Search Results (233)

Invasive plant species threaten biodiversity, disrupt ecosystems, and are costly to manage. Standard control methods, such as mechanical and chemical (herbicides), are usually ineffective and time-consuming and negatively affect the environment, especially in the latter case. This review explores the potential of smart chip technology (SCT) as a sustainable, precision approach tool for invasive species management. Integrating microchip sensors with artificial intelligence (AI) into the Internet of Things (IoT) and remote sensing technology allows for real-time monitoring, predictive modelling, and focused action, significantly improving management effectiveness. As one of many examples discussed herein, AI-driven decision-making systems can process real-time data from IoT-enabled environmental sensors to optimize invasive species detection. Smart chip technology also offers real-time monitoring of invasive species’ life processes, spread, and environmental effects, enabling artificial intelligence-powered eco-friendly control strategies that minimize herbicide usage and lessen collateral ecosystem damage. Despite the potential of SCT, challenges remain, including cost, biodegradability, and regulatory constraints. However, recent advances in biodegradable electronics and AI-driven automation offer promising solutions to many identified obstacles. Future research should focus on scalable deployment, improved predictive analytics, and interdisciplinary collaboration to drive innovation. Using SCT can help make invasive species control more sustainable while supporting biodiversity and strengthening agricultural systems. Full article

Hemorrhagic neurovascular diseases, with high mortality and poor outcomes, urge novel biomarker discovery and therapeutic targets. Micro-ribonucleic acids (miRNAs) are potent post-transcriptional regulators of gene expression. They have been studied in association with disease states and implicated in mechanistic gene interactions in various pathologies. Their presence and stability in circulating fluids also suggest a role as biomarkers. This review summarizes the current state of knowledge about miRNAs in the context of cerebral cavernous malformations (CCMs), a disease involving cerebrovascular dysmorphism and hemorrhage, with known genetic underpinnings. We also review common and distinct miRNAs of CCM compared to other diseases with brain vascular dysmorphism and hemorrhage. A systematic search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline, queried all peer-reviewed articles published in English as of January 2025 and reported miRNAs associated with four hemorrhagic neurovascular diseases: CCM, arteriovenous malformations, moyamoya disease, and intracerebral hemorrhage. The PubMed systematic search retrieved 154 articles that met the inclusion criteria, reporting a total of 267 unique miRNAs identified in the literature on these four hemorrhagic neurovascular diseases. Of these 267 miRNAs, 164 were identified in preclinical studies, while 159 were identified in human subjects. Seventeen miRNAs were common to CCM and other hemorrhagic diseases. Common and unique disease-associated miRNAs in this systematic review motivate novel mechanistic hypotheses and have potential applications in diagnostic, predictive, prognostic, and therapeutic contexts of use. Much of current research can be considered hypothesis-generating, reflecting association rather than causation. Future areas of mechanistic investigation are proposed alongside approaches to analytic and clinical validations of contexts of use for biomarkers. Full article

Neem oil nanoemulsions (NO NEs) have gained attention as natural antibacterial agents due to toxicity concerns surrounding synthetic preservatives. This study aimed to prepare a response surface methodology (RSM)-optimized NO NE < 200 nm to achieve a stable dip solution to maintain the sensory quality of Oreochromis niloticus fillets. The NO NE achieved a stable formulation with a particle size of 160.2 ± 0.04 nm on average. The polydispersity index (PDI) was 0.1 ± 0.05, and the zeta potential was found to be 18.2 ± 0.09 mV. Gas chromatography confirmed the presence of nimbiol, nimbandiol, 6-deacetyl nimbinene, and azadirachtin in NO after ultrasonic homogenization for 10 min (alternating between 30 s rest and 30 s work time). The NE had a spherical shape with a smooth surface, as was evident from transmission electron microscopy (TEM). Furthermore, NO:PM (neem oil–potassium metabisulphite) had an MIC (minimum inhibitory concentration) value of 150 ppm, compared to 210 ppm for the NO NE alone, against Staphylococcus aureus. Time–kill dynamics revealed the more effective control of S. aureus until 72 h with NO:PM. Moreover, DNA and protein leakage also increased from 0.145 ± 0.001 to 0.769 ± 0.002 OD (optical density) and from 0.142 ± 0.002 to 0.740 ± 0.001 OD, respectively, with the co-formulation of NO:PM. Conclusively, NO:PM inhibited S. aureus at a lower dose compared to the NO NE alone. Time–kill dynamics revealed complete inhibition of S. aureus in vitro for a period of 72 h. On the other hand, a proximate analysis of O. niloticus fillets showed no alteration in pH, no protein loss, and juiciness/moisture retention during 30 days of storage (4 °C). Sensory panelists reported that O. niloticus fillets treated with NE NO had improved color, flavor, juiciness, aroma, and overall quality. These results show that NE NO is a suitable green preservative for fish and possibly other meat-based products. Full article

