Incomplete Knowledge Research Articles

Religion, religiosity and spirituality: Conceptual differences in Brazilian psychotherapists' point of view

AbstractThe study aimed at identifying conceptions of Brazilian psychotherapists about “religion,” “religiosity” and “spirituality.” This is an exploratory study with a qualitative approach. Twenty‐four psychotherapists from two cities, one in the state of São Paulo and the other in the state of Minas Gerais, with a mean of 10.54 years in clinical practice, participated in the study. The participants completed a semistructured interview, and the data were organised through content analysis. It was identified that the participants have vague or incomplete knowledge about these concepts and their differences when comparing the Psychology and Health literature. This seems to be justified by the historical–scientific social context of the training process and by the fact that they do not always attribute real importance to the theoretical precision of these terms in clinical management with their patients. The absence of more precise definitions and discussions of these concepts and terms can increase the chances of the issue being neglected or infrequently addressed in sessions. This can affect the performance, the bond with the patient and the behaviour itself in sessions.

Three-Dimensional Digitalized Virtual Planning of Free Anterior Tibial Artery Perforator Flap for Repairing Soft Tissue Defects in Extremities.

Traditional research methods have limited the application of anterior tibial artery perforator flap due to incomplete knowledge of the perforator. This study aimed to investigate the feasibility of three-dimensional digitalized virtual planning of free anterior tibial artery perforator flap for repairing soft tissue defects in extremities. A total of 11 patients with soft tissue defects in extremities were included. The patient underwent computed tomography angiography (CTA) of bilateral lower limbs, and then the three-dimensional models of bones, arteries, and skin were constructed. Septocutaneous perforators with appropriate length and diameter were selected to design anterior tibial artery perforator flaps in software, and the virtual flaps were superimposed onto the patient's donor site in a translucent state. During the operation, the flaps were dissected and anastomosed to the proximal blood vessel of the defects as designed. Three-dimensional modeling showed clear anatomical relationships between bones, arteries, and skin. The origin, course, location, diameter, and length of the perforator obtained during the operation were consistent with those observed preoperatively. Eleven anterior tibial artery perforator flaps were successfully dissected and transplanted. Postoperative venous crisis occurred in one flap, partial epidermis necrosis occurred in another flap, while the remaining flaps completely survived. One flap was treated with debulking operation. The remaining flaps maintained aesthetic appearance, which did not affect the function of the affected limbs. Three-dimensional digitalized technology can provide comprehensive information on anterior tibial artery perforators, thus assisting in planning and dissecting patient-specific flaps for repairing soft tissue defects in extremities.

A Study to Assess the Knowledge and Attitude Regarding Organ Donation among Undergraduate Students in Selected Colleges of City with View to Prepare an Information Booklet

For all those who are born, there is one event which is certain and inevitable and that is death. Yet we all live as we are immortal when faced with death in the family or in the cause of professional work we are completely unprepared and rudely shocked out of our illusions. Organ donation is a born to medical industry as it has helped in saving the lives of those who would have died otherwise. There is a great need for human organs for transplantation. In fact, the need for exceeds the supply of transplantable organs. There are several reasons for the shortage of organs. Perhaps the most common is that people are hesitant to donate organs because they don’t have adequate knowledge regarding it. In other case people don’t wishes to donate his or her organs may be due to the false beliefs surrounding organ donation. There has been lack of initiative from the government after the organ transplantation act was passed so there is a great need for education and motivation both from the part of people as well as government since main reason for organ shortage is just because of improper or incomplete knowledge of people in donating their organ.

