The calculation of manual tightening torque applied by clinicians plays a critical role in achieving optimal preload for dental implants. However, there is a research gap in understanding the specific calculus involved in this process. This study aims to address this gap by analyzing the bending and torsional moments during manual tightening torque application by physicians of various specialties and genders. Additionally, the rates of early complications associated with clinician-calculated preload will be evaluated. The findings of this study will contribute to enhancing the understanding of clinician-controlled preload and guide future practices for successful dental implant outcomes.

Nowadays, many people are forced to accelerated rhythms cause of frenetic life and work. Both private and professional life intertwine, and finding the time for other activities is challenging. Time is one of the goods that has been lost or is difficult to obtain anyway. This experimental and prospective study aims to evaluate the heart rate cycle of medical doctors during their private and working life. Thanks to technology, we can monitor these parameters constantly and for each of us. The results of this study derive from a literature analysis and a trial carried out on three professionals regarding their work-related heart rate (Apple Watch®). The sample is certainly too small and does not provide significant results. This study represents the first step for a trial that can be carried out on a large scale; this preliminary study provides information about the heart rate cycle.

The aim of this study was to formulate and evaluate mucoadhesive sodium alginate microspheres for nasal administration of Pentoxifylline to avoid first-pass metabolism. Microspheres were prepared using an ionic gelation process using a 2³-factorial design. We investigated the effects of several factors on particle size and in vitro mucoadhesion, including drug-to-polymer weight ratio, calcium chloride (CaCl₂) concentration, and cross-linking time. Particle size of the mucoadhesive microsphre was found in the 27.01 to 33.78 µm range, were the in-vitro mucoadhesive result showed in the range 76.14 to 87.58 %. The microspheres were characterized by SEM to study the shape and distribution of drugs within the microspheres. The surface morphology studied by SEM showed spherical shape and smooth surface of pentoxifylline sodium alginate loaded microsphere containing 2% w/v of Carbopol prepared by ionotropic gelation method. F6 formulation shows highest percentage of in-vitro diffusion 84.78 %. In vitro dissolution tests were performed in pH 6.2 phosphate buffer indicated non- Fickenian type of transport for the diffusion of drug from the Pentoxyfylline mucoadhesive microsphere. It has been shown that the Hixson-Crowell model best describes the release of Pentoxyfylline from Carbopol. The F6 formulation utilized use of the Hixson-Crowell diffusion model of drug release, which was determined to be the model that best fit the data (r²=0.9697). The formulation showed that the Fickian mechanism of drug release was acting when the n value was less than 0.5.

Society relies mainly on fossil fuels for energy generation, which results in risks due to geopolitical conflicts, environmental damage, and climate change. By opting for renewable energy sources, including bioenergy derived from microorganisms, there is a potential solution to this predicament. By harnessing the energy-producing abilities of microorganisms, it is possible to generate renewable energy on a large scale without harming the environment or human activities. Thereby, the present work has as a neuralgic objective to develop a decision-making method for microbial energy generation. Using as a method the fuzzy logic of the Mamdani type for absorbing the uncertainties and inaccuracies, characteristic of this work theme. A structure with 4 levels of indicators was developed, using triangular and trapezoidal functions at the ends. In the development of the fuzzy rules, were used 5 input fuzzy sets and 5 output fuzzy sets when there were two indicators in the fuzzy machinery and three input fuzzy sets and 5 output fuzzy sets with 3 or more indicators in the fuzzy machinery. Five scenarios were developed, considering a scale of 0 to 10: High criticality (10-8), Tolerable (8-6), Adequate (6-4), Desirable (4-1.5) and Low criticality (1.5-0). Thus, it is expected that this model can optimize decision-making processes and promote renewable energy alternatives, potentially reducing the dependence on fossil fuels in the future.

Mono- and diacylglycerols of fatty acids are widely used as lipophilic nonionic emulsifiers, emulsion stabilizers in the production of food products. Identified deficiencies in the composition and properties of existing additives of this group created prerequisites for new developments. An acylglycerol emulsifier based on sunflower oil was obtained. The evaluation of its surface-active properties was carried out using the ring tear-off and the laying drop methods. The aggregative stability of the emulsion, the number of the hydrophilic-lipophilic balance were evaluated by the lifetime of individual drops of the emulsion near the interfacial surface. The results of determining the functional properties of the additive allowed to establish its technological usage as an effective emulsifier for food systems.

Ensuring the quality of meat is crucial to prevent health hazards caused by improper handling. To address this issue, a smart packaging system is necessary for continuous monitoring of beef quality and microbial population, benefiting both meat industries and end consumers. The presence of spoilage-causing microbes can be detected using an electronic nose (e-nose), a cost-effective and rapid instrument for beef quality classification. This research introduces the development of a mobile e-nose system for beef quality detection and monitoring. The system comprises a chemical gas sensor array, data acquisition system, data processing system, and pattern recognition system. The gas sensors utilized in the sensor array include MQ135, MQ137, MQ9, MQ3, TGS 2620, TGS 2610, TGS 2600, and TGS 822. The experiment involved a dataset with 1800 data points. The experimental results demonstrate the system's ability to accurately distinguish between fresh and spoiled beef. Furthermore, it exhibits promising classification accuracy for binary, three-class, and four-class classification tasks, achieving 94.11%, 87.72%, and 84.93% accuracy, respectively, using the support vector machine model. Therefore, this system presents a potential solution for a low-cost, user-friendly, and real-time meat quality monitoring system. This research contributes to the development of an accessible and efficient meat quality monitoring system, addressing the need for continuous assessment and ensuring consumer safety.