The potential consequences of interference on communication networks are one of the main challenges in the nature and efficiency of wireless communication links. The interruption is seen as additional noise to the device, which can have a major impact on the efficiency of the connection. The rapid expansion of broadband wireless networks and the increasing congestion of the radio frequency spectrum due to shared usage by terrestrial and satellite networks have heightened concerns about potential interference. To optimize spectrum utilization, multiple terrestrial and satellite networks often coexist within the same frequency bands allocated for satellite communications services. Spectrum interference in wireless networks is a topic of much interest in the current scenario as it can present a lot of challenges. This article provides a critical review of the coexistence and spectrum sharing in wireless networks. Along with this, mitigation techniques to avoid interference have also been discussed in detail. The article aims to give a detailed discussion on the challenges and opportunities in this field by reviewing significant recent works in this field. Full article

Insulin resistance (IR) is a significant factor in the development and progression of metabolic-related diseases like dyslipidemia, T2DM, hypertension, nonalcoholic fatty liver disease, cardiovascular and cerebrovascular disorders, and cancer. The pathogenesis of IR depends on multiple factors, including age, genetic predisposition, obesity, oxidative stress, among others. Abnormalities in the insulin-signaling cascade lead to IR in the host, including insulin receptor abnormalities, internal environment disturbances, and metabolic alterations in the muscle, liver, and cellular organelles. The complex and multifaceted characteristics of insulin signaling and insulin resistance envisage their thorough and comprehensive understanding at the cellular and molecular level. Therapeutic strategies for IR include exercise, dietary interventions, and pharmacotherapy. However, there are still gaps to be addressed, and more precise biomarkers for associated chronic diseases and lifestyle interventions are needed. Understanding these pathways is essential for developing effective treatments for IR, reducing healthcare costs, and improving quality of patient life. Full article

This study explores the integration of Building Information Modeling (BIM) and Geographic Information Systems (GISs) to enhance sustainable energy efficiency and flood resilience in residential buildings, with a case study from Quetta, Pakistan. The research leverages BIM to optimize energy performance through scenario-based energy consumption assessments, thermal efficiency, material properties, and groundwater considerations, ensuring structural integrity against water infiltration. Enhanced insulation and double-glazed windows reduced energy use by 11.78% and 5.8%, respectively, with monthly energy cost savings of up to 48.2%. GIS tools were employed for high-resolution flood risk analysis, utilizing Digital Elevation Models (DEMs) and hydrological data to simulate flood scenarios with depths of up to 2 m, identifying vulnerabilities and estimating non-structural damage costs at PKR 250,000 (~10% of total building costs). Groundwater data were also incorporated to evaluate their impact on foundation stability, ensuring the building’s resilience to surface and subsurface water challenges. A novel BIM-GIS integration framework provided precise 2D and 3D visualizations of flood impacts, facilitating accurate damage assessments and cost-effective resilience planning. The findings demonstrated that incorporating flood-resistant materials and design modifications could reduce repair costs by 30–50%, highlighting the cost-efficiency of sustainable resilience strategies. This research advances sustainable and resilient construction practices by showcasing the dual potential of BIM-GIS integration to address energy efficiency and groundwater-related structural vulnerabilities alongside hazard mitigation challenges. Future applications include automating workflows, integrating renewable energy systems, and validating models across diverse climatic regions to promote the global adoption of innovative urban planning solutions. Full article

