Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach.

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution.

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

Diversity and biological characteristics of macrofungi of district Bajaur, a remote area of Pakistan in the Hindu Kush range.

The present study was carried out to document the diversity and ecological characteristics of macrofungi of Bajaur, Pakistan. The diversity of macrofungi comprised 51 species belonging to 22 families and 37 genera. The families Agaricaceae (7 species) and Psathyrellaceae (7 species) were found dominant followed by Tricholomataceae (4 species), Fomitopsidaceae and Polyporaceae (4 species each) and Amanitaceae (3 species). White (23 species), brown (11 species), and yellow were the most prevalent morphological colours in basidiocarps (8 species). Among the identified species, 32 were saprophytic in nutrition followed by 7 parasitic, 6 saprophytic and parasitic both, while 6 mycorrhizal that make association with higher plants. The distribution of macrofungal species in the three tehsils of Bajaur was also evaluated based on Shannon diversity index, Simpson diversity index and evenness. The highest Shannon diversity index and Simpson diversity index were found for tehsil Utman Kheil at 3.73 and 0.97, while the maximum value of evenness for tehsil Khar with 0.92 value. The results indicate a very high species richness of the study site. Four species out of the total were identified to be new reports from Pakistan. This survey's findings suggested that there is a wide variety of macrofungi that might be used as food and alternative medications if further research is carried out.

Fabrication of Polymeric Hydrogels Containing Esomeprazole for Oral Delivery: In Vitro and In Vivo Pharmacokinetic Characterization.

Hydrogel is one of the most interesting and excellent candidates for oral drug delivery. The current study focuses on formulation development of hydrogels for controlled oral delivery of esomeprazole. The hydrogels were prepared by solution casting method by dissolving polymers in Polyvinyl alcohol (PVA) solution. Calcium alginate, Hydroxyl propyl methylcellulose (HPMC), acrylic acid and chondroitin sulfate were used in the preparation of hydrogels. Fourier transform infrared (FTIR) analysis showed no incompatibilities between drug and excipients used in the preparation of formulations. The hydrogels were characterized for size and surface morphology. Drug encapsulation efficiency was measured by Ultraviolet-visible (UV-VIS) spectroscopy. In vitro release studies were carried out using dissolution apparatus. The formulated hydrogels were then compared with the marketed product in vivo using rabbits. The result indicates that prepared hydrogels have a uniform size with a porous surface. The esomeprazole encapsulation efficiency of the prepared hydrogels was found to be 83.1 ± 2.16%. The esomeprazole-loaded hydrogel formulations showed optimum and Pharmacopeial acceptable range swelling behavior. The release of esomeprazole is controlled for 24 h (85.43 ± 0.32% in 24 h). The swelling and release of drug results make the prepared hydrogels a potential candidate for the controlled delivery of esomeprazole. The release of the drug from prepared hydrogel followed the super case transport-2 mechanism. The in vivo studies showed that prepared hydrogel formulations showed controlled and prolonged release of esomeprazole as compared to drug solution and marketed product. The formulations were kept for stability studies; there was no significant change observed in physical parameters, i.e., (appearance, color change and grittiness) at 40 °C ± 2/75% ± RH. There was a negligible difference in the drug content observed after the stability study suggested that all the formulations are stable under the given conditions for 60 days. The current study provides a valuable perspective on the controlled release profile of Hydroxyl propyl methylcellulose (HPMC) and calcium alginate-based esomeprazole hydrogels.

Fabrication of Novel Omeprazole-Based Chitosan Coated Nanoemulgel Formulation for Potential Anti-Microbia; In Vitro and Ex Vivo Characterizations.

Infectious diseases remain inevitable factors for high mortality and morbidity rate in the modern world to date. Repurposing is a novel approach to drug development has become an intriguing research topic in the literature. Omeprazole is one of the top ten proton pump inhibitors prescribed in the USA. The literature suggests that no reports based on omeprazole anti-microbial actions have been discovered to date. This study entails the potential of omeprazole to treat skin and soft tissue infections based on the literature's evident anti-microbial effects. To get a skin-friendly formulation, a chitosan-coated omeprazole-loaded nanoemulgel formulation was fabricated using olive oil, carbopol 940, Tween 80, Span 80, and triethanolamine by high-speed homogenization technique. The optimized formulation was physicochemically characterized for zeta potential, size distribution, pH, drug content, entrapment efficiency, viscosity, spreadability, extrudability, in-vitro drug release, ex-vivo permeation analysis, and minimum inhibitory concentration determination. The FTIR analysis indicated that there was no incompatibility between the drug and formulation excipients. The optimized formulation exhibited particle size, PDI, zeta potential, drug content, and entrapment efficiency of 369.7 ± 8.77 nm, 0.316, -15.3 ± 6.7 mV, 90.92 ± 1.37% and 78.23 ± 3.76%, respectively. In-vitro release and ex-vivo permeation data of optimized formulation showed 82.16% and 72.21 ± 1.71 µg/cm2, respectively. The results of minimum inhibitory concentration (1.25 mg/mL) against selected bacterial strains were satisfactory, suggesting a successful treatment approach for the topical application of omeprazole to treat microbial infections. Furthermore, chitosan coating synergistically increases the antibacterial activity of the drug.

