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Abstract This research purpose was focused to document the traditional ethnobotanical knowledge (TEK) associated with important wild plants and indigenous communities residing in mountainous zone of District Sudhnoti of Azad Jammu and Kashmir, Pakistan and to determine their conservation status with suggestions and recommendations to protect and propagate the rare and endangered wild flora of the area for sustainable use. The data regarding traditional ethnomedicines (TEMs) and phytogeography with conservation analysis of wild plants were collected via structured and semi structured interviews of 150 participants belonging to various professions and of both genders. Prior consent and permission were obtained from family heads and from all the participants, and the data were shared with all the informants through the local female translator guide. To validate the collected data, various microstatistical tools, such as the informant consensus factor (ICF), fidelity level (FL) and data matrix ranking (DMR) were used. The relative frequency of citations (RFC) and rank order of popularity (ROP) were also calculated to determine and authenticate the relative importance of TEMs which may be further studied in future research and used for drug discovery. In the present study, 150 locally important plants belonging to 69 families were documented, and the Rosaceae was the most dominant and prevalent family. The results indicated that many locally important plants have multiple uses such as food, medicine, fodder, shelter, ornamental and fuel. The TEMs obtained from these plants have been known to be useful for curing various infirmities such as flu, renal disorders, fever, malaria, cough, migraine, whooping cough, influenza, skin rashes, allergies, stomach aches, wounds and bruises, diabetes, tumours and joint pains. The ICF analysis revealed that renal calculus, malaria, fever, whooping cough, rheumatoid arthritis and arthritis were prevalent infirmities of the mountainous area. The FL analysis indicated the popularity of these plants used in various TEMs and among these Artemisia maritima and Berberis lycium were the most common. The DMR indicated that Pinus roxburghii was the most common species being used for multipurpose by the indigenous communities. The highest use value index was found for Indigofera linifolia. The relative frequency of citations was calculated to determine the importance of plants in traditional ethnomedicine, highlighting Morus nigra, Pinus wallichiana, and Rosa indica as significant species. The Jaccard index indicated a high level of novelty in the research which can be exploited for neo-drug discovery and drug development. In conclusion, this study has successfully documented the ethnobotanically and ethnomedicinally important plants in District Sudhnoti, Azad Jammu and Kashmir, Pakistan. The findings of this research contribute to the preservation and documentation of cultural heritage, as well as provide a foundation for further studies in ethnobotany, ethnopharmacology and biodiversity conservation efforts for sustainable provision of wild flora to the indigenous communities which will also assist in combating drasting climatic changes.

The model of circular bipolar complex fuzzy (Cir-BCF) sets computed based on the membership function, non-membership function, and radius among both functions for each value of the universal set. The technique of the Cir-BCF set is the modified or extended form of fuzzy sets, complex fuzzy sets, bipolar fuzzy sets, bipolar complex fuzzy sets, and simple circular bipolar fuzzy sets to cope with uncertain and vague information. In this manuscript, we describe the novel technique of frank operational laws based on Cir-BCF values for frank t-norm and frank t-conorm. Further, we simplify the model of Cir-BCF frank power averaging (Cir-BCFFPA), Cir-BCF frank power weighted averaging (Cir-BCFFPWA), Cir-BCF frank power geometric (Cir-BCFFPG), Cir-BCF frank power weighted geometric (Cir-BCFFPWG) operators, and highlighted their valuable properties, called idempotency, monotonicity, and boundedness. Moreover, analysis of renewable energy resources is very awkward and complicated, but it is natural sources of energy that are refilled constantly or relativeness quickly on a human timescale. Further, solar energy, hydroelectric energy, geothermal energy, biomass energy, and wind energy are five major sources of energy, but it is complex to find which one is the best and which one is worst. For evaluating the above problems, we construct the technique of evaluation based on the distance from the average solution (EDAS) method under the presence of the Cir-BCF numbers (Cir-BCFNs). At the end, we arrange the comparison between proposed and existing techniques based on some numerical examples to describe the validity and proficiency of the initiated information.

