Truth Values Research Articles

Commercially available activity monitors such as the fitbit charge and apple watch show poor validity in patients with gait aids after total knee arthroplasty

PurposeThe aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery.MethodsTwenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques.ResultsMean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from − 0.87 to 1.36 with limits of agreement (LOA) ranging between − 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between − 2.8 and 5.73 with LOA between − 13.51 and 24.97.ConclusionsThese devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution.

On The Preparation Of Training Data For 3D Tracking Of Position And Orientation Of Microswimmers Using Defocusing And Deep Learning

The use of image analysis algorithms based on deep convolutional neural networks (CNNs) is opening new possibilities in experimental techniques for fluid dynamics. In the field of microscopic 3D particle tracking based on defocusing, it has been shown on synthetic images that CNNs can be used to determine not only the depth position but also the orientation of non-spherical microscopic particles or specimens. However, the translation of such approaches to experimental setups is challenging, especially due to the difficulty of obtaining reliable labeled images to train the neural networks. In this work, we propose a method to obtain labeled images of microswimmers from conventional microscopy images, exploiting the properties of the swimming motion at low Reynolds numbers, where inertial effects are negligible. This method allows us to obtain ground truth values of orientation and depth position of a microorganism swimming at a regular pace that can be used as training data for CNNs. The proposed methodology was used to obtain labeled images from experimental videos of the micro-organism Euplotes Vannus. The labeled images were used to train a VGG16 network, providing an estimated uncertainty in the determination of the orientation angles psi, theta, phi, and the depth position Z of 2.4%, 1.4%, 2.3%, and 3.5%, respectively.

Multilattice as the set of truth values for fuzzy rough sets

Radzikowska and Kerre developed L-fuzzy rough set as a fuzzy generalisation of the notion of rough sets. Specifically, they have taken a residuated lattice L as the underlying set of truth degrees. However, in real life, we may encounter situations where truth degrees are not always linear, or where the existence of the least upper bound of two elements is no longer required. Instead, there may be the possibility of having minimal upper bounds, and dually, maximal lower bounds, multilattices have become a suitable algebraic structure for addressing such cases. Our aim is to extend Radzikowska and Kerre's work by replacing a residuated lattice with a multilattice M, thereby obtaining a more flexible structure. We define M-fuzzy rough sets using operators derived from adjoint pairs within the multilattice M. For a tolerance and an equivalence M-fuzzy relation, we define an appropriate M-fuzzy rough set and investigate its properties.

Head tracking using an optical soft tactile sensing surface.

This research proposes a sensor for tracking the motion of a human head via optical tactile sensing. It implements the use of a fibrescope a non-metal alternative to a webcam. Previous works have included robotics grippers to mimic the sensory features of human skin, that used monochrome cameras and depth cameras. Tactile sensing has shown advantages in feedback-based interactions between robots and their environment. The methodology in this paper is utilised to track motion of objects in physical contact with these sensors to replace external camera based motion capture systems. Our immediate application is related to detection of human head motion during radiotherapy procedures. The motion was analysed in two degrees of freedom, respective to the tactile sensor (translational in z-axis, and rotational around y-axis), to produce repeatable and accurate results. The movements were stimulated by a robot arm, which also provided ground truth values from its end-effector. The fibrescope was implemented to ensure the device's compatibility with electromagnetic waves. The cameras and the ground truth values were time synchronised using robotics operating systems tools. Image processing methods were compared between grayscale and binary image sequences, followed by motion tracking estimation using deterministic approaches. These included Lukas-Kanade Optical Flow and Simple Blob Detection, by OpenCV. The results showed that the grayscale image processing along with the Lukas-Kanade algorithm for motion tracking can produce better tracking abilities, although further exploration to improve the accuracy is still required.