Health Risk Assessment and Multivariate Statistical Analysis of Heavy Metals in Vegetables of Khyber Pakhtunkhwa Region, Pakistan.

The level of heavy metals in vegetables grown in soil irrigated with various sources of water and the health risks associated with the consumption of these vegetables were assessed in this study. Samples of water, soil, and vegetables were collected from farmer fields. After digestion in acidic solution, analytical measurements were made using an atomic absorption spectrophotometer. The mean concentration of Pb, Cr, Cd, Cu, Zn, Ni, Fe, and Mn in the soil of two sampling area were in the range from 60.00 to 84.00 mg kg-1, 68.00 to 98.00 mg kg-1, 1.60 to 2.60 mg kg-1, 26.10 to 33.20 mg kg-1, 22.60 to 30.80 mg kg-1, 50.10 to 78.30 mg kg-1, 420.00 to 471.00 mg kg-1, and 270.20 to 340.50 mg kg-1, respectively. Heavy metals in soil varied significantly at (P ≤ 0.001) among sampling area. The nine heavy metals were divided into two clusters for wastewater and soil, according to cluster analysis. The number of variables was reduced using principal component analysis, which yielded three latent factors, one for wastewater and one for soil. Pb, Cr, Cd, Cu, Zn, Ni, Fe, and Mn concentrations were significantly higher at P ≤ 0.001 in nine vegetables grown on soil irrigated with untreated wastewater than in vegetables grown on fresh-tube well-water-irrigated soil. The health risks associated with metal intake were assessed using the estimated daily intake of metals (EDIM), hazard quotients (HQs), and hazard index (HI). The rates of metal transfer to vegetables have been determined. Except for Pb and Cd, all of the elements' EDMI values were found to be lower than their RfD values. The corresponding HRI values of metals in the various vegetables were found to be below 1, implying that vegetable consumption in the studied region poses no carcinogenic risk. Constant determination of heavy metals in all fruits and vegetables is essential for the assessment of health risks associated with dietary metal exposure. The study has provided valuable information to the general public about the use of wastewater for irrigation of vegetables.

Quantitative Modeling of Protein Synthesis Using Ribosome Profiling Data.

Quantitative prediction on protein synthesis requires accurate translation initiation and codon translation rates. Ribosome profiling data, which provide steady-state distribution of relative ribosome occupancies along a transcript, can be used to extract these rate parameters. Various methods have been developed in the past few years to measure translation-initiation and codon translation rates from ribosome profiling data. In the review, we provide a detailed analysis of the key methods employed to extract the translation rate parameters from ribosome profiling data. We further discuss how these approaches were used to decipher the role of various structural and sequence-based features of mRNA molecules in the regulation of gene expression. The utilization of these accurate rate parameters in computational modeling of protein synthesis may provide new insights into the kinetic control of the process of gene expression.

Assessment of health and ecological risks of heavy metal contamination: a case study of agricultural soils in Thall, Dir-Kohistan.

Heavy metal (HM) contamination in agricultural soils has been a significant health concern worldwide due to their persistent and non-biodegradable nature and biomagnification to higher trophic levels. The present study was conducted to assess Cd and Pb concentrations in soil samples collected from potato-growing areas in Thall, Dir-Kohistan, and to determine their associated health and ecological risks. The contamination factor calculated for Cd (0 to 1.74) and Pb (0 to 0.91) showed their moderate to low contamination in the study area. Geo-accumulation indices of Cd and Pb were less than 1, indicating moderate soil pollution of these HM in the study area. However, the ecological risk factor (Eri) of Cd was greater than 40 for only three soil samples, indicating the moderate potential of ecological risks of respective soils. The principal component analysis (PCA) and Pearson correlation suggested that the contamination in different soils was lithogenic followed by anthropogenic activities. The hazard quotient (HQ) in children and adults was found in the following order: dermal > ingestion > inhalation. Moreover, the values of HQ through various exposure routes were higher in children compared to adults, which showed that adults were at a lower level of risk associated with HM contamination. The results of the present study can serve as baseline data for government agencies related to environmental protection, which could devise policies to minimize Cd and Pb contamination in the agricultural soils.