Colorectal cancer (CRC) is among most frequent cancers, and dietary habits play important role in elevating risk of CRC initiation. Invasive potential of cancer cells is particularly problematic in cancer treatment, and presents key step in metastatic process. Therefore, science is turning to natural products in order to ameliorate standard chemotherapeutical approaches. Royal jelly is well known bee product that has been traditionally used for centuries because of its significant pharmacological properties and beneficial effects on human wellbeing. Moreover, it already showed remarkable anticancer activity, especially regarding metastasis of various cancer types. Hence, we aimed to investigate royal jelly's potential on invasiveness of colorectal carcinoma cell line (SW-480) by applying Transwell assay with collagen layer, simulating extracellular matrix. Moreover, immunofluorescent technique was used to evaluate the protein expression of proinvasive markers Vimentin and Snail. Cells were treated with royal jelly (RJ), sampled from Serbia, in two selected concentrations 10 and 100 µg/mL, and results were analyzed after 24 h. We observed responsiveness of SW-480 cells to applied treatment when it comes to suppression of invasive potential, whereat this antiinvasive activity of RJ was showed to be dose-dependent. Also, the notable decrease of Vimentin and Snail expression was noticed, which is in correlation with RJ's antiinvasive property. Applied treatment was able to induce strong inhibition of Vimentin and Snail in SW-480 cells, when compared to control (untreated) cells, and more prominent inhibitory effect of RJ was noticed on expression of Vimentin in this cell line. The exerted effects of RJ on suppression of invasive properties of SW-480 cells was obviously due to reduced expression of Vimentin and Snail. In conclusion, our report indicate the possible molecular mechanism of antiinvasive activity of RJ on colorectal carcinoma cell line. This bee product showed pronounced and promising effects on carcinoma cells in vitro, however, our future research will focus on more detailed studies regarding invasion as significant process in cancer progression.

The importance of transportation cannot be overstated, with road maintenance and construction being among the most crucial sectors. However, this area has been slow to update its tools and procedures, despite the benefits of automation. By embracing automation, the road construction industry can realize benefits such as increased efficiency, reduced physical strain on workers, shorter construction times, and less economic loss. In the road construction environment, traffic cones are commonly used to delimit work areas. These cones must be placed by workers and moved as the project progresses. Automation can greatly accelerate this process, freeing up workers for more complex tasks. However, conventional robots require an operator to control the device, limiting the efficiency gains.

To address this inefficiency, we propose a solution based on a robot that can autonomously reach the desired position. Our objective is to develop a model of a robotic cone using reinforcement learning, enabling it to operate independently and improve the efficiency of road construction projects. The self-learning is based on a system of rewards and punishments to achieve the desired position. The cone is rewarded if it approaches or reaches the goal, but it is penalized if it moves away, exceeds the goal or is exploring a wrong quadrant. By using this method, the cone must choose between a 0º or 90º each step-time to maximize the long-term reward. The simulated robotic safety cones reach the target, but the large number of variables involved long training times.

To achieve long-term socioeconomic growth, "smart cities" incorporate a broad spectrum of technological advances and innovations which can support to achieve those goals. The innovation potential, information and communication technologies (ICTs) development, living conditions, citizens' willingness, and the willingness to adopt such technologies all contribute to the development of these ecologies. It is challenging to establish a healthy and economical environment and a high living standards for citizens in "mega-cities" with a population of 10 million or above. Modern Artificial Intelligence (AI) methods are essential for enhancing ICTs solutions and, by augmentation, the competitive nature of cities in order to make up for this deficiency. Focusing on the context of domestic innovation culture, this study aims to analyse the influence of AI application on citizens' behavioural intentions in smart cities. The statistical data analysis of 546 respondents using self-structured survey was performed using structural equation modelling in SmartPLS software. This study found that the citizens of smart cities are more likely to adopt modern technologies if they presume those technologies are useful, easy to use, and will improve their quality of life. It was also found that the presence of a rigorous innovation culture in a city bolstered this positive correlation. The paper provides new insight into how artificial intelligence strategies can enhance urban environment.

This research paper provides a comprehensive review of the emerging trends in AI-based stock market prediction. The paper highlights the key concepts, approaches, and techniques employed in AI-based stock market prediction, and discusses their strengths and limitations. Key topics covered include deep learning, natural language processing, sentiment analysis, and reinforcement learning. The paper also presents case studies and evaluates the performance of different AI-based models in predicting stock market trends. Overall, the research paper provides valuable insights into the latest advancements in AI-based stock market prediction and their potential implications for investors, financial analysts, and policy makers.