Librarians as guardians of information quality: navigating the challenges of misinformation and disinformation

Purpose This study aims to explore the pivotal role of libraries in combating misinformation and disinformation in the contemporary information landscape, emphasizing their significance as guardians of information integrity. Design/methodology/approach Drawing on a review of literature and surveying librarian perspectives, this study examines the multifaceted strategies used by libraries to address misinformation and disinformation. It delves into the roles of librarians in distinguishing between different forms of false information, promoting information literacy and fostering community engagement. Findings Libraries serve as bastions of knowledge, curating diverse and reliable resources while actively combating false information through educational initiatives, technological innovations and community outreach. Librarians play a crucial role in empowering users with the skills to critically evaluate information and navigate the complexities of the digital age. Originality/value This study contributes to the scholarly discourse by highlighting the evolving role of libraries as guardians of information integrity in the face of misinformation and disinformation. It underscores the importance of libraries in promoting a more informed and resilient society, enriching public discourse with the values of truth and understanding.

TRANSFORMASI SPIRITUALITAS

The research addressed the gap between the theological concept of the new man in Christ and its implementation in everyday Christian life, highlighting the need for a deeper understanding of this concept to address uncertainties and misconceptions among believers. Through a qualitative theological approach, the study explored the subjective meaning of religious beliefs, religious practices, and biblical texts. Findings indicated that the concept of the new man emphasized the importance of spiritual transformation and the embodiment of values of love and truth in Christian life practices. Implications included shifts in self-perception and perceptions of others, conflict resolution, and understanding of true life purposes. The novelty of thought lay in the comprehensive application of the concept of the new man in the text of Colossians 3:5-17, aiming to bring positive impacts to individuals and communities. Thus, understanding and embodying this concept called believers to live in freedom, sanctity, and authentic love.

On Culture and Cultural Values

Culture is not merely a simple concept; it is a complex entity encompassing the creative products and activities of humanity. It is the result of continuous labor and innovation, passed down from generation to generation, reflecting the core values of truth, goodness, and beauty. Culture embodies both the spiritual and material expressions that humans have created and continue to create, ranging from works of art, architecture, language, and customs to systems of values, ideologies, and beliefs. In today’s context, as the world undergoes profound transformations in economics, politics, and technology, the role of culture has become more crucial than ever. Culture serves as a bridge that fosters understanding and respect among nations and civilizations, while also acting as a powerful tool to address global challenges and conflicts. The sustainable development of a nation cannot solely rely on economic or military strength but must also include cultural development. A prosperous society is one that values and nurtures its cultural heritage. This article will delve into the analysis of cultural values and its indispensable role in modern social life. We will explore how culture impacts various aspects of life, from social relations and education to individual and community development. Additionally, the article will discuss the challenges that culture faces in the digital and globalized era and how we can preserve and promote cultural values amidst the ever-changing landscape of the world.

African Lovegrass Segmentation with Artificial Intelligence Using UAS-Based Multispectral and Hyperspectral Imagery

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.

Artificial Intelligence for Media: Opportunities or Threats

The emergence of artificial intelligence (AI) has ushered in an era of unprecedented possibilities and, at the same time, poses significant threats in the media landscape. As a potent tool, AI can streamline operations, offering innovative solutions like content personalization, predictive analytics, and augmented reality experiences that can enhance user engagement and boost the media industry’s economic viability. These opportunities allow for a more immersive and personalized user experience, leveraging data to create content that is finely tuned to individual preferences and trends. However, the integration of AI in media does not come without its challenges. The threat of deep fakes and the dissemination of misinformation stand as substantial concerns, with AI technologies facilitating the creation of highly convincing fake content that can manipulate perceptions and sow discord. Moreover, the autonomy given to AI systems can potentially lead to job displacements and raise ethical concerns regarding privacy and data security. Despite these threats, when wielded responsibly and ethically, AI can usher in a new golden age for media characterized by creativity, efficiency, and innovation. As the media industry stands at this crossroads, it must navigate the fine balance between leveraging AI’s opportunities and mitigating its potential threats, fostering a landscape that upholds truth, ethics, and human value at its core. It is imperative for stakeholders to collaborate, devising strategies and regulatory frameworks to ensure AI serves as a force for good, steering media towards a future that embodies progress and inclusivity.

