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Service quality has been a topic of extensive inquiry for decades that has emerged now in form of self-service technology (SST) which has profound effects on the way customers interact with firms to create positive service outcomes i.e. customer satisfaction, loyalty, and behavioral Intentions. Therefore, the main objective of this study is to examine that how the technology based Services i.e. SSTs impact the customer satisfaction, loyalty, and Behavioral Intentions in service sector of Pakistan. The data have been collected from the 238 SST’s users through the online survey. In order to test the model, Structural Equation Modeling is applied by using the LISREL program. The results of this study reveal positive and significant relationship between SSTs service quality, loyalty, and behavioral Intentions directly and indirectly via customer satisfaction. These results provide insights for the service sector of the Pakistan to invest in the new technology in order to enhance the consumer experience, satisfaction, loyalty, and Intentions.

In this study, the complete mitochondrial genomes of four species ( , , , and ) were sequenced, assembled and annotated. The four mitochondrial genomes are all composed of circular DNA molecules, with a total length ranging from 81,294 bp to 88,296 bp, containing 41-42 genes (14 protein-coding genes (PCGs), 2 rRNA genes, and 25-26 tRNA genes), the GC content is 30%-30.7% and the AT skew is positive. Phylogenetic analysis based on 14 PCGs and rRNA genes revealed that the four Xanthoparmelia species form a well-supported clade within Parmeliaceae.

Objectives: The study objective was to appraise the predictors affecting healthcare students’ perceptions concerning mental health challenges (MHCs) in a medical university. Methods: A cross-sectional observational study was conducted to associate the various predictors affecting the perception concerning the MHCs of health care students in a medical university in Malaysia. A self-prepared and validated questionnaire was distributed to the health care students in three different healthcare faculties (medical, pharmacy and dentistry) in a medical university. The required sample size for the current study was calculated using a convenient stratified sampling technique. The targeted minimum sample size was 250 participants from three different health care faculties. The Statistical Package for Social Science (SPSS) Version 24.0 was used to analyze and present the data. Results: From 284 study participants, female students had significantly better (p=0.003) perception than males. A statically significant association (p=0.032) was observed between faculty determinant and perception of pharmacy students. The final students had significantly better (p=0.018) perception as compared to pre-final year students. The students living in hostels had a better perception than non-hostellers. Parents’ education (p=0.029) and having health care professionals (p=0.002) in the family were directly associated with a better perception of health care students. Conclusion: Overall good perception of MHCs was observed among the studied cohort of the medical university’s health care students.

Cotton (Gossypium hirsutum L.) is the most important fiber crop worldwide. Characterizing genotype by environment interaction (GEI) is helpful to identify stable genotypes across diverse environments. This study was conducted in six environments to compare the performance and stability of 11 inbred lines and 30 intraspecific hybrids of cotton. Analysis of variance using the additive main effects and multiplicative interaction model revealed that genotype (G), environment (E), and GEI had highly significant effects on yield and fiber quality traits. Mean comparisons among genotypes showed that most hybrids had higher means for yield and fiber quality traits than inbred genotypes. Additionally, a larger portion of the total variability in yield traits was explained by E than G and GEI. However, G and GEI combined contributed more to the total variance in fiber traits than E. The first three interaction principal components explained the majority of GEI in all traits under study. For most traits, the environments were not clustered together, implying contrasting interaction with genotypes. Stability measurements indicated that most hybrids showed more stable performance than inbred lines for all traits. The hybrids SJ48-1 × Z98-15 and L28-2 × A2-10 displayed both better performance and stability in yield and fiber quality traits. Our results show the importance of hybridization for improving cotton yield and fiber quality in a wide range of environments.

Nanotechnology deals with the synthesis of materials and particles at nanoscale with dimensions of 1–100 nm. Biological synthesis of nanoparticles, using microbes and plants, is the most proficient method in terms of ease of handling and reliability. Core objectives of this study were to synthesize metallic nanoparticles using selenium metal salt from citrus fruit extracts, their characterization and evaluation for antimicrobial activities against pathogenic microbes. In methodology, simple green method was implicated using sodium selenite salt solution and citrus fruit extracts of Grapefruit and Lemon as precursors for synthesizing nanoparticles. Brick red color of the solution indicated towards the synthesis of selenium nanoparticles (SeNPs). Nanoparticle’s initial characterization was done by UV–Vis Spectrophotometry and later FTIR analysis and DLS graphs via Zetasizer were obtained for the confirmation of different physical and chemical parameters of the nanoparticles. Different concentrations of SeNPs were used for antimicrobial testing against E. coli , M. luteus , B. subtilis and K. pneumoniae comparative with the standard antibiotic Ciprofloxacin. SeNPs possessed significant antimicrobial activities against all the bacterial pathogens used. Conclusively, SeNPs made from citrus fruits can act as potent antibacterial candidates.