A Self-Learning Based Dynamic Multi-Objective Evolutionary Algorithm for Resilient Scheduling Problems in Steelmaking Plants

Scheduling is one of the most important missions for plant-wide optimization in steelmaking manufacturing systems. In the context of dynamic scheduling, the decision-maker should simultaneously minimize economic objectives within the decision space and violation penalty out of the decision space. In this study, we introduce a resilient scheduling model in steelmaking plants, which provides flexible decisions, including buffering times in between stages and controllable processing speeds in the casting stage, to enable the solution to absorb random disturbances and recover quickly. We formulate the dynamic steelmaking scheduling problem with resilient responding strategies, which is a variant of dynamic multi-objective optimization problems (DMOP), and propose a resilient scheduling optimization framework to solve it over time. First, we employ a vector with problem-specific knowledge to map the whole decision space to sub-schedules in the casting stage, which contains casting priority, casting speed and scaling ratio. Next, we form a multi-objective linear programming model to evaluate these problem-specific vectors. Last but not least, we develop a self-learning based dynamic multi-objective differential evolutionary algorithm to solve the variant DMOP, in which a hypothesis-testing technique is used to detect and identify environmental changes. The sensitivity analysis and algorithm comparisons are performed on a wide range of problem instances under dynamic environments. Experimental evidence validates that the proposed resilient model and the optimization framework is effective to solve the dynamic scheduling problem in steelmaking plants. Note to Practitioners—This paper investigates a dynamic scheduling problem that comes from steelmaking manufacturing systems and is extensively studied in existing works. Because a decision-maker only has incomplete knowledge about the realistic environments, this paper simultaneously considers that both the decision and objective space of a scheduling problem dynamically changed over time. We develop a resilient scheduling model which can absorb the different types or scales of disturbances caused by unforeseen events, and recover rapidly to the original state. We formulate the resilience scheduling model and propose a dynamic multi-objective algorithm based on the self-learning differential evolutionary algorithm to solve it. The effectiveness of the proposed model and algorithm is validated via sensitivity analysis and comparison to other well-known algorithms. Furthermore, the resilient scheduling model and its optimization framework can also be applied to dynamic scheduling problems in other industries.

Activity budgets, responses to disturbance and novel behaviours in captive mountain chicken frogs Leptodactylus fallax

Mountain chicken frogs Leptodactylus fallax are assessed as Critically Endangered on the IUCN Red List due to threats including chytridiomycosis and habitat loss. Ex-situ populations underpin species survival, but captive management is hampered by incomplete species knowledge, including its behavioural biology. In sixteen adult frogs, we investigated enclosure usage, nocturnal activity budgets, and behavioural responses to varying levels of husbandry-related disturbance through instantaneous scan sampling of camera trap footage over forty-two consecutive nights. Enclosure usage was quantified through the application of modified Spread of Participation Indices (mSPI). We present the first detailed activity budgets published for this species and found that broad activity patterns of captive animals corresponded well with their wild conspecifics; taking refuge during the day and emerging at dusk into exposed areas of the enclosure. Some behaviours, especially hunting and bathing, were partially phased throughout the nocturnal period. Enclosure use was not even, with disproportionately large amounts of time spent using nest boxes and deep leaf litter, even at night, and the converse in thin leaf litter and elevated perches. Principal Components Analysis (PCA) identified a bold-shy behavioural grouping (movement and resting opposed to refuge behaviours) that was consistent across husbandry-related disturbance conditions. Randomisation analyses showed that disturbance significantly affected multiple measured behaviour, especially feeding, social interactions and vocalisation; the impact was associated with the degree of disturbance. Novel behaviours were also documented, which are thought to be related to courtship. Our data provide insights into the behaviour of mountain chicken frogs and highlight potential trade-offs between enclosure maintenance, veterinary intervention, and animal welfare. Keywords: Amphibians, behaviour, mSPI, disturbance, welfare, Zoo

Competencies of Indian Medical Graduates in delivering Effective Tuberculosis care: A mixed method study among doctors at Chennai, India