On the quantum circuit implementation of modus ponens

The process of inference reflects the structure of propositions with assigned truth values, either true or false. Modus ponens is a fundamental form of inference that involves affirming the antecedent to affirm the consequent. Inspired by the quantum computer, the superposition of true and false is used for the parallel processing. In this work, we propose a quantum version of modus ponens. Additionally, we introduce two generations of quantum modus ponens: quantum modus ponens inference chain and multidimensional quantum modus ponens. Finally, a simple implementation of quantum modus ponens on the OriginQ quantum computing cloud platform is demonstrated.

Ultrafast T2 and T2* mapping using single-shot spatiotemporally encoded MRI with reduced field of view and spiral out-in-out-in trajectory.

T2 and T2* mapping are crucial components of quantitative magnetic resonance imaging, offering valuable insights into tissue characteristics and pathology. Single-shot methods can achieve ultrafast T2 or T2* mapping by acquiring multiple readout echo trains. However, the extended echo trains pose challenges, such as compromised image quality and diminished quantification accuracy. In this study, we develop a single-shot method for ultrafast T2 and T2* mapping with reduced echo train length. The proposed method is based on ultrafast single-shot spatiotemporally encoded (SPEN) MRI combined with reduced field of view (FOV) and spiral out-in-out-in (OIOI) trajectory. Specifically, a biaxial SPEN excitation scheme was employed to excite the spin signal into the spatiotemporal encoding domain. The OIOI trajectory with high acquisition efficiency was employed to acquire signals within targeted reduced FOV. Through non-Cartesian super-resolved (SR) reconstruction, 12 aliasing-free images with different echo times were obtained within 150ms. These images were subsequently fitted to generate T2 or T2* mapping simultaneously using a derived model. Accurate and co-registered T2 and T2* maps were generated, closely resembling the reference maps. Numerical simulations demonstrated substantial consistency (R2>0.99) with the ground truth values. A mean difference of 0.6% and 1.7% was observed in T2 and T2*, respectively, in in vivo rat brain experiments compared to the reference. Moreover, the proposed method successfully obtained T2 and T2* mappings of rat kidney in free-breathing mode, demonstrating its superiority over multishot methods lacking respiratory navigation. The results suggest that the proposed method can achieve ultrafast and accurate T2 and T2* mapping, potentially facilitating the application of T2 and T2* mapping in scenarios requiring high temporal resolution.

Research on Surveying and Mapping Data Processing Based on Nonlinear Mathematical Models and Deep Learning Optimization

In order to deeply understand the mapping data processing of nonlinear mathematical model optimization, this paper uses nonlinear model optimization theory to process mapping data. When the precision of parameter approximation is high, the calculation results of each algorithm are the same, and the iteration times of the fastest descent method and simplex method are significantly increased compared with the other three algorithms. Therefore x01 = 5.42 in the parameter is kept unchanged near the truth value, and the convergence range of Newton method, the fastest descent method, conjugate gradient method and simplex method away from the truth value of parameter x02 = −0.25 is investigated. When the approximate value of undetermined parameters is high, the results of Newton algorithm and simplex algorithm are consistent, but the Newton iterative algorithm has faster convergence speed and higher computational efficiency than the simplex algorithm. Lower value when the undetermined parameter approximation precision, namely the undetermined parameter approximation and its true value is far off, may complete failure type Newton iteration algorithm, the simplex algorithm can be a supplement of the Newton method for the most part, the simplex method significantly reduces the requirements for initial value of parameters, data calculation efficiency has improved significantly.