Green industry development focuses on balancing economic and financial growth with environmental stewardship and ensuring that companies and industries are contributing positively to both environmental sustainability and prosperity. This manuscript aims to develop the novel technique of circular complex p, q-quasirung orthopair fuzzy 2-tuple linguistic (CCp, q-QOF2-TL) set and their operational laws based on algebraic t-norms and Hamacher t-norms, where the algebraic t-norms and Einstein t-norms are the special cases of the Hamacher t-norms for parameter ϜℲs=l and ϜℲs=2. Further, we derive the Hamacher power aggregation operators based on any finite collection of CCp, q-QOF2-TL numbers (CCp, q-QOF2-TLNs), called CCp, q-QOF2-TL Hamacher power average (CCp, q-QOF2-TLHPA) operator, CCp, q-QOF2-TL Hamacher power weighted average (CCp, q-QOF2-TLHPWA) operator, CCp, q-QOF2-TL Hamacher power geometric (CCp, q-QOF2-TLHPG) operator, CCp, q-QOF2-TL Hamacher power weighted geometric (CCp, q-QOF2-TLHPWG) operator, and described their basic properties, called idempotency, monotonicity, and boundedness. Further, we demonstrate the technique of multi-attribute decision-making (MADM) problem based on the above operators to evaluate the major factor that will be playing in the development of the green industry. Finally, we compare the proposed ranking values with the obtained ranking values of existing techniques to show the supremacy and superiority of the initiated approaches.

The actions are oriented to delight travelers where existing protocols fall short of solving problems in the travel and tourism industry, seeking the retention of commuters by offering travel quality. The present study investigates the travel decision-making process by exploring the nexus between travel quality and re-travel intentions (RTI). The study also analyzes the relational switching cost's (RSC) unique role as a sequential mediator in driving travel quality through the theoretical lens of expectancy disconfirmation theory. Furthermore, the study also explores the role of gender in explaining RTI. 434 real-time responses were collected following the lifetime value (LTV) approach. The structural equation modeling (SEM) was used to test the hypotheses. The study reveals that travel quality significantly influences commuter satisfaction. The results also suggest the indirect effect of RSC on commuter satisfaction (CS) and (RTI). Finally, it is suggested that RTI is higher among female commuters than male commuters. The fundamental contribution of this study is developing and testing the travel decision-making process by extending the EDT model. Additionally, the TRAVQUAL scale contributes to measuring perceived and expected travel quality. Commuter satisfaction and relational switching costs act as sequential mediators, while gender emerges as a moderator, emphasizing the greater significance of re-travel intentions among female commuters post-RSC. These insights contribute valuable perspectives for enhancing travel experiences and decision-making processes in the travel and tourism industry.

Service failure is bound to happen, but dissatisfaction has always been undermined by scholars previously. The present study investigates dissatisfaction factors in travel decision-making leading towards re-travel intention with the help of the expectancy disconfirmation theory (EDT) model. Moreover, complaint behavior mediates the relationship between dissatisfaction and re-travel intention concerning the factors involved in it. For the findings, 434 real-time responses were collected through an offline survey following the lifetime value (LTV) approach. Kaiser-Meyer-Olkin (KMO) and Bartlett's tests were conducted to test the sphericity of the variables one by one. Furthermore, structural equation modeling (SEM) was implied to test the measurement and structural models. All the hypotheses supported and accepted the proposed research questions. The findings reveal that the dissatisfaction factors, with their interaction terms (transaction-based and experience-based), help study the variable dissatisfaction and its multifaceted concept in travel and tourism literature. Moreover, complaint behavior is identified as a key mediating factor in the relationship between dissatisfaction and re-travel intention, suggesting that commuters' response to dissatisfaction, including complaints, shapes their future re-travel intentions. The theoretical implications of the study are substantial, as it advances our understanding of commuter behavior in the context of public transport travel and tourism. Practically, the findings offer actionable insights for public transport authorities and service providers. Understanding the specific dissatisfaction factors influencing re-travel intention allows for targeted interventions to improve service quality and customer experience. Recognizing the importance of effective complaint management systems can help organizations better address customer grievances and mitigate the negative effects of dissatisfaction on re-travel intention.

The solid solution of pure ZnO nanoparticles and doped Polyvinylepyrrolidone (PVP) green hybrid nanomaterial’s synthesized by using the electro spinning method. The synthetic polymer matrix nonwoven fibrous mats containing innovative properties are shown to shrink and encapsulated the zinc oxide materials to change the surface morphology when the concentration of PVP is increased from 1 % to 2 % dopants. The crystalline nature and morphological studies were examined by using x-ray diffraction and a scanning electron microscope. The average crystallite sizes of easily formed ZnO nanoparticles and nanorods that are 21 nm and 62 nm respectively. Moreover, the electrical and optical properties of the fibrous mesh were determined by using electrical impedance analyzer. The electrical conductivity values were measured in the pure ZnO nanoparticles in contrast to the doped ZnO/PVP. The tangent loss, dielectric constant, capacitance and relaxation time values are revealed in this study. All the characterization has been carried out at room temperature. The relaxation time for 1 %, and 2 % ZnO/PVP is (0.8 ns) and (0.79 ns) respectively which is suitable for the application of trigger using devices. The overall findings of this study implemented in a wide range of technologies i.e. photonics, electronics and super capacitor devices etc.

