Abstract Objectives Restless legs syndrome (RLS) is a disease from the spectrum of movement disorders, the prevalence of which increases significantly during pregnancy and is associated with poor sleep, a drop in daytime energy, and the development of psychological disorders during pregnancy and the postpartum period. Methods The IRLSS scale was used to determine the presence of RLS symptoms. The total test sample that included the tested and control groups was (n=390) subjects. The examined group consisted of pregnant women (n=260), and the control group consisted of female students (n=130). In total, 260 pregnant women were monitored 6 months after pregnancy. Three measurements were performed, first in the third trimester of pregnancy, second two months after delivery, third 6 months after delivery, while one cross-sectional measurement was performed for the control group. Results The prevalence of RLS in pregnancy is highest in the third trimester and amounts to 26.5 %. In the postpartum period, a significant decrease in the prevalence of RLS was observed, measured two months after delivery (18.1 %). Postpartum, over time, a decrease in the prevalence of RLS was noticed, and six months after delivery it was (7.3 %), when it practically approached the prevalence of the control group (standard population) which was (6.2 %). Conclusions The prevalence of RLS is highest during the third trimester of pregnancy and decreases after delivery so that 6 months after delivery it approaches the prevalence of the standard population.

This research explores the initial attitudes of special educators towards socially assistive robots (SAR) and considers how cultural and socioeconomic backgrounds shape these attitudes. Special educators providing services to individuals with autism spectrum disorder (ASD) in the United States and Serbia were surveyed, representing distinct cultural and socioeconomic contexts. Comparing their attitudes offers valuable insights into facilitating SAR adoption for ASD treatment across different cultural landscapes. Additionally, this study conducts a comparative analysis of cultural dimensions in the two countries to contextualize attitudes toward SAR use in ASD treatment. A nonexperimental quantitative approach was employed utilizing a cross-sectional survey design with purposive samples of special educators who provide services to individuals with ASD in the U.S. and Serbia to establish initial attitudes toward SARs. The outcomes derived from this investigation reflect the pervasive influence of the cultural contexts. Apprehension has been identified as a significant factor shaping attitudes toward SAR adoption among the Serbian cohort, while a more favorable disposition towards this technology typifies the U.S. participants. The adoption and utilization of SARs, as reported by participants in the U.S., face relatively fewer attitudinal barriers.

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The main applications of civil explosives in soils are soil compaction, mass excavation, and in situ pile creation. The suitability of explosives for each of these applications strongly depends upon the explosive properties and the soil properties. For those reasons, a reliable estimation or process simulation regarding cost efficiency and explosive work ability in the soil with known soil parameters is relevant. This paper presents a numerical simulation study of different types of soil (different amounts of gravel, sand, silt, and clay) under a blast load modeled using Ansys 2020 R1 Autodyn 2D hydrocode, with different types of explosives. The calculated results from the Ansys 2020 R1 Autodyn 2D and the experimental results obtained from the in situ cavity formation caused by blasting are presented. The Jones–Wilkins–Lee (JWL) equation of state parameters was calculated using EXPLO5 V7.01.01 supported by experimental data, while the soil and explosive properties were measured in laboratory and in situ.

According to Mitchelmore [1], generalisations are the cornerstone of school mathematics, covering various aspects like numerical generalisation in algebra, spatial generalisation in geometry and measurement, as well as logical generalisations in diverse contexts. The process of generalising lies at the heart of mathematical activity, serving as the fundamental method for constructing new knowledge [2, 3]. In this paper we will generalise an interesting geometry problem that appeared in the 1995 edition of the International Mathematical Olympiad (IMO) [4].

This paper investigates the rate of convergence of a certain mixed monotone rational second-order difference equation with quadratic terms. More precisely we give the precise rate of convergence for all attractors of the difference equation $x_{n+1}=\frac{Ax_{n}^{2}+Ex_{n-1}}{x_{n}^{2}+f}$, where all parameters are positive and initial conditions are non-negative.The mentioned methods are illustrated in several characteristic examples. 2020 Mathematics Subject Classification. 39A10, 39A20, 65L20.

With the ever-increasing number of polymer materials and the current number of commercially available materials, the polymer gear design process, regarding the wear lifetime predictions, is a difficult task given that there are very limited data on wear coefficients that can be deployed to evaluate the wear behavior of polymer gears. This study focuses on the classic steel/polymer engagements that result in a wear-induced failure of polymer gears and proposes a simple methodology based on the employment of optical methods that can be used to assess the necessary wear coefficient. Polymer gear testing, performed on an open-loop test rig, along with VDI 2736 guidelines for polymer gear design, serves as a starting point for the detailed analysis of the wear process putting into service a digital microscope that leads to the evaluation of the wear coefficient. The same wear coefficient, as presented within the scope of this study, can be implemented in a rather simple wear prediction model, based on Archard’s wear formulation. The developed model is established on the iterative numerical procedure that accounts for the changes in tooth flank geometry due to wear and investigates the surface wear impact on the contact pressure distribution to completely describe the behavior of polymer gears in different stages of their lifetime. Although a simple one, the developed wear prediction model is sufficient for most engineering applications, as the model prediction and experimental data agree well with each other, and can be utilized to reduce the need to perform time-consuming testing.

In the article, we use the subset sum formula over a finite abelian group on the product of finite groups to derive the number of restricted partitions of elements in the group and to count the number of compositions over finite abelian groups. Later, we apply the formula for the multisubset sum problem on a group $\mathbb{Z}_n$ to produce a new technique for studying restricted partitions of positive integers. 2020 Mathematics Subject Classification. 05A17, 11P81

Who conducts biological research, where, and how the results are disseminated varies among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical first step towards addressing them. We documented perceived and observed biases in movement ecology. Movement ecology is a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. First, we surveyed attendees of an international conference, and discussed the results at the conference (comparing uninformed vs informed perceived bias). Although most researchers identified as bias-aware, only a subset of biases were discussed in conversation. Next, by considering author affiliations from publications in the journal Movement Ecology, we found among-country discrepancies between the country of the authors’ affiliation and study site location related to national economics. At the within-country scale, we found that race-gender identities of postgraduate biology researchers in the USA differed from national demographics. We discuss the role of potential specific causes for the emergence of bias in the sub-discipline, e.g. parachute-science or accessibility to fieldwork. Undertaking data-driven analysis of bias within research sub-disciplines can help identify specific barriers and first steps towards the inclusion of a greater diversity of participants in the scientific process.

Global trends in food fortification, particularly in milk, and the increasing availability of fermented milk products have raised awareness of the link between nutrition and health, leading to higher consumption of these products. Consumers’ decisions to purchase fermented milk products are influenced by economic, psychological, cultural, and socio-demographic factors, along with preferences related to brand, price, taste, and nutritional value. This study aims to examine the impact of socio-demographic characteristics on production and marketing factors when purchasing fermented milk products, as well as on the choice of specific brands. The research was conducted using a survey method, with a questionnaire as the primary data collection tool. A total of 326 consumers participated in the survey. Data were analyzed using descriptive statistics, frequency distribution, analysis of variance, and the chi-square test. The results indicate that socio-demographic characteristics significantly influence production and marketing considerations, as well as brand choice when it comes to fermented milk products. However, the chi-square test showed no statistically significant relationship between sociodemographic characteristics and consumer attitudes toward fermented milk products. These findings can help producers and marketers better understand consumer behavior and tailor their strategies accordingly.

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers.