Hasil untuk "General works"

F. Kermode

R. Jackson

A. Guram, R. Rennels, S. Buchwald

Aripin, Nur Azizah Zuhriyah

This study examines the impact of financial literacy, business management skills, and access to capital on the sustainability of women entrepreneurs' businesses. Using a quantitative approach, the findings reveal that financial literacy and business management skills significantly enhance business sustainability, equipping women entrepreneurs with essential decision-making and managerial capabilities. Access to capital also plays a crucial role in sustaining businesses by providing financial resources for growth and risk management. The moderating effects of access to capital present a nuanced dynamic. While access to capital strengthens the positive relationship between business management skills and business sustainability, it unexpectedly weakens the relationship between financial literacy and sustainability. This indicates that although financial resources are necessary, excessive reliance on external funding without strong financial management can hinder business success. These findings align with Resource-Based View, emphasizing the significance of both financial and managerial competencies. The study provides theoretical contributions by refining the understanding of financial literacy's role in business sustainability and highlighting the risks of financial mismanagement. Practically, it emphasizes the need for integrated policies that enhance financial education and managerial skills while ensuring responsible access to capital. Governments, financial institutions, and entrepreneurship programs in Indonesia should adopt a holistic approach by combining financial training with structured capital access. Despite its contributions, this study is limited by its cross-sectional design and geographical scope. Future research should explore longitudinal and qualitative perspectives to gain deeper insights. Overall, this study offers valuable recommendations for fostering sustainable women entrepreneurship, particularly in developing economies.

Ryoma Kumon, Hitomi Yanaka

The compositional generalization abilities of neural models have been sought after for human-like linguistic competence. The popular method to evaluate such abilities is to assess the models' input-output behavior. However, that does not reveal the internal mechanisms, and the underlying competence of such models in compositional generalization remains unclear. To address this problem, we explore the inner workings of a Transformer model by finding an existing subnetwork that contributes to the generalization performance and by performing causal analyses on how the model utilizes syntactic features. We find that the model depends on syntactic features to output the correct answer, but that the subnetwork with much better generalization performance than the whole model relies on a non-compositional algorithm in addition to the syntactic features. We also show that the subnetwork improves its generalization performance relatively slowly during the training compared to the in-distribution one, and the non-compositional solution is acquired in the early stages of the training.

C. T. Chantler

David P. Luebke, Carl Erikson

Robert Mamada

In the cultural industry, firms face significant challenges when contracting with artists due to the inherently unpredictable quality of experience goods. This study investigates how firms can leverage indices, as introduced by Spence, to make more informed contracting decisions under such uncertainty. By framing the problem as a sequential Bayesian game and applying Shannon information theory, it is demonstrated that indices — observable and unalterable attributes correlated with artistic quality — serve as reliable predictors in the artist selection process. The analysis reveals that when the cost of forging indices exceeds a certain threshold, artists are disincentivised from manipulation, allowing firms to avoid contracting with low-quality producers. This framework offers a robust solution to the contracting dilemma within the cultural industry and provides valuable insights for other markets characterised by high uncertainty and the unpredictability of experience goods.

Savchuk V. S., Plekhov A. S.

Widely used in modern technology welding of metals is a complex technological process. To assess the quality of a welded joint and its compliance with operational requirements, mathematical modeling methods are used. The mathematical model of a pulse transformer has been developed using the Matlab Simulink software package and software modules from Schneider Electric. The output data have been verified at the manufacturing company NPK Etalon (Rostov Region, Volgodonsk), where the obtained data have been compared with the theoretical base. The mathematical model of a pulse transformer allows implementing the STT (Surface Tension Transfer – heat and mass transfer due to the mechanism of surface tension forces) welding with a deeper calibration of current power pulses with clearly defined electrical parameters. It allows as well solving the problem of switching welding modes in complex operating scenarios; it is applicable to various types of welding (contact, arc, beam, etc.).

Ting-Ting Wu, Andik Asmara, Yueh-Min Huang et al.

Problem-solving skills are an ability that must be cultivated to equip students with the skills needed to deal with today’s increasingly complex and volatile environment. Computational thinking represents a new paradigm in problem-solving skills. After Wing proposed Computational Thinking as problem-solving skills in 2006, other scholars investigated this topic; nevertheless, the link between Computational Thinking and problem-solving has not been clearly discussed in previous studies. To uncover evidence for the connection between Computational Thinking and problem-solving skills, we conduct a systematic literature review of 37 papers collected from Web of Science database. The results indicate that (a) problem-solving is discussed in the 37 articles in the context of Computational Thinking, (b) the most frequently employed Computational Thinking stages in problem-solving skills are decomposition, pattern recognition, abstraction, and algorithm, (c) Computational Thinking is closely linked to problem-solving, and (d) Computational Thinking and problem-solving stages serve the same functions in solving problems. The results of this study will encourage the development of education research, particularly in the application of CT as a problem-solving tool in various real-life scenarios.