The advancement of artificial intelligence (AI) has become a crucial element in autonomous cars. A well-designed AI architecture will be necessary to attain the full potential of autonomous vehicles and will significantly accelerate the development and deployment of autonomous cars in the transportation sector. Promising autonomous cars for innovating modern transportation systems are anticipated to address many long-standing transporting challenges related to congestion, safety, parking, and energy conservation. Choosing the optimal AI architecture for autonomous vehicles is a multi-attribute decision-making (MADM) dilemma, as it requires making a complicated decision while considering a number of attributes, and these attributes can have two-dimensional uncertainty as well as indiscernibility. Thus, in this framework, we developed a novel mathematical framework “complex intuitionistic fuzzy rough set” for tackling both two-dimensional uncertainties and indiscernibility. We also developed the elementary operations of the deduced complex intuitionistic fuzzy rough set. Moreover, we developed complex intuitionistic fuzzy rough (weighted averaging, ordered weighted averaging, weighted geometric, and ordered weighted geometric) aggregation operators. Afterward, we developed a method of MADM by employing the devised operators and investigated the case study “Selection of optimal AI architecture for autonomous vehicles” to reveal the practicability of the devised method of MADM. Finally, to reveal the dominance and supremacy of our proposed work, a benchmark dilemma was used for comparison with various prevailing techniques.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant’s comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant’s comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA–GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.

Every problem in decision-making has a solution when the information that is available is properly and precisely modeled. This study focuses on non-binary data from N-soft sets and q-rung orthopair fuzzy values, referred to as group-based generalized q-rung orthopair fuzzy N-soft sets (GGq-ROFNSSs). The GGq-ROFNSSs model provides information simultaneously on numerous competing criteria, alternatives, sub-alternatives, and data summarization. We introduce properties of GGq-ROFNSSs such as distinct inclusion features of GGq-ROFNSSs, weak complements of the GGq-ROFNSS, top weak complements the GGq-ROFNSS, bottom weak complements the GGq-ROFNSS. We provide the notion of GGq-ROFNSWA and GGq-ROFNSWG operators as well as their idempotency, monotonicity, and boundedness features. The notion of GGq-ROFNSSs requires a sound methodology of multiple criteria decision making (MCDM) since GGq-ROFNSS combines numerous elements of complex decision-making. We provide a MCDM methodology for the GGq-ROFNSWA and GGq-ROFNSWG operators and depict it in a flowchart. The selection of solar panels for a city is a difficult procedure because it depends on several components such as environment, where the area is located, what kinds of needs are being met, etc. We find a solution to the problem of selecting a suitable solar panel for a city with their underlying characteristics. Finally, we provide a comparison of the suggested method with other techniques to demonstrate its advantages.

The model of bipolar complex fuzzy linguistic set is a very famous and dominant principle to cope with vague and uncertain information. The bipolar complex fuzzy linguistic set contained the positive membership function, negative membership function, and linguistic variable, where the technique of fuzzy sets to bipolar fuzzy sets are the special cases of the bipolar complex fuzzy linguistic set. In this manuscript, we describe the model of Aczel-Alsina operational laws for bipolar complex fuzzy linguistic values based on Aczel-Alsina t-norm and Aczel-Alsina t-conorm. Additionally, we compute the Aczel-Alsina power aggregation operators based on bipolar complex fuzzy linguistic data, called bipolar complex fuzzy linguistic Aczel-Alsina power averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power weighted averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power geometric operator, and bipolar complex fuzzy linguistic Aczel-Alsina power weighted geometric operator with some dominant and fundamental laws such as idempotency, monotonicity, and boundedness. Moreover, we initiate the model of the Weighted Aggregates Sum Product Assessment technique with the help of consequent theory. In the context of geographic information systems and spatial information systems, coupling aims to find out the relationships among different components within a geographic information system, where coupling can occur at many stages, for instance, spatial coupling, data coupling, and functional coupling. To evaluate the above dilemma, we perform the model of multi-attribute decision-making for invented operators to compute the best technique for addressing geographic information systems. In the last, we deliberate some numerical examples for comparing the ranking results of proposed and prevailing techniques.

