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Morphological adaptation is vital for biological organisms navigating changing environments. While robots have sought to emulate this adaptability with adjustable body structures, practical robotic applications remain constrained by the complexity of integrating advanced materials, sophisticated control systems, and novel design approaches. This paper introduces a bioinspired quadruped robot featuring both a laterally undulating spine and posture-changing mechanism, specifically designed for adaptation in complex terradynamic environments. The robot utilizes a symmetrical parallelogram mechanism to precisely control its height and width, enabling it to navigate diverse terrains adeptly, avoid collisions, pass through narrow channels, and negotiate obstacles. Furthermore, the robot achieves stability through lateral undulation, which actively counteracts instability arising from posture changes. This ensures the center of gravity remains within its support triangle for the majority of the locomotion cycle, thereby obviating the reliance on intricate posture-stabilizing sensors or learning algorithms. The experimental results demonstrate the robot’s capability to traverse both flat and significantly inclined surfaces (10° uphill and downhill), as well as successfully navigate confined tunnels, down to a narrow width. We observed notable variations in locomotion speed based on posture: certain configurations exhibited speeds that were up to 30% faster than others on surfaces with the least roughness, with similar trends holding for intermediate and maximum roughness. Furthermore, the robot demonstrates energy efficiency; while zero-degree posture showed a modest increase in average power consumption (around 18%) compared to others, the overall energy expenditure across various gaits remained consistently low. This work contributes to the development of versatile and autonomous robotic systems capable of operating in unstructured and unpredictable real-world scenarios, bridging the gap between theoretical adaptability and practical deployment in fields ranging from exploration to disaster response.

The ability to modify morphology in response to environmental changes represents a highly advantageous feature in biological organisms, facilitating their adaptation to diverse environmental conditions. While some robots have the capability to modify their morphology by utilizing adaptive body parts, the practical implementation of morphological transformations in robotic systems is still relatively restricted. This limitation can be attributed, in part, to the intricate nature of achieving such transformations, which necessitates the integration of advanced materials, control systems, and design approaches. In nature, a range of morphology adaptation strategies is employed to achieve optimal performance and efficiency, such as those employed by crocodiles and alligators, who adjust their body posture depending on the speed and the surface they traverse on. Drawing inspiration from these biological examples, this paper introduces Adjustbot, a quadruped robot with an undulating body capable of adjusting its body posture. Its adaptive morphology allows it to traverse a wide range of terradynamically challenging surfaces and facilitates avoidance of collisions, navigation through narrow channels, obstacle traversal, and incline negotiation.

This dissertation is a theoretical study on management of audit evidence. An audit is a process of collecting, evaluating and aggregating evidence. This dissertation deals with these three aspects of the audit process. It employs two popular normative techniques, Bayesian and belief-function formalisms, to model uncertainty. Further, mathematical programming techniques are used for optimization. A brief discussion of the four parts of the dissertation follows. The first part of the dissertation deals with the evaluation of the strength of evidence. There are no clear guidelines, in the auditing literature, for classifying and comparing different items of evidence. Further, there is no consistent numerical measure of the strength of evidence in auditing. Here, a numerical measure of the strength of evidence and the related concepts are discussed under both the Bayesian and the belief-function formalisms. The second part of the dissertation deals with belief revision in auditing. Belief revision among auditors has been the focus of many recent empirical studies. The findings of these studies are interesting and are not in complete compliance with the normative prescriptions of belief revision. In this part of the dissertation, the deviations from the normative prescriptions are analyzed and modelled them by relaxing some axioms of the theories. In the third part of the dissertation an audit risk model is formulated in the Bayesian framework. First, a network of audit evidence is constructed. The network contains various accounts, objectives and items of evidence, and it also represents various inter-relationships between objectives and accounts. Next, the strength of evidence is propagated through the network using local computations. Finally, aggregation of evidence across different accounts are discussed. The fourth and final part of the dissertation pertains to collection of evidence. Here the cost of collecting evidence is incorporated into the audit planning model. The cost aspect of the audit has been completely ignored in the auditing literature. It is desirable to obtain sufficient evidence at a minimum cost. The audit planning problem is formulated as a constrained optimization problem. Further, the solution is obtained using stochastic dynamic programming. Also, an alternative technique, one-step-look-ahead, is suggested to ease the computations in complex problems.

