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Fingerprints and blood samples are important for the identification of individuals and criminals. The present study aims to identify the predominant fingerprint patterns and the association between the fingerprint patterns and ABO–Rh blood groups in Omani population.

A cross-sectional study was conducted on 200 Omani individuals aged 18 years (104 males and 96 females). The imprints of all right and left-hand fingers were taken, and the types of the fingerprints were determined using a standard protocol. The blood group of all the subjects was recorded. Chi-square test was performed to identify the association between the fingerprint patterns and the ABO and Rh blood groups.

The loop fingerprint pattern was the most common in Omani subjects (49.4%), followed by whorl (44.9%) and arch (5.7%) pattern. A significant association (p < 0.001) was found between gender and fingerprint pattern. The loop was the highest occurring pattern in the females (54.6%), while the whorl was more in males (50.0%). The whorl pattern was the most common in the AB+ and O− groups. The loop was a predominant pattern in the A+, A−, B+, B− and O+ groups. The Chi-square test also revealed a significant correlation between different fingerprint patterns and blood groups of the subjects (p < 0.001).

The present study is an outcome of undergraduate student's research project thesis for the Doctor of Medicine (MD) program. The results of the present study may help in creating a data bank for biometrics, which can be useful for diagnosing associated diseases and also help in identification of individuals.

The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

The paper aims to throw light on the interactions taking place between the different chemical compositions at various temperatures. P-methylacetophenone is a polar dissolvable, which is positively related by dipole–dipole co-operations and is exceptionally compelling a direct result of the shortfall of any critical primary impacts because of the absence of hydrogen bonds; hence, it might work an enormous dipole moment (μ = 3.62 D). Alcohols additionally assume a significant part in industries and research facilities as reagents and pull in incredible consideration as helpful solvents in the green innovation. They are utilized as pressure-driven liquids in drugs, beauty care products, aromas, paints removers, flavors, dye stuffs and as a germ-free specialist.

Mixtures were prepared by mass in airtight ground stopper bottles. The mass measurements were performed on a digital electronic balance (Mettler Toledo AB135, Switzerland) with an uncertainty of ±0.0001 g. The uncertainty in mole fraction was thus estimated to be less than ±0.0001. The densities of pure liquids and their mixtures were determined using a density meter (DDH-2911, Rudolph Research Analytical). The instrument was calibrated frequently using deionized doubly distilled water and dry air. The estimated uncertainty associated with density measurements is ±0.0003 g.cm⁻³. Viscosities of the pure liquids and their mixtures were determined by using Ostwald’s viscometer. The viscometer was calibrated at each required temperature using doubly distilled water. The viscometer was cleaned, dried and is filled with the sample liquid in a bulb having capacity of 10 ml. The viscometer was then kept in a transparent walled water bath with a thermal stability of ±0.01K for about 20 min to obtain thermal equilibrium. An electronic digital stop watch with an uncertainty of ±0.01 s was used for the flow time measurements for each sample at least four readings were taken and then the average of these was taken.

Negative values of excess molar volume, excess isentropic compressibility and positive values of deviation in viscosity including excess Gibbs energy of activation of viscous flow at different temperatures (303.15, 308.15 and 313.15 K) may be attribution to the specific intermolecular interactions through the hetero-association interaction between the components of the mixtures, resulting in the formation of associated complexes through hydrogen bond interactions.

The excess molar volume (V^E) values were analyzed with the Prigogine–Flory–Patterson theory, which demonstrated that the free volume contribution is the one of the factors influencing negative values of excess molar quantities. The Jouyban–Acree model was used to correlate the experimental values of density, speed of sound and viscosity.

Menstruation is a part of the female reproductive cycle that begins with adolescence. Menstruation is a natural change; it relates to several malpractices and misconceptions that may contribute to adverse health outcomes.

The authors have searched relevant papers using Google Scholar and PubMed to write this mini review.

During menstruation, poor hygiene maintenance can cause serious illness, which includes the urinary tract and reproductive tract infection. Menstruation management is a hygienic system, and it is essential for females because poor hygiene maintenance during menstruation can cause some infections and numerous sexually transmitted diseases. There are a few nanotechnology-based products that have come into the market to offer some relief to females during their periods.