This study explored the effect of different drying methods (sun, shade, oven) on the total polyphenol content (TPC) using the Folin–Ciocalteu (FC) procedure, total flavonoid content (TFC) using the AlCl3 colorimetric method, volatile organic compounds using GC/MS and the antioxidant properties of the rose petals using the protocol of DPPH assy. The results demonstrated that the drying methods had a significant impact and the highest TPC (34.24 mg gallic acid equivalent (GAE)/ g fresh weight (FW)) and TFC (5.56 mg catechin equivalent (CE)/g FW) were obtained for the oven-dried sample. While, the fresh sample exhibited the lowest TPC (15.6 mg GAE/g FW) and TFC (3.83 mg CE/g FW), respectively. Similarly, the oven-dried sample showed the highest DPPH scavenging activity (60.30 %) and reducing power (absorbance 1.138) among all the samples. Fresh rose sample GC–MS analysis revealed that the there are two major compounds heptacosane 64.56 % and citronellyl propionate 28.35 %. Pentadecyl 2-phenylethyl ester oxalic acid was the second dominant compound in sun and oven-dried rose samples, 18.5 % and 14.79 % respectively.

The Schwannoma is a benign growth of the nerve sheath cells most commonly seen in the vestibulocochlear nerve. Its prevalence in the adrenal gland is 1-3%. Here we discuss a case that presented as an incidentaloma of the right adrenal gland in a young male patient who had vague abdominal symptoms and a normal hormonal profile. He underwent an excisional biopsy of the right adrenal gland due to the large size of the lesion (more than 4cm). The histopathology report helped to establish the diagnosis of Schwannoma. Incidentaloma is defined as a lesion of the adrenal gland encountered on any radiological investigation carried out for symptoms that are not associated with adrenal pathologies. After discovering such lesions, it is imperative to perform radiological and hormonal investigations in an organised manner to plan further management of such cases. Keywords: Schwannoma, Adrenal Incidentaloma, Adrenocortical Adenoma, Adrenocortical Carcinoma, Adrenalectomy.

In many decision-making situations, we are not restricted to two kinds of aspects, such as membership degree or nonmembership degree, and sometimes we need to include the abstinence degree (AD). However, many fuzzy set theories fail to cover issues, such as an intuitionistic fuzzy soft set, Pythagorean fuzzy soft set and q-rung orthopair fuzzy soft set. All the above notions can only consider membership degree and a nonmembership degree in their structures. The spherical fuzzy soft set compensates for these drawbacks in its structure. Moreover, the Dombi t-norm and Dombi t-conorm are the fundamental apparatuses to generalize the basic operational laws of sum and product. Therefore, in this article, based on the dominant features of spherical fuzzy soft sets and valuable features of the Dombi t-norm and Dombi t-conorm, we initially developed the basic Dombi operational laws for spherical fuzzy soft numbers. Moreover, based on these newly developed operational laws, we introduced aggregation operators called spherical fuzzy soft Dombi average (weighted, ordered weighted, hybrid) aggregation operators. We discussed the basic properties of these aggregation operators. Additionally, we have developed a multiple criteria decision making (MCDM) approach using an explanatory example via our approach to show its effective utilization. Furthermore, a comparative study of our approach shows the superiority of our introduced notions.