Introduction: In spite of the various advances in the Tuberculosis (TB) management system in our country, we are still struggling to achieve the End TB strategy. Addressing the health provider difficulties in TB care will definitely be potential tool to control TB. Objective: To assess the competency, challenges and solutions of Indian medical graduates to provide effective Tuberculosis care. Method: It is a mixed methodological study conducted among the Bachelor of Medicine and Bachelor of Surgery (MBBS) graduates practising in various parts of Chennai, Tamil Nadu, India. The competency of the Indian medical graduates in delivering TB care was assessed using a content validated e-survey questionnaire disseminated through social media affinity groups and free listing;pile sorting and in-depth interview was done in the qualitative part. Results:The proportion of participants having good, fair and poor competency in providing effective TB care was 35%,19% and 46%, respectively. Various challenges in attaining good competency were enlisted by the participants through free listing, a smaller number of TB patients in private medical college has attained the highest Smith S value followed by other eleven challenges. Based on the pile sorting and indepth interview various solutions have been suggested, with most of them emphasizing on compulsory TB chest clinic posting followed by others measures. Conclusion: The major reason identified in difficulties in delivering TB care was inadequate exposure in managing TB patients, incomplete knowledge on notification,referral and followup system. These lacunae can be overturned by compulsory posting in TB clinics during internship period, provision of required personal protective equipment (PPE) for doctors and patients in TB wards and outpatient department, mandatory teaching on updates of TB management.

Intercropping increases soil N-targeting enzyme activities: A meta-analysis

Intercropping rationally utilizes land resources and effectively increases soil biodiversity. The enzyme-mediated biological catabolism of soil organic and mineral components is a crucial step in controlling soil carbon and nutrient circulation and fixation in agroecosystems, and those enzymes involved in nitrogen (N) cycling are particularly important. However, when considering different crop combinations, the effects of intercropping on N-targeting enzyme activities remain uncertain. Given such incomplete knowledge of these belowground processes and interactions, we synthesized results for three kinds of N-targeting enzyme activities in response to intercropping based on 117 peer-reviewed papers, in which 454 observations were included. We found that intercropping significantly increased the activity of N-acetyl-glucosaminidase by 26.1%, protease by 10.2%, and urease by 22.2%. Our results suggest that the intercropping effect is mediated by responses of the soil microbes to the diversity of soil nutrients under different crop combinations. Soil N availability and crop competition for nutrients between plant crops mediate patterns of microbial nutrient acquisition strategies and secretion of N-targeting enzymes. Our comprehensive meta-analysis highlights the importance of intercropping for better soil quality and biodiversity and has implications for the role of intercropping in regulating soil carbon dynamics.

UnTiCk: Unsupervised Type-Aware Complex Logical Queries Reasoning over Knowledge Graphs

For the task of answering complex logical queries on large-scale incomplete knowledge graphs, the promising approach is to embed the knowledge graph and complex logical queries into a low-dimensional space and perform iterative reasoning to find the final answers. The general problem is that these models do not include entity types as an important feature, which reduces the reasoning potential. However, explicit type information is not always available on large-scale knowledge graphs. We innovatively propose an embedding-based framework for Unsupervised Type-Aware Complex Logical Queries (UnTiCk). Our approach implements unsupervised type constraints on multi-hop complex logical query processing. Moreover, it can capture the different representations of type features when entities are in different locations in the logical path. We designed type compatibility measurement meta-operators combined with popular Existential Positive First-Order (EPFO) neural logical operators to achieve type-aware EPFO complex query embedding. We validated the effectiveness of our framework on popular large-scale knowledge graphs by using the same embedding dimensionality as complex logical embedding methods. The results showed an average relative improvement of 1.9–12.8% on Hit@3 and up to 42.1% on the certain logical pattern.

Agenda Control under Policy Uncertainty

AbstractModels of agenda setting are central to the analysis of political institutions. Elaborations of the classical agenda‐setting model of Romer–Rosenthal have long been used to make predictions about policy outcomes and the distribution of influence among political actors. Although the canonical model is based on complete and perfect information about preferences and policy outcomes, some extensions relax these assumptions to include uncertainty about preferences and reversion points. We consider a different type of uncertainty: incomplete knowledge of the mapping between policies and outcomes. In characterizing the optimal agenda setting under this form of uncertainty, we show that it amends substantively the implications of the Romer–Rosenthal model. We then extend the model dynamically and show that rich dynamics emerge under policy uncertainty. Over a longer horizon, we find that agenda control suppresses the incentive of legislators to experiment with policy, leading to less policy learning and worse outcomes than are socially efficient.

