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MCDM Approach Combining DEA and AHP Methods in Sustainable Tourism: Case of Serbia
No full text183-195This paper focuses on the workforce capable of implementing new trends through the application of environmental tourism and IT knowledge. Multi-criteria optimization methods such as Data Envelopment Analysis (DEA) and Analytical Hierarchy Process (AHP) were used to solve a particular and sensitive business decision problem. A unique questionnaire on five global trends - renewable energy growth, pollution, electrification, cloudification, data boom and smartization - was developed to assess the capabilities of potential candidates in relation to environmental issues in tourism and to determine whether they are able to solve tasks in a sustainable way. This paper proposes an approach for the selection of candidates for sustainable and green tourism. From 200 candidates, data collected in a northern region of Serbia in the fall of 2023, the model resulted in the 5 best alternatives under 5 criteria. The final solution was the alternative/candidate B with the consistency index 0.03. The intention was that by combining AHP and DEA methods to evaluate efficiency, the subjectivity of decision making in the selection of candidates would be minimized. The new value of this work could be that advanced technologies are integrated into sustainable tourism in a practical and scalable way, and that methods for evaluating and implementing the technologies in question are developed. This could form the basis for future research and practical applications
MBO: A Novel Memory based Optimizer for Continuous and Discrete Optimization Problem
No full text170-182This study introduces a novel metaheuristic approach called Memory Based Optimizer (MBO), which emulates the
problem-solving process through multiple stages by utilizing the best solution obtained in terms of memory. Rooted in
principles of human psychology, MBO reflects the tendency for individuals to solve problems incrementally, using previous
learning to take small steps toward an optimal solution within a limited number of attempts. MBO is first evaluated on ten
CEC 2019 Benchmark Functions, and its results are compared with ten other metaheuristic algorithms under
similar execution conditions. In its binary form, MBO is also applied as a wrapper for feature selection in
supervised machine learning using the K-NN classifier on twelve benchmark classification datasets. The findings
indicate significant improvements in average accuracy and optimal feature selection compared to other metaheuristic
approaches. As per the simulation results, MBO has outperformed other metaheuristics approaches in 5 out of
10 continuous benchmark functions. Further, the MBO has achieved higher average accuracy in 11 out of 12 datasets, along
with better execution times in 9 out of 12 datasets when applied as a wrapper for feature selection tasks, the
average improvement in accuracy and F1 score is reported as 2.8746% and 3.1643% when compared with other
metaheuristic approaches under similar execution environment further validating its robustness and efficiency across
multiple optimization tasks
Simulative Performance Investigation of OFDM & f-OFDM for Optical Wireless Communication System
No full text162-169Internet of Things (IoT) is a fast-growing technology that requires innovative solutions and technologies to realize its
vision efficiently. Optical Wireless Communication (OWC) technology is one of the emerging connectivity technologies
that could benefit this IoT deployment. Orthogonal Frequency Division Multiplexing (OFDM) is regarded as a technique of
encoding data on multiple carriers as it promises high data rates and lays down the foundation for many standards of
wireless communication such as 5G network. However, large OOBE (Out-Of-Band Emission) and large Peak to Average
Power Ratio (PAPR) in OFDM makes it less potent to meet demand of high data rate. Therefore, Filtered-OFDM (f-OFDM)
act as promising candidate for future wireless generation networks. The motivation of this paper is to analyze the applicative
aspect of Multicarrier Modulation schemes (OFDM and f-OFDM) in implementation of OWC technology within the IoT.
The parameters used for evaluating the robustness of the designed system are namely- Bit error Rate (BER), Signal to noise
ratio (SNR) Peak to average power ratio (PAPR) and Power Spectral Density (PSD).The investigation reveals an increment
of 25% SNR and decrement of 16% occurrences of error during transmission for f-OFDM. Further, Quadrature Amplitude
Modulation (QAM) modulation scheme increase this SNR to 30% (approx.), thus promising the designed system as suitable
contender for upcoming linked OWC and wireless networks like IoT
Life Cycle Energy Assessment of Rajasthan’s Marble Processing Plant for Sustainable Environment Planning
No full text123-135The construction sector plays a vital role in achieving sustainability; therefore, monitoring and continuous improvement in energy and environmental performance in this sector are crucial. The Rajasthan state of India contains 64% of Indian marble resources, and approximately 90% of the marble is being processed in Rajasthan alone. In past decades, the production of marble stones has been in very high volume, leading to high energy consumption. Since the processing of marble worldwide is performed by Small-to-Medium Enterprises (SMEs), these industries lack technology, leading to low efficiency and more expensive production with significant waste generation. The objective of this study is to assess the energy consumption and environmental impacts of typical marble processing SMEs in Rajasthan and to propose strategies for enhancing production efficiency and reducing the ecological footprint. Through site surveys, power rating data were collected to quantify electrical energy usage across various operations of marble production, and further, each operating scenario's energy consumption was compiled. Environmental impacts, particularly CO2 emissions, were quantified using the GaBi® sustainability software. This study presents a consolidate index for assessing the economic and environmental performance of different operating scenarios and for ranking processing lines for One Square Feet (ft2) of processed marble stone, providing a comprehensive sustainability performance assessment. The findings highlight the potential for substantial environmental advantages by implementing energy-efficient practices and critical technological advancements to improve the marble processing industries' sustainability and operational efficiency, potentially assisting broader regional environmental initiatives. Eventually, the findings aim to contribute to the development of greener production practices in the sector, promoting both economic and environmental sustainability
Mechanochemical synthesis, simultaneous double cycloaddition reactions of bisnitrones and potential anticancer activities of the bis cycloadducts
No full text316-327Solid phase synthesis of bisnitrones and cycloaddition reactions of some bisnitrones using mechanochemical procedure
has been reported. Change in reaction rate and yields of the bisnitrones as well as bis cycloadducts are the key factors which
is highly encouraging after comparing microwave and coventional cycloaddition procedures. This study reports synthesis of
terephthalaldehyde and glyoxal derived bis-nitrones and their cycloaddition reactions with activated alkenes and electron
deficient alkynes along with significant anticancer activities of a few bis-cycloadducts
Lipid peroxidation level and histological changes in rat liver after the cisplatin and dexamethasone separate and combined action
No full text480-489Cisplatin is known to exhibit pro-oxidative properties, which are responsible for various toxicities caused by this drug,
including hepatotoxicity. Dexamethasone, which is known as anti-inflammatory and immunomodulatory drug, is used with
cisplatin to mitigate its side effects. However, dexamethasone, like other glucocorticoids, can induce oxidative stress and lipid
peroxidation processes. In addition, it is known that dexamethasone causes liver damage and hepatotoxicity.