Background Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. Methods In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. Results The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. Conclusion VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex ( Capra sibirica ) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

The combined heat and power economic dispatch (CHPED) and optimal power flow (OPF) are two power system optimization issues that are simultaneously studied in this work on IEEE-57 bus and IEEE 118-bus power network. The main contribution of the proposed work is to determine the OPF of CHPED problem on the IEEE 57 bus and IEEE 118 bus systems. Secondly, renewable energy sources such as wind-solar-EV are integrated with the aforesaid systems for lowering fuel cost, emission, active power loss (APL), aggregated voltage deviation (AVD), voltage stability index (VSI) and also cost, emision, APL, AVD, VSI are reduced simultaneously considering different cases for multi-objective functions.Proposed sine-cosine algorithm (SCA) embedded with quasi-oppositional based learning (QOBL), known as QOSCA is used to balance the exploration and exploitation ability in order to overcome shortcomings and provide global optimal solutions. Utilizing statistical analysis, the suggested technique’s robustness has been assessed. Moreover, an analysis of variance (ANOVA) test and box plot are used to thoroughly investigate this data to provide a more precise assessment of QOSCA’s robustness. After integrating wind-solar and EV, the numerical analysis for IEEE 57 bus and IEEE 118-bus utilizing QOSCA for single objective over generation cost is reduced by 21%, emission is reduced by 17.5%, APL is reduced by 0.17% and 2.59%. Additionally, the suggested method (QOSCA) is applied to a multiobjective function while taking AVD and VSI into account. This resulted in a reduction in AVD by 0.37% and VSI by 0.24%, demonstrating the superiority of the suggested method. Furthermore, it has been demonstrated that the computational efficiency in complex systems is 24% faster than that of conventional optimization methods.

The snow leopard ( Panthera uncia ) plays a vital role in maintaining the integrity of the high mountain ecosystem by regulating prey populations and maintaining plant community structure. Therefore, it is necessary to understand the role of the snow leopard and its interaction with prey species. Further, elucidating landscape use and co-occurrence of snow leopard and its prey species can be used to assess the differential use of habitat, allowing them to coexist. We used camera trapping and sign survey to study the interactions of snow leopard and its prey species (Siberian Ibex- Capra sibrica and Blue sheep- Pseudois nayaur ) in the Spiti valley Himachal Pradesh. Using the occupancy modelling, we examined whether these prey and predator species occur together more or less frequently than would be expected by chance. To understand this, we have used ten covariates considering the ecology of the studied species. Our results suggest habitat covariates, such as LULC16 (barren area), LULC10 (grassland), ASP (aspect), SLP (slope) and DW (distance to water), are important drivers of habitat use for the snow leopard as well as its prey species. Furthermore, we found that the snow leopard detection probability was high if the site was used by its prey species, i.e., ibex and blue sheep. Whereas, in the case of the prey species, the probability of detection was low when the predator (snow leopard) was present and detected. Besides this, our results suggested that both species were less likely to detect together than expected if they were independent (Snow leopard—Ibex, Delta = 0.29, and snow leopard—blue sheep, Delta = 0.28, both the values are <1, i.e., avoidance). Moreover, despite the predation pressure, the differential anti-predation habitat selection and restriction of temporal activities by the prey species when snow leopard is present allows them to co-exist. Therefore, considering the strong link between the habitat use by the snow leopard and its prey species, it is imperative to generate quantitative long-term data on predator-prey densities and the population dynamics of its prey species in the landscape.