Dynamic analysis for mechanical system with clearance joint considering dependent interval parameters

Uncertainties caused by measuring errors or incomplete knowledge affect the dynamic response and functional reliability of the mechanical system, particularly for the mechanisms with clearance joints. Considering that dependency between the interval parameters in the uncertain quantification of mechanical systems with clearance joints is prone to be ignored, in this article, a dynamic analysis procedure considering the correlation between interval parameters is presented. Firstly, the continuous contact force model and modified friction force model are embedded into the deterministic dynamic governing equations of the mechanical system with clearance joints derived by the Lagrangian method. To characterize the correlation between interval uncertainties, the convex ellipsoid model is employed, and a Chebyshev-based convex scanning method is developed to obtain the bounds of dynamic response. Finally, a robot manipulator with clearance joint considering dependent uncertainties is illustrated as an example to investigate the clearance effect and correlations between uncertainties on the dynamic behavior. In the numerical simulation, the geometrical parameters of the manipulator arm are regarded as dependent interval parameters. The results show that the dependence between interval uncertainties induces relatively conservative bounds and should be considered carefully in the case of a large clearance size existing in the mechanical system.

ការអនុវត្តវិធីសាស្ត្របង្រៀនតាមបែបចម្រុះ ដើម្បីជួយសិស្សឱ្យរៀនសកម្មក្នុងមុខវិជ្ជាវិទ្យាសាស្ត្រនៅកម្រិតថ្នាក់ទី៣

Teaching methods and strategies play an important role in encouraging students' active participation in learning and thinking, skills, knowledge, and attitudes, and they have a strong influence on students' learning outcomes and practices to help develop students' personalities. However, most teachers still use teaching methods that are not up to date with the new era (e.g., teacher-centered approach), which leads to students being inactive, not united and cooperative, not communicating well, not having critical thinking, not having problem solving skills, and having incomplete knowledge. The purpose of this research was to find out reasons for using blended learning to help students be active in their studies and to improve their achievement in science subjects in Grade 3. There were 19 students (five females) and four observers (four females) who participated in this study. Research tools such as observation checklists and interview guides were developed, and data were analyzed using thematic analysis. Pre-test and post-test and observation checklists were analyzed by Microsoft Excel, focusing on descriptive and inferential statistics. The results showed that blended learning helped students to be active in learning by improving lesson objectives, teaching materials, teacher methods, student learning behaviors, student satisfaction, and participation. Concerning learning activities, self-study at home with the help of parents or guardians helped improve students’ learning outcomes (Mean pre-test = 1.61, SD = 0.89 and Mean post-test = 6.82, SD = 1.74). The t-test results showed a significant difference (p<0.05), meaning that the results are 95% reliable. Overall, the use of blended learning has helped students to be active in learning and improving their academic achievement in science subjects in Grade 3.

Influence of Geometrical Parameters on the Optical Activity of Chiral Gold Nanorods

AbstractChiral metal nanoparticles (NPs) offer a powerful means of inducing and harnessing optical activity. However, due to the incomplete knowledge of the underlying growth mechanisms, there is still limited control over the achievable morphological detail and, consequently, over the resulting optical activity. Therefore, theoretical modeling is needed to guide experimental development toward optimizing the plasmonic chiroptical response. Toward filling this gap, herein an extensive parametric analysis is presented, via computer‐aided‐design (CAD) models and full‐wave electrodynamic simulations, which aims at systematically analyzing the influence of structural changes on the plasmonic circular dichroism (CD) spectra of rod‐shaped gold NPs comprising helical indentations on achiral nanorod cores. From this analysis, interesting patterns in the plasmon‐mediated resonant behavior are identified and cause–effect relationships are drawn that may serve as a go‐to recipe for the understanding and fabrication of these NPs and their applications, such as spectroscopic (bio)detection including CD spectral shifts and surface‐enhanced Raman optical activity.