Al Jameel Osama, Sabrinah Adam

This study examines the factors and motivations behind social entrepreneurial behavior in Saudi Arabia, specifically highlighting the main findings. The research employs the Web of Science and Scopus databases to conduct a literature review following the PRISMA statement guidelines. Using the VOS viewer software, the analysis and identification of key terms result in three primary classifications. (1) The study of social entrepreneurial behavior and its consequences. (2) The examination of organizational dynamics and the creation of social value, including social innovation and corporate social responsibility. (3) The exploration of the relationship between the entrepreneurial ecosystem and planned behavioral constructs. The study explores Saudi Arabia’s efforts to promote entrepreneurial development and tackle ongoing challenges like high unemployment rates. The research highlights the potential of social entrepreneurship to create positive societal change. The text emphasizes the importance of improving the entrepreneurial ecosystem by focusing on cultural factors, social networks, institutional frameworks, investment capital, and economic policies. The findings enhance our comprehension of planned behavior outcomes in the context of social entrepreneurial behavior in Saudi Arabia.

S. A. Gilmanov, A. V. Mironov, V. A. Mishchenko

Introduction. For students, mastering the methodological foundations of scientific research plays an important role in professional education, providing scientifically proven support for professional thinking and readiness to scientifically substantiate the practical activities of a specialist to form a mechanism for intellectual and personal self-development. Today, numerous educational literature is being published, and educational disciplines devoted to the methodology of science and scientific research are being introduced. However, in most of the educational and scientific works created by students (course projects, final qualification papers, etc.), methodological provisions are formulated with errors; descriptions of the organisation of research, interpretation and design of their results are unsatisfactory. Therefore, there is the problem of finding ways to improve methodological literacy, methodological competence, and methodological culture of students. The relevance of such research is conditioned by the need to study the causes of such a situation, to develop ways for improving student methodological competence, to highlight and summarise the experience of teaching methodological knowledge, and to suggest ways for improving the educational process. The solution to this problem is especially significant for the humanities specialists (in particular, for educators and psychologists), who deal with the influence on the personality and human consciousness included in the social regulation of human behaviour.                Aim. The present research aims to: 1) substantiate the theoretical approach to the scientific and methodological support of the process of mastering the methodological foundations of scientific research by students as to the development of scientific-methodological knowledge (SMK) based on the analysis of scientific and educational literature, the practice of teaching disciplines aimed at studying the methodological aspects of psychological and pedagogical research; 2) to identify the main guidelines for improving the ways to support the formation of SMK using examples from empirical studies of its development by students in the educational process, from the generalisation of the practical experience of the authors.                Methodology and research methods. The research methodological framework was based on the approaches of Russian scientists to the methodological foundations of science and scientific research as a holistic knowledge, in which principles, norms, methods and scientific and cognitive activity are developed at the philosophical, general scientific, concrete scientific, methodical levels. Analysis, generalisation and systematisation were used as theoretical research methods; testing, survey, included observation and generalisation of experience were applied as empirical methods. To prove the propositions put forward by the authors, four empirically verifiable hypotheses were formulated. Two of them were confirmed: one was partially confirmed; the second one (about the absence of a connection between direct and remote forms with the formation of SMK) was not confirmed. In the mathematical and statistical interpretation of individual research results (correlation and variance types of analysis), the IBM SPSS Statistics 23 was employed.                Results. The guidelines for supporting the development of SMK by students in the educational process were outlined: reliance on the creation of a research project by the trainee; placing the trainees in the position of an independent researcher; developing an understanding of the content and scope of the key concepts of research, its unity and integrity; paying special attention to the transformation of information and its presentation in various forms.                Scientific novelty. The authors proposed and substantiated the idea of the study of the research methodological foundations as the development of SMK, which equips the researcher with an arsenal of guidelines, means and methods of action, providing the closest possible approach to the truth in the search for new scientific knowledge.                Practical significance. The proposed approaches to the development of SMK can be used in the practice of studying disciplines devoted to the research methodological foundations in the social sciences and humanities, in support of students’ course projects and final qualifying works.

Henny Endah Anggraeni, Aep Setiawan, Suhendi Irawan

Kittens in their first four weeks are in their most critical period because they do not yet have the ability to thermoregulate their bodies, and it is still difficult for them to adapt to environmental temperatures. Due to this condition, veterinary clinics and cat-lover communities need facilities that can maintain a kitten’s body temperature within the normal range. One way to help in the care of these kittens is to use a special incubator for animals. Incubators are useful in situations where animals cannot control their body temperature conditions. The expected method to monitor the work system of the incubator is internet-based monitoring, as part of IoT (internet of things). Monitoring is very important for animal health workers and cat lovers in monitoring the temperature and humidity in the incubator using the internet, which allows monitoring to be carried out anytime and anywhere from a smartphone through the Blynk application. The purpose of this research is to create an IoT-based kitten incubator monitoring system through the blynk application so that the owner or nurse of the kitten can monitor in real time via a smartphone so that time efficiency can be improved, by using NodeMCU ESP8266 microcontroller with fuzzy logic method. The incubator can work automatically to regulate the temperature through lighting and air settings in it, with the applied temperature ranging from 26 degrees to 30 degrees Celsius.