This mini review will help researchers to design innovative female hygiene products that can relieve the discomfort caused to women during their reproductive age.

This paper aims to attempt to empirically investigate the impact of privatization on the performance of central public sector enterprises in India. Further attempt is made to explore whether privatization is a necessary or sufficient condition for improvement of performance of central public sector enterprises.

The scope of the study is limited to financial and operating performance analysis of 206 central public sector enterprises in India. Multiple regression analysis has been used to determine the magnitude and direction of relationship between dependent and independent variables and identify variables other than privatization which affects performance.

The study found that financial and operational performance of firms has improved significantly due to privatization. Further, firm-specific factors and other parallel reforms adopted by enterprises have significantly influenced their performance. The established regression model is highly significant with F-ratio of 31.825 at 99% significance level. The degree of explanation of the model is robust with adjusted R² at 0.956 implying that only 4.40% of explanation in the dependent variable cannot be explained by designated independent/explanatory variables.

The study would be useful to public policymakers to reach to a policy view on whether further disinvestment/privatization of central public sector enterprises need to be continued, and if so, then to what extent and direction.

Service design is a multidisciplinary approach that plays a key role in fostering service innovation. However, the lack of a comprehensive understanding of its multiple perspectives hampers this potential to be realized. Through an activity theory lens, the purpose of this paper is to examine core areas that inform service design, identifying shared concerns and complementary contributions.

The study involved a literature review in two stages, followed by a qualitative study based on selected focus groups. The first literature review identified core areas that contribute to service design. Based on this identification, the second literature review examined 135 references suggested by 13 world-leading researchers in this field. These references were qualitatively analyzed using the NVivo software. Results were validated and complemented by six multidisciplinary focus groups with service research centers in five countries.

Six core areas were identified and characterized as contributing to service design: service research, design, marketing, operations management, information systems and interaction design. Data analysis shows the various goals, objects, approaches and outcomes that multidisciplinary perspectives bring to service design, supporting them to enable service innovation.

This paper supports service design teams to better communicate and collaborate by providing an in-depth understanding of the multiple contributions they can integrate to create the conditions for new service.

This paper identifies and examines the core areas that inform service design, their shared concerns, complementarities and how they contribute to foster new forms of value co-creation, building a common ground to advance this approach and leverage its impact on service innovation.

The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the method of integrating convolutional neural network (CNN)-based object detection and the category-free grasping method. The considered scenario is related to mobile manipulating platforms that move freely between workstations and manipulate defined objects. In this application, the robot is not positioned with respect to the table and manipulated objects. The robot detects objects in the environment and uses grasping methods to determine the reference pose of the gripper.

The authors implemented the whole pipeline which includes object detection, grasp planning and motion execution on the real robot. The selected grasping method uses raw depth images to find the configuration of the gripper. The authors compared the proposed approach with a representative grasping method that uses a 3D point cloud as an input to determine the grasp for the robotic arm equipped with a two-fingered gripper. To measure and compare the efficiency of these methods, the authors measured the success rate in various scenarios. Additionally, they evaluated the accuracy of object detection and pose estimation modules.

The performed experiments revealed that the CNN-based object detection and the category-free grasping methods can be integrated to obtain the system which allows grasping defined objects in the unstructured environment. The authors also identified the specific limitations of neural-based and point cloud-based methods. They show how the determined properties influence the performance of the whole system.

The authors identified the limitations of the proposed methods and the improvements are envisioned as part of future research.

The evaluation of the grasping and object detection methods on the mobile manipulating robot may be useful for all researchers working on the autonomy of similar platforms in various applications.

The proposed method increases the autonomy of robots in applications in the small industry which is related to repetitive tasks in a noisy and potentially risky environment. This allows reducing the human workload in these types of environments.

The main contribution of this research is the integration of the state-of-the-art methods for grasping objects with object detection methods and evaluation of the whole system on the industrial robot. Moreover, the properties of each subsystem are identified and measured.