Contact Lens Wearer’s Outlook Towards Recommended Replacement Schedule of Contact Lens in Mumbai

Background: Non-compliance is one of the major reasons of CL related complications. Various research studies have been done over the past three decades to find reasons for patient non-compliances. Use of CLs over the recommended replacement schedule has been one of the major reasons leading to CLs complications. It is evident that the CLs user are often unaware of their non-compliance towards lens care and maintenance regimen. Even after introduction of daily disposable lenses and one-step multipurpose solution non-compliance towards CLs use is still a major problem in CLs patients. The complications due to patient noncompliance can be sometimes irreversible. This research will help us understand CL user reasons of intentionally or unintentionally being non-compliant to recommended replacement frequency.
 Method: All CL users willing to share their experience participated in this questionnaire based online survey which was designed with the help of focused group discussion.
 Results: Most of the CL wearer prefer buying pairs for at least 6 months at a time. Almost 80% of CL wearers do not replace their CL as per MRRF. 70% of those who participated wore CL for more than 10 hours per day. The male and female ratio is almost equal in the participants participated in the research.
 Conclusion: Research done in the past suggest incomplete knowledge imparted to the wearer or CL forgetting the instructions given to them at dispensing. Research also suggest some CL wearer knowingly are non-compliant to MRRF. This study tries to find loopholes in a CL practice by finding reasons of non-compliance as per CL wearers prospective and probable suggestion from the user itself. An ECP is a paramedical person who has the right to prescribe them, whether according to MRRF or as per their knowledge of the patient’s ocular status. For e.g., an antibiotic medicine is supposed to be prescribed for 3 days or 5 days, once or twice is decided by the medical practitioner prescribing. As this research shows over 50% of the CL wearer said that their 'ECP said its okay'.

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease.

The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.

Ensemble Classification Model for EV Identification From Smart Meter Recordings

Electric vehicles (EVs) often consume large amounts of energy, and uncoordinated charging of many EVs may lead to grid overload, adversely impacting other customers. Electricity distributors require full visibility on the EV distribution to better manage operation planning of their distribution grid. However, they often have incomplete knowledge of EV presence in their network. Identifying EV customers (charging at home) using smart meter data is a nontrivial task for the grid network and energy scheduling. The difficulties include recognizing charging patterns, balancing the number of EV and non-EV customers during modeling, and building an efficient classification model. In this article, we propose a periodic pattern recognition method to extract useful EV charging patterns. Real world smart meter datasets are unbalanced with few EVs and majority of energy customers are those without EVs. We improve Kmedoids evaluated by dynamic time warping to obtain the representative non-EV training samples so that balanced samples over EV and non-EV customers can be obtained. We develop an ensemble classification model (ECM) by taking advantages of multiple classifiers, in which the optimization consists of obtaining the optimal subset of periodic patterns and the optimal parameters in each classifier and the optimal weights for combining classifiers. The superiority of the proposed ECM is demonstrated in comparison to several baseline models.

Schmidtea mediterranea as a Model Organism to Study the Molecular Background of Human Motile Ciliopathies.

Cilia and flagella are evolutionarily conserved organelles that form protrusions on the surface of many growth-arrested or differentiated eukaryotic cells. Due to the structural and functional differences, cilia can be roughly classified as motile and non-motile (primary). Genetically determined dysfunction of motile cilia is the basis of primary ciliary dyskinesia (PCD), a heterogeneous ciliopathy affecting respiratory airways, fertility, and laterality. In the face of the still incomplete knowledge of PCD genetics and phenotype-genotype relations in PCD and the spectrum of PCD-like diseases, a continuous search for new causative genes is required. The use of model organisms has been a great part of the advances in understanding molecular mechanisms and the genetic basis of human diseases; the PCD spectrum is not different in this respect. The planarian model (Schmidtea mediterranea) has been intensely used to study regeneration processes, and-in the context of cilia-their evolution, assembly, and role in cell signaling. However, relatively little attention has been paid to the use of this simple and accessible model for studying the genetics of PCD and related diseases. The recent rapid development of the available planarian databases with detailed genomic and functional annotations prompted us to review the potential of the S. mediterranea model for studying human motile ciliopathies.