Diversifying available natural resources to cope with abrupt climatic changes and the necessity to equalize rising agricultural production with improved ability to endure environmental influence is the dire need of the day. Inherent allelic variability regarding significant economic traits featuring both enhanced productivity and environmental adaptability is one such prominent need. To address this requirement, a series of analyses were conducted in this study for exploring natural diploid wheat germplasm resources. The current study involved 98 Recombinant Inbred Lines (RILs) populations developed by crossing two diploid ‘A’ sub-genome wheat species, Triticummonococcum and Triticum boeoticum, enriched with valuable alleles controlling, in particular, biotic and abiotic stresses tolerance. Their 12 phenotypic traits were explored to reveal germplasm value. All traits exhibited vast diversity among parents and RILs via multivariate analysis. Most of the investigated traits depicted significant (p < 0.05) positive correlations enlightening spikelet per spike, total biomass, seed weight per spike, number of seeds per spike, plant height, and days to heading as considerably focused traits for improving hexaploid wheat. Principal component analysis (PCA) exhibited 61.513% of total variation with three PCs for 12 traits. Clustering of genotypes happened in three clades, and the two parents were separated into two extreme clusters, validating their enrichment of diversity. This study provided beneficial aspects of parental resources rich in diverse alleles. They can be efficiently exploited in wheat improvement programs focusing on introgression breeding and the recovery of eroded genetic factors in currently available commercial wheat cultivars to sustain calamities of environmental fluctuations.

Nanotechnology is a vast field of science with the most vibrant and conspicuous applications. The green synthesis approach is cost-effective, eco-friendly, and produces the most stable metal-based nanoparticles without the use of toxic chemicals. This study presents the green synthesis of iron nanoparticles (FeNPs). For biosynthesis of FeNPs, Phoenix dactylifera extract was used as a reducing agent and iron sulfate heptahydrate (FeSO 4 ·7H 2 O) was used as a substrate. FeNPs were characterized by different techniques including UV–Visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), and nano zeta-sizer analysis. The antimicrobial activity of FeNPs synthesized by using an aqueous extract of Phoenix dactylifera was evaluated against Escherichia coli , Bacillus subtilis , Micrococcus leutus, and Klebsiella pneumoniae . A notable color change from yellow to black confirmed the synthesis of FeNPs. The sharp peak at 450 nm UV–Visible spectroscopy confirmed the synthesis of FeNPs. FTIR showed the presence of O–H and C=C stretching due to the presence of phenol and alkene functional groups. The average size of FeNPs was 6092 d.nm. The results of antimicrobial activity showed that FeNPs exhibit different potential against different bacterial strains with a maximum 25 ± 0.360 zone of inhibition against Escherichia coli. Thus, green synthesized FeNPs could be used as potential antimicrobial agents.

Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ABSA encompasses a set of sub-tasks that together facilitate a detailed understanding of the multifaceted sentiment expressions. These tasks include aspect and opinion terms extraction (ATE and OTE), classification of sentiment at the aspect level (ALSC), the coupling of aspect and opinion terms extraction (AOE and AOPE), and the challenging integration of these elements into sentiment triplets (ASTE). Our research introduces a comprehensive framework capable of addressing the entire gamut of ABSA sub-tasks. This framework leverages the contextual strengths of BERT for nuanced language comprehension and employs a biaffine attention mechanism for the precise delineation of word relationships. To address the relational complexity inherent in ABSA, we incorporate a Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) that utilizes advanced linguistic features to refine the model’s interpretive capabilities. We also introduce a systematic refinement approach within MLEGCN to enhance word-pair representations, which leverages the implicit outcomes of aspect and opinion extractions to ascertain the compatibility of word pairs. We conduct extensive experiments on benchmark datasets, where our model significantly outperforms existing approaches. Our contributions establish a new paradigm for sentiment analysis, offering a robust tool for the nuanced extraction of sentiment information across diverse text corpora. This work is anticipated to have significant implications for the advancement of sentiment analysis technology, providing deeper insights into consumer preferences and opinions for a wide range of applications.

Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Pakistan, and processed into an RGB orthomosaic with a 3 cm ground sampling distance. The You Only Look Once (YOLOv7) framework was trained on an annotated dataset to detect individual mango trees. The resultant bounding boxes were used as prompts for the segment anything model (SAM) for precise delineation of canopy boundaries. Validation against ground truth data of 175 manually digitized trees showed a strong correlation (R2 = 0.97), indicating high accuracy and minimal bias. The proposed method achieved a mean absolute percentage error (MAPE) of 4.94% and root mean square error (RMSE) of 80.23 sq ft against manually digitized tree canopies with an average size of 1290.14 sq ft. The proposed approach effectively addresses common issues such as inaccurate bounding boxes and over- or under-segmentation of tree canopies. The enhanced accuracy can substantially assist in various downstream tasks such as tree location mapping, canopy volume estimation, health monitoring, and crop yield estimation.

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients’ treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.