Mango (Mangifera Indica L.), a key horticultural crop, particularly in Pakistan, has been primarily studied locally using low- to medium-resolution satellite imagery, usually focusing on a particular phenological stage. The large canopy size, complex tree structure, and unique phenology of mango trees further accentuate intrinsic challenges posed by low-spatiotemporal-resolution data. The absence of mango-specific vegetation indices compounds the problem of accurate health classification and yield estimation at the tree level. To overcome these issues, this study utilizes high-resolution multi-spectral UAV imagery collected from two mango orchards in Multan, Pakistan, throughout the annual phenological cycle. It introduces MangiSpectra, an integrated two-staged framework based on Long Short-Term Memory (LSTM) networks. In the first stage, nine conventional and three mango-specific vegetation indices derived from UAV imagery were processed through fine-tuned LSTM networks to classify the health of individual mango trees. In the second stage, associated data such as the trees’ age, variety, canopy volume, height, and weather data were combined with predicted health classes for yield estimation through a decision tree algorithm. Three mango-specific indices, namely the Mango Tree Yellowness Index (MTYI), Weighted Yellowness Index (WYI), and Normalized Automatic Flowering Detection Index (NAFDI), were developed to measure the degree of canopy covered by flowers to enhance the robustness of the framework. In addition, a Cumulative Health Index (CHI) derived from imagery analysis after every flight is also proposed for proactive orchard management. MangiSpectra outperformed the comparative benchmarks of AdaBoost and Random Forest in health classification by achieving 93% accuracy and AUC scores of 0.85, 0.96, and 0.92 for the healthy, moderate and weak classes, respectively. Yield estimation accuracy was reasonable with $R^2=0.21$, and $\mathrm{RMSE}=50.18$. Results underscore MangiSpectra’s potential as a scalable precision agriculture tool for sustainable mango orchard management, which can be improved further by fine-tuning algorithms using ground-based spectrometry, IoT-based orchard monitoring systems, computer vision-based counting of fruit on control trees, and smartphone-based data collection and insight dissemination applications. Full article

Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data. Full article

Based on social exchange theory, social psychology theories, and despotic leadership theory, this study explored the impact of petty tyranny on employee turnover intentions. Specifically, the authors examined the mediating effect of toxic workplace environments through emotional exhaustion on this relationship among academicians. The authors surveyed 421 employees using a five-point Likert scale across six universities in Lahore, Pakistan and employed a time-lag research design. Partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses, including performance comparisons of various algorithms, were used to test the relationships among the variables. The analysis results of the study suggested that petty tyranny does not significantly and directly contribute to employee turnover intentions; however, this relationship is positively and significantly mediated by toxic workplace environments and emotional exhaustion. The results indicated that toxic workplace environments and emotional exhaustion also have a direct effect on employee turnover intentions. A serial full mediation was found between petty tyranny and turnover intentions, mediated through a toxic workplace environment and emotional exhaustion. Similarly, results from the performance comparison of various algorithms reveal trade-offs between precision, recall, and processing time, with ZeroR and Stacking REP Tree emerging as the most effective in terms of overall model accuracy. This study contributes to the literature by examining petty tyranny, workplace environment, and emotional exhaustion, highlighting the need to address tyrannical behavior to improve employee retention in academic organizations. Our study offers valuable practical implications, emphasizing addressing these issues to reduce turnover in academic organizations. Our study also provides recommendations for future research directions. Full article