Hybrid Decision-Making-Method-Based Intelligent System for Integrated Bogie Welding Manufacturing

To address the challenges of incomplete knowledge representation, independent decision ranges, and insufficient causal decisions in bogie welding decisions, this paper proposes a hybrid decision-making method and develops a corresponding intelligent system. The collaborative case, rule, and knowledge graph approach is used to support structured documents and domain causality decisions. In addition, we created a knowledge model of bogie welding characteristics and proposed a case-matching method based on empirical weights. Several entity categorizations and relationship extraction models were trained under supervised conditions while building the knowledge graph. CRF and CR-CNN obtained high combined F1 scores (0.710 for CRF and 0.802 for CR-CNN) in the entity classification and relationship extraction tasks, respectively. We designed and developed an intelligent decision system based on the proposed method to implement engineering applications. This system was validated with some actual engineering data. The results show that the system obtained a high score on the accuracy test (0.947 for Corrected Accuracy) and can effectively complete structured document and causality decision-making tasks, having large research significance and engineering value.

A new measure of physicians' erroneous assumptions towards adults with intellectual disability: A first study.

Incomplete knowledge and unfamiliarity with intellectual disability (ID) contribute to erroneous assumptions of physicians towards ID, which negatively impact the health equity of people with ID. This study aimed to identify the erroneous assumptions that, based on the ID stakeholders' perceptions, were the most prevalent in physicians and damaging for the healthcare of adults with ID, verify their unidimensionality and that no personal characteristics of ID stakeholders were associated with their ratings of erroneous assumptions' prevalence and damage. Seventy-four possible physician erroneous assumptions were developed concerning health, daily living skills and quality of life of individuals with ID. ID stakeholders rated each one for perceived prevalence in physicians and damage for the healthcare of adults with ID. Frequency analysis, exploratory factor analysis and correlations were run separately for participants' prevalence and damage ratings. Twenty-seven erroneous assumptions were identified as those perceived most prevalent and damaging. Their unidimensionality was ascertained and participants' characteristics were not associated with their prevalence and damage ratings. The identified assumptions are appropriate to represent the items of a new instrument that can be used in medical education to guide the development of curricula to change erroneous assumptions.

A survey of pediatric oncology nurses’ oral health knowledge, attitudes, practices, and perceived barriers in a Singapore Tertiary Children’s Hospital

AimTo explore oral health-related knowledge, abilities, attitudes, practices, and barriers of pediatric oncology nurses at an Asian children’s hospital.MethodsA cross-sectional study was conducted via a self-administered anonymized questionnaire. Data was analyzed to summarize knowledge, confidence, and practice behaviors.ResultsAll sixty-three pediatric oncology nurses responded. Fifteen participants had >80% of the knowledge questions correct. Majority (97.3%) agreed on their roles in helping patients maintain their oral health. However, 75.8% of participants felt need for training in giving oral health advice. Notably, 74.6% checked patients’ mouths at least once daily but only 57.1% felt adequately trained. Though a high proportion (>90%) of nurses felt confident to assist with oral care, only 65% would assist patients to do so; “Uncooperative patient” was the major barrier reported.DiscussionNurses have high general awareness of importance of oral health, but had incomplete knowledge. Compared to previous studies, most (90.5%) did not find performing oral care unpleasant but other barriers might have hindered actual oral care practice.ConclusionNurses were motivated to assist in oral care of children with cancer but sometimes felt ill-equipped. Updated national and institution guidelines, didactic and hands-on training, and implementation of practical support could be considered.

Boundary conditions to represent the wave impedance characteristics of axial compressors

Thermo-acoustic oscillations can damage gas turbines. Numerical predictions guide the combustor design to prevent the occurrence of this hazardous resonance phenomenon. Uncertainties, such as incomplete knowledge of boundary conditions, challenge this strategy. For the appropriate estimation of combustor inlet boundary conditions, we develop a model mimicking the reflection characteristics of an axial compressor. The non-compact formulation relies on individual blade rows’ performance data and allows thereby estimating the phase delay information. The evolution of the reflection coefficients is analysed for different operating conditions and related to the slope of the compressor characteristics. We find that the impedance at the compressor discharge varies significantly with frequency, resulting in wave interactions with all blade rows. Only towards compressor choke, high flow velocities at the compressor discharge restrict the penetration depth of incident waves resulting in reflection coefficients with minor frequency dependence. That even small phase components of the reflection coefficient are crucial for thermo-acoustic stability prediction is demonstrated in an example.