Purpose The purpose of this paper is to explore the multi-level ambidexterity challenges through the metaroutine lens. Further, while confronting the ambidexterity challenges, it is found that what is paradox at one level can be understood as tradeoffs at another level. This study uses an in-depth multi-level case study of Tata Motors, an Indian automotive giant highlighting the ambidexterity dynamics across strategic, business unit and functional levels to demonstrate that paradoxes at the strategic level are converted to manageable tradeoffs at the business unit/ operational level. Also, metaroutine-enabled ambidexterity explains a possible way through which multi-level ambidexterity can be promoted and managed within organizations. Design/methodology/approach This study uses a case-based methodology (Eisenhardt, 1989) similar to the approach of Adler et al. (1999). The field research consisted of in-depth interviews, which focus on gathering information from the key involved members in the field, thus enabling us to understand how multi-level ambidexterity is promoted within Tata Motors. A semi-structured interview guide was used to collect data for this study. Findings The metaroutine lens offers an alternative route to explain the multi-level ambidexterity dynamics at Tata Motors. The ambidexterity questions at different levels in Tata Motors seem to be a mixture of paradoxes and tradeoffs. However, a key trend emerges. At the strategic and business unit level, the firm wanted to exploit their existing products and explore new customer segments. At the product level, the strategic and NPI core team wanted to best combine the customer centric explorations with exploitations resulting in cost savings. The ambidexterity questions at these two levels seem to be paradoxes. However, as the authors analyze the functional domains, it appears that each individual domain was working under increased constraints. Hence, the ambidexterity questions at the domain level seem to be a tradeoff based on the constraints faced by individual functional domains. Originality/value This study presents an in-depth multi-level case study of an Indian automotive giant, Tata Motors. The authors present the role of metaroutines in shaping the ambidexterity issues during the development of passenger vehicles. This study builds on the seminal work of Adler et al., 1999, and extends the discussion to the framing of the ambidexterity question as a paradox and/or a tradeoff. The core argument of this paper is that balancing opposite polarities in business models is basically a paradoxical issue in the exploitation/ exploration relationship.

With the development of the Internet, the use of social media has increased dramatically over time and has emerged as the most powerful networking tool of the twenty-first century. From youngsters of ten years to senior citizens of sixty years, everyone is profoundly active in social media. Social media, due to its easy accessibility, has become a major part of our lives in all segments. Social media became a crucial platform for communication during the COVID-19 pandemic as people were socially isolated and had little access to others. The use of social media platforms helped keep the world connected. Those who were stuck at home alone turned to social media to keep in touch with friends and find entertainment. However, with the positives of social media come along a very dark negative side too. The greater involvement of social media has given rise to cyberbully where someone bullies or harasses others over social media. People, especially teenagers are found to write and post negative comments in facebook, instagram, youtube etc. This has become a major social cause as such activities are extremely disturbing for the victim. In this paper, we have proposed a comparative study between various machine learning frameworks used to detect cyberbullying in social media comments. In addition, the comments are also classified according to the severity of cyberbullying. The main contribution of this work is to collect a sizable data or comments based on the Hinglish language (a mixture of English and Hindi terms written in Latin script) and then detect cyberbullying in the Hinglish language.

Intermolecular Coulombic decay (ICD) is a process whereby photoexcited molecules relax by ionizing their neighbouring molecules. ICD is efficient when intermolecular interactions are active and consequently it is observed only in weakly bound systems, such as clusters and hydrogen-bonded systems. Here we report an efficient ICD between unbound molecules excited at ambient-light intensities. On the photoexcitation of gas-phase pyridine monomers, well below the ionization threshold and at low laser intensities, we detected the parent and heavier-than-parent cations. The isotropic emission of slow electrons revealed ICD as the underlying process. π – π * excitation in unbounded pyridine monomers triggered an associative interaction between them, which leads to an efficient three-centre ICD. The cation resulting from the molecular association of the three pyridine centres relaxed through fragmentation. This below-threshold ionization under ambient light has implications for the understanding of radiation damage and astrochemistry. Intermolecular Coulombic decay (ICD) is a process whereby a photoexcited molecule relaxes while ionizing a neighbouring molecule. ICD is efficient when intermolecular interactions are active and consequently it is usually observed in weakly bound systems. Now, an efficient ICD is shown to occur even between unbound pyridine molecules excited at ambient-light intensities.