We report super broad non-Hermitian line shape from out-of-phase and in-phase photon-phonon dressing (quantization) in Eu³⁺: NaYF₄ and Eu³⁺: BiPO₄ nanocrystals. The line shape is controlled by changing time gate position, time gate width, power, temperature, sample, photomultiplier tubes, and laser. We observed that the fluorescence (FL) line-shape contrasts are 69.23% for Eu³⁺: BiPO₄ and 43.75% for Eu³⁺: NaYF₄, owing to the stronger out-of-phase photon-phonon dressing (destructive quantization). Moreover, we observed that the spontaneous four-wave mixing (SFWM) line shape was approximately three times wider at 300 K than at 77 K for the [(12:1)-phase] Eu³⁺: NaYF₄ due to more high-frequency in-phase phonon dressing (strong constructive quantization). Furthermore, we showed that the noise line-shape width remains unchanged for Eu³⁺: BiPO₄ (16 nm) and Eu³⁺: NaYF₄ (12 nm) due to out-of-phase and in-phase photon-phonon dressing balance. Such results have potential applications in multi-channel band stop filter. Full article

Constraining the collision timing of India and Asia requires reliable information from the coeval geological record along the ~2400 km long collisional margin. This study provides insights into the India–Asia collision at the westernmost margin of the Indian Plate using combined U-Pb geochronological data and sandstone petrography. The study area is situated in the vicinity of Fort Munro, Pakistan, along the western margin of the Indian Plate, and consists of the Paleocene Dunghan Formation and Eocene Ghazij Formation. The U-Pb ages of detrital zircons from the Dunghan Formation are mainly clustered between ~453 and 1100 Ma with a second minor cluster between ~1600 and 2600 Ma. These ages suggest that the major source contributing to the Dunghan Formation was likely derived from basement rocks and the cover sequence exposed mainly in Tethyan Himalaya (TH), Lesser Himalaya (LH), and Higher Himalayan (HH). Petrographic results suggest that the quartz-rich samples from the Dunghan Formation are mineralogically mature and have likely experienced log-distance transportation, which is possible in the case of an already established and well-developed river system delivering the sediments from the Craton Interior provenance. Samples of the overlying Ghazij Formation show a major detrital zircon age clustered at ~272–600 Ma in the lower part of the formation, comparable to the TH. In the middle part, the major cluster is at ~400–1100 Ma, and a minor cluster at ~1600–2600 Ma similar to the age patterns of TH, LH, and HH. However, in the uppermost part of the Ghazij Formation, ages of <100 Ma are recorded along with 110–166 Ma, ~400–1100 Ma, and ~1600–2600 Ma clusters. The <100 Ma ages were mainly attributed to the northern source, which was the Kohistan-Ladakh arc (KLA). The ~110–166 Ma ages are possibly associated with the TH volcanic rocks, ophiolitic source, and Karakoram block (KB). The Paleozoic to Archean-aged zircons in the Ghazij Formation represent an Indian source. This contrasting provenance shift from India to Asia is also reflected in the sandstone petrography, where the sample KZ-09 is plotted in a dissected arc field. By combining the U-Pb ages of the detrital zircons with sandstone petrography, we attribute this provenance change to the Asia–India collision that caused the provenance shift from the southern (Indian Craton) provenance to the northern (KLA and KB) provenance. In view of the upper age limit of the Ghazij Formation, we suggest the onset of Asian–Indian collision along its western part occurred at ca. 50–48 Ma, which is younger than the collision ages reported from central and northwestern segments of the Indian plate margin with 70–59 Ma and 56 Ma, respectively. Full article

Distance learning has grown rapidly in recent years. E-learning can aid teachers of students with disabilities, particularly visually impaired students (VISs), by offering versatility, accessibility, enhanced communication, adaptability, and a wide range of multimedia and non-verbal teaching methods. However, the shift from traditional face-to-face instruction to online platforms, especially during the pandemic, introduced unique challenges for VISs, with respect to including instructional methodologies, accessibility, and the integration of suitable technology. Recent research has shown that the resources and facilities of educational institutions pose challenges for teachers of visually impaired students (TVISs). This study conducts a literature review of research studies from the years 2000 to 2024 to identify significant issues encountered by TVISs with online learning to show the effects of distance learning before, during, and after the pandemic. This systematic literature review examines 25 publications. The evaluation reveals technological problems affecting the educational experience of visually impaired educators through a methodical categorization and analysis of these papers. The results emphasize important problems and suggest solutions, providing valuable knowledge for experts in education and legislation. The study recommends technology solutions to support instructors in providing inclusive online learning environments for VISs. Full article