Linear-time logics -- a coalgebraic perspective

We describe a general approach to deriving linear-time logics for a wide variety of state-based, quantitative systems, by modelling the latter as coalgebras whose type incorporates both branching and linear behaviour. Concretely, we define logics whose syntax is determined by the type of linear behaviour, and whose domain of truth values is determined by the type of branching behaviour, and we provide two semantics for them: a step-wise semantics akin to that of standard coalgebraic logics, and a path-based semantics akin to that of standard linear-time logics. The former semantics is useful for model checking, whereas the latter is the more natural semantics, as it measures the extent with which qualitative properties hold along computation paths from a given state. Our main result is the equivalence of the two semantics. We also provide a semantic characterisation of a notion of logical distance induced by these logics. Instances of our logics support reasoning about the possibility, likelihood or minimal cost of exhibiting a given linear-time property.

Development of Arduino Uno-Based Free Fall Motion Props to Increase Learning Motivation Towards Practicum Results of 11th Grade Students

Research has been conducted on the development of Arduino Uno-based Free Fall Motion (FFM) teaching aids to increase learning motivation towards the results of practicum for 11th grade students of MAN 1 Mataram. This study aims, (1) to design arduino-based teaching aids on free fall motion (FFM) material, (2) to determine the validity level of teaching aids to 3 media experts and 3 material experts, (3) to determine the increase in student learning motivation, (4) to determine the results of student practicum, and (5) to determine the effect of learning motivation on student practicum results. This type of tool development is Research and Development (R&D). the subject model of this research is MAN 1 MATARAM students as many as 35 students. The instrument used was a questionnaire. The development model in this study uses a 3D model. 3D development model (ThreeD). This model is a modification of the Thiagarajan model which includes 4 stages called the 4D (Four-D) model. Based on the research objectives, a student's practicum teaching aid on GJB material was made. Based on the validity test results, the teaching aids are suitable for use as learning media in class 11 MAN 1 Mataram. Furthermore, the results of motivation based on the value before applying FFM props amounted to 51.60% (pretest). While the interpretation after the practicum (posttest) was 90.44% and the N-Gain was 0.46. This means that there are changes in learning activities in students. For the results of student practicum through the satisfaction interval with a very high category of 11 students, a high category of 23 students, a low category of 1 student and very low 0 students. Meanwhile, the correlation of learning motivation to student practicum results shows rcount> table (0.450>0.344) and obtained tcount> table (2.895>2.035). From the truth value of the hypothesis it can be concluded that there is an influence between learning motivation (X) and practicum learning outcomes (Y) on GJB material.

Solvability of systems of partial fuzzy relational equations revisited – a short note

Fuzzy inference systems have been widely investigated from different perspectives, including their logical correctness. It is not surprising that the logical correctness led mostly to the questions on the preservation of modus ponens. Indeed, whenever such a system processes an input equivalent to one of the rule antecedents, it is natural to expect the modus ponens to be preserved and the inferred output to be identical to the respective rule consequent. This leads to the related systems of fuzzy relational equations where the antecedent and consequent fuzzy sets are known values, the inference is represented either by the direct product related to the compositional rule of inference or by the Bandler-Kohout subproduct, and the fuzzy relation that represents the fuzzy rule base is the unknown element in the equations. The most important question is whether such systems are solvable, i.e., whether there even exists a fuzzy relation that models the given fuzzy rule base in such a way that the modus ponens is preserved.Partiality allows dealing with partially defined truth values which allows to deal with situations, where we cannot define the truth value for a given predicate. The partial logics have been recently extended to partial fuzzy logics and the partial fuzzy set theory has been developed. Partial fuzzy sets then may have undefined membership degrees for some values from the given universe.This background leads naturally to the problem of solvability of partial fuzzy relational equations, which are equations with partial fuzzy sets in the role of antecedents and consequents and with partial fuzzy relation as the model of the given fuzzy rule base. Such a setting led to a recent publication that uncovers the solvability and even the shape of the solutions. In this contribution, we revisit the problem and consider a specific case that allows partiality only in the input. In other words, antecedents, as well as consequents expressing the knowledge, are fully defined. Thus, the model of the fuzzy rules is one of the standard and fully defined relations. We investigate what happens if the input differs from one of the antecedents only by a few undefined values. This mimics the situation when a description of an observed object misses a few values in its feature vector. We show that under specific conditions, we still may preserve the modified modus ponens, i.e., that the inferred output is identical with the fully defined consequent of the respective rule.