To reduce the number of alcohol-exposed pregnancies in antenatal care in the UK, the CHAMPION study objective was to design appropriate training and resources to develop the midwifery skills and confidence required to have an effective conversation about alcohol with pregnant women. Women with lived experience of drinking during pregnancy and ‘birth mums’ of a child with fetal alcohol spectrum disorders (FASD) were central to the co-creation process of the CHAMPION study. Some remarkable unintended positive outcomes resulted from this collaboration process for the women involved. They include increased confidence levels from newfound friendships with other mothers of children with FASD, and an acceptance that societal influences had a significant role to play in their personal situations. Reflecting on this authentic co-creation process has elicited important learning outcomes to inform future research design. The significance of excellent communication channels, robust support networks and genuinely valuing and respecting experts by experience as equal partners should not be underestimated.

Aleksandar Nikolov, Haohua Tang

We investigate unbiased high-dimensional mean estimators in differential privacy. We consider differentially private mechanisms whose expected output equals the mean of the input dataset, for every dataset drawn from a fixed bounded $d$-dimensional domain $K$. A classical approach to private mean estimation is to compute the true mean and add unbiased, but possibly correlated, Gaussian noise to it. In the first part of this paper, we study the optimal error achievable by a Gaussian noise mechanism for a given domain $K$ when the error is measured in the $\ell_p$ norm for some $p \ge 2$. We give algorithms that compute the optimal covariance for the Gaussian noise for a given $K$ under suitable assumptions, and prove a number of nice geometric properties of the optimal error. These results generalize the theory of factorization mechanisms from domains $K$ that are symmetric and finite (or, equivalently, symmetric polytopes) to arbitrary bounded domains. In the second part of the paper we show that Gaussian noise mechanisms achieve nearly optimal error among all private unbiased mean estimation mechanisms in a very strong sense. In particular, for every input dataset, an unbiased mean estimator satisfying concentrated differential privacy introduces approximately at least as much error as the best Gaussian noise mechanism. We extend this result to local differential privacy, and to approximate differential privacy, but for the latter the error lower bound holds either for a dataset or for a neighboring dataset, and this relaxation is necessary.

Pierre Nazé

The nonlinear response of the excess work, when made via series expansion in the parameter perturbation of the average thermodynamic work, requires adjustments to agree with the Second Law of Thermodynamics. In this work, I present a well-behaved nonlinear response of the irreversible work, based on its well-known cumulant series expansion. From the generalization of the fluctuation-dissipation relation derived from it, I define the terms of the series expansion in the parameter perturbation of the irreversible work by the terms of the cumulants. Since every cumulant depends on raw moments, I define from them the generalized relaxation functions, whose arbitrary constants were chosen guaranteeing the accomplishment of the Second Law of Thermodynamics. A procedure to calculate the nonlinear response of the irreversible work is then provided.

Ralph P. Eatough, Gregory Desvignes, Kuo Liu et al.

BlackHoleCam is a project funded by a European Research Council Synergy Grant to build a complete astrophysical description of nearby supermassive black holes by using a combination of radio imaging, pulsar observations, stellar astrometry and general relativistic magneto-hydrodynamic models. BlackHoleCam scientists are active partners of the Event Horizon Telescope Consortium. In this talk I will discuss the use of pulsars orbiting Sagittarius A* for tests of General Relativity, the current difficulties in detecting such sources, recent results from the Galactic Centre magnetar PSR J1745-2900 and how BlackHoleCam aims to search for undiscovered pulsars in the Galactic Centre.

Xintong Zhou, Chang-jun Liu

J. Langford, Lihong Li, Tong Zhang

We propose a general method called truncated gradient to induce sparsity in the weights of online-learning algorithms with convex loss. This method has several essential properties. First, the degree of sparsity is continuous—a parameter controls the rate of sparsification from no sparsification to total sparsification. Second, the approach is theoretically motivated, and an instance of it can be regarded as an online counterpart of the popular L1-regularization method in the batch setting. We prove small rates of sparsification result in only small additional regret with respect to typical online-learning guarantees. Finally, the approach works well empirically. We apply it to several datasets and find for datasets with large numbers of features, substantial sparsity is discoverable.

Xiwei Liu, Tianping Chen