Presently, the provision of safe drinking water is becoming a big challenge all over the world. In developing countries like Pakistan, many technical, financial and policy-related issues are hindering clean drinking water supply to communities. This study evaluates the performance of the drinking water supply system in Islamabad, starting from the Khanpur Dam to the consumer end via the Sangjani water treatment plant (SG-WTP). For this purpose, different physicochemical and biological parameters of water quality were analyzed and compared at four different locations in the Islamabad water supply network (also called the Khanpur Dam water supply network) for a period of one year. Statistical analyses such as the t-test, principal component analysis (PCA) and cluster analysis (CA) were performed to observe the variations in water quality parameters at the four locations. The results illustrate that the water quality upstream of the SG-WTP is declining due to various anthropogenic activities adding a variety of organic and inorganic pollutants into the water channel coming from the Khanpur Dam to the Sangjani plant. The water quality at the consumer end is deteriorating mainly due to algal growth and cracks in the water distribution network. As far as the performance of the SG-WTP is concerned, it is currently in good working condition and treating most of the water pollution coming from the Khanpur Dam water. Proper repair, maintenance and regular monitoring are necessary for sustainable operation of the Islamabad water supply system. Full article

Background and objectives: Hereditary spastic paraplegia (HSP) is characterized by unsteady gait, motor incoordination, speech impairment, abnormal eye movement, progressive spasticity and lower limb weakness. Spastic paraplegia 75 (SPG75) results from a mutation in the gene that encodes myelin associated glycoprotein (MAG). Only a limited number of MAG variants associated with SPG75 in families of European, Middle Eastern, North African, Turkish and Palestinian ancestry have been documented so far. This study aims to provide further insight into the clinical and molecular manifestations of HSP. Methods: Using whole-exome sequencing, we investigated a consanguineous Pakistani family where three individuals presented with clinical signs of HSP. Sanger sequencing was used to carry out segregation analysis on available family members, and a minigene splicing assay was utilized to evaluate the effect of the splicing variant. Results: We identified a novel homozygous pathogenic splice donor variant in MAG (c.46 + 1G > T) associated with SPG75. RNA analysis revealed exon skipping that resulted in the loss of a start codon for ENST00000361922.8 isoform. Affected individuals exhibited variable combinations of nystagmus, developmental delay, cognitive impairments, spasticity, dysarthria, delayed gait and ataxia. The proband displayed a quadrupedal stride, and his siblings experienced frequent falls and ataxic gait as one of the prominent features that have not been previously reported in SPG75. Conclusions: Thus, the present study presents an uncommon manifestation of SPG75, the first from the Pakistani population, and broadens the spectrum of MAG variants. Full article

Object detection in maritime environments is a challenging problem because of the continuously changing background and moving objects resulting in shearing, occlusion, noise, etc. Unluckily, this problem is of critical importance since such failure may result in significant loss of human lives and economic loss. The available object detection methods rely on radar and sonar sensors. Even with the advances in electro-optical sensors, their employment in maritime object detection is rarely considered. The proposed research aims to employ both electro-optical and near-infrared sensors for effective maritime object detection. For this, dedicated deep learning detection models are trained on electro-optical and near-infrared (NIR) sensor datasets. For this, (ResNet-50, ResNet-101, and SSD MobileNet) are utilized in both electro-optical and near-infrared space. Then, dedicated ensemble classifications are constructed on each collection of base learners from electro-optical and near-infrared spaces. After this, decisions about object detection from these spaces are combined using logical-disjunction-based final ensemble classification. This strategy is utilized to reduce false negatives effectively. To evaluate the performance of the proposed methodology, the publicly available standard Singapore Maritime Dataset is used and the results show that the proposed methodology outperforms the contemporary maritime object detection techniques with a significantly improved mean average precision. Full article