Chase Wrenn, The True and the Good: A Strong Virtue Theory of the Value of Truth

Chase Wrenn, The True and the Good: A Strong Virtue Theory of the Value of Truth

The Rumors Are True

Abstract This essay examines the role of rumors in F. W. Murnau’s films as well as in later writings about his life and career. While Murnau perpetuates long-standing figurations of gossip as a frivolous or even malicious activity, he also grants unverified information a truth value. Where Heidegger claimed that Gerede (idle talk) is based on “groundlessness” and “indifferent intelligibility,” this essay shows that rumors in Murnau’s films are often well-founded and also essential to a nuanced understanding of his work. Bringing together film analyses, archival materials, and philosophical texts, the essay sheds light on an underexamined aspect of Murnau’s oeuvre and addresses broader questions about the status of speculative, uncertain, or contested knowledge. A study of Gerede does not entail a concealment of authentic discourse but illuminates Murnau’s philosophy of rumors as part of a dialectic of destabilized knowledge.

𝕃-valued variable precision rough sets

In this study, we focus on exploring a novel rough sets model, L -valued variable precision rough sets, employing a complete residuated lattice within the context of residuated lattices to offer an expansive perspective on lattice truth values. This model comprises three main elements: L -fuzzy set H representing the universal set, an L -valued relation on H , and an L -fuzzy subset of H . Also, the classical L -fuzzy rough set, L -valued fuzzy rough set, and L -fuzzy variable precision rough sets can be regarded as special cases of this model. Moreover, through constructive approaches, this paper comprehensively characterizes L -valued variable precision rough sets. Finally, we examine the connection between an L -quasi-topology and L -valued variable precision rough sets on the L -set H .

PENGAJARAN AGAMA KATOLIK DI SEKOLAH MENUMBUH NILAI KEJUJURAN DAN KEBENARAN DALAM DIRI PESERTA DIDIK

Honesty and truth are the important values ​​for human life because they are related to the good behaviors. Educational institution has an important role in educating students’ character of being honesty and truth in the life of students at school. Catholic religious teaching in schools is an effort made by a Catholic religious teacher to instill so called Christian values of honesty and truth in life of students. The research conducted with the following aims: 1) to analyze the extent of efforts made by Catholic religious teacher to instill the values ​​of honesty and truth in students’ life; and 2) to analyze the extent to which those values taught by Catholic religious teachers have a positive impact on the students' behavior. The research was conducted using qualitative methods. Research data was obtained through individual interviews. Research data analysis used an inductive approach. The results of research data analysis revealed that majority of informants revealed that they had tried their best to instill the values ​​of honesty and truth in the life of students through Catholic religious teaching in schools.

Peran Media Sosial dalam Katekese guna Membangun Iman di Era Digital

This article discusses the role of social media in catechesis to build faith in the digital age. The digital age has brought about major changes in the way we communicate and access information. Social media, as a product of the digital technology revolution, has become a highly efficient and effective means of communication, connecting individuals around the world quickly and easily. The Catholic Church views social media as an important tool for evangelization, faith formation, and spreading the values of love and truth. Through social media, the Church can influence more people to take the message of Christ to different corners of the world and offer new opportunities for catechesis to reach out to people and nurture their faith. Digital catechesis is a very effective tool in educating and deepening the faith of the people, creating a lively and dynamic atmosphere of faith. The church has an important role in guiding its people to integrate their faith in digital life, making technology a tool to strengthen faith and Christian values. This article provides some suggestions for the effective use of social media in catechesis, such as the development of innovative catechesis programs, digital education for parishioners, collaboration with communities and technologists, and evaluation of the impact of social media use. In this strategy, the Church can utilize social media to strengthen the faith of the people and create a more faithful and dignified community in the digital age.