Background: Endoscopic radiofrequency ablation (RFA) is used for the treatment of unresectable malignant biliary obstruction (MBO). The postoperative adverse events associated with RFA treatment have gained importance. Objective: To investigate the early adverse events and their risk factors associated with RFA for the treatment of MBO. Design: Observational retrospective study. Methods: We collected data from patients diagnosed with MBO and treated with endoscopic RFA at our hospital between January 2010 and June 2022. Based on the collected data, the patients were divided into two groups: the adverse event group and the nonadverse event group. Early postoperative adverse events were recorded, and risk factors were assessed. Results: One hundred and twenty patients with MBO underwent endoscopic RFA, with 20 developing adverse events (16.6%; 20/120). Among these, 13 patients (10.8%) developed biliary infection after RFA treatment, while 7 (5.8%) developed acute pancreatitis, and no bleeding or perforation occurred. Type 2 diabetes mellitus, bile duct stricture length >2.5 cm, segmental RFA, and the proportion of patients receiving single stent drainage were all significantly greater in the adverse event group compared to the nonadverse event group (p < 0.05). The results of the logistic regression analysis showed that type 2 diabetes, segmental RFA, and single stent drainage were the three independent risk factors for getting a biliary infection after RFA therapy. Conclusion: Unresectable MBO combined with type 2 diabetes mellitus, segmental RFA, and postoperative single stent drainage can be the risk factors for adverse events after RFA. More attention should be paid to patients with multiple risk factors and preventive measures should be taken. Plain language summary: Assessment of safety and adverse events in endoscopic radiofrequency ablation for malignant biliary obstruction The number of cases of malignant biliary obstruction (MBO) has been increasing recently, and nonsurgical treatment of MBO is a major problem. Radiofrequency ablation (RFA) has shown promising effects as palliative therapy. However, adverse events after the RFA treatment are a point of concern. In this retrospective study, we analyzed data from 120 patients who received RFA treatment. [ABSTRACT FROM AUTHOR]

Leptadenia pyrotechnica is considered a wild herb used to enhance the palatability of food, particularly in the Gulf region. The effect of microwave (5, 8, and 10 min at 900 W) and hot-air heating (60, 120, and 180 min at 100 °C) on the phenolic compounds and antioxidants of L. pyrotechnica stems was investigated. The results showed that microwave heating gave high values of the total phenolic content (TPC), total flavonoid content (TFC), and antioxidant properties, while the control sample was inferior in all these attributes. Moreover, microwave heating, particularly for 8 min, produced the highest TPC, TFC, and DPPH values (significant at p < 0.05) and reduced power in the L. pyrotechnica stem. However, prolonging the heating time caused adverse effects on the bioactive potential of the samples. The HPLC analysis indicates that both processes caused a significant increment in the phenolic compounds of the sample. Tannic acid, vanillin, and acetyl salicylic acid were found to be higher in the microwaved-treated samples than in those heated with hot air. The tannic acid was found to be high after microwave heating for 8 min at 116.06 mg/100 g, while the higher value of acetylsalicylic acid 119.08 mg/100 g was observed after microwave heating for 5 min. The partial least regression (PLS) validation model revealed that microwave heating, particularly at an 8 min application time, offered better results, and owing to its short processing time, which might be adopted for heating the L. pyrotechnica stems in the food industry, and it can be useful for preparing functional foods.

In the early 1990s, Mehrotra and Nichani developed a filtering-based corner detection method, which, though conceptually intriguing, suffered from limited reliability, leading to minimal references in the literature. Despite its underappreciation, the core concept of this method, rooted in the half-edge concept and directional truncated first derivative of Gaussian, holds significant promise. This article presents a comprehensive assessment of the enhanced corner detection algorithm, combining both qualitative and quantitative evaluations. We thoroughly explore the strengths, limitations, and overall effectiveness of our approach by incorporating visual examples and conducting evaluations. Through experiments conducted on both synthetic and real images, we demonstrate the efficiency and reliability of the proposed algorithm. Collectively, our experimental assessments substantiate that our modifications have transformed the method into one that outperforms established benchmark techniques. Due to its ease of implementation, our improved corner detection process has the potential to become a valuable reference for the computer vision community when dealing with corner detection algorithms. This article thus highlights the quantitative achievements of our refined corner detection algorithm, building upon the groundwork laid by Mehrotra and Nichani, and offers valuable insights for the computer vision community seeking robust corner detection solutions.

Abstract This work presents a gallium nitride (GaN) high electron mobility transistor (HEMT)–based cascaded multistage power amplifier (MPA) in class‐AB for L‐band radar applications. The purpose of this endeavour is to develop an MPA using GaN HEMT devices to achieve optimised parameters such as high gain, high power, better efficiency, and linearity in a compact size. In an MPA design with multiple stages, oscillations are common owing to unwanted high gain at the lower frequency range. To overcome this issue, we introduced interstage harmonic termination networks as a novel approach to suppress high gain at low frequencies, which are prone to oscillations. The proposed cascaded MPA provides the maximum radio‐frequency output power of 89 W and a power gain of 52 dB with an associated power‐added efficiency of 51%. Second and third harmonic levels are −32.5 and −37 dBc, respectively. Two‐tone measurements are performed with a frequency separation of 10 MHz, and an intermodulation level of less than −33 dBc is achieved.