The aim of this study was to clarify how dexamethasone, having a similar effect to cisplatin, alleviates the side effects
caused by this antitumor drug.
Our studies have shown that cisplatin and dexamethasone increase the formation of lipid peroxidation products conjugated
dienes and trienes of rat’s liver to varying degrees extent in the case of separate and combined injection. In addition, cisplatin
and dexamethasone were shown to increase the amount of lipid peroxidation marker malondialdehyde (MDA), in the rat liver
tissue homogenate after separate and combined administration. These changes, as well as a decrease in the activity of the
antioxidant enzyme catalase, confirm the pro-oxidative nature of cisplatin and dexamethasone. Moreover, the histopathological
studies also testify to their hepatotoxic effect.
However, contrary to the expected synergistic enhancement of both lipid peroxidation processes, and histological changes, a
reduction in cisplatin effect by dexamethasone was observed.
Thus, it is hypothesized that this “deterrent” effect of dexamethasone, combined with its anti-inflammatory and
immunomodulatory properties, allows mitigating the side effects of cisplatin
Establishing PCOS in Wistar rats: A reliable model for understanding polycystic ovary syndrome
No full text301-311Polycystic ovary syndrome (PCOS) is a complex endocrine disorder affecting reproductive-age women, characterised by
metabolic and reproductive abnormalities. This study aimed to develop and evaluate a comprehensive rat model of PCOS
that accurately replicates both the metabolic and reproductive facets of the syndrome. Female Wistar rats were treated with
dehydroepiandrosterone (DHEA), high-fat diet (HFD), and a combination of both for 20 and 30 days. The study assessed
body weight, estrous cyclicity, serum biochemistry, hormone levels, and ovarian histology. The DHEA+HFD combination
model effectively mimicked PCOS characteristics and exhibited significant increases in body weight, disrupted estrous
cycles, blood glucose, lipid levels, testosterone, estrogen, and LH levels, with decreased FSH levels. Liver and kidney
function markers were also altered, indicating systemic effects. Further, histological examination of ovaries revealed cystlike
follicles and reduced corpus luteum formation, resembling PCOS ovarian morphology. Moreover, DHEA alone induces
reproductive changes without significant metabolic alterations, and HFD alone showed slow progression of metabolic
features, but the combination group rapidly induced both metabolic and reproductive abnormalities within 20 to 30 days.
The synergistic effect highlights the potential role of diet in exacerbating PCOS symptoms. Current study presents a rat
model that comprehensively replicates PCOS features in a shorter timeframe. This combination model is valuable for
investigating PCOS pathophysiology and potential therapeutic interventions. Furthermore, these findings underscore the
importance of considering nutritional factors in PCOS management and open new avenues for research into the intricate
relationship between PCOS-related metabolic and reproductive abnormalities
PneuSwin: An Advanced X-Ray-Based Diagnostic System Integrating Ensemble Deep Learning Architectures and Swin Transformers for Pneumonia Detection
No full text421-434Pneumonia continues to pose a considerable global health concern, characterized by elevated fatality rates globally. Xrays
are the primary radiological imaging technique for detecting pneumonia because of their widespread availability and
inexpensive cost in medicine. Researchers have employed a variety of Deep Learning (DL)-based procedures to solve the
issue, but only a small number of studies have amalgamated DL methods with Swin transformers. The Swin Transformer,
distinguished for its ability to capture long-range dependencies and spatial associations, subsequently processes the refined
features. This investigation introduces PneuSwin, a novel X-ray-based diagnostic system for efficient detection. The study
utilized the PneuData dataset, a composite of three public X-ray datasets. It had Bacterial Pneumonia (BP), Viral Pneumonia
(VP), and normal classes with balanced instances. Initially, the study paired various DL architectures (CapsNet, DenseNet-
121, EfficientNet-B3, and ResNet-101) with the swin transformer. The DLs extracted relevant features from the PneuData
images, sent them to Principal Component Analysis (PCA) to diminish their dimension and pick out the relevant features,
and then inserted them as patches into the swin transformer for either binary or multi-class classification. Conversely, the
PneuSwin concatenates the retained features, transferring them as a feature matrix to the PCA for relevant features and
feeding them to the swin transformer. In both binary and multi-class classification, PnewSwin outperformed in comparison
to ensemble DLs. In binary, PneuSwin had 97.21% accuracy, a 96.95% F1 score, and a 0.967 AUC, while in multi-class it
had 97.67% accuracy, a 97.31% F1 score, and a 0.973 AUC. The results indicate that PneuSwin is proficient in detecting
pneumonia in both binary and multi-class classifications
