reactions by patients. Barriers experienced were stigma, socio-economic and cultural influences, provider-patient communication gaps, disconnect between facility-based services and patients’ lived experiences, and inadequate community care services. Patients suggested empowering community-based solutions to strengthen their disease self-management, including dedicated multidisciplinary diabetes services, counselling services; strengthened family support; patient buddies; patient-led community projects, and advocacy. Providers suggested contextualised communication using audio-visual technologies and patient-centred provider consultations. Conclusions Community-based dedicated multidisciplinary chronic disease healthcare teams, chronic disease counselling services, patient-driven projects and advocacy are needed to improve patient self-management.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.